{ "file_map": { "0": { "path": "std/aes128.nr", "source": "// docs:start:aes128\n// Given a plaintext as an array of bytes, returns the corresponding aes128 ciphertext (CBC mode). Input padding is performed using PKCS#7, so that the output length is `input.len() + (16 - input.len() % 16)`.\npub fn aes128_encrypt(\n input: [u8; N],\n iv: [u8; 16],\n key: [u8; 16],\n) -> [u8; N + 16 - N % 16] {\n let padding_length = (16 - N % 16) as u8;\n let mut padded_input: [u8; N + 16 - N % 16] = [0; N + 16 - N % 16];\n for i in 0..N {\n padded_input[i] = input[i];\n }\n for i in N..N + 16 - N % 16 {\n padded_input[i] = padding_length;\n }\n let output = aes128_encrypt_padded_input(padded_input, iv, key);\n output\n}\n\n#[foreign(aes128_encrypt)]\nfn aes128_encrypt_padded_input(input: [u8; N], iv: [u8; 16], key: [u8; 16]) -> [u8; N] {}\n\n// docs:end:aes128\n" }, "3": { "path": "std/array/mod.nr", "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a vector.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let vector = array.as_vector();\n /// assert_eq(vector, [1, 2].as_vector());\n /// ```\n #[builtin(as_vector)]\n pub fn as_vector(self) -> [T] {}\n\n /// Returns this array as a vector.\n /// This method is deprecated in favor of `as_vector`.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let vector = array.as_slice();\n /// assert_eq(vector, [1, 2].as_vector());\n /// ```\n #[builtin(as_vector)]\n #[deprecated(\"This method has been renamed to `as_vector`\")]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the [`Self::sort_via`] function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n" }, "5": { "path": "std/cmp.nr", "source": "use crate::meta::derive_via;\n\n#[derive_via(derive_eq)]\n// docs:start:eq-trait\npub trait Eq {\n fn eq(self, other: Self) -> bool;\n}\n// docs:end:eq-trait\n\n// docs:start:derive_eq\ncomptime fn derive_eq(s: TypeDefinition) -> Quoted {\n let signature = quote { fn eq(_self: Self, _other: Self) -> bool };\n let for_each_field = |name| quote { (_self.$name == _other.$name) };\n let body = |fields| {\n if s.fields_as_written().len() == 0 {\n quote { true }\n } else {\n fields\n }\n };\n crate::meta::make_trait_impl(\n s,\n quote { $crate::cmp::Eq },\n signature,\n for_each_field,\n quote { & },\n body,\n )\n}\n// docs:end:derive_eq\n\nimpl Eq for Field {\n fn eq(self, other: Field) -> bool {\n self == other\n }\n}\n\nimpl Eq for u128 {\n fn eq(self, other: u128) -> bool {\n self == other\n }\n}\nimpl Eq for u64 {\n fn eq(self, other: u64) -> bool {\n self == other\n }\n}\nimpl Eq for u32 {\n fn eq(self, other: u32) -> bool {\n self == other\n }\n}\nimpl Eq for u16 {\n fn eq(self, other: u16) -> bool {\n self == other\n }\n}\nimpl Eq for u8 {\n fn eq(self, other: u8) -> bool {\n self == other\n }\n}\nimpl Eq for u1 {\n fn eq(self, other: u1) -> bool {\n self == other\n }\n}\n\nimpl Eq for i8 {\n fn eq(self, other: i8) -> bool {\n self == other\n }\n}\nimpl Eq for i16 {\n fn eq(self, other: i16) -> bool {\n self == other\n }\n}\nimpl Eq for i32 {\n fn eq(self, other: i32) -> bool {\n self == other\n }\n}\nimpl Eq for i64 {\n fn eq(self, other: i64) -> bool {\n self == other\n }\n}\n\nimpl Eq for () {\n fn eq(_self: Self, _other: ()) -> bool {\n true\n }\n}\nimpl Eq for bool {\n fn eq(self, other: bool) -> bool {\n self == other\n }\n}\n\nimpl Eq for [T; N]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T; N]) -> bool {\n let mut result = true;\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for [T]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T]) -> bool {\n let mut result = self.len() == other.len();\n if result {\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n }\n result\n }\n}\n\nimpl Eq for str {\n fn eq(self, other: str) -> bool {\n let self_bytes = self.as_bytes();\n let other_bytes = other.as_bytes();\n self_bytes == other_bytes\n }\n}\n\nimpl Eq for (A, B)\nwhere\n A: Eq,\n B: Eq,\n{\n fn eq(self, other: (A, B)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1)\n }\n}\n\nimpl Eq for (A, B, C)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n{\n fn eq(self, other: (A, B, C)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2)\n }\n}\n\nimpl Eq for (A, B, C, D)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n{\n fn eq(self, other: (A, B, C, D)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2) & self.3.eq(other.3)\n }\n}\n\nimpl Eq for (A, B, C, D, E)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n E: Eq,\n{\n fn eq(self, other: (A, B, C, D, E)) -> bool {\n self.0.eq(other.0)\n & self.1.eq(other.1)\n & self.2.eq(other.2)\n & self.3.eq(other.3)\n & self.4.eq(other.4)\n }\n}\n\nimpl Eq for Ordering {\n fn eq(self, other: Ordering) -> bool {\n self.result == other.result\n }\n}\n\n// Noir doesn't have enums yet so we emulate (Lt | Eq | Gt) with a struct\n// that has 3 public functions for constructing the struct.\npub struct Ordering {\n result: Field,\n}\n\nimpl Ordering {\n // Implementation note: 0, 1, and 2 for Lt, Eq, and Gt are built\n // into the compiler, do not change these without also updating\n // the compiler itself!\n pub fn less() -> Ordering {\n Ordering { result: 0 }\n }\n\n pub fn equal() -> Ordering {\n Ordering { result: 1 }\n }\n\n pub fn greater() -> Ordering {\n Ordering { result: 2 }\n }\n}\n\n#[derive_via(derive_ord)]\n// docs:start:ord-trait\npub trait Ord {\n fn cmp(self, other: Self) -> Ordering;\n}\n// docs:end:ord-trait\n\n// docs:start:derive_ord\ncomptime fn derive_ord(s: TypeDefinition) -> Quoted {\n let name = quote { $crate::cmp::Ord };\n let signature = quote { fn cmp(_self: Self, _other: Self) -> $crate::cmp::Ordering };\n let for_each_field = |name| quote {\n if result == $crate::cmp::Ordering::equal() {\n result = _self.$name.cmp(_other.$name);\n }\n };\n let body = |fields| quote {\n let mut result = $crate::cmp::Ordering::equal();\n $fields\n result\n };\n crate::meta::make_trait_impl(s, name, signature, for_each_field, quote {}, body)\n}\n// docs:end:derive_ord\n\n// Note: Field deliberately does not implement Ord\n\nimpl Ord for u128 {\n fn cmp(self, other: u128) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\nimpl Ord for u64 {\n fn cmp(self, other: u64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u32 {\n fn cmp(self, other: u32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u16 {\n fn cmp(self, other: u16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u8 {\n fn cmp(self, other: u8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i8 {\n fn cmp(self, other: i8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i16 {\n fn cmp(self, other: i16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i32 {\n fn cmp(self, other: i32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i64 {\n fn cmp(self, other: i64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for () {\n fn cmp(_self: Self, _other: ()) -> Ordering {\n Ordering::equal()\n }\n}\n\nimpl Ord for bool {\n fn cmp(self, other: bool) -> Ordering {\n if self {\n if other {\n Ordering::equal()\n } else {\n Ordering::greater()\n }\n } else if other {\n Ordering::less()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for [T; N]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T; N]) -> Ordering {\n let mut result = Ordering::equal();\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for [T]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T]) -> Ordering {\n let self_len = self.len();\n let other_len = other.len();\n let min_len = if self_len < other_len {\n self_len\n } else {\n other_len\n };\n\n let mut result = Ordering::equal();\n for i in 0..min_len {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n\n if result != Ordering::equal() {\n result\n } else {\n self_len.cmp(other_len)\n }\n }\n}\n\nimpl Ord for (A, B)\nwhere\n A: Ord,\n B: Ord,\n{\n fn cmp(self, other: (A, B)) -> Ordering {\n let result = self.0.cmp(other.0);\n\n if result != Ordering::equal() {\n result\n } else {\n self.1.cmp(other.1)\n }\n }\n}\n\nimpl Ord for (A, B, C)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n{\n fn cmp(self, other: (A, B, C)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n{\n fn cmp(self, other: (A, B, C, D)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D, E)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n E: Ord,\n{\n fn cmp(self, other: (A, B, C, D, E)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n if result == Ordering::equal() {\n result = self.4.cmp(other.4);\n }\n\n result\n }\n}\n\n// Compares and returns the maximum of two values.\n//\n// Returns the second argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::max(1, 2), 2);\n// assert_eq(cmp::max(2, 2), 2);\n// ```\npub fn max(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v1\n } else {\n v2\n }\n}\n\n// Compares and returns the minimum of two values.\n//\n// Returns the first argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::min(1, 2), 1);\n// assert_eq(cmp::min(2, 2), 2);\n// ```\npub fn min(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v2\n } else {\n v1\n }\n}\n\nmod cmp_tests {\n use super::{Eq, max, min, Ord};\n\n #[test]\n fn sanity_check_min() {\n assert_eq(min(0_u64, 1), 0);\n assert_eq(min(0_u64, 0), 0);\n assert_eq(min(1_u64, 1), 1);\n assert_eq(min(255_u8, 0), 0);\n }\n\n #[test]\n fn sanity_check_max() {\n assert_eq(max(0_u64, 1), 1);\n assert_eq(max(0_u64, 0), 0);\n assert_eq(max(1_u64, 1), 1);\n assert_eq(max(255_u8, 0), 255);\n }\n\n #[test]\n fn correctly_handles_unequal_length_vectors() {\n let vector_1 = [0, 1, 2, 3].as_vector();\n let vector_2 = [0, 1, 2].as_vector();\n assert(!vector_1.eq(vector_2));\n }\n\n #[test]\n fn lexicographic_ordering_for_vectors() {\n assert(\n [2_u32].as_vector().cmp([1_u32, 1_u32, 1_u32].as_vector())\n == super::Ordering::greater(),\n );\n assert(\n [1_u32, 2_u32].as_vector().cmp([1_u32, 2_u32, 3_u32].as_vector())\n == super::Ordering::less(),\n );\n }\n}\n" }, "6": { "path": "std/collections/bounded_vec.nr", "source": "use crate::{cmp::Eq, convert::From, runtime::is_unconstrained, static_assert};\n\n/// A `BoundedVec` is a growable storage similar to a built-in vector except that it\n/// is bounded with a maximum possible length. `BoundedVec` is also not\n/// subject to the same restrictions vectors are (notably, nested vectors are disallowed).\n///\n/// Since a BoundedVec is backed by a normal array under the hood, growing the BoundedVec by\n/// pushing an additional element is also more efficient - the length only needs to be increased\n/// by one.\n///\n/// For these reasons `BoundedVec` should generally be preferred over vectors when there\n/// is a reasonable maximum bound that can be placed on the vector.\n///\n/// Example:\n///\n/// ```noir\n/// let mut vector: BoundedVec = BoundedVec::new();\n/// for i in 0..5 {\n/// vector.push(i);\n/// }\n/// assert(vector.len() == 5);\n/// assert(vector.max_len() == 10);\n/// ```\npub struct BoundedVec {\n storage: [T; MaxLen],\n len: u32,\n}\n\nimpl BoundedVec {\n /// Creates a new, empty vector of length zero.\n ///\n /// Since this container is backed by an array internally, it still needs an initial value\n /// to give each element. To resolve this, each element is zeroed internally. This value\n /// is guaranteed to be inaccessible unless `get_unchecked` is used.\n ///\n /// Example:\n ///\n /// ```noir\n /// let empty_vector: BoundedVec = BoundedVec::new();\n /// assert(empty_vector.len() == 0);\n /// ```\n ///\n /// Note that whenever calling `new` the maximum length of the vector should always be specified\n /// via a type signature:\n ///\n /// ```noir\n /// fn good() -> BoundedVec {\n /// // Ok! MaxLen is specified with a type annotation\n /// let v1: BoundedVec = BoundedVec::new();\n /// let v2 = BoundedVec::new();\n ///\n /// // Ok! MaxLen is known from the type of `good`'s return value\n /// v2\n /// }\n ///\n /// fn bad() {\n /// // Error: Type annotation needed\n /// // The compiler can't infer `MaxLen` from the following code:\n /// let mut v3 = BoundedVec::new();\n /// v3.push(5);\n /// }\n /// ```\n ///\n /// This defaulting of `MaxLen` (and numeric generics in general) to zero may change in future noir versions\n /// but for now make sure to use type annotations when using bounded vectors. Otherwise, you will receive a\n /// constraint failure at runtime when the vec is pushed to.\n pub fn new() -> Self {\n let zeroed = crate::mem::zeroed();\n BoundedVec { storage: [zeroed; MaxLen], len: 0 }\n }\n\n /// Retrieves an element from the vector at the given index, starting from zero.\n ///\n /// If the given index is equal to or greater than the length of the vector, this\n /// will issue a constraint failure.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn foo(v: BoundedVec) {\n /// let first = v.get(0);\n /// let last = v.get(v.len() - 1);\n /// assert(first != last);\n /// }\n /// ```\n pub fn get(self, index: u32) -> T {\n assert(index < self.len, \"Attempted to read past end of BoundedVec\");\n self.get_unchecked(index)\n }\n\n /// Retrieves an element from the vector at the given index, starting from zero, without\n /// performing a bounds check.\n ///\n /// Since this function does not perform a bounds check on length before accessing the element,\n /// it is unsafe! Use at your own risk!\n ///\n /// Example:\n ///\n /// ```noir\n /// fn sum_of_first_three(v: BoundedVec) -> u32 {\n /// // Always ensure the length is larger than the largest\n /// // index passed to get_unchecked\n /// assert(v.len() > 2);\n /// let first = v.get_unchecked(0);\n /// let second = v.get_unchecked(1);\n /// let third = v.get_unchecked(2);\n /// first + second + third\n /// }\n /// ```\n pub fn get_unchecked(self, index: u32) -> T {\n self.storage[index]\n }\n\n /// Writes an element to the vector at the given index, starting from zero.\n ///\n /// If the given index is equal to or greater than the length of the vector, this will issue a constraint failure.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn foo(v: BoundedVec) {\n /// let first = v.get(0);\n /// assert(first != 42);\n /// v.set(0, 42);\n /// let new_first = v.get(0);\n /// assert(new_first == 42);\n /// }\n /// ```\n pub fn set(&mut self, index: u32, value: T) {\n assert(index < self.len, \"Attempted to write past end of BoundedVec\");\n self.set_unchecked(index, value)\n }\n\n /// Writes an element to the vector at the given index, starting from zero, without performing a bounds check.\n ///\n /// Since this function does not perform a bounds check on length before accessing the element, it is unsafe! Use at your own risk!\n ///\n /// Example:\n ///\n /// ```noir\n /// fn set_unchecked_example() {\n /// let mut vec: BoundedVec = BoundedVec::new();\n /// vec.extend_from_array([1, 2]);\n ///\n /// // Here we're safely writing within the valid range of `vec`\n /// // `vec` now has the value [42, 2]\n /// vec.set_unchecked(0, 42);\n ///\n /// // We can then safely read this value back out of `vec`.\n /// // Notice that we use the checked version of `get` which would prevent reading unsafe values.\n /// assert_eq(vec.get(0), 42);\n ///\n /// // We've now written past the end of `vec`.\n /// // As this index is still within the maximum potential length of `v`,\n /// // it won't cause a constraint failure.\n /// vec.set_unchecked(2, 42);\n /// println(vec);\n ///\n /// // This will write past the end of the maximum potential length of `vec`,\n /// // it will then trigger a constraint failure.\n /// vec.set_unchecked(5, 42);\n /// println(vec);\n /// }\n /// ```\n pub fn set_unchecked(&mut self, index: u32, value: T) {\n self.storage[index] = value;\n }\n\n /// Pushes an element to the end of the vector. This increases the length\n /// of the vector by one.\n ///\n /// Panics if the new length of the vector will be greater than the max length.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n ///\n /// v.push(1);\n /// v.push(2);\n ///\n /// // Panics with failed assertion \"push out of bounds\"\n /// v.push(3);\n /// ```\n pub fn push(&mut self, elem: T) {\n assert(self.len < MaxLen, \"push out of bounds\");\n\n self.storage[self.len] = elem;\n self.len += 1;\n }\n\n /// Returns the current length of this vector\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n /// assert(v.len() == 0);\n ///\n /// v.push(100);\n /// assert(v.len() == 1);\n ///\n /// v.push(200);\n /// v.push(300);\n /// v.push(400);\n /// assert(v.len() == 4);\n ///\n /// let _ = v.pop();\n /// let _ = v.pop();\n /// assert(v.len() == 2);\n /// ```\n pub fn len(self) -> u32 {\n self.len\n }\n\n /// Returns the maximum length of this vector. This is always\n /// equal to the `MaxLen` parameter this vector was initialized with.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n ///\n /// assert(v.max_len() == 5);\n /// v.push(10);\n /// assert(v.max_len() == 5);\n /// ```\n pub fn max_len(_self: BoundedVec) -> u32 {\n MaxLen\n }\n\n /// Returns the internal array within this vector.\n ///\n /// Since arrays in Noir are immutable, mutating the returned storage array will not mutate\n /// the storage held internally by this vector.\n ///\n /// Note that uninitialized elements may be zeroed out!\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n ///\n /// assert(v.storage() == [0, 0, 0, 0, 0]);\n ///\n /// v.push(57);\n /// assert(v.storage() == [57, 0, 0, 0, 0]);\n /// ```\n pub fn storage(self) -> [T; MaxLen] {\n self.storage\n }\n\n /// Pushes each element from the given array to this vector.\n ///\n /// Panics if pushing each element would cause the length of this vector\n /// to exceed the maximum length.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut vec: BoundedVec = BoundedVec::new();\n /// vec.extend_from_array([2, 4]);\n ///\n /// assert(vec.len == 2);\n /// assert(vec.get(0) == 2);\n /// assert(vec.get(1) == 4);\n /// ```\n pub fn extend_from_array(&mut self, array: [T; Len]) {\n let new_len = self.len + array.len();\n assert(new_len <= MaxLen, \"extend_from_array out of bounds\");\n for i in 0..array.len() {\n self.storage[self.len + i] = array[i];\n }\n self.len = new_len;\n }\n\n /// Pushes each element from the given vector to this vector.\n ///\n /// Panics if pushing each element would cause the length of this vector\n /// to exceed the maximum length.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut vec: BoundedVec = BoundedVec::new();\n /// vec.extend_from_vector([2, 4].as_vector());\n ///\n /// assert(vec.len == 2);\n /// assert(vec.get(0) == 2);\n /// assert(vec.get(1) == 4);\n /// ```\n pub fn extend_from_vector(&mut self, vector: [T]) {\n let new_len = self.len + vector.len();\n assert(new_len <= MaxLen, \"extend_from_vector out of bounds\");\n for i in 0..vector.len() {\n self.storage[self.len + i] = vector[i];\n }\n self.len = new_len;\n }\n\n /// Pushes each element from the other vector to this vector. The length of\n /// the other vector is left unchanged.\n ///\n /// Panics if pushing each element would cause the length of this vector\n /// to exceed the maximum length.\n ///\n /// ```noir\n /// let mut v1: BoundedVec = BoundedVec::new();\n /// let mut v2: BoundedVec = BoundedVec::new();\n ///\n /// v2.extend_from_array([1, 2, 3]);\n /// v1.extend_from_bounded_vec(v2);\n ///\n /// assert(v1.storage() == [1, 2, 3, 0, 0]);\n /// assert(v2.storage() == [1, 2, 3, 0, 0, 0, 0]);\n /// ```\n pub fn extend_from_bounded_vec(&mut self, vec: BoundedVec) {\n let append_len = vec.len();\n let new_len = self.len + append_len;\n assert(new_len <= MaxLen, \"extend_from_bounded_vec out of bounds\");\n\n if is_unconstrained() {\n for i in 0..append_len {\n self.storage[self.len + i] = vec.get_unchecked(i);\n }\n } else {\n for i in 0..Len {\n if i < append_len {\n self.storage[self.len + i] = vec.get_unchecked(i);\n }\n }\n }\n self.len = new_len;\n }\n\n /// Creates a new vector, populating it with values derived from an array input.\n /// The maximum length of the vector is determined based on the type signature.\n ///\n /// Example:\n ///\n /// ```noir\n /// let bounded_vec: BoundedVec = BoundedVec::from_array([1, 2, 3])\n /// ```\n pub fn from_array(array: [T; Len]) -> Self {\n static_assert(Len <= MaxLen, \"from array out of bounds\");\n let mut vec: BoundedVec = BoundedVec::new();\n vec.extend_from_array(array);\n vec\n }\n\n /// Pops the element at the end of the vector. This will decrease the length\n /// of the vector by one.\n ///\n /// Panics if the vector is empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n /// v.push(1);\n /// v.push(2);\n ///\n /// let two = v.pop();\n /// let one = v.pop();\n ///\n /// assert(two == 2);\n /// assert(one == 1);\n ///\n /// // error: cannot pop from an empty vector\n /// let _ = v.pop();\n /// ```\n pub fn pop(&mut self) -> T {\n assert(self.len > 0, \"cannot pop from an empty vector\");\n self.len -= 1;\n\n let elem = self.storage[self.len];\n self.storage[self.len] = crate::mem::zeroed();\n elem\n }\n\n /// Returns true if the given predicate returns true for any element\n /// in this vector.\n ///\n /// Example:\n ///\n /// ```noir\n /// let mut v: BoundedVec = BoundedVec::new();\n /// v.extend_from_array([2, 4, 6]);\n ///\n /// let all_even = !v.any(|elem: u32| elem % 2 != 0);\n /// assert(all_even);\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n if is_unconstrained() {\n for i in 0..self.len {\n ret |= predicate(self.storage[i]);\n }\n } else {\n let mut exceeded_len = false;\n for i in 0..MaxLen {\n exceeded_len |= i == self.len;\n if !exceeded_len {\n ret |= predicate(self.storage[i]);\n }\n }\n }\n ret\n }\n\n /// Creates a new vector of equal size by calling a closure on each element in this vector.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n /// let result = vec.map(|value| value * 2);\n ///\n /// let expected = BoundedVec::from_array([2, 4, 6, 8]);\n /// assert_eq(result, expected);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> BoundedVec {\n let mut ret = BoundedVec::new();\n ret.len = self.len();\n\n if is_unconstrained() {\n for i in 0..self.len() {\n ret.storage[i] = f(self.get_unchecked(i));\n }\n } else {\n for i in 0..MaxLen {\n if i < self.len() {\n ret.storage[i] = f(self.get_unchecked(i));\n }\n }\n }\n\n ret\n }\n\n /// Creates a new vector of equal size by calling a closure on each element\n /// in this vector, along with its index.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n /// let result = vec.mapi(|i, value| i + value * 2);\n ///\n /// let expected = BoundedVec::from_array([2, 5, 8, 11]);\n /// assert_eq(result, expected);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> BoundedVec {\n let mut ret = BoundedVec::new();\n ret.len = self.len();\n\n if is_unconstrained() {\n for i in 0..self.len() {\n ret.storage[i] = f(i, self.get_unchecked(i));\n }\n } else {\n for i in 0..MaxLen {\n if i < self.len() {\n ret.storage[i] = f(i, self.get_unchecked(i));\n }\n }\n }\n\n ret\n }\n\n /// Calls a closure on each element in this vector.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n /// let mut result = BoundedVec::::new();\n /// vec.for_each(|value| result.push(value * 2));\n ///\n /// let expected = BoundedVec::from_array([2, 4, 6, 8]);\n /// assert_eq(result, expected);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n if is_unconstrained() {\n for i in 0..self.len() {\n f(self.get_unchecked(i));\n }\n } else {\n for i in 0..MaxLen {\n if i < self.len() {\n f(self.get_unchecked(i));\n }\n }\n }\n }\n\n /// Calls a closure on each element in this vector, along with its index.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n /// let mut result = BoundedVec::::new();\n /// vec.for_eachi(|i, value| result.push(i + value * 2));\n ///\n /// let expected = BoundedVec::from_array([2, 5, 8, 11]);\n /// assert_eq(result, expected);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n if is_unconstrained() {\n for i in 0..self.len() {\n f(i, self.get_unchecked(i));\n }\n } else {\n for i in 0..MaxLen {\n if i < self.len() {\n f(i, self.get_unchecked(i));\n }\n }\n }\n }\n\n /// Creates a new BoundedVec from the given array and length.\n /// The given length must be less than or equal to the length of the array.\n ///\n /// This function will zero out any elements at or past index `len` of `array`.\n /// This incurs an extra runtime cost of O(MaxLen). If you are sure your array is\n /// zeroed after that index, you can use [`from_parts_unchecked`][Self::from_parts_unchecked] to remove the extra loop.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_parts([1, 2, 3, 0], 3);\n /// assert_eq(vec.len(), 3);\n /// ```\n pub fn from_parts(mut array: [T; MaxLen], len: u32) -> Self {\n assert(len <= MaxLen);\n let zeroed = crate::mem::zeroed();\n\n if is_unconstrained() {\n for i in len..MaxLen {\n array[i] = zeroed;\n }\n } else {\n for i in 0..MaxLen {\n if i >= len {\n array[i] = zeroed;\n }\n }\n }\n\n BoundedVec { storage: array, len }\n }\n\n /// Creates a new BoundedVec from the given array and length.\n /// The given length must be less than or equal to the length of the array.\n ///\n /// This function is unsafe because it expects all elements past the `len` index\n /// of `array` to be zeroed, but does not check for this internally. Use `from_parts`\n /// for a safe version of this function which does zero out any indices past the\n /// given length. Invalidating this assumption can notably cause `BoundedVec::eq`\n /// to give incorrect results since it will check even elements past `len`.\n ///\n /// Example:\n ///\n /// ```noir\n /// let vec: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 0], 3);\n /// assert_eq(vec.len(), 3);\n ///\n /// // invalid use!\n /// let vec1: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 1], 3);\n /// let vec2: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 2], 3);\n ///\n /// // both vecs have length 3 so we'd expect them to be equal, but this\n /// // fails because elements past the length are still checked in eq\n /// assert_eq(vec1, vec2); // fails\n /// ```\n pub fn from_parts_unchecked(array: [T; MaxLen], len: u32) -> Self {\n assert(len <= MaxLen);\n BoundedVec { storage: array, len }\n }\n}\n\nimpl Eq for BoundedVec\nwhere\n T: Eq,\n{\n fn eq(self, other: BoundedVec) -> bool {\n // TODO: https://github.com/noir-lang/noir/issues/4837\n //\n // We make the assumption that the user has used the proper interface for working with `BoundedVec`s\n // rather than directly manipulating the internal fields as this can result in an inconsistent internal state.\n if self.len == other.len {\n self.storage == other.storage\n } else {\n false\n }\n }\n}\n\nimpl From<[T; Len]> for BoundedVec {\n fn from(array: [T; Len]) -> BoundedVec {\n BoundedVec::from_array(array)\n }\n}\n\nmod bounded_vec_tests {\n\n mod get {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test(should_fail_with = \"Attempted to read past end of BoundedVec\")]\n fn panics_when_reading_elements_past_end_of_vec() {\n let vec: BoundedVec = BoundedVec::new();\n\n let _ = vec.get(0);\n }\n\n #[test(should_fail_with = \"Attempted to read past end of BoundedVec\")]\n fn panics_when_reading_beyond_length() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n let _ = vec.get(3);\n }\n\n #[test]\n fn get_works_within_bounds() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4, 5]);\n assert_eq(vec.get(0), 1);\n assert_eq(vec.get(2), 3);\n assert_eq(vec.get(4), 5);\n }\n\n #[test]\n fn get_unchecked_works() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n assert_eq(vec.get_unchecked(0), 1);\n assert_eq(vec.get_unchecked(2), 3);\n }\n\n #[test]\n fn get_unchecked_works_past_len() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n assert_eq(vec.get_unchecked(4), 0);\n }\n }\n\n mod set {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn set_updates_values_properly() {\n let mut vec = BoundedVec::from_array([0, 0, 0, 0, 0]);\n\n vec.set(0, 42);\n assert_eq(vec.storage, [42, 0, 0, 0, 0]);\n\n vec.set(1, 43);\n assert_eq(vec.storage, [42, 43, 0, 0, 0]);\n\n vec.set(2, 44);\n assert_eq(vec.storage, [42, 43, 44, 0, 0]);\n\n vec.set(1, 10);\n assert_eq(vec.storage, [42, 10, 44, 0, 0]);\n\n vec.set(0, 0);\n assert_eq(vec.storage, [0, 10, 44, 0, 0]);\n }\n\n #[test(should_fail_with = \"Attempted to write past end of BoundedVec\")]\n fn panics_when_writing_elements_past_end_of_vec() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.set(0, 42);\n }\n\n #[test(should_fail_with = \"Attempted to write past end of BoundedVec\")]\n fn panics_when_setting_beyond_length() {\n let mut vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n vec.set(3, 4);\n }\n\n #[test]\n fn set_unchecked_operations() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.push(2);\n\n vec.set_unchecked(0, 10);\n assert_eq(vec.get(0), 10);\n }\n\n #[test(should_fail_with = \"Attempted to read past end of BoundedVec\")]\n fn set_unchecked_operations_past_len() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.push(2);\n\n vec.set_unchecked(3, 40);\n assert_eq(vec.get(3), 40);\n }\n\n #[test]\n fn set_preserves_other_elements() {\n let mut vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4, 5]);\n\n vec.set(2, 30);\n assert_eq(vec.get(0), 1);\n assert_eq(vec.get(1), 2);\n assert_eq(vec.get(2), 30);\n assert_eq(vec.get(3), 4);\n assert_eq(vec.get(4), 5);\n }\n }\n\n mod any {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn returns_false_if_predicate_not_satisfied() {\n let vec: BoundedVec = BoundedVec::from_array([false, false, false, false]);\n let result = vec.any(|value| value);\n\n assert(!result);\n }\n\n #[test]\n fn returns_true_if_predicate_satisfied() {\n let vec: BoundedVec = BoundedVec::from_array([false, false, true, true]);\n let result = vec.any(|value| value);\n\n assert(result);\n }\n\n #[test]\n fn returns_false_on_empty_boundedvec() {\n let vec: BoundedVec = BoundedVec::new();\n let result = vec.any(|value| value);\n\n assert(!result);\n }\n\n #[test]\n fn any_with_complex_predicates() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4, 5]);\n\n assert(vec.any(|x| x > 3));\n assert(!vec.any(|x| x > 10));\n assert(vec.any(|x| x % 2 == 0)); // has a even number\n assert(vec.any(|x| x == 3)); // has a specific value\n }\n\n #[test]\n fn any_with_partial_vector() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.push(2);\n\n assert(vec.any(|x| x == 1));\n assert(vec.any(|x| x == 2));\n assert(!vec.any(|x| x == 3));\n }\n }\n\n mod map {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn applies_function_correctly() {\n // docs:start:bounded-vec-map-example\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = vec.map(|value| value * 2);\n // docs:end:bounded-vec-map-example\n let expected = BoundedVec::from_array([2, 4, 6, 8]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn applies_function_that_changes_return_type() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = vec.map(|value| (value * 2) as Field);\n let expected: BoundedVec = BoundedVec::from_array([2, 4, 6, 8]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn does_not_apply_function_past_len() {\n let vec: BoundedVec = BoundedVec::from_array([0, 1]);\n let result = vec.map(|value| if value == 0 { 5 } else { value });\n let expected = BoundedVec::from_array([5, 1]);\n\n assert_eq(result, expected);\n assert_eq(result.get_unchecked(2), 0);\n }\n\n #[test]\n fn map_with_conditional_logic() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n\n let result = vec.map(|x| if x % 2 == 0 { x * 2 } else { x });\n let expected = BoundedVec::from_array([1, 4, 3, 8]);\n assert_eq(result, expected);\n }\n\n #[test]\n fn map_preserves_length() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = vec.map(|x| x * 2);\n\n assert_eq(result.len(), vec.len());\n assert_eq(result.max_len(), vec.max_len());\n }\n\n #[test]\n fn map_on_empty_vector() {\n let vec: BoundedVec = BoundedVec::new();\n let result = vec.map(|x| x * 2);\n assert_eq(result, vec);\n assert_eq(result.len(), 0);\n assert_eq(result.max_len(), 5);\n }\n }\n\n mod mapi {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn applies_function_correctly() {\n // docs:start:bounded-vec-mapi-example\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = vec.mapi(|i, value| i + value * 2);\n // docs:end:bounded-vec-mapi-example\n let expected = BoundedVec::from_array([2, 5, 8, 11]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn applies_function_that_changes_return_type() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = vec.mapi(|i, value| (i + value * 2) as Field);\n let expected: BoundedVec = BoundedVec::from_array([2, 5, 8, 11]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn does_not_apply_function_past_len() {\n let vec: BoundedVec = BoundedVec::from_array([0, 1]);\n let result = vec.mapi(|_, value| if value == 0 { 5 } else { value });\n let expected = BoundedVec::from_array([5, 1]);\n\n assert_eq(result, expected);\n assert_eq(result.get_unchecked(2), 0);\n }\n\n #[test]\n fn mapi_with_index_branching_logic() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n\n let result = vec.mapi(|i, x| if i % 2 == 0 { x * 2 } else { x });\n let expected = BoundedVec::from_array([2, 2, 6, 4]);\n assert_eq(result, expected);\n }\n }\n\n mod for_each {\n use crate::collections::bounded_vec::BoundedVec;\n\n // map in terms of for_each\n fn for_each_map(\n input: BoundedVec,\n f: fn[Env](T) -> U,\n ) -> BoundedVec {\n let mut output = BoundedVec::::new();\n let output_ref = &mut output;\n input.for_each(|x| output_ref.push(f(x)));\n output\n }\n\n #[test]\n fn smoke_test() {\n let mut acc = 0;\n let acc_ref = &mut acc;\n // docs:start:bounded-vec-for-each-example\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n vec.for_each(|value| { *acc_ref += value; });\n // docs:end:bounded-vec-for-each-example\n assert_eq(acc, 6);\n }\n\n #[test]\n fn applies_function_correctly() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = for_each_map(vec, |value| value * 2);\n let expected = BoundedVec::from_array([2, 4, 6, 8]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn applies_function_that_changes_return_type() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = for_each_map(vec, |value| (value * 2) as Field);\n let expected: BoundedVec = BoundedVec::from_array([2, 4, 6, 8]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn does_not_apply_function_past_len() {\n let vec: BoundedVec = BoundedVec::from_array([0, 1]);\n let result = for_each_map(vec, |value| if value == 0 { 5 } else { value });\n let expected = BoundedVec::from_array([5, 1]);\n\n assert_eq(result, expected);\n assert_eq(result.get_unchecked(2), 0);\n }\n\n #[test]\n fn for_each_on_empty_vector() {\n let vec: BoundedVec = BoundedVec::new();\n let mut count = 0;\n let count_ref = &mut count;\n vec.for_each(|_| { *count_ref += 1; });\n assert_eq(count, 0);\n }\n\n #[test]\n fn for_each_with_side_effects() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n let mut seen = BoundedVec::::new();\n let seen_ref = &mut seen;\n vec.for_each(|x| seen_ref.push(x));\n assert_eq(seen, vec);\n }\n }\n\n mod for_eachi {\n use crate::collections::bounded_vec::BoundedVec;\n\n // mapi in terms of for_eachi\n fn for_eachi_mapi(\n input: BoundedVec,\n f: fn[Env](u32, T) -> U,\n ) -> BoundedVec {\n let mut output = BoundedVec::::new();\n let output_ref = &mut output;\n input.for_eachi(|i, x| output_ref.push(f(i, x)));\n output\n }\n\n #[test]\n fn smoke_test() {\n let mut acc = 0;\n let acc_ref = &mut acc;\n // docs:start:bounded-vec-for-eachi-example\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n vec.for_eachi(|i, value| { *acc_ref += i * value; });\n // docs:end:bounded-vec-for-eachi-example\n\n // 0 * 1 + 1 * 2 + 2 * 3\n assert_eq(acc, 8);\n }\n\n #[test]\n fn applies_function_correctly() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = for_eachi_mapi(vec, |i, value| i + value * 2);\n let expected = BoundedVec::from_array([2, 5, 8, 11]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn applies_function_that_changes_return_type() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3, 4]);\n let result = for_eachi_mapi(vec, |i, value| (i + value * 2) as Field);\n let expected: BoundedVec = BoundedVec::from_array([2, 5, 8, 11]);\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn does_not_apply_function_past_len() {\n let vec: BoundedVec = BoundedVec::from_array([0, 1]);\n let result = for_eachi_mapi(vec, |_, value| if value == 0 { 5 } else { value });\n let expected = BoundedVec::from_array([5, 1]);\n\n assert_eq(result, expected);\n assert_eq(result.get_unchecked(2), 0);\n }\n\n #[test]\n fn for_eachi_on_empty_vector() {\n let vec: BoundedVec = BoundedVec::new();\n let mut count = 0;\n let count_ref = &mut count;\n vec.for_eachi(|_, _| { *count_ref += 1; });\n assert_eq(count, 0);\n }\n\n #[test]\n fn for_eachi_with_index_tracking() {\n let vec: BoundedVec = BoundedVec::from_array([10, 20, 30]);\n let mut indices = BoundedVec::::new();\n let indices_ref = &mut indices;\n vec.for_eachi(|i, _| indices_ref.push(i));\n\n let expected = BoundedVec::from_array([0, 1, 2]);\n assert_eq(indices, expected);\n }\n\n }\n\n mod from_array {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn empty() {\n let empty_array: [Field; 0] = [];\n let bounded_vec = BoundedVec::from_array([]);\n\n assert_eq(bounded_vec.max_len(), 0);\n assert_eq(bounded_vec.len(), 0);\n assert_eq(bounded_vec.storage(), empty_array);\n }\n\n #[test]\n fn equal_len() {\n let array = [1, 2, 3];\n let bounded_vec = BoundedVec::from_array(array);\n\n assert_eq(bounded_vec.max_len(), 3);\n assert_eq(bounded_vec.len(), 3);\n assert_eq(bounded_vec.storage(), array);\n }\n\n #[test]\n fn max_len_greater_then_array_len() {\n let array = [1, 2, 3];\n let bounded_vec: BoundedVec = BoundedVec::from_array(array);\n\n assert_eq(bounded_vec.max_len(), 10);\n assert_eq(bounded_vec.len(), 3);\n assert_eq(bounded_vec.get(0), 1);\n assert_eq(bounded_vec.get(1), 2);\n assert_eq(bounded_vec.get(2), 3);\n }\n\n #[test(should_fail_with = \"from array out of bounds\")]\n fn max_len_lower_then_array_len() {\n let _: BoundedVec = BoundedVec::from_array([0; 3]);\n }\n\n #[test]\n fn from_array_preserves_order() {\n let array = [5, 3, 1, 4, 2];\n let vec: BoundedVec = BoundedVec::from_array(array);\n for i in 0..array.len() {\n assert_eq(vec.get(i), array[i]);\n }\n }\n\n #[test]\n fn from_array_with_different_types() {\n let bool_array = [true, false, true];\n let bool_vec: BoundedVec = BoundedVec::from_array(bool_array);\n assert_eq(bool_vec.len(), 3);\n assert_eq(bool_vec.get(0), true);\n assert_eq(bool_vec.get(1), false);\n }\n }\n\n mod trait_from {\n use crate::collections::bounded_vec::BoundedVec;\n use crate::convert::From;\n\n #[test]\n fn simple() {\n let array = [1, 2];\n let bounded_vec: BoundedVec = BoundedVec::from(array);\n\n assert_eq(bounded_vec.max_len(), 10);\n assert_eq(bounded_vec.len(), 2);\n assert_eq(bounded_vec.get(0), 1);\n assert_eq(bounded_vec.get(1), 2);\n }\n }\n\n mod trait_eq {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn empty_equality() {\n let mut bounded_vec1: BoundedVec = BoundedVec::new();\n let mut bounded_vec2: BoundedVec = BoundedVec::new();\n\n assert_eq(bounded_vec1, bounded_vec2);\n }\n\n #[test]\n fn inequality() {\n let mut bounded_vec1: BoundedVec = BoundedVec::new();\n let mut bounded_vec2: BoundedVec = BoundedVec::new();\n bounded_vec1.push(1);\n bounded_vec2.push(2);\n\n assert(bounded_vec1 != bounded_vec2);\n }\n }\n\n mod from_parts {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn from_parts() {\n // docs:start:from-parts\n let vec: BoundedVec = BoundedVec::from_parts([1, 2, 3, 0], 3);\n assert_eq(vec.len(), 3);\n\n // Any elements past the given length are zeroed out, so these\n // two BoundedVecs will be completely equal\n let vec1: BoundedVec = BoundedVec::from_parts([1, 2, 3, 1], 3);\n let vec2: BoundedVec = BoundedVec::from_parts([1, 2, 3, 2], 3);\n assert_eq(vec1, vec2);\n // docs:end:from-parts\n }\n\n #[test]\n fn from_parts_unchecked() {\n // docs:start:from-parts-unchecked\n let vec: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 0], 3);\n assert_eq(vec.len(), 3);\n\n // invalid use!\n let vec1: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 1], 3);\n let vec2: BoundedVec = BoundedVec::from_parts_unchecked([1, 2, 3, 2], 3);\n\n // both vecs have length 3 so we'd expect them to be equal, but this\n // fails because elements past the length are still checked in eq\n assert(vec1 != vec2);\n // docs:end:from-parts-unchecked\n }\n }\n\n mod push_pop {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn push_and_pop_operations() {\n let mut vec: BoundedVec = BoundedVec::new();\n\n assert_eq(vec.len(), 0);\n\n vec.push(1);\n assert_eq(vec.len(), 1);\n assert_eq(vec.get(0), 1);\n\n vec.push(2);\n assert_eq(vec.len(), 2);\n assert_eq(vec.get(1), 2);\n\n let popped = vec.pop();\n assert_eq(popped, 2);\n assert_eq(vec.len(), 1);\n\n let popped2 = vec.pop();\n assert_eq(popped2, 1);\n assert_eq(vec.len(), 0);\n }\n\n #[test(should_fail_with = \"push out of bounds\")]\n fn push_to_full_vector() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.push(2);\n vec.push(3); // should panic\n }\n\n #[test(should_fail_with = \"cannot pop from an empty vector\")]\n fn pop_from_empty_vector() {\n let mut vec: BoundedVec = BoundedVec::new();\n let _ = vec.pop(); // should panic\n }\n\n #[test]\n fn push_pop_cycle() {\n let mut vec: BoundedVec = BoundedVec::new();\n\n // push to full\n vec.push(1);\n vec.push(2);\n vec.push(3);\n assert_eq(vec.len(), 3);\n\n // pop all\n assert_eq(vec.pop(), 3);\n assert_eq(vec.pop(), 2);\n assert_eq(vec.pop(), 1);\n assert_eq(vec.len(), 0);\n\n // push again\n vec.push(4);\n assert_eq(vec.len(), 1);\n assert_eq(vec.get(0), 4);\n }\n }\n\n mod extend {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn extend_from_array() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.extend_from_array([2, 3]);\n\n assert_eq(vec.len(), 3);\n assert_eq(vec.get(0), 1);\n assert_eq(vec.get(1), 2);\n assert_eq(vec.get(2), 3);\n }\n\n #[test]\n fn extend_from_vector() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.extend_from_vector([2, 3].as_vector());\n\n assert_eq(vec.len(), 3);\n assert_eq(vec.get(0), 1);\n assert_eq(vec.get(1), 2);\n assert_eq(vec.get(2), 3);\n }\n\n #[test]\n fn extend_from_bounded_vec() {\n let mut vec1: BoundedVec = BoundedVec::new();\n let mut vec2: BoundedVec = BoundedVec::new();\n\n vec1.push(1);\n vec2.push(2);\n vec2.push(3);\n\n vec1.extend_from_bounded_vec(vec2);\n\n assert_eq(vec1.len(), 3);\n assert_eq(vec1.get(0), 1);\n assert_eq(vec1.get(1), 2);\n assert_eq(vec1.get(2), 3);\n }\n\n #[test(should_fail_with = \"extend_from_array out of bounds\")]\n fn extend_array_beyond_max_len() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.extend_from_array([2, 3, 4]); // should panic\n }\n\n #[test(should_fail_with = \"extend_from_vector out of bounds\")]\n fn extend_vector_beyond_max_len() {\n let mut vec: BoundedVec = BoundedVec::new();\n vec.push(1);\n vec.extend_from_vector([2, 3, 4].as_vector()); // S]should panic\n }\n\n #[test(should_fail_with = \"extend_from_bounded_vec out of bounds\")]\n fn extend_bounded_vec_beyond_max_len() {\n let mut vec: BoundedVec = BoundedVec::new();\n let other: BoundedVec = BoundedVec::from_array([1, 2, 3, 4, 5]);\n vec.extend_from_bounded_vec(other); // should panic\n }\n\n #[test]\n fn extend_with_empty_collections() {\n let mut vec: BoundedVec = BoundedVec::new();\n let original_len = vec.len();\n\n vec.extend_from_array([]);\n assert_eq(vec.len(), original_len);\n\n vec.extend_from_vector([].as_vector());\n assert_eq(vec.len(), original_len);\n\n let empty: BoundedVec = BoundedVec::new();\n vec.extend_from_bounded_vec(empty);\n assert_eq(vec.len(), original_len);\n }\n }\n\n mod storage {\n use crate::collections::bounded_vec::BoundedVec;\n\n #[test]\n fn storage_consistency() {\n let mut vec: BoundedVec = BoundedVec::new();\n\n // test initial storage state\n assert_eq(vec.storage(), [0, 0, 0, 0, 0]);\n\n vec.push(1);\n vec.push(2);\n\n // test storage after modifications\n assert_eq(vec.storage(), [1, 2, 0, 0, 0]);\n\n // storage doesn't change length\n assert_eq(vec.len(), 2);\n assert_eq(vec.max_len(), 5);\n }\n\n #[test]\n fn storage_after_pop() {\n let mut vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n\n let _ = vec.pop();\n // after pop, the last element should be zeroed\n assert_eq(vec.storage(), [1, 2, 0]);\n assert_eq(vec.len(), 2);\n }\n\n #[test]\n fn vector_immutable() {\n let vec: BoundedVec = BoundedVec::from_array([1, 2, 3]);\n let storage = vec.storage();\n\n assert_eq(storage, [1, 2, 3]);\n\n // Verify that the original vector is unchanged\n assert_eq(vec.len(), 3);\n assert_eq(vec.get(0), 1);\n assert_eq(vec.get(1), 2);\n assert_eq(vec.get(2), 3);\n }\n }\n}\n" }, "16": { "path": "std/embedded_curve_ops.nr", "source": "use crate::cmp::Eq;\nuse crate::hash::Hash;\nuse crate::ops::arith::{Add, Neg, Sub};\n\n/// A point on the embedded elliptic curve\n/// By definition, the base field of the embedded curve is the scalar field of the proof system curve, i.e the Noir Field.\n/// x and y denotes the Weierstrass coordinates of the point, if is_infinite is false.\npub struct EmbeddedCurvePoint {\n pub x: Field,\n pub y: Field,\n pub is_infinite: bool,\n}\n\nimpl EmbeddedCurvePoint {\n /// Elliptic curve point doubling operation\n /// returns the doubled point of a point P, i.e P+P\n pub fn double(self) -> EmbeddedCurvePoint {\n embedded_curve_add(self, self)\n }\n\n /// Returns the null element of the curve; 'the point at infinity'\n pub fn point_at_infinity() -> EmbeddedCurvePoint {\n EmbeddedCurvePoint { x: 0, y: 0, is_infinite: true }\n }\n\n /// Returns the curve's generator point.\n pub fn generator() -> EmbeddedCurvePoint {\n // Generator point for the grumpkin curve (y^2 = x^3 - 17)\n EmbeddedCurvePoint {\n x: 1,\n y: 17631683881184975370165255887551781615748388533673675138860, // sqrt(-16)\n is_infinite: false,\n }\n }\n}\n\nimpl Add for EmbeddedCurvePoint {\n /// Adds two points P+Q, using the curve addition formula, and also handles point at infinity\n fn add(self, other: EmbeddedCurvePoint) -> EmbeddedCurvePoint {\n embedded_curve_add(self, other)\n }\n}\n\nimpl Sub for EmbeddedCurvePoint {\n /// Points subtraction operation, using addition and negation\n fn sub(self, other: EmbeddedCurvePoint) -> EmbeddedCurvePoint {\n self + other.neg()\n }\n}\n\nimpl Neg for EmbeddedCurvePoint {\n /// Negates a point P, i.e returns -P, by negating the y coordinate.\n /// If the point is at infinity, then the result is also at infinity.\n fn neg(self) -> EmbeddedCurvePoint {\n EmbeddedCurvePoint { x: self.x, y: -self.y, is_infinite: self.is_infinite }\n }\n}\n\nimpl Eq for EmbeddedCurvePoint {\n /// Checks whether two points are equal\n fn eq(self: Self, b: EmbeddedCurvePoint) -> bool {\n (self.is_infinite & b.is_infinite)\n | ((self.is_infinite == b.is_infinite) & (self.x == b.x) & (self.y == b.y))\n }\n}\n\nimpl Hash for EmbeddedCurvePoint {\n fn hash(self, state: &mut H)\n where\n H: crate::hash::Hasher,\n {\n if self.is_infinite {\n self.is_infinite.hash(state);\n } else {\n self.x.hash(state);\n self.y.hash(state);\n }\n }\n}\n\n/// Scalar for the embedded curve represented as low and high limbs\n/// By definition, the scalar field of the embedded curve is base field of the proving system curve.\n/// It may not fit into a Field element, so it is represented with two Field elements; its low and high limbs.\npub struct EmbeddedCurveScalar {\n pub lo: Field,\n pub hi: Field,\n}\n\nimpl EmbeddedCurveScalar {\n pub fn new(lo: Field, hi: Field) -> Self {\n EmbeddedCurveScalar { lo, hi }\n }\n\n #[field(bn254)]\n pub fn from_field(scalar: Field) -> EmbeddedCurveScalar {\n let (a, b) = crate::field::bn254::decompose(scalar);\n EmbeddedCurveScalar { lo: a, hi: b }\n }\n\n //Bytes to scalar: take the first (after the specified offset) 16 bytes of the input as the lo value, and the next 16 bytes as the hi value\n #[field(bn254)]\n pub(crate) fn from_bytes(bytes: [u8; 64], offset: u32) -> EmbeddedCurveScalar {\n let mut v = 1;\n let mut lo = 0 as Field;\n let mut hi = 0 as Field;\n for i in 0..16 {\n lo = lo + (bytes[offset + 31 - i] as Field) * v;\n hi = hi + (bytes[offset + 15 - i] as Field) * v;\n v = v * 256;\n }\n let sig_s = crate::embedded_curve_ops::EmbeddedCurveScalar { lo, hi };\n sig_s\n }\n}\n\nimpl Eq for EmbeddedCurveScalar {\n fn eq(self, other: Self) -> bool {\n (other.hi == self.hi) & (other.lo == self.lo)\n }\n}\n\nimpl Hash for EmbeddedCurveScalar {\n fn hash(self, state: &mut H)\n where\n H: crate::hash::Hasher,\n {\n self.hi.hash(state);\n self.lo.hash(state);\n }\n}\n\n// Computes a multi scalar multiplication over the embedded curve.\n// For bn254, We have Grumpkin and Baby JubJub.\n// For bls12-381, we have JubJub and Bandersnatch.\n//\n// The embedded curve being used is decided by the\n// underlying proof system.\n//\n// IMPORTANT: Prefer `multi_scalar_mul()` over repeated `embedded_curve_add()`\n// for adding multiple points. This is significantly more efficient.\n// For adding exactly 2 points, use `embedded_curve_add()` directly.\n// docs:start:multi_scalar_mul\npub fn multi_scalar_mul(\n points: [EmbeddedCurvePoint; N],\n scalars: [EmbeddedCurveScalar; N],\n) -> EmbeddedCurvePoint\n// docs:end:multi_scalar_mul\n{\n multi_scalar_mul_array_return(points, scalars, true)[0]\n}\n\n#[foreign(multi_scalar_mul)]\npub(crate) fn multi_scalar_mul_array_return(\n points: [EmbeddedCurvePoint; N],\n scalars: [EmbeddedCurveScalar; N],\n predicate: bool,\n) -> [EmbeddedCurvePoint; 1] {}\n\n// docs:start:fixed_base_scalar_mul\npub fn fixed_base_scalar_mul(scalar: EmbeddedCurveScalar) -> EmbeddedCurvePoint\n// docs:end:fixed_base_scalar_mul\n{\n multi_scalar_mul([EmbeddedCurvePoint::generator()], [scalar])\n}\n\n/// Elliptic curve addition\n/// IMPORTANT: this function is expected to perform a full addition in order to handle all corner cases:\n/// - points on the curve\n/// - point doubling\n/// - point at infinity\n/// As a result, you may not get optimal performance, depending on the assumptions of your inputs.\n// docs:start:embedded_curve_add\npub fn embedded_curve_add(\n point1: EmbeddedCurvePoint,\n point2: EmbeddedCurvePoint,\n) -> EmbeddedCurvePoint {\n // docs:end:embedded_curve_add\n if crate::runtime::is_unconstrained() {\n // avoid calling the black box function for trivial cases\n if point1.is_infinite {\n point2\n } else if point2.is_infinite {\n point1\n } else {\n embedded_curve_add_inner(point1, point2)\n }\n } else {\n embedded_curve_add_inner(point1, point2)\n }\n}\n\n#[foreign(embedded_curve_add)]\nfn embedded_curve_add_array_return(\n _point1: EmbeddedCurvePoint,\n _point2: EmbeddedCurvePoint,\n _predicate: bool,\n) -> [EmbeddedCurvePoint; 1] {}\n\n/// EC addition wrapper for the foreign function\nfn embedded_curve_add_inner(\n point1: EmbeddedCurvePoint,\n point2: EmbeddedCurvePoint,\n) -> EmbeddedCurvePoint {\n embedded_curve_add_array_return(point1, point2, true)[0]\n}\n" }, "17": { "path": "std/field/bn254.nr", "source": "use crate::field::field_less_than;\nuse crate::runtime::is_unconstrained;\n\n// The low and high decomposition of the field modulus\npub(crate) global PLO: Field = 53438638232309528389504892708671455233;\npub(crate) global PHI: Field = 64323764613183177041862057485226039389;\n\npub(crate) global TWO_POW_128: Field = 0x100000000000000000000000000000000;\n\n// Decomposes a single field into two 16 byte fields.\nfn compute_decomposition(x: Field) -> (Field, Field) {\n // Here's we're taking advantage of truncating 128 bit limbs from the input field\n // and then subtracting them from the input such the field division is equivalent to integer division.\n let low = (x as u128) as Field;\n let high = (x - low) / TWO_POW_128;\n\n (low, high)\n}\n\npub(crate) unconstrained fn decompose_hint(x: Field) -> (Field, Field) {\n compute_decomposition(x)\n}\n\nunconstrained fn lte_hint(x: Field, y: Field) -> bool {\n if x == y {\n true\n } else {\n field_less_than(x, y)\n }\n}\n\n// Assert that (alo > blo && ahi >= bhi) || (alo <= blo && ahi > bhi)\nfn assert_gt_limbs(a: (Field, Field), b: (Field, Field)) {\n let (alo, ahi) = a;\n let (blo, bhi) = b;\n // Safety: borrow is enforced to be boolean due to its type.\n // if borrow is 0, it asserts that (alo > blo && ahi >= bhi)\n // if borrow is 1, it asserts that (alo <= blo && ahi > bhi)\n unsafe {\n let borrow = lte_hint(alo, blo);\n\n let rlo = alo - blo - 1 + (borrow as Field) * TWO_POW_128;\n let rhi = ahi - bhi - (borrow as Field);\n\n rlo.assert_max_bit_size::<128>();\n rhi.assert_max_bit_size::<128>();\n }\n}\n\n/// Decompose a single field into two 16 byte fields.\npub fn decompose(x: Field) -> (Field, Field) {\n if is_unconstrained() {\n compute_decomposition(x)\n } else {\n // Safety: decomposition is properly checked below\n unsafe {\n // Take hints of the decomposition\n let (xlo, xhi) = decompose_hint(x);\n\n // Range check the limbs\n xlo.assert_max_bit_size::<128>();\n xhi.assert_max_bit_size::<128>();\n\n // Check that the decomposition is correct\n assert_eq(x, xlo + TWO_POW_128 * xhi);\n\n // Assert that the decomposition of P is greater than the decomposition of x\n assert_gt_limbs((PLO, PHI), (xlo, xhi));\n (xlo, xhi)\n }\n }\n}\n\npub fn assert_gt(a: Field, b: Field) {\n if is_unconstrained() {\n assert(\n // Safety: already unconstrained\n unsafe { field_less_than(b, a) },\n );\n } else {\n // Decompose a and b\n let a_limbs = decompose(a);\n let b_limbs = decompose(b);\n\n // Assert that a_limbs is greater than b_limbs\n assert_gt_limbs(a_limbs, b_limbs)\n }\n}\n\npub fn assert_lt(a: Field, b: Field) {\n assert_gt(b, a);\n}\n\npub fn gt(a: Field, b: Field) -> bool {\n if is_unconstrained() {\n // Safety: unsafe in unconstrained\n unsafe {\n field_less_than(b, a)\n }\n } else if a == b {\n false\n } else {\n // Safety: Take a hint of the comparison and verify it\n unsafe {\n if field_less_than(a, b) {\n assert_gt(b, a);\n false\n } else {\n assert_gt(a, b);\n true\n }\n }\n }\n}\n\npub fn lt(a: Field, b: Field) -> bool {\n gt(b, a)\n}\n\nmod tests {\n // TODO: Allow imports from \"super\"\n use crate::field::bn254::{assert_gt, decompose, gt, lt, lte_hint, PHI, PLO, TWO_POW_128};\n\n #[test]\n fn check_decompose() {\n assert_eq(decompose(TWO_POW_128), (0, 1));\n assert_eq(decompose(TWO_POW_128 + 0x1234567890), (0x1234567890, 1));\n assert_eq(decompose(0x1234567890), (0x1234567890, 0));\n }\n\n #[test]\n unconstrained fn check_lte_hint() {\n assert(lte_hint(0, 1));\n assert(lte_hint(0, 0x100));\n assert(lte_hint(0x100, TWO_POW_128 - 1));\n assert(!lte_hint(0 - 1, 0));\n\n assert(lte_hint(0, 0));\n assert(lte_hint(0x100, 0x100));\n assert(lte_hint(0 - 1, 0 - 1));\n }\n\n #[test]\n fn check_gt() {\n assert(gt(1, 0));\n assert(gt(0x100, 0));\n assert(gt((0 - 1), (0 - 2)));\n assert(gt(TWO_POW_128, 0));\n assert(!gt(0, 0));\n assert(!gt(0, 0x100));\n assert(gt(0 - 1, 0 - 2));\n assert(!gt(0 - 2, 0 - 1));\n assert_gt(0 - 1, 0);\n }\n\n #[test]\n fn check_plo_phi() {\n assert_eq(PLO + PHI * TWO_POW_128, 0);\n let p_bytes = crate::field::modulus_le_bytes();\n let mut p_low: Field = 0;\n let mut p_high: Field = 0;\n\n let mut offset = 1;\n for i in 0..16 {\n p_low += (p_bytes[i] as Field) * offset;\n p_high += (p_bytes[i + 16] as Field) * offset;\n offset *= 256;\n }\n assert_eq(p_low, PLO);\n assert_eq(p_high, PHI);\n }\n\n #[test]\n fn check_decompose_edge_cases() {\n assert_eq(decompose(0), (0, 0));\n assert_eq(decompose(TWO_POW_128 - 1), (TWO_POW_128 - 1, 0));\n assert_eq(decompose(TWO_POW_128 + 1), (1, 1));\n assert_eq(decompose(TWO_POW_128 * 2), (0, 2));\n assert_eq(decompose(TWO_POW_128 * 2 + 0x1234567890), (0x1234567890, 2));\n }\n\n #[test]\n fn check_decompose_large_values() {\n let large_field = 0xffffffffffffffff;\n let (lo, hi) = decompose(large_field);\n assert_eq(large_field, lo + TWO_POW_128 * hi);\n\n let large_value = large_field - TWO_POW_128;\n let (lo2, hi2) = decompose(large_value);\n assert_eq(large_value, lo2 + TWO_POW_128 * hi2);\n }\n\n #[test]\n fn check_lt_comprehensive() {\n assert(lt(0, 1));\n assert(!lt(1, 0));\n assert(!lt(0, 0));\n assert(!lt(42, 42));\n\n assert(lt(TWO_POW_128 - 1, TWO_POW_128));\n assert(!lt(TWO_POW_128, TWO_POW_128 - 1));\n }\n}\n" }, "18": { "path": "std/field/mod.nr", "source": "pub mod bn254;\nuse crate::{runtime::is_unconstrained, static_assert};\nuse bn254::lt as bn254_lt;\n\nimpl Field {\n /// Asserts that `self` can be represented in `bit_size` bits.\n ///\n /// # Failures\n /// Causes a constraint failure for `Field` values exceeding `2^{bit_size}`.\n // docs:start:assert_max_bit_size\n pub fn assert_max_bit_size(self) {\n // docs:end:assert_max_bit_size\n static_assert(\n BIT_SIZE < modulus_num_bits() as u32,\n \"BIT_SIZE must be less than modulus_num_bits\",\n );\n __assert_max_bit_size(self, BIT_SIZE);\n }\n\n /// Decomposes `self` into its little endian bit decomposition as a `[u1; N]` array.\n /// This array will be zero padded should not all bits be necessary to represent `self`.\n ///\n /// # Failures\n /// Causes a constraint failure for `Field` values exceeding `2^N` as the resulting array will not\n /// be able to represent the original `Field`.\n ///\n /// # Safety\n /// The bit decomposition returned is canonical and is guaranteed to not overflow the modulus.\n // docs:start:to_le_bits\n pub fn to_le_bits(self: Self) -> [u1; N] {\n // docs:end:to_le_bits\n let bits = __to_le_bits(self);\n\n if !is_unconstrained() {\n // Ensure that the byte decomposition does not overflow the modulus\n let p = modulus_le_bits();\n assert(bits.len() <= p.len());\n let mut ok = bits.len() != p.len();\n for i in 0..N {\n if !ok {\n if (bits[N - 1 - i] != p[N - 1 - i]) {\n assert(p[N - 1 - i] == 1);\n ok = true;\n }\n }\n }\n assert(ok);\n }\n bits\n }\n\n /// Decomposes `self` into its big endian bit decomposition as a `[u1; N]` array.\n /// This array will be zero padded should not all bits be necessary to represent `self`.\n ///\n /// # Failures\n /// Causes a constraint failure for `Field` values exceeding `2^N` as the resulting array will not\n /// be able to represent the original `Field`.\n ///\n /// # Safety\n /// The bit decomposition returned is canonical and is guaranteed to not overflow the modulus.\n // docs:start:to_be_bits\n pub fn to_be_bits(self: Self) -> [u1; N] {\n // docs:end:to_be_bits\n let bits = __to_be_bits(self);\n\n if !is_unconstrained() {\n // Ensure that the decomposition does not overflow the modulus\n let p = modulus_be_bits();\n assert(bits.len() <= p.len());\n let mut ok = bits.len() != p.len();\n for i in 0..N {\n if !ok {\n if (bits[i] != p[i]) {\n assert(p[i] == 1);\n ok = true;\n }\n }\n }\n assert(ok);\n }\n bits\n }\n\n /// Decomposes `self` into its little endian byte decomposition as a `[u8;N]` array\n /// This array will be zero padded should not all bytes be necessary to represent `self`.\n ///\n /// # Failures\n /// The length N of the array must be big enough to contain all the bytes of the 'self',\n /// and no more than the number of bytes required to represent the field modulus\n ///\n /// # Safety\n /// The result is ensured to be the canonical decomposition of the field element\n // docs:start:to_le_bytes\n pub fn to_le_bytes(self: Self) -> [u8; N] {\n // docs:end:to_le_bytes\n static_assert(\n N <= modulus_le_bytes().len(),\n \"N must be less than or equal to modulus_le_bytes().len()\",\n );\n // Compute the byte decomposition\n let bytes = self.to_le_radix(256);\n\n if !is_unconstrained() {\n // Ensure that the byte decomposition does not overflow the modulus\n let p = modulus_le_bytes();\n assert(bytes.len() <= p.len());\n let mut ok = bytes.len() != p.len();\n for i in 0..N {\n if !ok {\n if (bytes[N - 1 - i] != p[N - 1 - i]) {\n assert(bytes[N - 1 - i] < p[N - 1 - i]);\n ok = true;\n }\n }\n }\n assert(ok);\n }\n bytes\n }\n\n /// Decomposes `self` into its big endian byte decomposition as a `[u8;N]` array of length required to represent the field modulus\n /// This array will be zero padded should not all bytes be necessary to represent `self`.\n ///\n /// # Failures\n /// The length N of the array must be big enough to contain all the bytes of the 'self',\n /// and no more than the number of bytes required to represent the field modulus\n ///\n /// # Safety\n /// The result is ensured to be the canonical decomposition of the field element\n // docs:start:to_be_bytes\n pub fn to_be_bytes(self: Self) -> [u8; N] {\n // docs:end:to_be_bytes\n static_assert(\n N <= modulus_le_bytes().len(),\n \"N must be less than or equal to modulus_le_bytes().len()\",\n );\n // Compute the byte decomposition\n let bytes = self.to_be_radix(256);\n\n if !is_unconstrained() {\n // Ensure that the byte decomposition does not overflow the modulus\n let p = modulus_be_bytes();\n assert(bytes.len() <= p.len());\n let mut ok = bytes.len() != p.len();\n for i in 0..N {\n if !ok {\n if (bytes[i] != p[i]) {\n assert(bytes[i] < p[i]);\n ok = true;\n }\n }\n }\n assert(ok);\n }\n bytes\n }\n\n fn to_le_radix(self: Self, radix: u32) -> [u8; N] {\n // Brillig does not need an immediate radix\n if !crate::runtime::is_unconstrained() {\n static_assert(1 < radix, \"radix must be greater than 1\");\n static_assert(radix <= 256, \"radix must be less than or equal to 256\");\n static_assert(radix & (radix - 1) == 0, \"radix must be a power of 2\");\n }\n __to_le_radix(self, radix)\n }\n\n fn to_be_radix(self: Self, radix: u32) -> [u8; N] {\n // Brillig does not need an immediate radix\n if !crate::runtime::is_unconstrained() {\n static_assert(1 < radix, \"radix must be greater than 1\");\n static_assert(radix <= 256, \"radix must be less than or equal to 256\");\n static_assert(radix & (radix - 1) == 0, \"radix must be a power of 2\");\n }\n __to_be_radix(self, radix)\n }\n\n // Returns self to the power of the given exponent value.\n // Caution: we assume the exponent fits into 32 bits\n // using a bigger bit size impacts negatively the performance and should be done only if the exponent does not fit in 32 bits\n pub fn pow_32(self, exponent: Field) -> Field {\n let mut r: Field = 1;\n let b: [u1; 32] = exponent.to_le_bits();\n\n for i in 1..33 {\n r *= r;\n r = (b[32 - i] as Field) * (r * self) + (1 - b[32 - i] as Field) * r;\n }\n r\n }\n\n // Parity of (prime) Field element, i.e. sgn0(x mod p) = 0 if x `elem` {0, ..., p-1} is even, otherwise sgn0(x mod p) = 1.\n pub fn sgn0(self) -> u1 {\n self as u1\n }\n\n pub fn lt(self, another: Field) -> bool {\n if crate::compat::is_bn254() {\n bn254_lt(self, another)\n } else {\n lt_fallback(self, another)\n }\n }\n\n /// Convert a little endian byte array to a field element.\n /// If the provided byte array overflows the field modulus then the Field will silently wrap around.\n pub fn from_le_bytes(bytes: [u8; N]) -> Field {\n static_assert(\n N <= modulus_le_bytes().len(),\n \"N must be less than or equal to modulus_le_bytes().len()\",\n );\n let mut v = 1;\n let mut result = 0;\n\n for i in 0..N {\n result += (bytes[i] as Field) * v;\n v = v * 256;\n }\n result\n }\n\n /// Convert a big endian byte array to a field element.\n /// If the provided byte array overflows the field modulus then the Field will silently wrap around.\n pub fn from_be_bytes(bytes: [u8; N]) -> Field {\n let mut v = 1;\n let mut result = 0;\n\n for i in 0..N {\n result += (bytes[N - 1 - i] as Field) * v;\n v = v * 256;\n }\n result\n }\n}\n\n#[builtin(apply_range_constraint)]\nfn __assert_max_bit_size(value: Field, bit_size: u32) {}\n\n// `_radix` must be less than 256\n#[builtin(to_le_radix)]\nfn __to_le_radix(value: Field, radix: u32) -> [u8; N] {}\n\n// `_radix` must be less than 256\n#[builtin(to_be_radix)]\nfn __to_be_radix(value: Field, radix: u32) -> [u8; N] {}\n\n/// Decomposes `self` into its little endian bit decomposition as a `[u1; N]` array.\n/// This array will be zero padded should not all bits be necessary to represent `self`.\n///\n/// # Failures\n/// Causes a constraint failure for `Field` values exceeding `2^N` as the resulting array will not\n/// be able to represent the original `Field`.\n///\n/// # Safety\n/// Values of `N` equal to or greater than the number of bits necessary to represent the `Field` modulus\n/// (e.g. 254 for the BN254 field) allow for multiple bit decompositions. This is due to how the `Field` will\n/// wrap around due to overflow when verifying the decomposition.\n#[builtin(to_le_bits)]\nfn __to_le_bits(value: Field) -> [u1; N] {}\n\n/// Decomposes `self` into its big endian bit decomposition as a `[u1; N]` array.\n/// This array will be zero padded should not all bits be necessary to represent `self`.\n///\n/// # Failures\n/// Causes a constraint failure for `Field` values exceeding `2^N` as the resulting array will not\n/// be able to represent the original `Field`.\n///\n/// # Safety\n/// Values of `N` equal to or greater than the number of bits necessary to represent the `Field` modulus\n/// (e.g. 254 for the BN254 field) allow for multiple bit decompositions. This is due to how the `Field` will\n/// wrap around due to overflow when verifying the decomposition.\n#[builtin(to_be_bits)]\nfn __to_be_bits(value: Field) -> [u1; N] {}\n\n#[builtin(modulus_num_bits)]\npub comptime fn modulus_num_bits() -> u64 {}\n\n#[builtin(modulus_be_bits)]\npub comptime fn modulus_be_bits() -> [u1] {}\n\n#[builtin(modulus_le_bits)]\npub comptime fn modulus_le_bits() -> [u1] {}\n\n#[builtin(modulus_be_bytes)]\npub comptime fn modulus_be_bytes() -> [u8] {}\n\n#[builtin(modulus_le_bytes)]\npub comptime fn modulus_le_bytes() -> [u8] {}\n\n/// An unconstrained only built in to efficiently compare fields.\n#[builtin(field_less_than)]\nunconstrained fn __field_less_than(x: Field, y: Field) -> bool {}\n\npub(crate) unconstrained fn field_less_than(x: Field, y: Field) -> bool {\n __field_less_than(x, y)\n}\n\n// Convert a 32 byte array to a field element by modding\npub fn bytes32_to_field(bytes32: [u8; 32]) -> Field {\n // Convert it to a field element\n let mut v = 1;\n let mut high = 0 as Field;\n let mut low = 0 as Field;\n\n for i in 0..16 {\n high = high + (bytes32[15 - i] as Field) * v;\n low = low + (bytes32[16 + 15 - i] as Field) * v;\n v = v * 256;\n }\n // Abuse that a % p + b % p = (a + b) % p and that low < p\n low + high * v\n}\n\nfn lt_fallback(x: Field, y: Field) -> bool {\n if is_unconstrained() {\n // Safety: unconstrained context\n unsafe {\n field_less_than(x, y)\n }\n } else {\n let x_bytes: [u8; 32] = x.to_le_bytes();\n let y_bytes: [u8; 32] = y.to_le_bytes();\n let mut x_is_lt = false;\n let mut done = false;\n for i in 0..32 {\n if (!done) {\n let x_byte = x_bytes[32 - 1 - i] as u8;\n let y_byte = y_bytes[32 - 1 - i] as u8;\n let bytes_match = x_byte == y_byte;\n if !bytes_match {\n x_is_lt = x_byte < y_byte;\n done = true;\n }\n }\n }\n x_is_lt\n }\n}\n\nmod tests {\n use crate::{panic::panic, runtime, static_assert};\n use super::{\n field_less_than, modulus_be_bits, modulus_be_bytes, modulus_le_bits, modulus_le_bytes,\n };\n\n #[test]\n // docs:start:to_be_bits_example\n fn test_to_be_bits() {\n let field = 2;\n let bits: [u1; 8] = field.to_be_bits();\n assert_eq(bits, [0, 0, 0, 0, 0, 0, 1, 0]);\n }\n // docs:end:to_be_bits_example\n\n #[test]\n // docs:start:to_le_bits_example\n fn test_to_le_bits() {\n let field = 2;\n let bits: [u1; 8] = field.to_le_bits();\n assert_eq(bits, [0, 1, 0, 0, 0, 0, 0, 0]);\n }\n // docs:end:to_le_bits_example\n\n #[test]\n // docs:start:to_be_bytes_example\n fn test_to_be_bytes() {\n let field = 2;\n let bytes: [u8; 8] = field.to_be_bytes();\n assert_eq(bytes, [0, 0, 0, 0, 0, 0, 0, 2]);\n assert_eq(Field::from_be_bytes::<8>(bytes), field);\n }\n // docs:end:to_be_bytes_example\n\n #[test]\n // docs:start:to_le_bytes_example\n fn test_to_le_bytes() {\n let field = 2;\n let bytes: [u8; 8] = field.to_le_bytes();\n assert_eq(bytes, [2, 0, 0, 0, 0, 0, 0, 0]);\n assert_eq(Field::from_le_bytes::<8>(bytes), field);\n }\n // docs:end:to_le_bytes_example\n\n #[test]\n // docs:start:to_be_radix_example\n fn test_to_be_radix() {\n // 259, in base 256, big endian, is [1, 3].\n // i.e. 3 * 256^0 + 1 * 256^1\n let field = 259;\n\n // The radix (in this example, 256) must be a power of 2.\n // The length of the returned byte array can be specified to be\n // >= the amount of space needed.\n let bytes: [u8; 8] = field.to_be_radix(256);\n assert_eq(bytes, [0, 0, 0, 0, 0, 0, 1, 3]);\n assert_eq(Field::from_be_bytes::<8>(bytes), field);\n }\n // docs:end:to_be_radix_example\n\n #[test]\n // docs:start:to_le_radix_example\n fn test_to_le_radix() {\n // 259, in base 256, little endian, is [3, 1].\n // i.e. 3 * 256^0 + 1 * 256^1\n let field = 259;\n\n // The radix (in this example, 256) must be a power of 2.\n // The length of the returned byte array can be specified to be\n // >= the amount of space needed.\n let bytes: [u8; 8] = field.to_le_radix(256);\n assert_eq(bytes, [3, 1, 0, 0, 0, 0, 0, 0]);\n assert_eq(Field::from_le_bytes::<8>(bytes), field);\n }\n // docs:end:to_le_radix_example\n\n #[test(should_fail_with = \"radix must be greater than 1\")]\n fn test_to_le_radix_1() {\n // this test should only fail in constrained mode\n if !runtime::is_unconstrained() {\n let field = 2;\n let _: [u8; 8] = field.to_le_radix(1);\n } else {\n panic(\"radix must be greater than 1\");\n }\n }\n\n // Updated test to account for Brillig restriction that radix must be greater than 2\n #[test(should_fail_with = \"radix must be greater than 1\")]\n fn test_to_le_radix_brillig_1() {\n // this test should only fail in constrained mode\n if !runtime::is_unconstrained() {\n let field = 1;\n let _: [u8; 8] = field.to_le_radix(1);\n } else {\n panic(\"radix must be greater than 1\");\n }\n }\n\n #[test(should_fail_with = \"radix must be a power of 2\")]\n fn test_to_le_radix_3() {\n // this test should only fail in constrained mode\n if !runtime::is_unconstrained() {\n let field = 2;\n let _: [u8; 8] = field.to_le_radix(3);\n } else {\n panic(\"radix must be a power of 2\");\n }\n }\n\n #[test]\n fn test_to_le_radix_brillig_3() {\n // this test should only fail in constrained mode\n if runtime::is_unconstrained() {\n let field = 1;\n let out: [u8; 8] = field.to_le_radix(3);\n let mut expected = [0; 8];\n expected[0] = 1;\n assert(out == expected, \"unexpected result\");\n }\n }\n\n #[test(should_fail_with = \"radix must be less than or equal to 256\")]\n fn test_to_le_radix_512() {\n // this test should only fail in constrained mode\n if !runtime::is_unconstrained() {\n let field = 2;\n let _: [u8; 8] = field.to_le_radix(512);\n } else {\n panic(\"radix must be less than or equal to 256\")\n }\n }\n\n #[test(should_fail_with = \"Field failed to decompose into specified 16 limbs\")]\n unconstrained fn not_enough_limbs_brillig() {\n let _: [u8; 16] = 0x100000000000000000000000000000000.to_le_bytes();\n }\n\n #[test(should_fail_with = \"Field failed to decompose into specified 16 limbs\")]\n fn not_enough_limbs() {\n let _: [u8; 16] = 0x100000000000000000000000000000000.to_le_bytes();\n }\n\n #[test]\n unconstrained fn test_field_less_than() {\n assert(field_less_than(0, 1));\n assert(field_less_than(0, 0x100));\n assert(field_less_than(0x100, 0 - 1));\n assert(!field_less_than(0 - 1, 0));\n }\n\n #[test]\n unconstrained fn test_large_field_values_unconstrained() {\n let large_field = 0xffffffffffffffff;\n\n let bits: [u1; 64] = large_field.to_le_bits();\n assert_eq(bits[0], 1);\n\n let bytes: [u8; 8] = large_field.to_le_bytes();\n assert_eq(Field::from_le_bytes::<8>(bytes), large_field);\n\n let radix_bytes: [u8; 8] = large_field.to_le_radix(256);\n assert_eq(Field::from_le_bytes::<8>(radix_bytes), large_field);\n }\n\n #[test]\n fn test_large_field_values() {\n let large_val = 0xffffffffffffffff;\n\n let bits: [u1; 64] = large_val.to_le_bits();\n assert_eq(bits[0], 1);\n\n let bytes: [u8; 8] = large_val.to_le_bytes();\n assert_eq(Field::from_le_bytes::<8>(bytes), large_val);\n\n let radix_bytes: [u8; 8] = large_val.to_le_radix(256);\n assert_eq(Field::from_le_bytes::<8>(radix_bytes), large_val);\n }\n\n #[test]\n fn test_decomposition_edge_cases() {\n let zero_bits: [u1; 8] = 0.to_le_bits();\n assert_eq(zero_bits, [0; 8]);\n\n let zero_bytes: [u8; 8] = 0.to_le_bytes();\n assert_eq(zero_bytes, [0; 8]);\n\n let one_bits: [u1; 8] = 1.to_le_bits();\n let expected: [u1; 8] = [1, 0, 0, 0, 0, 0, 0, 0];\n assert_eq(one_bits, expected);\n\n let pow2_bits: [u1; 8] = 4.to_le_bits();\n let expected: [u1; 8] = [0, 0, 1, 0, 0, 0, 0, 0];\n assert_eq(pow2_bits, expected);\n }\n\n #[test]\n fn test_pow_32() {\n assert_eq(2.pow_32(3), 8);\n assert_eq(3.pow_32(2), 9);\n assert_eq(5.pow_32(0), 1);\n assert_eq(7.pow_32(1), 7);\n\n assert_eq(2.pow_32(10), 1024);\n\n assert_eq(0.pow_32(5), 0);\n assert_eq(0.pow_32(0), 1);\n\n assert_eq(1.pow_32(100), 1);\n }\n\n #[test]\n fn test_sgn0() {\n assert_eq(0.sgn0(), 0);\n assert_eq(2.sgn0(), 0);\n assert_eq(4.sgn0(), 0);\n assert_eq(100.sgn0(), 0);\n\n assert_eq(1.sgn0(), 1);\n assert_eq(3.sgn0(), 1);\n assert_eq(5.sgn0(), 1);\n assert_eq(101.sgn0(), 1);\n }\n\n #[test(should_fail_with = \"Field failed to decompose into specified 8 limbs\")]\n fn test_bit_decomposition_overflow() {\n // 8 bits can't represent large field values\n let large_val = 0x1000000000000000;\n let _: [u1; 8] = large_val.to_le_bits();\n }\n\n #[test(should_fail_with = \"Field failed to decompose into specified 4 limbs\")]\n fn test_byte_decomposition_overflow() {\n // 4 bytes can't represent large field values\n let large_val = 0x1000000000000000;\n let _: [u8; 4] = large_val.to_le_bytes();\n }\n\n #[test]\n fn test_to_from_be_bytes_bn254_edge_cases() {\n if crate::compat::is_bn254() {\n // checking that decrementing this byte produces the expected 32 BE bytes for (modulus - 1)\n let mut p_minus_1_bytes: [u8; 32] = modulus_be_bytes().as_array();\n assert(p_minus_1_bytes[32 - 1] > 0);\n p_minus_1_bytes[32 - 1] -= 1;\n\n let p_minus_1 = Field::from_be_bytes::<32>(p_minus_1_bytes);\n assert_eq(p_minus_1 + 1, 0);\n\n // checking that converting (modulus - 1) from and then to 32 BE bytes produces the same bytes\n let p_minus_1_converted_bytes: [u8; 32] = p_minus_1.to_be_bytes();\n assert_eq(p_minus_1_converted_bytes, p_minus_1_bytes);\n\n // checking that incrementing this byte produces 32 BE bytes for (modulus + 1)\n let mut p_plus_1_bytes: [u8; 32] = modulus_be_bytes().as_array();\n assert(p_plus_1_bytes[32 - 1] < 255);\n p_plus_1_bytes[32 - 1] += 1;\n\n let p_plus_1 = Field::from_be_bytes::<32>(p_plus_1_bytes);\n assert_eq(p_plus_1, 1);\n\n // checking that converting p_plus_1 to 32 BE bytes produces the same\n // byte set to 1 as p_plus_1_bytes and otherwise zeroes\n let mut p_plus_1_converted_bytes: [u8; 32] = p_plus_1.to_be_bytes();\n assert_eq(p_plus_1_converted_bytes[32 - 1], 1);\n p_plus_1_converted_bytes[32 - 1] = 0;\n assert_eq(p_plus_1_converted_bytes, [0; 32]);\n\n // checking that Field::from_be_bytes::<32> on the Field modulus produces 0\n assert_eq(modulus_be_bytes().len(), 32);\n let p = Field::from_be_bytes::<32>(modulus_be_bytes().as_array());\n assert_eq(p, 0);\n\n // checking that converting 0 to 32 BE bytes produces 32 zeroes\n let p_bytes: [u8; 32] = 0.to_be_bytes();\n assert_eq(p_bytes, [0; 32]);\n }\n }\n\n #[test]\n fn test_to_from_le_bytes_bn254_edge_cases() {\n if crate::compat::is_bn254() {\n // checking that decrementing this byte produces the expected 32 LE bytes for (modulus - 1)\n let mut p_minus_1_bytes: [u8; 32] = modulus_le_bytes().as_array();\n assert(p_minus_1_bytes[0] > 0);\n p_minus_1_bytes[0] -= 1;\n\n let p_minus_1 = Field::from_le_bytes::<32>(p_minus_1_bytes);\n assert_eq(p_minus_1 + 1, 0);\n\n // checking that converting (modulus - 1) from and then to 32 BE bytes produces the same bytes\n let p_minus_1_converted_bytes: [u8; 32] = p_minus_1.to_le_bytes();\n assert_eq(p_minus_1_converted_bytes, p_minus_1_bytes);\n\n // checking that incrementing this byte produces 32 LE bytes for (modulus + 1)\n let mut p_plus_1_bytes: [u8; 32] = modulus_le_bytes().as_array();\n assert(p_plus_1_bytes[0] < 255);\n p_plus_1_bytes[0] += 1;\n\n let p_plus_1 = Field::from_le_bytes::<32>(p_plus_1_bytes);\n assert_eq(p_plus_1, 1);\n\n // checking that converting p_plus_1 to 32 LE bytes produces the same\n // byte set to 1 as p_plus_1_bytes and otherwise zeroes\n let mut p_plus_1_converted_bytes: [u8; 32] = p_plus_1.to_le_bytes();\n assert_eq(p_plus_1_converted_bytes[0], 1);\n p_plus_1_converted_bytes[0] = 0;\n assert_eq(p_plus_1_converted_bytes, [0; 32]);\n\n // checking that Field::from_le_bytes::<32> on the Field modulus produces 0\n assert_eq(modulus_le_bytes().len(), 32);\n let p = Field::from_le_bytes::<32>(modulus_le_bytes().as_array());\n assert_eq(p, 0);\n\n // checking that converting 0 to 32 LE bytes produces 32 zeroes\n let p_bytes: [u8; 32] = 0.to_le_bytes();\n assert_eq(p_bytes, [0; 32]);\n }\n }\n\n /// Convert a little endian bit array to a field element.\n /// If the provided bit array overflows the field modulus then the Field will silently wrap around.\n fn from_le_bits(bits: [u1; N]) -> Field {\n static_assert(\n N <= modulus_le_bits().len(),\n \"N must be less than or equal to modulus_le_bits().len()\",\n );\n let mut v = 1;\n let mut result = 0;\n\n for i in 0..N {\n result += (bits[i] as Field) * v;\n v = v * 2;\n }\n result\n }\n\n /// Convert a big endian bit array to a field element.\n /// If the provided bit array overflows the field modulus then the Field will silently wrap around.\n fn from_be_bits(bits: [u1; N]) -> Field {\n let mut v = 1;\n let mut result = 0;\n\n for i in 0..N {\n result += (bits[N - 1 - i] as Field) * v;\n v = v * 2;\n }\n result\n }\n\n #[test]\n fn test_to_from_be_bits_bn254_edge_cases() {\n if crate::compat::is_bn254() {\n // checking that decrementing this bit produces the expected 254 BE bits for (modulus - 1)\n let mut p_minus_1_bits: [u1; 254] = modulus_be_bits().as_array();\n assert(p_minus_1_bits[254 - 1] > 0);\n p_minus_1_bits[254 - 1] -= 1;\n\n let p_minus_1 = from_be_bits::<254>(p_minus_1_bits);\n assert_eq(p_minus_1 + 1, 0);\n\n // checking that converting (modulus - 1) from and then to 254 BE bits produces the same bits\n let p_minus_1_converted_bits: [u1; 254] = p_minus_1.to_be_bits();\n assert_eq(p_minus_1_converted_bits, p_minus_1_bits);\n\n // checking that incrementing this bit produces 254 BE bits for (modulus + 4)\n let mut p_plus_4_bits: [u1; 254] = modulus_be_bits().as_array();\n assert(p_plus_4_bits[254 - 3] < 1);\n p_plus_4_bits[254 - 3] += 1;\n\n let p_plus_4 = from_be_bits::<254>(p_plus_4_bits);\n assert_eq(p_plus_4, 4);\n\n // checking that converting p_plus_4 to 254 BE bits produces the same\n // bit set to 1 as p_plus_4_bits and otherwise zeroes\n let mut p_plus_4_converted_bits: [u1; 254] = p_plus_4.to_be_bits();\n assert_eq(p_plus_4_converted_bits[254 - 3], 1);\n p_plus_4_converted_bits[254 - 3] = 0;\n assert_eq(p_plus_4_converted_bits, [0; 254]);\n\n // checking that Field::from_be_bits::<254> on the Field modulus produces 0\n assert_eq(modulus_be_bits().len(), 254);\n let p = from_be_bits::<254>(modulus_be_bits().as_array());\n assert_eq(p, 0);\n\n // checking that converting 0 to 254 BE bytes produces 254 zeroes\n let p_bits: [u1; 254] = 0.to_be_bits();\n assert_eq(p_bits, [0; 254]);\n }\n }\n\n #[test]\n fn test_to_from_le_bits_bn254_edge_cases() {\n if crate::compat::is_bn254() {\n // checking that decrementing this bit produces the expected 254 LE bits for (modulus - 1)\n let mut p_minus_1_bits: [u1; 254] = modulus_le_bits().as_array();\n assert(p_minus_1_bits[0] > 0);\n p_minus_1_bits[0] -= 1;\n\n let p_minus_1 = from_le_bits::<254>(p_minus_1_bits);\n assert_eq(p_minus_1 + 1, 0);\n\n // checking that converting (modulus - 1) from and then to 254 BE bits produces the same bits\n let p_minus_1_converted_bits: [u1; 254] = p_minus_1.to_le_bits();\n assert_eq(p_minus_1_converted_bits, p_minus_1_bits);\n\n // checking that incrementing this bit produces 254 LE bits for (modulus + 4)\n let mut p_plus_4_bits: [u1; 254] = modulus_le_bits().as_array();\n assert(p_plus_4_bits[2] < 1);\n p_plus_4_bits[2] += 1;\n\n let p_plus_4 = from_le_bits::<254>(p_plus_4_bits);\n assert_eq(p_plus_4, 4);\n\n // checking that converting p_plus_4 to 254 LE bits produces the same\n // bit set to 1 as p_plus_4_bits and otherwise zeroes\n let mut p_plus_4_converted_bits: [u1; 254] = p_plus_4.to_le_bits();\n assert_eq(p_plus_4_converted_bits[2], 1);\n p_plus_4_converted_bits[2] = 0;\n assert_eq(p_plus_4_converted_bits, [0; 254]);\n\n // checking that Field::from_le_bits::<254> on the Field modulus produces 0\n assert_eq(modulus_le_bits().len(), 254);\n let p = from_le_bits::<254>(modulus_le_bits().as_array());\n assert_eq(p, 0);\n\n // checking that converting 0 to 254 LE bytes produces 254 zeroes\n let p_bits: [u1; 254] = 0.to_le_bits();\n assert_eq(p_bits, [0; 254]);\n }\n }\n}\n" }, "19": { "path": "std/hash/mod.nr", "source": "// Exposed only for usage in `std::meta`\npub(crate) mod poseidon2;\n\nuse crate::default::Default;\nuse crate::embedded_curve_ops::{\n EmbeddedCurvePoint, EmbeddedCurveScalar, multi_scalar_mul, multi_scalar_mul_array_return,\n};\nuse crate::meta::derive_via;\n\n#[foreign(sha256_compression)]\n// docs:start:sha256_compression\npub fn sha256_compression(input: [u32; 16], state: [u32; 8]) -> [u32; 8] {}\n// docs:end:sha256_compression\n\n#[foreign(keccakf1600)]\n// docs:start:keccakf1600\npub fn keccakf1600(input: [u64; 25]) -> [u64; 25] {}\n// docs:end:keccakf1600\n\npub mod keccak {\n #[deprecated(\"This function has been moved to std::hash::keccakf1600\")]\n pub fn keccakf1600(input: [u64; 25]) -> [u64; 25] {\n super::keccakf1600(input)\n }\n}\n\n#[foreign(blake2s)]\n// docs:start:blake2s\npub fn blake2s(input: [u8; N]) -> [u8; 32]\n// docs:end:blake2s\n{}\n\n// docs:start:blake3\npub fn blake3(input: [u8; N]) -> [u8; 32]\n// docs:end:blake3\n{\n if crate::runtime::is_unconstrained() {\n // Temporary measure while Barretenberg is main proving system.\n // Please open an issue if you're working on another proving system and running into problems due to this.\n crate::static_assert(\n N <= 1024,\n \"Barretenberg cannot prove blake3 hashes with inputs larger than 1024 bytes\",\n );\n }\n __blake3(input)\n}\n\n#[foreign(blake3)]\nfn __blake3(input: [u8; N]) -> [u8; 32] {}\n\n// docs:start:pedersen_commitment\npub fn pedersen_commitment(input: [Field; N]) -> EmbeddedCurvePoint {\n // docs:end:pedersen_commitment\n pedersen_commitment_with_separator(input, 0)\n}\n\n#[inline_always]\npub fn pedersen_commitment_with_separator(\n input: [Field; N],\n separator: u32,\n) -> EmbeddedCurvePoint {\n let mut points = [EmbeddedCurveScalar { lo: 0, hi: 0 }; N];\n for i in 0..N {\n // we use the unsafe version because the multi_scalar_mul will constrain the scalars.\n points[i] = from_field_unsafe(input[i]);\n }\n let generators = derive_generators(\"DEFAULT_DOMAIN_SEPARATOR\".as_bytes(), separator);\n multi_scalar_mul(generators, points)\n}\n\n// docs:start:pedersen_hash\npub fn pedersen_hash(input: [Field; N]) -> Field\n// docs:end:pedersen_hash\n{\n pedersen_hash_with_separator(input, 0)\n}\n\n#[no_predicates]\npub fn pedersen_hash_with_separator(input: [Field; N], separator: u32) -> Field {\n let mut scalars: [EmbeddedCurveScalar; N + 1] = [EmbeddedCurveScalar { lo: 0, hi: 0 }; N + 1];\n let mut generators: [EmbeddedCurvePoint; N + 1] =\n [EmbeddedCurvePoint::point_at_infinity(); N + 1];\n crate::assert_constant(separator);\n let domain_generators: [EmbeddedCurvePoint; N] =\n derive_generators(\"DEFAULT_DOMAIN_SEPARATOR\".as_bytes(), separator);\n\n for i in 0..N {\n scalars[i] = from_field_unsafe(input[i]);\n generators[i] = domain_generators[i];\n }\n scalars[N] = EmbeddedCurveScalar { lo: N as Field, hi: 0 as Field };\n\n let length_generator: [EmbeddedCurvePoint; 1] =\n derive_generators(\"pedersen_hash_length\".as_bytes(), 0);\n generators[N] = length_generator[0];\n multi_scalar_mul_array_return(generators, scalars, true)[0].x\n}\n\n#[field(bn254)]\n#[inline_always]\npub fn derive_generators(\n domain_separator_bytes: [u8; M],\n starting_index: u32,\n) -> [EmbeddedCurvePoint; N] {\n crate::assert_constant(domain_separator_bytes);\n crate::assert_constant(starting_index);\n __derive_generators(domain_separator_bytes, starting_index)\n}\n\n#[builtin(derive_pedersen_generators)]\n#[field(bn254)]\nfn __derive_generators(\n domain_separator_bytes: [u8; M],\n starting_index: u32,\n) -> [EmbeddedCurvePoint; N] {}\n\n#[field(bn254)]\n// Decompose the input 'bn254 scalar' into two 128 bits limbs.\n// It is called 'unsafe' because it does not assert the limbs are 128 bits\n// Assuming the limbs are 128 bits:\n// Assert the decomposition does not overflow the field size.\nfn from_field_unsafe(scalar: Field) -> EmbeddedCurveScalar {\n // Safety: xlo and xhi decomposition is checked below\n let (xlo, xhi) = unsafe { crate::field::bn254::decompose_hint(scalar) };\n // Check that the decomposition is correct\n assert_eq(scalar, xlo + crate::field::bn254::TWO_POW_128 * xhi);\n // Check that the decomposition does not overflow the field size\n let (a, b) = if xhi == crate::field::bn254::PHI {\n (xlo, crate::field::bn254::PLO)\n } else {\n (xhi, crate::field::bn254::PHI)\n };\n crate::field::bn254::assert_lt(a, b);\n\n EmbeddedCurveScalar { lo: xlo, hi: xhi }\n}\n\npub fn poseidon2_permutation(input: [Field; N], state_len: u32) -> [Field; N] {\n assert_eq(input.len(), state_len);\n poseidon2_permutation_internal(input)\n}\n\n#[foreign(poseidon2_permutation)]\nfn poseidon2_permutation_internal(input: [Field; N]) -> [Field; N] {}\n\n// Generic hashing support.\n// Partially ported and impacted by rust.\n\n// Hash trait shall be implemented per type.\n#[derive_via(derive_hash)]\npub trait Hash {\n fn hash(self, state: &mut H)\n where\n H: Hasher;\n}\n\n// docs:start:derive_hash\ncomptime fn derive_hash(s: TypeDefinition) -> Quoted {\n let name = quote { $crate::hash::Hash };\n let signature = quote { fn hash(_self: Self, _state: &mut H) where H: $crate::hash::Hasher };\n let for_each_field = |name| quote { _self.$name.hash(_state); };\n crate::meta::make_trait_impl(\n s,\n name,\n signature,\n for_each_field,\n quote {},\n |fields| fields,\n )\n}\n// docs:end:derive_hash\n\n// Hasher trait shall be implemented by algorithms to provide hash-agnostic means.\n// TODO: consider making the types generic here ([u8], [Field], etc.)\npub trait Hasher {\n fn finish(self) -> Field;\n\n fn write(&mut self, input: Field);\n}\n\n// BuildHasher is a factory trait, responsible for production of specific Hasher.\npub trait BuildHasher {\n type H: Hasher;\n\n fn build_hasher(self) -> H;\n}\n\npub struct BuildHasherDefault;\n\nimpl BuildHasher for BuildHasherDefault\nwhere\n H: Hasher + Default,\n{\n type H = H;\n\n fn build_hasher(_self: Self) -> H {\n H::default()\n }\n}\n\nimpl Default for BuildHasherDefault\nwhere\n H: Hasher + Default,\n{\n fn default() -> Self {\n BuildHasherDefault {}\n }\n}\n\nimpl Hash for Field {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self);\n }\n}\n\nimpl Hash for u1 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for u8 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for u16 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for u32 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for u64 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for u128 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for i8 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as u8 as Field);\n }\n}\n\nimpl Hash for i16 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as u16 as Field);\n }\n}\n\nimpl Hash for i32 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as u32 as Field);\n }\n}\n\nimpl Hash for i64 {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as u64 as Field);\n }\n}\n\nimpl Hash for bool {\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n H::write(state, self as Field);\n }\n}\n\nimpl Hash for () {\n fn hash(_self: Self, _state: &mut H)\n where\n H: Hasher,\n {}\n}\n\nimpl Hash for [T; N]\nwhere\n T: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n for elem in self {\n elem.hash(state);\n }\n }\n}\n\nimpl Hash for [T]\nwhere\n T: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self.len().hash(state);\n for elem in self {\n elem.hash(state);\n }\n }\n}\n\nimpl Hash for (A, B)\nwhere\n A: Hash,\n B: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self.0.hash(state);\n self.1.hash(state);\n }\n}\n\nimpl Hash for (A, B, C)\nwhere\n A: Hash,\n B: Hash,\n C: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self.0.hash(state);\n self.1.hash(state);\n self.2.hash(state);\n }\n}\n\nimpl Hash for (A, B, C, D)\nwhere\n A: Hash,\n B: Hash,\n C: Hash,\n D: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self.0.hash(state);\n self.1.hash(state);\n self.2.hash(state);\n self.3.hash(state);\n }\n}\n\nimpl Hash for (A, B, C, D, E)\nwhere\n A: Hash,\n B: Hash,\n C: Hash,\n D: Hash,\n E: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self.0.hash(state);\n self.1.hash(state);\n self.2.hash(state);\n self.3.hash(state);\n self.4.hash(state);\n }\n}\n\n// Some test vectors for Pedersen hash and Pedersen Commitment.\n// They have been generated using the same functions so the tests are for now useless\n// but they will be useful when we switch to Noir implementation.\n#[test]\nfn assert_pedersen() {\n assert_eq(\n pedersen_hash_with_separator([1], 1),\n 0x1b3f4b1a83092a13d8d1a59f7acb62aba15e7002f4440f2275edb99ebbc2305f,\n );\n assert_eq(\n pedersen_commitment_with_separator([1], 1),\n EmbeddedCurvePoint {\n x: 0x054aa86a73cb8a34525e5bbed6e43ba1198e860f5f3950268f71df4591bde402,\n y: 0x209dcfbf2cfb57f9f6046f44d71ac6faf87254afc7407c04eb621a6287cac126,\n is_infinite: false,\n },\n );\n\n assert_eq(\n pedersen_hash_with_separator([1, 2], 2),\n 0x26691c129448e9ace0c66d11f0a16d9014a9e8498ee78f4d69f0083168188255,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2], 2),\n EmbeddedCurvePoint {\n x: 0x2e2b3b191e49541fe468ec6877721d445dcaffe41728df0a0eafeb15e87b0753,\n y: 0x2ff4482400ad3a6228be17a2af33e2bcdf41be04795f9782bd96efe7e24f8778,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3], 3),\n 0x0bc694b7a1f8d10d2d8987d07433f26bd616a2d351bc79a3c540d85b6206dbe4,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3], 3),\n EmbeddedCurvePoint {\n x: 0x1fee4e8cf8d2f527caa2684236b07c4b1bad7342c01b0f75e9a877a71827dc85,\n y: 0x2f9fedb9a090697ab69bf04c8bc15f7385b3e4b68c849c1536e5ae15ff138fd1,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4], 4),\n 0xdae10fb32a8408521803905981a2b300d6a35e40e798743e9322b223a5eddc,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4], 4),\n EmbeddedCurvePoint {\n x: 0x07ae3e202811e1fca39c2d81eabe6f79183978e6f12be0d3b8eda095b79bdbc9,\n y: 0x0afc6f892593db6fbba60f2da558517e279e0ae04f95758587760ba193145014,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5], 5),\n 0xfc375b062c4f4f0150f7100dfb8d9b72a6d28582dd9512390b0497cdad9c22,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5], 5),\n EmbeddedCurvePoint {\n x: 0x1754b12bd475a6984a1094b5109eeca9838f4f81ac89c5f0a41dbce53189bb29,\n y: 0x2da030e3cfcdc7ddad80eaf2599df6692cae0717d4e9f7bfbee8d073d5d278f7,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5, 6], 6),\n 0x1696ed13dc2730062a98ac9d8f9de0661bb98829c7582f699d0273b18c86a572,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5, 6], 6),\n EmbeddedCurvePoint {\n x: 0x190f6c0e97ad83e1e28da22a98aae156da083c5a4100e929b77e750d3106a697,\n y: 0x1f4b60f34ef91221a0b49756fa0705da93311a61af73d37a0c458877706616fb,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5, 6, 7], 7),\n 0x128c0ff144fc66b6cb60eeac8a38e23da52992fc427b92397a7dffd71c45ede3,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5, 6, 7], 7),\n EmbeddedCurvePoint {\n x: 0x015441e9d29491b06563fac16fc76abf7a9534c715421d0de85d20dbe2965939,\n y: 0x1d2575b0276f4e9087e6e07c2cb75aa1baafad127af4be5918ef8a2ef2fea8fc,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5, 6, 7, 8], 8),\n 0x2f960e117482044dfc99d12fece2ef6862fba9242be4846c7c9a3e854325a55c,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5, 6, 7, 8], 8),\n EmbeddedCurvePoint {\n x: 0x1657737676968887fceb6dd516382ea13b3a2c557f509811cd86d5d1199bc443,\n y: 0x1f39f0cb569040105fa1e2f156521e8b8e08261e635a2b210bdc94e8d6d65f77,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5, 6, 7, 8, 9], 9),\n 0x0c96db0790602dcb166cc4699e2d306c479a76926b81c2cb2aaa92d249ec7be7,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5, 6, 7, 8, 9], 9),\n EmbeddedCurvePoint {\n x: 0x0a3ceae42d14914a432aa60ec7fded4af7dad7dd4acdbf2908452675ec67e06d,\n y: 0xfc19761eaaf621ad4aec9a8b2e84a4eceffdba78f60f8b9391b0bd9345a2f2,\n is_infinite: false,\n },\n );\n assert_eq(\n pedersen_hash_with_separator([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 10),\n 0x2cd37505871bc460a62ea1e63c7fe51149df5d0801302cf1cbc48beb8dff7e94,\n );\n assert_eq(\n pedersen_commitment_with_separator([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 10),\n EmbeddedCurvePoint {\n x: 0x2fb3f8b3d41ddde007c8c3c62550f9a9380ee546fcc639ffbb3fd30c8d8de30c,\n y: 0x300783be23c446b11a4c0fabf6c91af148937cea15fcf5fb054abf7f752ee245,\n is_infinite: false,\n },\n );\n}\n" }, "25": { "path": "std/meta/expr.nr", "source": "//! Contains methods on the built-in `Expr` type for quoted, syntactically valid expressions.\n\nuse crate::meta::op::BinaryOp;\nuse crate::meta::op::UnaryOp;\nuse crate::option::Option;\n\nimpl Expr {\n /// If this expression is an array literal `[elem1, ..., elemN]`, this returns a vector of each element in the array.\n #[builtin(expr_as_array)]\n // docs:start:as_array\n pub comptime fn as_array(self) -> Option<[Expr]> {}\n // docs:end:as_array\n\n /// If this expression is an assert, this returns the assert expression and the optional message.\n #[builtin(expr_as_assert)]\n // docs:start:as_assert\n pub comptime fn as_assert(self) -> Option<(Expr, Option)> {}\n // docs:end:as_assert\n\n /// If this expression is an assert_eq, this returns the left-hand-side and right-hand-side\n /// expressions, together with the optional message.\n #[builtin(expr_as_assert_eq)]\n // docs:start:as_assert_eq\n pub comptime fn as_assert_eq(self) -> Option<(Expr, Expr, Option)> {}\n // docs:end:as_assert_eq\n\n /// If this expression is an assignment, this returns a tuple with the left hand side\n /// and right hand side in order.\n #[builtin(expr_as_assign)]\n // docs:start:as_assign\n pub comptime fn as_assign(self) -> Option<(Expr, Expr)> {}\n // docs:end:as_assign\n\n /// If this expression is a binary operator operation ` `,\n /// return the left-hand side, operator, and the right-hand side of the operation.\n #[builtin(expr_as_binary_op)]\n // docs:start:as_binary_op\n pub comptime fn as_binary_op(self) -> Option<(Expr, BinaryOp, Expr)> {}\n // docs:end:as_binary_op\n\n /// If this expression is a block `{ stmt1; stmt2; ...; stmtN }`, return\n /// a vector containing each statement.\n #[builtin(expr_as_block)]\n // docs:start:as_block\n pub comptime fn as_block(self) -> Option<[Expr]> {}\n // docs:end:as_block\n\n /// If this expression is a boolean literal, return that literal.\n #[builtin(expr_as_bool)]\n // docs:start:as_bool\n pub comptime fn as_bool(self) -> Option {}\n // docs:end:as_bool\n\n /// If this expression is a cast expression `expr as type`, returns the casted\n /// expression and the type to cast to.\n // docs:start:as_cast\n #[builtin(expr_as_cast)]\n pub comptime fn as_cast(self) -> Option<(Expr, UnresolvedType)> {}\n // docs:end:as_cast\n\n /// If this expression is a `comptime { stmt1; stmt2; ...; stmtN }` block,\n /// return each statement in the block.\n #[builtin(expr_as_comptime)]\n // docs:start:as_comptime\n pub comptime fn as_comptime(self) -> Option<[Expr]> {}\n // docs:end:as_comptime\n\n /// If this expression is a constructor `Type { field1: expr1, ..., fieldN: exprN }`,\n /// return the type and the fields.\n #[builtin(expr_as_constructor)]\n // docs:start:as_constructor\n pub comptime fn as_constructor(self) -> Option<(UnresolvedType, [(Quoted, Expr)])> {}\n // docs:end:as_constructor\n\n /// If this expression is a for statement over a single expression, return the identifier,\n /// the expression and the for loop body.\n #[builtin(expr_as_for)]\n // docs:start:as_for\n pub comptime fn as_for(self) -> Option<(Quoted, Expr, Expr)> {}\n // docs:end:as_for\n\n /// If this expression is a for statement over a range, return the identifier,\n /// the range start, the range end, whether the range is inclusive, and the for loop body.\n #[builtin(expr_as_for_range)]\n // docs:start:as_for_range\n pub comptime fn as_for_range(self) -> Option<(Quoted, Expr, Expr, bool, Expr)> {}\n // docs:end:as_for_range\n\n /// If this expression is a function call `foo(arg1, ..., argN)`, return\n /// the function and a vector of each argument.\n #[builtin(expr_as_function_call)]\n // docs:start:as_function_call\n pub comptime fn as_function_call(self) -> Option<(Expr, [Expr])> {}\n // docs:end:as_function_call\n\n /// If this expression is an `if condition { then_branch } else { else_branch }`,\n /// return the condition, then branch, and else branch. If there is no else branch,\n /// `None` is returned for that branch instead.\n #[builtin(expr_as_if)]\n // docs:start:as_if\n pub comptime fn as_if(self) -> Option<(Expr, Expr, Option)> {}\n // docs:end:as_if\n\n /// If this expression is an index into an array `array[index]`, return the\n /// array and the index.\n #[builtin(expr_as_index)]\n // docs:start:as_index\n pub comptime fn as_index(self) -> Option<(Expr, Expr)> {}\n // docs:end:as_index\n\n /// If this expression is an integer literal, return the integer as a field\n /// as well as whether the integer is negative (true) or not (false).\n #[builtin(expr_as_integer)]\n // docs:start:as_integer\n pub comptime fn as_integer(self) -> Option<(Field, bool)> {}\n // docs:end:as_integer\n\n /// If this expression is a lambda, returns the parameters, return type and body.\n #[builtin(expr_as_lambda)]\n // docs:start:as_lambda\n pub comptime fn as_lambda(\n self,\n ) -> Option<([(Expr, Option)], Option, Expr)> {}\n // docs:end:as_lambda\n\n /// If this expression is a let statement, returns the let pattern as an `Expr`,\n /// the optional type annotation, and the assigned expression.\n #[builtin(expr_as_let)]\n // docs:start:as_let\n pub comptime fn as_let(self) -> Option<(Expr, Option, Expr)> {}\n // docs:end:as_let\n\n /// If this expression is a member access `foo.bar`, return the struct/tuple\n /// expression and the field. The field will be represented as a quoted value.\n #[builtin(expr_as_member_access)]\n // docs:start:as_member_access\n pub comptime fn as_member_access(self) -> Option<(Expr, Quoted)> {}\n // docs:end:as_member_access\n\n /// If this expression is a method call `foo.bar::(arg1, ..., argN)`, return\n /// the receiver, method name, a vector of each generic argument, and a vector of each argument.\n #[builtin(expr_as_method_call)]\n // docs:start:as_method_call\n pub comptime fn as_method_call(self) -> Option<(Expr, Quoted, [UnresolvedType], [Expr])> {}\n // docs:end:as_method_call\n\n /// If this expression is a repeated element array `[elem; length]`, return\n /// the repeated element and the length expressions.\n #[builtin(expr_as_repeated_element_array)]\n // docs:start:as_repeated_element_array\n pub comptime fn as_repeated_element_array(self) -> Option<(Expr, Expr)> {}\n // docs:end:as_repeated_element_array\n\n /// If this expression is a repeated element vector `[elem; length]`, return\n /// the repeated element and the length expressions.\n #[builtin(expr_as_repeated_element_vector)]\n // docs:start:as_repeated_element_vector\n pub comptime fn as_repeated_element_vector(self) -> Option<(Expr, Expr)> {}\n // docs:end:as_repeated_element_vector\n\n /// If this expression is a repeated element vector `[elem; length]`, return\n /// the repeated element and the length expressions.\n /// This method is deprecated in favor of `as_repeated_element_vector`\n #[builtin(expr_as_repeated_element_vector)]\n #[deprecated(\"This method has been renamed to `as_repeated_element_vector`\")]\n pub comptime fn as_repeated_element_slice(self) -> Option<(Expr, Expr)> {}\n\n /// If this expression is a vector literal `@[elem1, ..., elemN]`,\n /// return each element of the vector.\n #[builtin(expr_as_vector)]\n // docs:start:as_vector\n pub comptime fn as_vector(self) -> Option<[Expr]> {}\n // docs:end:as_vector\n\n /// If this expression is a vector literal `@[elem1, ..., elemN]`,\n /// return each element of the vector.\n /// This method is deprecated in favor of `as_vector`\n #[builtin(expr_as_vector)]\n #[deprecated(\"This method has been renamed to `as_vector`\")]\n pub comptime fn as_slice(self) -> Option<[Expr]> {}\n\n /// If this expression is a tuple `(field1, ..., fieldN)`,\n /// return each element of the tuple.\n #[builtin(expr_as_tuple)]\n // docs:start:as_tuple\n pub comptime fn as_tuple(self) -> Option<[Expr]> {}\n // docs:end:as_tuple\n\n /// If this expression is a unary operation ` `,\n /// return the unary operator as well as the right-hand side expression.\n #[builtin(expr_as_unary_op)]\n // docs:start:as_unary_op\n pub comptime fn as_unary_op(self) -> Option<(UnaryOp, Expr)> {}\n // docs:end:as_unary_op\n\n /// If this expression is an `unsafe { stmt1; ...; stmtN }` block,\n /// return each statement inside in a vector.\n #[builtin(expr_as_unsafe)]\n // docs:start:as_unsafe\n pub comptime fn as_unsafe(self) -> Option<[Expr]> {}\n // docs:end:as_unsafe\n\n /// Returns `true` if this expression is trailed by a semicolon.\n ///\n /// Example:\n ///\n /// ```noir\n /// comptime {\n /// let expr1 = quote { 1 + 2 }.as_expr().unwrap();\n /// let expr2 = quote { 1 + 2; }.as_expr().unwrap();\n ///\n /// assert(expr1.as_binary_op().is_some());\n /// assert(expr2.as_binary_op().is_some());\n ///\n /// assert(!expr1.has_semicolon());\n /// assert(expr2.has_semicolon());\n /// }\n /// ```\n #[builtin(expr_has_semicolon)]\n // docs:start:has_semicolon\n pub comptime fn has_semicolon(self) -> bool {}\n // docs:end:has_semicolon\n\n /// Returns `true` if this expression is `break`.\n #[builtin(expr_is_break)]\n // docs:start:is_break\n pub comptime fn is_break(self) -> bool {}\n // docs:end:is_break\n\n /// Returns `true` if this expression is `continue`.\n #[builtin(expr_is_continue)]\n // docs:start:is_continue\n pub comptime fn is_continue(self) -> bool {}\n // docs:end:is_continue\n\n /// Applies a mapping function to this expression and to all of its sub-expressions.\n /// `f` will be applied to each sub-expression first, then applied to the expression itself.\n ///\n /// This happens recursively for every expression within `self`.\n ///\n /// For example, calling `modify` on `(@[1], @[2, 3])` with an `f` that returns `Option::some`\n /// for expressions that are integers, doubling them, would return `(@[2], @[4, 6])`.\n // docs:start:modify\n pub comptime fn modify(self, f: fn[Env](Expr) -> Option) -> Expr {\n // docs:end:modify\n let result = modify_array(self, f);\n let result = result.or_else(|| modify_assert(self, f));\n let result = result.or_else(|| modify_assert_eq(self, f));\n let result = result.or_else(|| modify_assign(self, f));\n let result = result.or_else(|| modify_binary_op(self, f));\n let result = result.or_else(|| modify_block(self, f));\n let result = result.or_else(|| modify_cast(self, f));\n let result = result.or_else(|| modify_comptime(self, f));\n let result = result.or_else(|| modify_constructor(self, f));\n let result = result.or_else(|| modify_if(self, f));\n let result = result.or_else(|| modify_index(self, f));\n let result = result.or_else(|| modify_for(self, f));\n let result = result.or_else(|| modify_for_range(self, f));\n let result = result.or_else(|| modify_lambda(self, f));\n let result = result.or_else(|| modify_let(self, f));\n let result = result.or_else(|| modify_function_call(self, f));\n let result = result.or_else(|| modify_member_access(self, f));\n let result = result.or_else(|| modify_method_call(self, f));\n let result = result.or_else(|| modify_repeated_element_array(self, f));\n let result = result.or_else(|| modify_repeated_element_vector(self, f));\n let result = result.or_else(|| modify_vector(self, f));\n let result = result.or_else(|| modify_tuple(self, f));\n let result = result.or_else(|| modify_unary_op(self, f));\n let result = result.or_else(|| modify_unsafe(self, f));\n if result.is_some() {\n let result = result.unwrap_unchecked();\n let modified = f(result);\n modified.unwrap_or(result)\n } else {\n f(self).unwrap_or(self)\n }\n }\n\n /// Returns this expression as a `Quoted` value. It's the same as `quote { $self }`.\n // docs:start:quoted\n pub comptime fn quoted(self) -> Quoted {\n // docs:end:quoted\n quote { $self }\n }\n\n /// Resolves and type-checks this expression and returns the result as a `TypedExpr`.\n ///\n /// The `in_function` argument specifies where the expression is resolved:\n /// - If it's `none`, the expression is resolved in the function where `resolve` was called\n /// - If it's `some`, the expression is resolved in the given function\n ///\n /// If any names used by this expression are not in scope or if there are any type errors,\n /// this will give compiler errors as if the expression was written directly into\n /// the current `comptime` function.\n #[builtin(expr_resolve)]\n // docs:start:resolve\n pub comptime fn resolve(self, in_function: Option) -> TypedExpr {}\n // docs:end:resolve\n}\n\ncomptime fn modify_array(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_array().map(|exprs| {\n let exprs = modify_expressions(exprs, f);\n new_array(exprs)\n })\n}\n\ncomptime fn modify_assert(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_assert().map(|(predicate, msg)| {\n let predicate = predicate.modify(f);\n let msg = msg.map(|msg| msg.modify(f));\n new_assert(predicate, msg)\n })\n}\n\ncomptime fn modify_assert_eq(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_assert_eq().map(|(lhs, rhs, msg)| {\n let lhs = lhs.modify(f);\n let rhs = rhs.modify(f);\n let msg = msg.map(|msg| msg.modify(f));\n new_assert_eq(lhs, rhs, msg)\n })\n}\n\ncomptime fn modify_assign(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_assign().map(|expr| {\n let (lhs, rhs) = expr;\n let lhs = lhs.modify(f);\n let rhs = rhs.modify(f);\n new_assign(lhs, rhs)\n })\n}\n\ncomptime fn modify_binary_op(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_binary_op().map(|(lhs, op, rhs)| {\n let lhs = lhs.modify(f);\n let rhs = rhs.modify(f);\n new_binary_op(lhs, op, rhs)\n })\n}\n\ncomptime fn modify_block(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_block().map(|exprs| {\n let exprs = modify_expressions(exprs, f);\n new_block(exprs)\n })\n}\n\ncomptime fn modify_cast(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_cast().map(|(expr, typ)| {\n let expr = expr.modify(f);\n new_cast(expr, typ)\n })\n}\n\ncomptime fn modify_comptime(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_comptime().map(|exprs| {\n let exprs = exprs.map(|expr| expr.modify(f));\n new_comptime(exprs)\n })\n}\n\ncomptime fn modify_constructor(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_constructor().map(|(typ, fields)| {\n let fields = fields.map(|(name, value)| (name, value.modify(f)));\n new_constructor(typ, fields)\n })\n}\n\ncomptime fn modify_function_call(\n expr: Expr,\n f: fn[Env](Expr) -> Option,\n) -> Option {\n expr.as_function_call().map(|(function, arguments)| {\n let function = function.modify(f);\n let arguments = arguments.map(|arg| arg.modify(f));\n new_function_call(function, arguments)\n })\n}\n\ncomptime fn modify_if(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_if().map(|(condition, consequence, alternative)| {\n let condition = condition.modify(f);\n let consequence = consequence.modify(f);\n let alternative = alternative.map(|alternative| alternative.modify(f));\n new_if(condition, consequence, alternative)\n })\n}\n\ncomptime fn modify_index(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_index().map(|(object, index)| {\n let object = object.modify(f);\n let index = index.modify(f);\n new_index(object, index)\n })\n}\n\ncomptime fn modify_for(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_for().map(|(identifier, array, body)| {\n let array = array.modify(f);\n let body = body.modify(f);\n new_for(identifier, array, body)\n })\n}\n\ncomptime fn modify_for_range(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_for_range().map(|(identifier, from, to, inclusive, body)| {\n let from = from.modify(f);\n let to = to.modify(f);\n let body = body.modify(f);\n new_for_range(identifier, from, to, inclusive, body)\n })\n}\n\ncomptime fn modify_lambda(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_lambda().map(|(params, return_type, body)| {\n let params = params.map(|(name, typ)| (name.modify(f), typ));\n let body = body.modify(f);\n new_lambda(params, return_type, body)\n })\n}\n\ncomptime fn modify_let(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_let().map(|(pattern, typ, expr)| {\n let pattern = pattern.modify(f);\n let expr = expr.modify(f);\n new_let(pattern, typ, expr)\n })\n}\n\ncomptime fn modify_member_access(\n expr: Expr,\n f: fn[Env](Expr) -> Option,\n) -> Option {\n expr.as_member_access().map(|(object, name)| {\n let object = object.modify(f);\n new_member_access(object, name)\n })\n}\n\ncomptime fn modify_method_call(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_method_call().map(|(object, name, generics, arguments)| {\n let object = object.modify(f);\n let arguments = arguments.map(|arg| arg.modify(f));\n new_method_call(object, name, generics, arguments)\n })\n}\n\ncomptime fn modify_repeated_element_array(\n expr: Expr,\n f: fn[Env](Expr) -> Option,\n) -> Option {\n expr.as_repeated_element_array().map(|(expr, length)| {\n let expr = expr.modify(f);\n let length = length.modify(f);\n new_repeated_element_array(expr, length)\n })\n}\n\ncomptime fn modify_repeated_element_vector(\n expr: Expr,\n f: fn[Env](Expr) -> Option,\n) -> Option {\n expr.as_repeated_element_vector().map(|(expr, length)| {\n let expr = expr.modify(f);\n let length = length.modify(f);\n new_repeated_element_vector(expr, length)\n })\n}\n\ncomptime fn modify_vector(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_vector().map(|exprs| {\n let exprs = modify_expressions(exprs, f);\n new_vector(exprs)\n })\n}\n\ncomptime fn modify_tuple(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_tuple().map(|exprs| {\n let exprs = modify_expressions(exprs, f);\n new_tuple(exprs)\n })\n}\n\ncomptime fn modify_unary_op(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_unary_op().map(|(op, rhs)| {\n let rhs = rhs.modify(f);\n new_unary_op(op, rhs)\n })\n}\n\ncomptime fn modify_unsafe(expr: Expr, f: fn[Env](Expr) -> Option) -> Option {\n expr.as_unsafe().map(|exprs| {\n let exprs = exprs.map(|expr| expr.modify(f));\n new_unsafe(exprs)\n })\n}\n\ncomptime fn modify_expressions(exprs: [Expr], f: fn[Env](Expr) -> Option) -> [Expr] {\n exprs.map(|expr| expr.modify(f))\n}\n\ncomptime fn new_array(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { , });\n quote { [$exprs]}.as_expr().unwrap()\n}\n\ncomptime fn new_assert(predicate: Expr, msg: Option) -> Expr {\n if msg.is_some() {\n let msg = msg.unwrap();\n quote { assert($predicate, $msg) }.as_expr().unwrap()\n } else {\n quote { assert($predicate) }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_assert_eq(lhs: Expr, rhs: Expr, msg: Option) -> Expr {\n if msg.is_some() {\n let msg = msg.unwrap();\n quote { assert_eq($lhs, $rhs, $msg) }.as_expr().unwrap()\n } else {\n quote { assert_eq($lhs, $rhs) }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_assign(lhs: Expr, rhs: Expr) -> Expr {\n quote { $lhs = $rhs }.as_expr().unwrap()\n}\n\ncomptime fn new_binary_op(lhs: Expr, op: BinaryOp, rhs: Expr) -> Expr {\n let op = op.quoted();\n quote { ($lhs) $op ($rhs) }.as_expr().unwrap()\n}\n\ncomptime fn new_block(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { ; });\n quote { { $exprs }}.as_expr().unwrap()\n}\n\ncomptime fn new_cast(expr: Expr, typ: UnresolvedType) -> Expr {\n quote { ($expr) as $typ }.as_expr().unwrap()\n}\n\ncomptime fn new_comptime(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { ; });\n quote { comptime { $exprs }}.as_expr().unwrap()\n}\n\ncomptime fn new_constructor(typ: UnresolvedType, fields: [(Quoted, Expr)]) -> Expr {\n let fields = fields.map(|(name, value)| quote { $name: $value }).join(quote { , });\n quote { $typ { $fields }}.as_expr().unwrap()\n}\n\ncomptime fn new_if(condition: Expr, consequence: Expr, alternative: Option) -> Expr {\n if alternative.is_some() {\n let alternative = alternative.unwrap();\n quote { if $condition { $consequence } else { $alternative }}.as_expr().unwrap()\n } else {\n quote { if $condition { $consequence } }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_for(identifier: Quoted, array: Expr, body: Expr) -> Expr {\n quote { for $identifier in $array { $body } }.as_expr().unwrap()\n}\n\ncomptime fn new_for_range(\n identifier: Quoted,\n from: Expr,\n to: Expr,\n inclusive: bool,\n body: Expr,\n) -> Expr {\n let dots = if inclusive {\n quote { ..= }\n } else {\n quote { .. }\n };\n quote { for $identifier in $from $dots $to { $body } }.as_expr().unwrap()\n}\n\ncomptime fn new_index(object: Expr, index: Expr) -> Expr {\n quote { $object[$index] }.as_expr().unwrap()\n}\n\ncomptime fn new_lambda(\n params: [(Expr, Option)],\n return_type: Option,\n body: Expr,\n) -> Expr {\n let params = params\n .map(|(name, typ)| {\n if typ.is_some() {\n let typ = typ.unwrap();\n quote { $name: $typ }\n } else {\n quote { $name }\n }\n })\n .join(quote { , });\n\n if return_type.is_some() {\n let return_type = return_type.unwrap();\n quote { |$params| -> $return_type { $body } }.as_expr().unwrap()\n } else {\n quote { |$params| { $body } }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_let(pattern: Expr, typ: Option, expr: Expr) -> Expr {\n if typ.is_some() {\n let typ = typ.unwrap();\n quote { let $pattern : $typ = $expr; }.as_expr().unwrap()\n } else {\n quote { let $pattern = $expr; }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_member_access(object: Expr, name: Quoted) -> Expr {\n quote { $object.$name }.as_expr().unwrap()\n}\n\ncomptime fn new_function_call(function: Expr, arguments: [Expr]) -> Expr {\n let arguments = join_expressions(arguments, quote { , });\n\n quote { $function($arguments) }.as_expr().unwrap()\n}\n\ncomptime fn new_method_call(\n object: Expr,\n name: Quoted,\n generics: [UnresolvedType],\n arguments: [Expr],\n) -> Expr {\n let arguments = join_expressions(arguments, quote { , });\n\n if generics.len() == 0 {\n quote { $object.$name($arguments) }.as_expr().unwrap()\n } else {\n let generics = generics.map(|generic| quote { $generic }).join(quote { , });\n quote { $object.$name::<$generics>($arguments) }.as_expr().unwrap()\n }\n}\n\ncomptime fn new_repeated_element_array(expr: Expr, length: Expr) -> Expr {\n quote { [$expr; $length] }.as_expr().unwrap()\n}\n\ncomptime fn new_repeated_element_vector(expr: Expr, length: Expr) -> Expr {\n quote { @[$expr; $length] }.as_expr().unwrap()\n}\n\ncomptime fn new_vector(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { , });\n quote { @[$exprs] }.as_expr().unwrap()\n}\n\ncomptime fn new_tuple(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { , });\n quote { ($exprs) }.as_expr().unwrap()\n}\n\ncomptime fn new_unary_op(op: UnaryOp, rhs: Expr) -> Expr {\n let op = op.quoted();\n quote { $op($rhs) }.as_expr().unwrap()\n}\n\ncomptime fn new_unsafe(exprs: [Expr]) -> Expr {\n let exprs = join_expressions(exprs, quote { ; });\n quote { \n // Safety: generated by macro\n unsafe { $exprs }\n }\n .as_expr()\n .unwrap()\n}\n\ncomptime fn join_expressions(exprs: [Expr], separator: Quoted) -> Quoted {\n exprs.map(|expr| expr.quoted()).join(separator)\n}\n" }, "39": { "path": "std/ops/arith.nr", "source": "use crate::convert::AsPrimitive;\n\n// docs:start:add-trait\npub trait Add {\n fn add(self, other: Self) -> Self;\n}\n// docs:end:add-trait\n\nimpl Add for Field {\n fn add(self, other: Field) -> Field {\n self + other\n }\n}\n\nimpl Add for u128 {\n fn add(self, other: u128) -> u128 {\n self + other\n }\n}\nimpl Add for u64 {\n fn add(self, other: u64) -> u64 {\n self + other\n }\n}\nimpl Add for u32 {\n fn add(self, other: u32) -> u32 {\n self + other\n }\n}\nimpl Add for u16 {\n fn add(self, other: u16) -> u16 {\n self + other\n }\n}\nimpl Add for u8 {\n fn add(self, other: u8) -> u8 {\n self + other\n }\n}\nimpl Add for u1 {\n fn add(self, other: u1) -> u1 {\n self + other\n }\n}\n\nimpl Add for i8 {\n fn add(self, other: i8) -> i8 {\n self + other\n }\n}\nimpl Add for i16 {\n fn add(self, other: i16) -> i16 {\n self + other\n }\n}\nimpl Add for i32 {\n fn add(self, other: i32) -> i32 {\n self + other\n }\n}\nimpl Add for i64 {\n fn add(self, other: i64) -> i64 {\n self + other\n }\n}\n\n// docs:start:sub-trait\npub trait Sub {\n fn sub(self, other: Self) -> Self;\n}\n// docs:end:sub-trait\n\nimpl Sub for Field {\n fn sub(self, other: Field) -> Field {\n self - other\n }\n}\n\nimpl Sub for u128 {\n fn sub(self, other: u128) -> u128 {\n self - other\n }\n}\nimpl Sub for u64 {\n fn sub(self, other: u64) -> u64 {\n self - other\n }\n}\nimpl Sub for u32 {\n fn sub(self, other: u32) -> u32 {\n self - other\n }\n}\nimpl Sub for u16 {\n fn sub(self, other: u16) -> u16 {\n self - other\n }\n}\nimpl Sub for u8 {\n fn sub(self, other: u8) -> u8 {\n self - other\n }\n}\nimpl Sub for u1 {\n fn sub(self, other: u1) -> u1 {\n self - other\n }\n}\n\nimpl Sub for i8 {\n fn sub(self, other: i8) -> i8 {\n self - other\n }\n}\nimpl Sub for i16 {\n fn sub(self, other: i16) -> i16 {\n self - other\n }\n}\nimpl Sub for i32 {\n fn sub(self, other: i32) -> i32 {\n self - other\n }\n}\nimpl Sub for i64 {\n fn sub(self, other: i64) -> i64 {\n self - other\n }\n}\n\n// docs:start:mul-trait\npub trait Mul {\n fn mul(self, other: Self) -> Self;\n}\n// docs:end:mul-trait\n\nimpl Mul for Field {\n fn mul(self, other: Field) -> Field {\n self * other\n }\n}\n\nimpl Mul for u128 {\n fn mul(self, other: u128) -> u128 {\n self * other\n }\n}\nimpl Mul for u64 {\n fn mul(self, other: u64) -> u64 {\n self * other\n }\n}\nimpl Mul for u32 {\n fn mul(self, other: u32) -> u32 {\n self * other\n }\n}\nimpl Mul for u16 {\n fn mul(self, other: u16) -> u16 {\n self * other\n }\n}\nimpl Mul for u8 {\n fn mul(self, other: u8) -> u8 {\n self * other\n }\n}\nimpl Mul for u1 {\n fn mul(self, other: u1) -> u1 {\n self * other\n }\n}\n\nimpl Mul for i8 {\n fn mul(self, other: i8) -> i8 {\n self * other\n }\n}\nimpl Mul for i16 {\n fn mul(self, other: i16) -> i16 {\n self * other\n }\n}\nimpl Mul for i32 {\n fn mul(self, other: i32) -> i32 {\n self * other\n }\n}\nimpl Mul for i64 {\n fn mul(self, other: i64) -> i64 {\n self * other\n }\n}\n\n// docs:start:div-trait\npub trait Div {\n fn div(self, other: Self) -> Self;\n}\n// docs:end:div-trait\n\nimpl Div for Field {\n fn div(self, other: Field) -> Field {\n self / other\n }\n}\n\nimpl Div for u128 {\n fn div(self, other: u128) -> u128 {\n self / other\n }\n}\nimpl Div for u64 {\n fn div(self, other: u64) -> u64 {\n self / other\n }\n}\nimpl Div for u32 {\n fn div(self, other: u32) -> u32 {\n self / other\n }\n}\nimpl Div for u16 {\n fn div(self, other: u16) -> u16 {\n self / other\n }\n}\nimpl Div for u8 {\n fn div(self, other: u8) -> u8 {\n self / other\n }\n}\nimpl Div for u1 {\n fn div(self, other: u1) -> u1 {\n self / other\n }\n}\n\nimpl Div for i8 {\n fn div(self, other: i8) -> i8 {\n self / other\n }\n}\nimpl Div for i16 {\n fn div(self, other: i16) -> i16 {\n self / other\n }\n}\nimpl Div for i32 {\n fn div(self, other: i32) -> i32 {\n self / other\n }\n}\nimpl Div for i64 {\n fn div(self, other: i64) -> i64 {\n self / other\n }\n}\n\n// docs:start:rem-trait\npub trait Rem {\n fn rem(self, other: Self) -> Self;\n}\n// docs:end:rem-trait\n\nimpl Rem for u128 {\n fn rem(self, other: u128) -> u128 {\n self % other\n }\n}\nimpl Rem for u64 {\n fn rem(self, other: u64) -> u64 {\n self % other\n }\n}\nimpl Rem for u32 {\n fn rem(self, other: u32) -> u32 {\n self % other\n }\n}\nimpl Rem for u16 {\n fn rem(self, other: u16) -> u16 {\n self % other\n }\n}\nimpl Rem for u8 {\n fn rem(self, other: u8) -> u8 {\n self % other\n }\n}\nimpl Rem for u1 {\n fn rem(self, other: u1) -> u1 {\n self % other\n }\n}\n\nimpl Rem for i8 {\n fn rem(self, other: i8) -> i8 {\n self % other\n }\n}\nimpl Rem for i16 {\n fn rem(self, other: i16) -> i16 {\n self % other\n }\n}\nimpl Rem for i32 {\n fn rem(self, other: i32) -> i32 {\n self % other\n }\n}\nimpl Rem for i64 {\n fn rem(self, other: i64) -> i64 {\n self % other\n }\n}\n\n// docs:start:neg-trait\npub trait Neg {\n fn neg(self) -> Self;\n}\n// docs:end:neg-trait\n\n// docs:start:neg-trait-impls\nimpl Neg for Field {\n fn neg(self) -> Field {\n -self\n }\n}\n\nimpl Neg for i8 {\n fn neg(self) -> i8 {\n -self\n }\n}\nimpl Neg for i16 {\n fn neg(self) -> i16 {\n -self\n }\n}\nimpl Neg for i32 {\n fn neg(self) -> i32 {\n -self\n }\n}\nimpl Neg for i64 {\n fn neg(self) -> i64 {\n -self\n }\n}\n// docs:end:neg-trait-impls\n\n// docs:start:wrapping-add-trait\npub trait WrappingAdd {\n fn wrapping_add(self, y: Self) -> Self;\n}\n// docs:end:wrapping-add-trait\n\nimpl WrappingAdd for u1 {\n fn wrapping_add(self: u1, y: u1) -> u1 {\n self ^ y\n }\n}\n\nimpl WrappingAdd for u8 {\n fn wrapping_add(self: u8, y: u8) -> u8 {\n wrapping_add_hlp(self, y)\n }\n}\n\nimpl WrappingAdd for u16 {\n fn wrapping_add(self: u16, y: u16) -> u16 {\n wrapping_add_hlp(self, y)\n }\n}\n\nimpl WrappingAdd for u32 {\n fn wrapping_add(self: u32, y: u32) -> u32 {\n wrapping_add_hlp(self, y)\n }\n}\n\nimpl WrappingAdd for u64 {\n fn wrapping_add(self: u64, y: u64) -> u64 {\n wrapping_add_hlp(self, y)\n }\n}\n\nimpl WrappingAdd for u128 {\n fn wrapping_add(self: u128, y: u128) -> u128 {\n wrapping_add_hlp(self, y)\n }\n}\n\nimpl WrappingAdd for i8 {\n fn wrapping_add(self: i8, y: i8) -> i8 {\n let x = self as u8;\n x.wrapping_add(y as u8) as i8\n }\n}\n\nimpl WrappingAdd for i16 {\n fn wrapping_add(self: i16, y: i16) -> i16 {\n let x = self as u16;\n x.wrapping_add(y as u16) as i16\n }\n}\n\nimpl WrappingAdd for i32 {\n fn wrapping_add(self: i32, y: i32) -> i32 {\n let x = self as u32;\n x.wrapping_add(y as u32) as i32\n }\n}\n\nimpl WrappingAdd for i64 {\n fn wrapping_add(self: i64, y: i64) -> i64 {\n let x = self as u64;\n x.wrapping_add(y as u64) as i64\n }\n}\nimpl WrappingAdd for Field {\n fn wrapping_add(self: Field, y: Field) -> Field {\n self + y\n }\n}\n\n// docs:start:wrapping-sub-trait\npub trait WrappingSub {\n fn wrapping_sub(self, y: Self) -> Self;\n}\n// docs:start:wrapping-sub-trait\n\nimpl WrappingSub for u1 {\n fn wrapping_sub(self: u1, y: u1) -> u1 {\n self ^ y\n }\n}\n\nimpl WrappingSub for u8 {\n fn wrapping_sub(self: u8, y: u8) -> u8 {\n wrapping_sub_hlp(self, y) as u8\n }\n}\n\nimpl WrappingSub for u16 {\n fn wrapping_sub(self: u16, y: u16) -> u16 {\n wrapping_sub_hlp(self, y) as u16\n }\n}\n\nimpl WrappingSub for u32 {\n fn wrapping_sub(self: u32, y: u32) -> u32 {\n wrapping_sub_hlp(self, y) as u32\n }\n}\nimpl WrappingSub for u64 {\n fn wrapping_sub(self: u64, y: u64) -> u64 {\n wrapping_sub_hlp(self, y) as u64\n }\n}\nimpl WrappingSub for u128 {\n fn wrapping_sub(self: u128, y: u128) -> u128 {\n wrapping_sub_hlp(self, y) as u128\n }\n}\n\nimpl WrappingSub for i8 {\n fn wrapping_sub(self: i8, y: i8) -> i8 {\n let x = self as u8;\n x.wrapping_sub(y as u8) as i8\n }\n}\n\nimpl WrappingSub for i16 {\n fn wrapping_sub(self: i16, y: i16) -> i16 {\n let x = self as u16;\n x.wrapping_sub(y as u16) as i16\n }\n}\n\nimpl WrappingSub for i32 {\n fn wrapping_sub(self: i32, y: i32) -> i32 {\n let x = self as u32;\n x.wrapping_sub(y as u32) as i32\n }\n}\nimpl WrappingSub for i64 {\n fn wrapping_sub(self: i64, y: i64) -> i64 {\n let x = self as u64;\n x.wrapping_sub(y as u64) as i64\n }\n}\nimpl WrappingSub for Field {\n fn wrapping_sub(self: Field, y: Field) -> Field {\n self - y\n }\n}\n\n// docs:start:wrapping-mul-trait\npub trait WrappingMul {\n fn wrapping_mul(self, y: Self) -> Self;\n}\n// docs:start:wrapping-mul-trait\n\nimpl WrappingMul for u1 {\n fn wrapping_mul(self: u1, y: u1) -> u1 {\n self & y\n }\n}\n\nimpl WrappingMul for u8 {\n fn wrapping_mul(self: u8, y: u8) -> u8 {\n wrapping_mul_hlp(self, y)\n }\n}\n\nimpl WrappingMul for u16 {\n fn wrapping_mul(self: u16, y: u16) -> u16 {\n wrapping_mul_hlp(self, y)\n }\n}\n\nimpl WrappingMul for u32 {\n fn wrapping_mul(self: u32, y: u32) -> u32 {\n wrapping_mul_hlp(self, y)\n }\n}\nimpl WrappingMul for u64 {\n fn wrapping_mul(self: u64, y: u64) -> u64 {\n wrapping_mul_hlp(self, y)\n }\n}\n\nimpl WrappingMul for i8 {\n fn wrapping_mul(self: i8, y: i8) -> i8 {\n let x = self as u8;\n x.wrapping_mul(y as u8) as i8\n }\n}\n\nimpl WrappingMul for i16 {\n fn wrapping_mul(self: i16, y: i16) -> i16 {\n let x = self as u16;\n x.wrapping_mul(y as u16) as i16\n }\n}\n\nimpl WrappingMul for i32 {\n fn wrapping_mul(self: i32, y: i32) -> i32 {\n let x = self as u32;\n x.wrapping_mul(y as u32) as i32\n }\n}\n\nimpl WrappingMul for i64 {\n fn wrapping_mul(self: i64, y: i64) -> i64 {\n let x = self as u64;\n x.wrapping_mul(y as u64) as i64\n }\n}\n\nimpl WrappingMul for u128 {\n fn wrapping_mul(self: u128, y: u128) -> u128 {\n wrapping_mul128_hlp(self, y)\n }\n}\nimpl WrappingMul for Field {\n fn wrapping_mul(self: Field, y: Field) -> Field {\n self * y\n }\n}\n\nfn wrapping_add_hlp(x: T, y: T) -> T\nwhere\n T: AsPrimitive,\n Field: AsPrimitive,\n{\n AsPrimitive::as_(x.as_() + y.as_())\n}\n\nfn wrapping_sub_hlp(x: T, y: T) -> Field\nwhere\n T: AsPrimitive,\n{\n //340282366920938463463374607431768211456 is 2^128, it is used to avoid underflow\n x.as_() + 340282366920938463463374607431768211456 - y.as_()\n}\n\nfn wrapping_mul_hlp(x: T, y: T) -> T\nwhere\n T: AsPrimitive,\n Field: AsPrimitive,\n{\n AsPrimitive::as_(x.as_() * y.as_())\n}\n\nglobal two_pow_64: u128 = 0x10000000000000000;\n/// Splits a 128 bits number into two 64 bits limbs\nunconstrained fn split64(x: u128) -> (u64, u64) {\n let lo = x as u64;\n let hi = (x / two_pow_64) as u64;\n (lo, hi)\n}\n\n/// Split a 128 bits number into two 64 bits limbs\n/// It will fail if the number is more than 128 bits\nfn split_into_64_bit_limbs(x: u128) -> (u64, u64) {\n // Safety: the limbs are constrained below\n let (x_lo, x_hi) = unsafe { split64(x) };\n assert(x as Field == x_lo as Field + x_hi as Field * two_pow_64 as Field);\n (x_lo, x_hi)\n}\n\n#[field(bn254)]\nfn wrapping_mul128_hlp(x: u128, y: u128) -> u128 {\n let (x_lo, x_hi) = split_into_64_bit_limbs(x);\n let (y_lo, y_hi) = split_into_64_bit_limbs(y);\n // Multiplication using the limbs:(x_lo + 2**64*x_hi)*(y_lo + 2**64*y_hi)=x_lo*y_lo+...\n // and skipping the terms over 2**128\n // Working with u64 limbs ensures that we cannot overflow the field modulus.\n let low = x_lo as Field * y_lo as Field;\n let lo = low as u64 as Field;\n let carry = (low - lo) / two_pow_64 as Field;\n let high = x_lo as Field * y_hi as Field + x_hi as Field * y_lo as Field + carry;\n let hi = high as u64 as Field;\n (lo + two_pow_64 as Field * hi) as u128\n}\n\nmod tests {\n #[test(should_fail_with = \"custom message\")]\n fn test_static_assert_custom_message() {\n crate::static_assert(1 == 2, \"custom message\");\n }\n\n mod arithmetic {\n use crate::ops::arith::{Add, Div, Mul, Neg, Rem, Sub};\n #[test]\n fn test_basic_arithmetic_traits() {\n // add\n assert_eq(5.add(3), 8);\n assert_eq(0u8.add(255u8), 255u8);\n assert_eq(42.add(58), 100);\n\n // sub\n assert_eq(10.sub(3), 7);\n assert_eq(100.sub(42), 58);\n\n // mul\n assert_eq(6.mul(7), 42);\n\n // div\n assert_eq(15.div(3), 5);\n assert_eq(10u8.div(3u8), 3u8);\n assert_eq(15.div(3), 5);\n\n // rem\n assert_eq(17.rem(5), 2);\n assert_eq(10u8.rem(3u8), 1u8);\n\n // neg\n assert_eq(42.neg(), -42);\n assert_eq((-10).neg(), 10);\n assert_eq(42.neg(), -42);\n }\n\n #[test]\n fn test_division() {\n // test division by one\n assert_eq(42.div(1), 42);\n assert_eq(0.div(1), 0);\n assert_eq(255u8.div(1u8), 255u8);\n\n // test division by self\n assert_eq(42.div(42), 1);\n assert_eq(1.div(1), 1);\n\n // test remainder\n assert_eq(42.rem(42), 0);\n assert_eq(0.rem(42), 0);\n assert_eq(1.rem(42), 1);\n }\n\n #[test(should_fail)]\n fn test_u8_sub_overflow_failure() {\n let _ = 0u8.sub(1u8);\n }\n\n #[test(should_fail)]\n fn test_u8_add_overflow_failure() {\n let _ = 255u8.add(1u8);\n }\n\n #[test(should_fail)]\n fn test_u8_mul_overflow_failure() {\n let _ = 255u8.mul(2u8);\n }\n\n #[test(should_fail)]\n fn test_u16_sub_overflow_failure() {\n let _ = 0u16.sub(1u16);\n }\n\n #[test(should_fail)]\n fn test_u16_add_overflow_failure() {\n let _ = 65535u16.add(1u16);\n }\n\n #[test(should_fail)]\n fn test_u16_mul_overflow_failure() {\n let _ = 65535u16.mul(2u16);\n }\n\n #[test(should_fail)]\n fn test_signed_sub_overflow_failure() {\n let val: i8 = -128;\n let _ = val.sub(1i8);\n }\n\n #[test(should_fail)]\n fn test_signed_overflow_failure() {\n let _ = 127i8.add(1i8);\n }\n\n #[test]\n fn test_field() {\n let zero: Field = 0;\n let one: Field = 1;\n\n // test Field basic operations\n assert_eq(zero.add(one), one);\n assert_eq(one.add(zero), one);\n assert_eq(one.sub(one), zero);\n assert_eq(one.mul(one), one);\n assert_eq(one.div(one), one);\n assert_eq(zero.neg(), zero);\n assert_eq(one.neg(), -one);\n }\n\n }\n\n mod wrapping_arithmetic {\n use crate::ops::arith::{Add, Div, Mul, Neg, Sub, WrappingAdd, WrappingMul, WrappingSub};\n #[test]\n fn test_wrapping_add() {\n assert_eq(255u8.wrapping_add(1u8), 0u8);\n assert_eq(255u8.wrapping_add(255u8), 254u8);\n assert_eq(0u8.wrapping_add(0u8), 0u8);\n assert_eq(128u8.wrapping_add(128u8), 0u8);\n\n // test u16 wrapping add\n assert_eq(65535u16.wrapping_add(1u16), 0u16);\n assert_eq(65535u16.wrapping_add(65535u16), 65534u16);\n\n // test u32 wrapping add\n assert_eq(0xffffffffu32.wrapping_add(1u32), 0u32);\n assert_eq(0xffffffffu32.wrapping_add(0xffffffffu32), 0xfffffffeu32);\n\n // test u64 wrapping add\n assert_eq(0xffffffffffffffffu64.wrapping_add(1u64), 0u64);\n assert_eq(\n 0xffffffffffffffffu64.wrapping_add(0xffffffffffffffffu64),\n 0xfffffffffffffffeu64,\n );\n\n // test u128 wrapping add\n assert_eq(0xffffffffffffffffffffffffffffffffu128.wrapping_add(1u128), 0u128);\n\n // test signed types\n assert_eq(127i8.wrapping_add(1i8), -128i8);\n let val: i8 = -128;\n assert_eq(val.wrapping_add(-1i8), 127i8);\n\n // test Field wrapping add\n let forty_two: Field = 42;\n let fifty_eight: Field = 58;\n let hundred: Field = 100;\n let neg_two: Field = -2;\n let two: Field = 2;\n let zero: Field = 0;\n let neg_two_hundred: Field = -200;\n let neg_one_ninety_eight: Field = -198;\n assert_eq(forty_two.wrapping_add(fifty_eight), hundred);\n assert_eq(neg_two.wrapping_add(two), zero);\n assert_eq(neg_two_hundred.wrapping_add(two), neg_one_ninety_eight);\n }\n\n #[test]\n fn test_wrapping_sub() {\n assert_eq(0u8.wrapping_sub(1u8), 255u8);\n assert_eq(255u8.wrapping_sub(255u8), 0u8);\n assert_eq(0u8.wrapping_sub(0u8), 0u8);\n assert_eq(1u8.wrapping_sub(2u8), 255u8);\n\n // test u16 wrapping sub\n assert_eq(0u16.wrapping_sub(1u16), 65535u16);\n assert_eq(65535u16.wrapping_sub(65535u16), 0u16);\n\n // test u32 wrapping sub\n assert_eq(0u32.wrapping_sub(1u32), 0xffffffffu32);\n assert_eq(0xffffffffu32.wrapping_sub(0xffffffffu32), 0u32);\n\n // test u64 wrapping sub\n assert_eq(0u64.wrapping_sub(1u64), 0xffffffffffffffffu64);\n assert_eq(0xffffffffffffffffu64.wrapping_sub(0xffffffffffffffffu64), 0u64);\n\n // test u128 wrapping sub\n assert_eq(0u128.wrapping_sub(1u128), 0xffffffffffffffffffffffffffffffffu128);\n\n // test signed types\n let val: i8 = -128;\n assert_eq(val.wrapping_sub(1i8), 127i8);\n assert_eq(127i8.wrapping_sub(-1i8), -128i8);\n\n // test Field wrapping sub\n let forty_two: Field = 42;\n let fifty_eight: Field = 58;\n let neg_sixteen: Field = -16;\n assert_eq(forty_two.wrapping_sub(fifty_eight), neg_sixteen);\n }\n\n #[test]\n fn test_wrapping_mul() {\n let zero: u128 = 0;\n let one: u128 = 1;\n let two_pow_64: u128 = 0x10000000000000000;\n let u128_max: u128 = 0xffffffffffffffffffffffffffffffff;\n\n assert_eq(zero, zero.wrapping_mul(one));\n assert_eq(zero, one.wrapping_mul(zero));\n assert_eq(one, one.wrapping_mul(one));\n assert_eq(zero, zero.wrapping_mul(two_pow_64));\n assert_eq(zero, two_pow_64.wrapping_mul(zero));\n assert_eq(two_pow_64, two_pow_64.wrapping_mul(one));\n assert_eq(two_pow_64, one.wrapping_mul(two_pow_64));\n assert_eq(zero, two_pow_64.wrapping_mul(two_pow_64));\n assert_eq(one, u128_max.wrapping_mul(u128_max));\n\n // test u8 wrapping mul\n assert_eq(255u8.wrapping_mul(2u8), 254u8);\n assert_eq(255u8.wrapping_mul(255u8), 1u8);\n assert_eq(128u8.wrapping_mul(2u8), 0u8);\n\n // test u16 wrapping mul\n assert_eq(65535u16.wrapping_mul(2u16), 65534u16);\n assert_eq(65535u16.wrapping_mul(65535u16), 1u16);\n\n // test u32 wrapping mul\n assert_eq(0xffffffffu32.wrapping_mul(2u32), 0xfffffffeu32);\n assert_eq(0xffffffffu32.wrapping_mul(0xffffffffu32), 1u32);\n\n // test u64 wrapping mul\n // 0xffffffffffffffffu64 is 2^64 - 1\n assert_eq(0xffffffffffffffffu64.wrapping_mul(2u64), 0xfffffffffffffffeu64);\n assert_eq(0xffffffffffffffffu64.wrapping_mul(0xffffffffffffffffu64), 1u64);\n\n // test signed types\n assert_eq(127i8.wrapping_mul(2i8), -2i8);\n let val: i8 = -128;\n assert_eq(val.wrapping_mul(-1i8), -128i8);\n\n // test Field wrapping mul\n let six: Field = 6;\n let seven: Field = 7;\n let forty_two: Field = 42;\n let neg_two: Field = -2;\n let two: Field = 2;\n let neg_four: Field = -4;\n assert_eq(six.wrapping_mul(seven), forty_two);\n assert_eq(neg_two.wrapping_mul(two), neg_four);\n }\n\n #[test]\n fn test_u1_behavior() {\n // u1 wrapping add is XOR\n assert_eq(0u1.wrapping_add(0u1), 0u1);\n assert_eq(0u1.wrapping_add(1u1), 1u1);\n assert_eq(1u1.wrapping_add(0u1), 1u1);\n assert_eq(1u1.wrapping_add(1u1), 0u1);\n\n // u1 wrapping sub is XOR\n assert_eq(0u1.wrapping_sub(0u1), 0u1);\n assert_eq(0u1.wrapping_sub(1u1), 1u1);\n assert_eq(1u1.wrapping_sub(0u1), 1u1);\n assert_eq(1u1.wrapping_sub(1u1), 0u1);\n\n // u1 wrapping mul is AND\n assert_eq(0u1.wrapping_mul(0u1), 0u1);\n assert_eq(0u1.wrapping_mul(1u1), 0u1);\n assert_eq(1u1.wrapping_mul(0u1), 0u1);\n assert_eq(1u1.wrapping_mul(1u1), 1u1);\n }\n\n // test wrapping operations is the same as the regular operations\n #[test]\n fn test_wrapping_vs_regular() {\n let u64_large = 0x123456789abcdef0u64;\n let u128_large = 0x123456789abcdef0123456789abcdef0u128;\n\n assert_eq(u64_large.wrapping_add(1u64), u64_large + 1u64);\n assert_eq(u64_large.wrapping_sub(1u64), u64_large - 1u64);\n assert_eq(u64_large.wrapping_mul(2u64), u64_large * 2u64);\n\n assert_eq(u128_large.wrapping_add(1u128), u128_large + 1u128);\n assert_eq(u128_large.wrapping_sub(1u128), u128_large - 1u128);\n assert_eq(u128_large.wrapping_mul(2u128), u128_large * 2u128);\n }\n\n #[test]\n fn test_field_wrapping_operations() {\n let zero: Field = 0;\n let one: Field = 1;\n let large_val = 0xffffffffffffffff;\n\n // test Field wrapping operations\n assert_eq(zero.wrapping_add(one), one);\n assert_eq(one.wrapping_add(large_val), one + large_val);\n assert_eq(zero.wrapping_sub(one), -one);\n assert_eq(one.wrapping_sub(large_val), one - large_val);\n assert_eq(zero.wrapping_mul(one), zero);\n assert_eq(one.wrapping_mul(large_val), large_val);\n\n // test Field basic operations\n assert_eq(zero.add(one), one);\n assert_eq(one.add(zero), one);\n assert_eq(one.sub(one), zero);\n assert_eq(one.mul(one), one);\n assert_eq(one.div(one), one);\n assert_eq(zero.neg(), zero);\n assert_eq(one.neg(), -one);\n }\n\n }\n\n mod split_functions {\n\n use crate::ops::arith::{split64, split_into_64_bit_limbs};\n\n // test split64 and split_into_64_bit_limbs functions\n #[test]\n fn test_split_functions() {\n let small_val = 0x123456789abcdefu128;\n let large_val = 0x123456789abcdef0123456789abcdef0u128;\n let max_val = 0xffffffffffffffffffffffffffffffffu128;\n\n // test split64 (unconstrained)\n // Safety: testing\n unsafe {\n let (lo, hi) = split64(small_val);\n assert_eq(lo, 0x123456789abcdefu64);\n assert_eq(hi, 0u64);\n\n let (lo2, hi2) = split64(large_val);\n assert_eq(lo2, 0x123456789abcdef0u64);\n assert_eq(hi2, 0x123456789abcdef0u64);\n }\n\n // test split_into_64_bit_limbs (constrained)\n let (lo3, hi3) = split_into_64_bit_limbs(small_val);\n assert_eq(lo3, 0x123456789abcdefu64);\n assert_eq(hi3, 0u64);\n\n let (lo4, hi4) = split_into_64_bit_limbs(large_val);\n assert_eq(lo4, 0x123456789abcdef0u64);\n assert_eq(hi4, 0x123456789abcdef0u64);\n\n let (lo5, hi5) = split_into_64_bit_limbs(max_val);\n assert_eq(lo5, 0xffffffffffffffffu64);\n assert_eq(hi5, 0xffffffffffffffffu64);\n }\n }\n}\n" }, "42": { "path": "std/option.nr", "source": "use crate::cmp::{Eq, Ord, Ordering};\nuse crate::default::Default;\nuse crate::hash::{Hash, Hasher};\n\npub struct Option {\n _is_some: bool,\n _value: T,\n}\n\nimpl Option {\n /// Constructs a None value\n pub fn none() -> Self {\n Self { _is_some: false, _value: crate::mem::zeroed() }\n }\n\n /// Constructs a Some wrapper around the given value\n pub fn some(_value: T) -> Self {\n Self { _is_some: true, _value }\n }\n\n /// True if this Option is None\n pub fn is_none(self) -> bool {\n !self._is_some\n }\n\n /// True if this Option is Some\n pub fn is_some(self) -> bool {\n self._is_some\n }\n\n /// Asserts `self.is_some()` and returns the wrapped value.\n pub fn unwrap(self) -> T {\n assert(self._is_some);\n self._value\n }\n\n /// Returns the inner value without asserting `self.is_some()`\n /// Note that if `self` is `None`, there is no guarantee what value will be returned,\n /// only that it will be of type `T`.\n pub fn unwrap_unchecked(self) -> T {\n self._value\n }\n\n /// Returns the wrapped value if `self.is_some()`. Otherwise, returns the given default value.\n pub fn unwrap_or(self, default: T) -> T {\n if self._is_some {\n self._value\n } else {\n default\n }\n }\n\n /// Returns the wrapped value if `self.is_some()`. Otherwise, calls the given function to return\n /// a default value.\n pub fn unwrap_or_else(self, default: fn[Env]() -> T) -> T {\n if self._is_some {\n self._value\n } else {\n default()\n }\n }\n\n /// Asserts `self.is_some()` with a provided custom message and returns the contained `Some` value\n pub fn expect(self, message: fmtstr) -> T {\n assert(self.is_some(), message);\n self._value\n }\n\n /// If self is `Some(x)`, this returns `Some(f(x))`. Otherwise, this returns `None`.\n pub fn map(self, f: fn[Env](T) -> U) -> Option {\n if self._is_some {\n Option::some(f(self._value))\n } else {\n Option::none()\n }\n }\n\n /// If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns the given default value.\n pub fn map_or(self, default: U, f: fn[Env](T) -> U) -> U {\n if self._is_some {\n f(self._value)\n } else {\n default\n }\n }\n\n /// If self is `Some(x)`, this returns `f(x)`. Otherwise, this returns `default()`.\n pub fn map_or_else(self, default: fn[Env1]() -> U, f: fn[Env2](T) -> U) -> U {\n if self._is_some {\n f(self._value)\n } else {\n default()\n }\n }\n\n /// Returns None if self is None. Otherwise, this returns `other`.\n pub fn and(self, other: Self) -> Self {\n if self.is_none() {\n Option::none()\n } else {\n other\n }\n }\n\n /// If self is None, this returns None. Otherwise, this calls the given function\n /// with the Some value contained within self, and returns the result of that call.\n ///\n /// In some languages this function is called `flat_map` or `bind`.\n pub fn and_then(self, f: fn[Env](T) -> Option) -> Option {\n if self._is_some {\n f(self._value)\n } else {\n Option::none()\n }\n }\n\n /// If self is Some, return self. Otherwise, return `other`.\n pub fn or(self, other: Self) -> Self {\n if self._is_some {\n self\n } else {\n other\n }\n }\n\n /// If self is Some, return self. Otherwise, return `default()`.\n pub fn or_else(self, default: fn[Env]() -> Self) -> Self {\n if self._is_some {\n self\n } else {\n default()\n }\n }\n\n // If only one of the two Options is Some, return that option.\n // Otherwise, if both options are Some or both are None, None is returned.\n pub fn xor(self, other: Self) -> Self {\n if self._is_some {\n if other._is_some {\n Option::none()\n } else {\n self\n }\n } else if other._is_some {\n other\n } else {\n Option::none()\n }\n }\n\n /// Returns `Some(x)` if self is `Some(x)` and `predicate(x)` is true.\n /// Otherwise, this returns `None`\n pub fn filter(self, predicate: fn[Env](T) -> bool) -> Self {\n if self._is_some {\n if predicate(self._value) {\n self\n } else {\n Option::none()\n }\n } else {\n Option::none()\n }\n }\n\n /// Flattens an Option> into a Option.\n /// This returns None if the outer Option is None. Otherwise, this returns the inner Option.\n pub fn flatten(option: Option>) -> Option {\n if option._is_some {\n option._value\n } else {\n Option::none()\n }\n }\n}\n\nimpl Default for Option {\n fn default() -> Self {\n Option::none()\n }\n}\n\nimpl Eq for Option\nwhere\n T: Eq,\n{\n fn eq(self, other: Self) -> bool {\n if self._is_some == other._is_some {\n if self._is_some {\n self._value == other._value\n } else {\n true\n }\n } else {\n false\n }\n }\n}\n\nimpl Hash for Option\nwhere\n T: Hash,\n{\n fn hash(self, state: &mut H)\n where\n H: Hasher,\n {\n self._is_some.hash(state);\n if self._is_some {\n self._value.hash(state);\n }\n }\n}\n\n// For this impl we're declaring Option::none < Option::some\nimpl Ord for Option\nwhere\n T: Ord,\n{\n fn cmp(self, other: Self) -> Ordering {\n if self._is_some {\n if other._is_some {\n self._value.cmp(other._value)\n } else {\n Ordering::greater()\n }\n } else if other._is_some {\n Ordering::less()\n } else {\n Ordering::equal()\n }\n }\n}\n" }, "43": { "path": "std/panic.nr", "source": "pub fn panic(message: T) -> U\nwhere\n T: StringLike,\n{\n assert(false, message);\n crate::mem::zeroed()\n}\n\ntrait StringLike {}\n\nimpl StringLike for str {}\nimpl StringLike for fmtstr {}\n" }, "75": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/capsules/mod.nr", "source": "use crate::oracle::capsules;\nuse crate::protocol::{address::AztecAddress, traits::{Deserialize, Serialize}};\n\n/// A dynamically sized array backed by PXE's non-volatile database (called capsules). Values are persisted until\n/// deleted, so they can be e.g. stored during simulation of a transaction and later retrieved during witness\n/// generation. All values are scoped per contract address, so external contracts cannot access them.\npub struct CapsuleArray {\n contract_address: AztecAddress,\n /// The base slot is where the array length is stored in capsules. Array elements are stored in consecutive slots\n /// after the base slot. For example, with base slot 5: the length is at slot 5, the first element (index 0) is at\n /// slot 6, the second element (index 1) is at slot 7, and so on.\n base_slot: Field,\n /// Scope for capsule isolation. Capsule operations are scoped to the given address, allowing multiple independent\n /// namespaces within the same contract.\n scope: AztecAddress,\n}\n\nimpl CapsuleArray {\n /// Returns a CapsuleArray scoped to a specific address.\n ///\n /// Array elements are stored in contiguous slots\n /// following the base slot, so there should be sufficient space between array base slots to accommodate elements.\n /// A reasonable strategy is to make the base slot a hash of a unique value.\n pub unconstrained fn at(contract_address: AztecAddress, base_slot: Field, scope: AztecAddress) -> Self {\n Self { contract_address, base_slot, scope }\n }\n\n /// Returns the number of elements stored in the array.\n pub unconstrained fn len(self) -> u32 {\n // An uninitialized array defaults to a length of 0.\n capsules::load(self.contract_address, self.base_slot, self.scope).unwrap_or(0) as u32\n }\n\n /// Stores a value at the end of the array.\n pub unconstrained fn push(self, value: T)\n where\n T: Serialize,\n {\n let current_length = self.len();\n\n // The slot corresponding to the index `current_length` is the first slot immediately after the end of the\n // array, which is where we want to place the new value.\n capsules::store(\n self.contract_address,\n self.slot_at(current_length),\n value,\n self.scope,\n );\n\n // Then we simply update the length.\n let new_length = current_length + 1;\n capsules::store(\n self.contract_address,\n self.base_slot,\n new_length,\n self.scope,\n );\n }\n\n /// Retrieves the value stored in the array at `index`. Throws if the index is out of bounds.\n pub unconstrained fn get(self, index: u32) -> T\n where\n T: Deserialize,\n {\n assert(index < self.len(), \"Attempted to read past the length of a CapsuleArray\");\n\n capsules::load(self.contract_address, self.slot_at(index), self.scope).unwrap()\n }\n\n /// Deletes the value stored in the array at `index`. Throws if the index is out of bounds.\n pub unconstrained fn remove(self, index: u32) {\n let current_length = self.len();\n assert(index < current_length, \"Attempted to delete past the length of a CapsuleArray\");\n\n // In order to be able to remove elements at arbitrary indices, we need to shift the entire contents of the\n // array past the removed element one slot backward so that we don't end up with a gap and preserve the\n // contiguous slots. We can skip this when deleting the last element however.\n if index != current_length - 1 {\n // The source and destination regions overlap, but `copy` supports this.\n capsules::copy(\n self.contract_address,\n self.slot_at(index + 1),\n self.slot_at(index),\n current_length - index - 1,\n self.scope,\n );\n }\n\n // We can now delete the last element (which has either been copied to the slot immediately before it, or was\n // the element we meant to delete in the first place) and update the length.\n capsules::delete(\n self.contract_address,\n self.slot_at(current_length - 1),\n self.scope,\n );\n capsules::store(\n self.contract_address,\n self.base_slot,\n current_length - 1,\n self.scope,\n );\n }\n\n /// Calls a function on each element of the array.\n ///\n /// The function `f` is called once with each array value and its corresponding index. The order in which values\n /// are processed is arbitrary.\n ///\n /// ## Array Mutation\n ///\n /// It is safe to delete the current element (and only the current element) from inside the callback via `remove`:\n /// ```noir\n /// array.for_each(|index, value| {\n /// if some_condition(value) {\n /// array.remove(index); // safe only for this index\n /// }\n /// }\n /// ```\n ///\n /// If all elements in the array need to iterated over and then removed, then using `for_each` results in optimal\n /// efficiency.\n ///\n /// It is **not** safe to push new elements into the array from inside the callback.\n pub unconstrained fn for_each(self, f: unconstrained fn[Env](u32, T) -> ())\n where\n T: Deserialize,\n {\n // Iterating over all elements is simple, but we want to do it in such a way that a) deleting the current\n // element is safe to do, and b) deleting *all* elements is optimally efficient. This is because CapsuleArrays\n // are typically used to hold pending tasks, so iterating them while clearing completed tasks (sometimes\n // unconditionally, resulting in a full clear) is a very common access pattern.\n //\n // The way we achieve this is by iterating backwards: each element can always be deleted since it won't change\n // any preceding (lower) indices, and if every element is deleted then every element will (in turn) be the last\n // element. This results in an optimal full clear since `remove` will be able to skip the `capsules::copy` call\n // to shift any elements past the deleted one (because there will be none).\n let mut i = self.len();\n while i > 0 {\n i -= 1;\n f(i, self.get(i));\n }\n }\n\n unconstrained fn slot_at(self, index: u32) -> Field {\n // Elements are stored immediately after the base slot, so we add 1 to it to compute the slot for the first\n // element.\n self.base_slot + 1 + index as Field\n }\n}\n\nmod test {\n use crate::protocol::address::AztecAddress;\n use crate::test::helpers::test_environment::TestEnvironment;\n use super::CapsuleArray;\n\n global SLOT: Field = 1230;\n global SCOPE: AztecAddress = AztecAddress { inner: 0xface };\n\n #[test]\n unconstrained fn empty_array() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array: CapsuleArray = CapsuleArray::at(contract_address, SLOT, SCOPE);\n assert_eq(array.len(), 0);\n });\n }\n\n #[test(should_fail_with = \"Attempted to read past the length of a CapsuleArray\")]\n unconstrained fn empty_array_read() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n let _: Field = array.get(0);\n });\n }\n\n #[test]\n unconstrained fn array_push() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n array.push(5);\n\n assert_eq(array.len(), 1);\n assert_eq(array.get(0), 5);\n });\n }\n\n #[test(should_fail_with = \"Attempted to read past the length of a CapsuleArray\")]\n unconstrained fn read_past_len() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n array.push(5);\n\n let _ = array.get(1);\n });\n }\n\n #[test]\n unconstrained fn array_remove_last() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(5);\n array.remove(0);\n\n assert_eq(array.len(), 0);\n });\n }\n\n #[test]\n unconstrained fn array_remove_some() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(7);\n array.push(8);\n array.push(9);\n\n assert_eq(array.len(), 3);\n assert_eq(array.get(0), 7);\n assert_eq(array.get(1), 8);\n assert_eq(array.get(2), 9);\n\n array.remove(1);\n\n assert_eq(array.len(), 2);\n assert_eq(array.get(0), 7);\n assert_eq(array.get(1), 9);\n });\n }\n\n #[test]\n unconstrained fn array_remove_all() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(7);\n array.push(8);\n array.push(9);\n\n array.remove(1);\n array.remove(1);\n array.remove(0);\n\n assert_eq(array.len(), 0);\n });\n }\n\n #[test]\n unconstrained fn for_each_called_with_all_elements() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(4);\n array.push(5);\n array.push(6);\n\n // We store all values that we were called with and check that all (value, index) tuples are present. Note\n // that we do not care about the order in which each tuple was passed to the closure.\n let called_with = &mut BoundedVec::<(u32, Field), 3>::new();\n array.for_each(|index, value| { called_with.push((index, value)); });\n\n assert_eq(called_with.len(), 3);\n assert(called_with.any(|(index, value)| (index == 0) & (value == 4)));\n assert(called_with.any(|(index, value)| (index == 1) & (value == 5)));\n assert(called_with.any(|(index, value)| (index == 2) & (value == 6)));\n });\n }\n\n #[test]\n unconstrained fn for_each_remove_some() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(4);\n array.push(5);\n array.push(6);\n\n array.for_each(|index, _| {\n if index == 1 {\n array.remove(index);\n }\n });\n\n assert_eq(array.len(), 2);\n assert_eq(array.get(0), 4);\n assert_eq(array.get(1), 6);\n });\n }\n\n #[test]\n unconstrained fn for_each_remove_all() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(4);\n array.push(5);\n array.push(6);\n\n array.for_each(|index, _| { array.remove(index); });\n\n assert_eq(array.len(), 0);\n });\n }\n\n #[test]\n unconstrained fn for_each_remove_all_no_copy() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let array = CapsuleArray::at(contract_address, SLOT, SCOPE);\n\n array.push(4);\n array.push(5);\n array.push(6);\n\n // We test that the aztec_utl_copyCapsule was never called, which is the expensive operation we want to\n // avoid.\n let mock = std::test::OracleMock::mock(\"aztec_utl_copyCapsule\");\n\n array.for_each(|index, _| { array.remove(index); });\n\n assert_eq(mock.times_called(), 0);\n });\n }\n\n #[test]\n unconstrained fn different_scopes_are_isolated() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let scope_a = AztecAddress { inner: 0xaaa };\n let scope_b = AztecAddress { inner: 0xbbb };\n\n let array_a = CapsuleArray::at(contract_address, SLOT, scope_a);\n let array_b = CapsuleArray::at(contract_address, SLOT, scope_b);\n\n array_a.push(10);\n array_a.push(20);\n array_b.push(99);\n\n assert_eq(array_a.len(), 2);\n assert_eq(array_a.get(0), 10);\n assert_eq(array_a.get(1), 20);\n\n assert_eq(array_b.len(), 1);\n assert_eq(array_b.get(0), 99);\n });\n }\n}\n" }, "76": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/calls.nr", "source": "use crate::protocol::{abis::function_selector::FunctionSelector, address::AztecAddress, traits::{Deserialize, ToField}};\n\nuse crate::context::{gas::GasOpts, PrivateContext, PublicContext};\nuse crate::hash::{hash_args, hash_calldata_array};\nuse crate::oracle::execution_cache;\n\n// Having T associated on the structs and then instantiating it with `std::mem::zeroed()` is ugly but we need to do it\n// like this to avoid forcing users to specify the return type when calling functions on the structs (that's the only\n// way how we can specify the type when we generate the call stubs. The return types are specified in the\n// `external_functions_stubs.nr` file.)\n\n// PrivateCall\n\n#[must_use = \"Your private call needs to be passed into the `self.call(...)` method to be executed (e.g. `self.call(MyContract::at(address).my_private_function(...args))`\"]\npub struct PrivateCall {\n pub target_contract: AztecAddress,\n pub selector: FunctionSelector,\n pub name: str,\n args_hash: Field,\n pub args: [Field; N],\n return_type: T,\n}\n\nimpl PrivateCall {\n pub fn new(target_contract: AztecAddress, selector: FunctionSelector, name: str, args: [Field; N]) -> Self {\n let args_hash = hash_args(args);\n Self { target_contract, selector, name, args_hash, args, return_type: std::mem::zeroed() }\n }\n}\n\nimpl PrivateCall\nwhere\n T: Deserialize,\n{\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.call(MyContract::at(address).my_private_function(...args))` instead of\n /// manually constructing and calling `PrivateCall`.\n pub fn call(self, context: &mut PrivateContext) -> T {\n execution_cache::store(self.args, self.args_hash);\n let returns_hash =\n context.call_private_function_with_args_hash(self.target_contract, self.selector, self.args_hash, false);\n\n // If T is () (i.e. if the function does not return anything) then `get_preimage` will constrain that the\n // returns hash is empty as per the protocol rules.\n returns_hash.get_preimage()\n }\n}\n\n// PrivateStaticCall\n\n#[must_use = \"Your private static call needs to be passed into the `self.view(...)` method to be executed (e.g. `self.view(MyContract::at(address).my_private_static_function(...args))`\"]\npub struct PrivateStaticCall {\n pub target_contract: AztecAddress,\n pub selector: FunctionSelector,\n pub name: str,\n args_hash: Field,\n pub args: [Field; N],\n return_type: T,\n}\n\nimpl PrivateStaticCall {\n pub fn new(target_contract: AztecAddress, selector: FunctionSelector, name: str, args: [Field; N]) -> Self {\n let args_hash = hash_args(args);\n Self { target_contract, selector, name, args_hash, args, return_type: std::mem::zeroed() }\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.view(MyContract::at(address).my_private_static_function(...args))`\n /// instead of manually constructing and calling `PrivateCall`.\n pub fn view(self, context: &mut PrivateContext) -> T\n where\n T: Deserialize,\n {\n execution_cache::store(self.args, self.args_hash);\n let returns =\n context.call_private_function_with_args_hash(self.target_contract, self.selector, self.args_hash, true);\n returns.get_preimage()\n }\n}\n\n// PublicCall\n\n#[must_use = \"Your public call needs to be passed into the `self.call(...)`, `self.enqueue(...)` or `self.enqueue_incognito(...)` method to be executed (e.g. `self.call(MyContract::at(address).my_public_function(...args))`\"]\npub struct PublicCall {\n pub target_contract: AztecAddress,\n pub selector: FunctionSelector,\n pub name: str,\n pub args: [Field; N],\n gas_opts: GasOpts,\n return_type: T,\n}\n\nimpl PublicCall {\n pub fn new(target_contract: AztecAddress, selector: FunctionSelector, name: str, args: [Field; N]) -> Self {\n Self { target_contract, selector, name, args, gas_opts: GasOpts::default(), return_type: std::mem::zeroed() }\n }\n\n pub fn with_gas(mut self, gas_opts: GasOpts) -> Self {\n self.gas_opts = gas_opts;\n self\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.call(MyContract::at(address).my_public_function(...args))` instead of\n /// manually constructing and calling `PublicCall`.\n pub unconstrained fn call(self, context: PublicContext) -> T\n where\n T: Deserialize,\n {\n let returns = context.call_public_function(self.target_contract, self.selector, self.args, self.gas_opts);\n // If T is () (i.e. if the function does not return anything) then `as_array` will constrain that `returns` has\n // a length of 0 (since that is ()'s deserialization length).\n Deserialize::deserialize(returns.as_array())\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.enqueue(MyContract::at(address).my_public_function(...args))` instead of\n /// manually constructing and calling `PublicCall`.\n pub fn enqueue(self, context: &mut PrivateContext) {\n self.enqueue_impl(context, false, false)\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.enqueue_incognito(MyContract::at(address).my_public_function(...args))`\n /// instead of manually constructing and calling `PublicCall`.\n pub fn enqueue_incognito(self, context: &mut PrivateContext) {\n self.enqueue_impl(context, false, true)\n }\n\n fn enqueue_impl(self, context: &mut PrivateContext, is_static_call: bool, hide_msg_sender: bool) {\n let calldata = [self.selector.to_field()].concat(self.args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n context.call_public_function_with_calldata_hash(\n self.target_contract,\n calldata_hash,\n is_static_call,\n hide_msg_sender,\n )\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.set_as_teardown(MyContract::at(address).my_public_function(...args))`\n /// instead of manually constructing and setting the teardown function `PublicCall`.\n pub fn set_as_teardown(self, context: &mut PrivateContext) {\n self.set_as_teardown_impl(context, false);\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API:\n /// `self.set_as_teardown_incognito(MyContract::at(address).my_public_function(...args))` instead of manually\n /// constructing and setting the teardown function `PublicCall`.\n pub fn set_as_teardown_incognito(self, context: &mut PrivateContext) {\n self.set_as_teardown_impl(context, true);\n }\n\n fn set_as_teardown_impl(self, context: &mut PrivateContext, hide_msg_sender: bool) {\n let calldata = [self.selector.to_field()].concat(self.args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n context.set_public_teardown_function_with_calldata_hash(\n self.target_contract,\n calldata_hash,\n false,\n hide_msg_sender,\n )\n }\n}\n\n// PublicStaticCall\n\n#[must_use = \"Your public static call needs to be passed into the `self.view(...)`, `self.enqueue_view(...)` or `self.enqueue_view_incognito(...)` method to be executed (e.g. `self.view(MyContract::at(address).my_public_static_function(...args))`\"]\npub struct PublicStaticCall {\n pub target_contract: AztecAddress,\n pub selector: FunctionSelector,\n pub name: str,\n pub args: [Field; N],\n return_type: T,\n gas_opts: GasOpts,\n}\n\nimpl PublicStaticCall {\n pub fn new(target_contract: AztecAddress, selector: FunctionSelector, name: str, args: [Field; N]) -> Self {\n Self { target_contract, selector, name, args, return_type: std::mem::zeroed(), gas_opts: GasOpts::default() }\n }\n\n pub fn with_gas(mut self, gas_opts: GasOpts) -> Self {\n self.gas_opts = gas_opts;\n self\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API: `self.view(MyContract::at(address).my_public_static_function(...args))`\n /// instead of manually constructing and calling `PublicStaticCall`.\n pub unconstrained fn view(self, context: PublicContext) -> T\n where\n T: Deserialize,\n {\n let returns =\n context.static_call_public_function(self.target_contract, self.selector, self.args, self.gas_opts);\n Deserialize::deserialize(returns.as_array())\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API:\n /// `self.enqueue_view(MyContract::at(address).my_public_static_function(...args))` instead of manually\n /// constructing and calling `PublicStaticCall`.\n pub fn enqueue_view(self, context: &mut PrivateContext) {\n let calldata = [self.selector.to_field()].concat(self.args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n context\n .call_public_function_with_calldata_hash(\n self.target_contract,\n calldata_hash,\n /*static=*/\n true,\n false,\n )\n }\n\n /// **DEPRECATED**.\n ///\n /// Please use the new contract API:\n /// `self.enqueue_view_incognito(MyContract::at(address).my_public_static_function(...args))` instead of manually\n /// constructing and calling `PublicStaticCall`.\n pub fn enqueue_view_incognito(self, context: &mut PrivateContext) {\n let calldata = [self.selector.to_field()].concat(self.args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n context\n .call_public_function_with_calldata_hash(\n self.target_contract,\n calldata_hash,\n /*static=*/\n true,\n true,\n )\n }\n}\n\n// UtilityCall\n\npub struct UtilityCall {\n pub target_contract: AztecAddress,\n pub selector: FunctionSelector,\n pub name: str,\n args_hash: Field,\n pub args: [Field; N],\n return_type: T,\n}\n\nimpl UtilityCall {\n pub fn new(target_contract: AztecAddress, selector: FunctionSelector, name: str, args: [Field; N]) -> Self {\n let args_hash = hash_args(args);\n Self { target_contract, selector, name, args_hash, args, return_type: std::mem::zeroed() }\n }\n}\n" }, "84": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/nullifier_existence_request.nr", "source": "use crate::protocol::address::aztec_address::AztecAddress;\n\n/// A request to assert the existence of a nullifier.\n///\n/// Used by [`crate::context::private_context::PrivateContext::assert_nullifier_exists`].\npub struct NullifierExistenceRequest {\n nullifier: Field,\n maybe_contract_address: Option,\n}\n\nimpl NullifierExistenceRequest {\n /// Creates an existence request for a pending nullifier.\n ///\n /// Pending nullifiers have not been siloed with the contract address. These requests are created using the\n /// unsiloed value and address of the contract that emitted the nullifier.\n pub fn for_pending(unsiloed_nullifier: Field, contract_address: AztecAddress) -> Self {\n // The kernel doesn't take options; it takes a nullifier and an address, and infers whether the request is\n // siloed based on whether the address is zero or non-zero. When passing the value to the kernel, we use\n // `maybe_addr.unwrap_or(Address::ZERO)`. Therefore, passing a zero address to `for_pending` is not allowed\n // since it would be interpreted by the kernel as a settled request.\n assert(!contract_address.is_zero(), \"Can't read a pending nullifier with a zero contract address\");\n Self { nullifier: unsiloed_nullifier, maybe_contract_address: Option::some(contract_address) }\n }\n\n /// Creates an existence request for a settled nullifier.\n ///\n /// Unlike pending nullifiers, settled nullifiers have been siloed with their contract addresses before adding to\n /// the nullifier tree, and their existence request is created using the siloed value.\n pub fn for_settled(siloed_nullifier: Field) -> Self {\n Self { nullifier: siloed_nullifier, maybe_contract_address: Option::none() }\n }\n\n pub(crate) fn nullifier(self) -> Field {\n self.nullifier\n }\n\n pub(crate) fn maybe_contract_address(self) -> Option {\n self.maybe_contract_address\n }\n}\n" }, "85": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/private_context.nr", "source": "use crate::{\n context::{inputs::PrivateContextInputs, NoteExistenceRequest, NullifierExistenceRequest, ReturnsHash},\n hash::{hash_args, hash_calldata_array},\n keys::constants::{NULLIFIER_INDEX, NUM_KEY_TYPES, OUTGOING_INDEX, public_key_domain_separators},\n messaging::process_l1_to_l2_message,\n oracle::{\n block_header::get_block_header_at,\n call_private_function::call_private_function_internal,\n execution_cache,\n key_validation_request::get_key_validation_request,\n logs::notify_created_contract_class_log,\n notes::notify_nullified_note,\n nullifiers::notify_created_nullifier,\n public_call::assert_valid_public_call_data,\n tx_phase::{is_execution_in_revertible_phase, notify_revertible_phase_start},\n },\n};\nuse crate::logging::aztecnr_trace_log_format;\nuse crate::protocol::{\n abis::{\n block_header::BlockHeader,\n call_context::CallContext,\n function_selector::FunctionSelector,\n gas_settings::GasSettings,\n log_hash::LogHash,\n note_hash::NoteHash,\n nullifier::Nullifier,\n private_call_request::PrivateCallRequest,\n private_circuit_public_inputs::PrivateCircuitPublicInputs,\n private_log::{PrivateLog, PrivateLogData},\n public_call_request::PublicCallRequest,\n validation_requests::{KeyValidationRequest, KeyValidationRequestAndSeparator},\n },\n address::{AztecAddress, EthAddress},\n constants::{\n CONTRACT_CLASS_LOG_SIZE_IN_FIELDS, MAX_CONTRACT_CLASS_LOGS_PER_CALL, MAX_ENQUEUED_CALLS_PER_CALL,\n MAX_KEY_VALIDATION_REQUESTS_PER_CALL, MAX_L2_TO_L1_MSGS_PER_CALL, MAX_NOTE_HASH_READ_REQUESTS_PER_CALL,\n MAX_NOTE_HASHES_PER_CALL, MAX_NULLIFIER_READ_REQUESTS_PER_CALL, MAX_NULLIFIERS_PER_CALL,\n MAX_PRIVATE_CALL_STACK_LENGTH_PER_CALL, MAX_PRIVATE_LOGS_PER_CALL, MAX_TX_LIFETIME,\n NULL_MSG_SENDER_CONTRACT_ADDRESS, PRIVATE_LOG_CIPHERTEXT_LEN,\n },\n hash::poseidon2_hash,\n messaging::l2_to_l1_message::L2ToL1Message,\n side_effect::{Counted, scoped::Scoped},\n traits::{Empty, Hash, ToField},\n utils::arrays::{ClaimedLengthArray, trimmed_array_length_hint},\n};\n\n/// # PrivateContext\n///\n/// The **main interface** between an #[external(\"private\")] function and the Aztec blockchain.\n///\n/// An instance of the PrivateContext is initialized automatically at the outset of every private function, within the\n/// #[external(\"private\")] macro, so you'll never need to consciously instantiate this yourself.\n///\n/// The instance is always named `context`, and it is always be available within the body of every\n/// #[external(\"private\")] function in your smart contract.\n///\n/// > For those used to \"vanilla\" Noir, it might be jarring to have access to > `context` without seeing a declaration\n/// `let context = PrivateContext::new(...)` > within the body of your function. This is just a consequence of using >\n/// macros to tidy-up verbose boilerplate. You can use `nargo expand` to > expand all macros, if you dare.\n///\n/// Typical usage for a smart contract developer will be to call getter methods of the PrivateContext.\n///\n/// _Pushing_ data and requests to the context is mostly handled within aztec-nr's own functions, so typically a smart\n/// contract developer won't need to call any setter methods directly.\n///\n/// > Advanced users might occasionally wish to push data to the context > directly for lower-level control. If you\n/// find yourself doing this, please > open an issue on GitHub to describe your use case: it might be that > new\n/// functionality should be added to aztec-nr.\n///\n/// ## Responsibilities\n/// - Exposes contextual data to a private function:\n/// - Data relating to how this private function was called.\n/// - msg_sender\n/// - this_address - (the contract address of the private function being executed)\n/// - See `CallContext` for more data.\n/// - Data relating to the transaction in which this private function is being executed.\n/// - chain_id\n/// - version\n/// - gas_settings\n/// - Provides state access:\n/// - Access to the \"Anchor block\" header. Recall, a private function cannot read from the \"current\" block header, but\n/// must read from some historical block header, because as soon as private function execution begins (asynchronously,\n/// on a user's device), the public state of the chain (the \"current state\") will have progressed forward. We call this\n/// reference the \"Anchor block\". See `BlockHeader`.\n/// - Enables consumption of L1->L2 messages.\n/// - Enables calls to functions of other smart contracts:\n/// - Private function calls\n/// - Enqueueing of public function call requests (Since public functions are executed at a later time, by a block\n/// proposer, we say they are \"enqueued\").\n/// - Writes data to the blockchain:\n/// - New notes\n/// - New nullifiers\n/// - Private logs (for sending encrypted note contents or encrypted events)\n/// - New L2->L1 messages.\n/// - Provides args to the private function (handled by the #[external(\"private\")] macro).\n/// - Returns the return values of this private function (handled by the\n/// #[external(\"private\")] macro).\n/// - Makes Key Validation Requests.\n/// - Private functions are not allowed to see master secret keys, because we do not trust them. They are instead given\n/// \"app-siloed\" secret keys with a claim that they relate to a master public key. They can then request validation of\n/// this claim, by making a \"key validation request\" to the protocol's kernel circuits (which _are_ allowed to see\n/// certain master secret keys).\n///\n/// ## Advanced Responsibilities\n///\n/// - Ultimately, the PrivateContext is responsible for constructing the PrivateCircuitPublicInputs of the private\n/// function being executed. All private functions on Aztec must have public inputs which adhere to the rigid layout of\n/// the PrivateCircuitPublicInputs, in order to be compatible with the protocol's kernel circuits. A well-known\n/// misnomer:\n/// - \"public inputs\" contain both inputs and outputs of this function.\n/// - By \"outputs\" we mean a lot more side-effects than just the \"return values\" of the function.\n/// - Most of the so-called \"public inputs\" are kept _private_, and never leak to the outside world, because they are\n/// 'swallowed' by the protocol's kernel circuits before the tx is sent to the network. Only the following are exposed\n/// to the outside world:\n/// - New note_hashes\n/// - New nullifiers\n/// - New private logs\n/// - New L2->L1 messages\n/// - New enqueued public function call requests All the above-listed arrays of side-effects can be padded by the\n/// user's wallet (through instructions to the kernel circuits, via the PXE) to obscure their true lengths.\n///\n/// ## Syntax Justification\n///\n/// Both user-defined functions _and_ most functions in aztec-nr need access to the PrivateContext instance to\n/// read/write data. This is why you'll see the arguably-ugly pervasiveness of the \"context\" throughout your smart\n/// contract and the aztec-nr library. For example, `&mut context` is prevalent. In some languages, you can access and\n/// mutate a global variable (such as a PrivateContext instance) from a function without polluting the function's\n/// parameters. With Noir, a function must explicitly pass control of a mutable variable to another function, by\n/// reference. Since many functions in aztec-nr need to be able to push new data to the PrivateContext, they need to be\n/// handed a mutable reference _to_ the context as a parameter. For example, `Context` is prevalent as a generic\n/// parameter, to give better type safety at compile time. Many `aztec-nr` functions don't make sense if they're called\n/// in a particular runtime (private, public or utility), and so are intentionally only implemented over certain\n/// [Private|Public|Utility]Context structs. This gives smart contract developers a much faster feedback loop if\n/// they're making a mistake, as an error will be thrown by the LSP or when they compile their contract.\n///\n#[derive(Eq)]\npub struct PrivateContext {\n // docs:start:private-context\n pub inputs: PrivateContextInputs,\n pub side_effect_counter: u32,\n\n pub min_revertible_side_effect_counter: u32,\n pub is_fee_payer: bool,\n\n pub args_hash: Field,\n pub return_hash: Field,\n\n pub expiration_timestamp: u64,\n\n pub(crate) note_hash_read_requests: BoundedVec>, MAX_NOTE_HASH_READ_REQUESTS_PER_CALL>,\n pub(crate) nullifier_read_requests: BoundedVec>, MAX_NULLIFIER_READ_REQUESTS_PER_CALL>,\n key_validation_requests_and_separators: BoundedVec,\n\n pub note_hashes: BoundedVec, MAX_NOTE_HASHES_PER_CALL>,\n pub nullifiers: BoundedVec, MAX_NULLIFIERS_PER_CALL>,\n\n pub private_call_requests: BoundedVec,\n pub public_call_requests: BoundedVec, MAX_ENQUEUED_CALLS_PER_CALL>,\n pub public_teardown_call_request: PublicCallRequest,\n pub l2_to_l1_msgs: BoundedVec, MAX_L2_TO_L1_MSGS_PER_CALL>,\n // docs:end:private-context\n\n // Header of a block whose state is used during private execution (not the block the transaction is included in).\n pub anchor_block_header: BlockHeader,\n\n pub private_logs: BoundedVec, MAX_PRIVATE_LOGS_PER_CALL>,\n pub contract_class_logs_hashes: BoundedVec, MAX_CONTRACT_CLASS_LOGS_PER_CALL>,\n\n // Contains the last key validation request for each key type. This is used to cache the last request and avoid\n // fetching the same request multiple times. The index of the array corresponds to the key type (0 nullifier, 1\n // incoming, 2 outgoing, 3 tagging).\n pub last_key_validation_requests: [Option; NUM_KEY_TYPES],\n\n pub expected_non_revertible_side_effect_counter: u32,\n pub expected_revertible_side_effect_counter: u32,\n}\n\nimpl PrivateContext {\n pub fn new(inputs: PrivateContextInputs, args_hash: Field) -> PrivateContext {\n PrivateContext {\n inputs,\n side_effect_counter: inputs.start_side_effect_counter + 1,\n min_revertible_side_effect_counter: 0,\n is_fee_payer: false,\n args_hash,\n return_hash: 0,\n expiration_timestamp: inputs.anchor_block_header.timestamp() + MAX_TX_LIFETIME,\n note_hash_read_requests: BoundedVec::new(),\n nullifier_read_requests: BoundedVec::new(),\n key_validation_requests_and_separators: BoundedVec::new(),\n note_hashes: BoundedVec::new(),\n nullifiers: BoundedVec::new(),\n anchor_block_header: inputs.anchor_block_header,\n private_call_requests: BoundedVec::new(),\n public_call_requests: BoundedVec::new(),\n public_teardown_call_request: PublicCallRequest::empty(),\n l2_to_l1_msgs: BoundedVec::new(),\n private_logs: BoundedVec::new(),\n contract_class_logs_hashes: BoundedVec::new(),\n last_key_validation_requests: [Option::none(); NUM_KEY_TYPES],\n expected_non_revertible_side_effect_counter: 0,\n expected_revertible_side_effect_counter: 0,\n }\n }\n\n /// Returns the contract address that initiated this function call.\n ///\n /// This is similar to `msg.sender` in Solidity (hence the name).\n ///\n /// Important Note: Since Aztec doesn't have a concept of an EoA (Externally-owned Account), the msg_sender is\n /// \"none\" for the first function call of every transaction. The first function call of a tx is likely to be a call\n /// to the user's account contract, so this quirk will most often be handled by account contract developers.\n ///\n /// # Returns\n /// * `Option` - The address of the smart contract that called this function (be it an app contract\n /// or a user's account contract). Returns `Option::none` for the first function call of the tx. No\n /// other _private_ function calls in the tx will have a `none` msg_sender, but _public_ function calls might (see\n /// the PublicContext).\n pub fn maybe_msg_sender(self) -> Option {\n let maybe_msg_sender = self.inputs.call_context.msg_sender;\n if maybe_msg_sender == NULL_MSG_SENDER_CONTRACT_ADDRESS {\n Option::none()\n } else {\n Option::some(maybe_msg_sender)\n }\n }\n\n /// Returns the contract address of the current function being executed.\n ///\n /// This is equivalent to `address(this)` in Solidity (hence the name). Use this to identify the current contract's\n /// address, commonly needed for access control or when interacting with other contracts.\n ///\n /// # Returns\n /// * `AztecAddress` - The contract address of the current function being executed.\n ///\n pub fn this_address(self) -> AztecAddress {\n self.inputs.call_context.contract_address\n }\n\n /// Returns the chain ID of the current network.\n ///\n /// This is similar to `block.chainid` in Solidity. Returns the unique identifier for the blockchain network this\n /// transaction is executing on.\n ///\n /// Helps prevent cross-chain replay attacks. Useful if implementing multi-chain contract logic.\n ///\n /// # Returns\n /// * `Field` - The chain ID as a field element\n ///\n pub fn chain_id(self) -> Field {\n self.inputs.tx_context.chain_id\n }\n\n /// Returns the Aztec protocol version that this transaction is executing under. Different versions may have\n /// different rules, opcodes, or cryptographic primitives.\n ///\n /// This is similar to how Ethereum has different EVM versions.\n ///\n /// Useful for forward/backward compatibility checks\n ///\n /// Not to be confused with contract versions; this is the protocol version.\n ///\n /// # Returns\n /// * `Field` - The protocol version as a field element\n ///\n pub fn version(self) -> Field {\n self.inputs.tx_context.version\n }\n\n /// Returns the gas settings for the current transaction.\n ///\n /// This provides information about gas limits and pricing for the transaction, similar to `tx.gasprice` and gas\n /// limits in Ethereum. However, Aztec has a more sophisticated gas model with separate accounting for L2\n /// computation and data availability (DA) costs.\n ///\n /// # Returns\n /// * `GasSettings` - Struct containing gas limits and fee information\n ///\n pub fn gas_settings(self) -> GasSettings {\n self.inputs.tx_context.gas_settings\n }\n\n /// Returns the function selector of the currently executing function.\n ///\n /// Low-level function: Ordinarily, smart contract developers will not need to access this.\n ///\n /// This is similar to `msg.sig` in Solidity, which returns the first 4 bytes of the function signature. In Aztec,\n /// the selector uniquely identifies which function within the contract is being called.\n ///\n /// # Returns\n /// * `FunctionSelector` - The 4-byte function identifier\n ///\n /// # Advanced\n /// Only #[external(\"private\")] functions have a function selector as a protocol- enshrined concept. The function\n /// selectors of private functions are baked into the preimage of the contract address, and are used by the\n /// protocol's kernel circuits to identify each private function and ensure the correct one is being executed.\n ///\n /// Used internally for function dispatch and call verification.\n ///\n pub fn selector(self) -> FunctionSelector {\n self.inputs.call_context.function_selector\n }\n\n /// Returns the hash of the arguments passed to the current function.\n ///\n /// Very low-level function: You shouldn't need to call this. The #[external(\"private\")] macro calls this, and it\n /// makes the arguments neatly available to the body of your private function.\n ///\n /// # Returns\n /// * `Field` - Hash of the function arguments\n ///\n /// # Advanced\n /// * Arguments are hashed to reduce proof size and verification time\n /// * Enables efficient argument passing in recursive function calls\n /// * The hash can be used to retrieve the original arguments from the PXE.\n ///\n pub fn get_args_hash(self) -> Field {\n self.args_hash\n }\n\n /// Pushes a new note_hash to the Aztec blockchain's global Note Hash Tree (a state tree).\n ///\n /// A note_hash is a commitment to a piece of private state.\n ///\n /// Low-level function: Ordinarily, smart contract developers will not need to manually call this. Aztec-nr's state\n /// variables (see `../state_vars/`) are designed to understand when to create and push new note hashes.\n ///\n /// # Arguments\n /// * `note_hash` - The new note_hash.\n ///\n /// # Advanced\n /// From here, the protocol's kernel circuits will take over and insert the note_hash into the protocol's \"note\n /// hash tree\" (in the Base Rollup circuit). Before insertion, the protocol will:\n /// - \"Silo\" the `note_hash` with the contract address of this function, to yield a `siloed_note_hash`. This\n /// prevents state collisions between different smart contracts.\n /// - Ensure uniqueness of the `siloed_note_hash`, to prevent Faerie-Gold attacks, by hashing the\n /// `siloed_note_hash` with a unique value, to yield a `unique_siloed_note_hash` (see the protocol spec for more).\n ///\n /// In addition to calling this function, aztec-nr provides the contents of the newly-created note to the PXE, via\n /// the `notify_created_note` oracle.\n ///\n /// > Advanced users might occasionally wish to push data to the context > directly for lower-level control. If you\n /// find yourself doing this, > please open an issue on GitHub to describe your use case: it might be > that new\n /// functionality should be added to aztec-nr.\n ///\n pub fn push_note_hash(&mut self, note_hash: Field) {\n self.note_hashes.push(Counted::new(note_hash, self.next_counter()));\n }\n\n /// Creates a new [nullifier](crate::nullifier).\n ///\n /// ## Safety\n ///\n /// This is a low-level function that must be used with great care to avoid subtle corruption of contract state.\n /// Instead of calling this function, consider using the higher-level [`crate::state_vars::SingleUseClaim`].\n ///\n /// In particular, callers must ensure all nullifiers created by a contract are properly domain-separated, so that\n /// unrelated components don't interfere with one another (e.g. a transaction nullifier accidentally marking a\n /// variable as initialized). Only [`PrivateContext::push_nullifier_for_note_hash`] should be used for note\n /// nullifiers, never this one.\n ///\n /// ## Advanced\n ///\n /// The raw `nullifier` is not what is inserted into the Aztec state tree: it will be first siloed by contract\n /// address via [`crate::protocol::hash::compute_siloed_nullifier`] in order to prevent accidental or malicious\n /// interference of nullifiers from different contracts.\n pub fn push_nullifier(&mut self, nullifier: Field) {\n notify_created_nullifier(nullifier);\n self.nullifiers.push(Nullifier { value: nullifier, note_hash: 0 }.count(self.next_counter()));\n }\n\n /// Creates a new [nullifier](crate::nullifier) associated with a note.\n ///\n /// This is a variant of [`PrivateContext::push_nullifier`] that is used for note nullifiers, i.e. nullifiers that\n /// correspond to a note. If a note and its nullifier are created in the same transaction, then the private kernels\n /// will 'squash' these values, deleting them both as if they never existed and reducing transaction fees.\n ///\n /// The `nullification_note_hash` must be the result of calling\n /// [`crate::note::utils::compute_confirmed_note_hash_for_nullification`] for pending notes, and `0` for settled\n /// notes (which cannot be squashed).\n ///\n /// ## Safety\n ///\n /// This is a low-level function that must be used with great care to avoid subtle corruption of contract state.\n /// Instead of calling this function, consider using the higher-level [`crate::note::lifecycle::destroy_note`].\n ///\n /// The precautions listed for [`PrivateContext::push_nullifier`] apply here as well, and callers should\n /// additionally ensure `nullification_note_hash` corresponds to a note emitted by this contract, with its hash\n /// computed in the same transaction execution phase as the call to this function. Finally, only this function\n /// should be used for note nullifiers, never [`PrivateContext::push_nullifier`].\n ///\n /// Failure to do these things can result in unprovable contexts, accidental deletion of notes, or double-spend\n /// attacks.\n pub fn push_nullifier_for_note_hash(&mut self, nullifier: Field, nullification_note_hash: Field) {\n let nullifier_counter = self.next_counter();\n notify_nullified_note(nullifier, nullification_note_hash, nullifier_counter);\n self.nullifiers.push(Nullifier { value: nullifier, note_hash: nullification_note_hash }.count(\n nullifier_counter,\n ));\n }\n\n /// Returns the anchor block header - the historical block header that this private function is reading from.\n ///\n /// A private function CANNOT read from the \"current\" block header, but must read from some older block header,\n /// because as soon as private function execution begins (asynchronously, on a user's device), the public state of\n /// the chain (the \"current state\") will have progressed forward.\n ///\n /// # Returns\n /// * `BlockHeader` - The anchor block header.\n ///\n /// # Advanced\n /// * All private functions of a tx read from the same anchor block header.\n /// * The protocol asserts that the `expiration_timestamp` of every tx is at most 24 hours beyond the timestamp of\n /// the tx's chosen anchor block header. This enables the network's nodes to safely prune old txs from the mempool.\n /// Therefore, the chosen block header _must_ be one from within the last 24 hours.\n ///\n pub fn get_anchor_block_header(self) -> BlockHeader {\n self.anchor_block_header\n }\n\n /// Returns the header of any historical block at or before the anchor block.\n ///\n /// This enables private contracts to access information from even older blocks than the anchor block header.\n ///\n /// Useful for time-based contract logic that needs to compare against multiple historical points.\n ///\n /// # Arguments\n /// * `block_number` - The block number to retrieve (must be <= anchor block number)\n ///\n /// # Returns\n /// * `BlockHeader` - The header of the requested historical block\n ///\n /// # Advanced\n /// This function uses an oracle to fetch block header data from the user's PXE. Depending on how much blockchain\n /// data the user's PXE has been set up to store, this might require a query from the PXE to another Aztec node to\n /// get the data. > This is generally true of all oracle getters (see `../oracle`).\n ///\n /// Each block header gets hashed and stored as a leaf in the protocol's Archive Tree. In fact, the i-th block\n /// header gets stored at the i-th leaf index of the Archive Tree. Behind the scenes, this `get_block_header_at`\n /// function will add Archive Tree merkle-membership constraints (~3k) to your smart contract function's circuit,\n /// to prove existence of the block header in the Archive Tree.\n ///\n /// Note: we don't do any caching, so avoid making duplicate calls for the same block header, because each call\n /// will add duplicate constraints.\n ///\n /// Calling this function is more expensive (constraint-wise) than getting the anchor block header (via\n /// `get_block_header`). This is because the anchor block's merkle membership proof is handled by Aztec's protocol\n /// circuits, and is only performed once for the entire tx because all private functions of a tx share a common\n /// anchor block header. Therefore, the cost (constraint-wise) of calling `get_block_header` is effectively free.\n ///\n pub fn get_block_header_at(self, block_number: u32) -> BlockHeader {\n get_block_header_at(block_number, self)\n }\n\n /// Sets the hash of the return values for this private function.\n ///\n /// Very low-level function: this is called by the #[external(\"private\")] macro.\n ///\n /// # Arguments\n /// * `serialized_return_values` - The serialized return values as a field array\n ///\n pub fn set_return_hash(&mut self, serialized_return_values: [Field; N]) {\n let return_hash = hash_args(serialized_return_values);\n self.return_hash = return_hash;\n execution_cache::store(serialized_return_values, return_hash);\n }\n\n /// Builds the PrivateCircuitPublicInputs for this private function, to ensure compatibility with the protocol's\n /// kernel circuits.\n ///\n /// Very low-level function: This function is automatically called by the #[external(\"private\")] macro.\n pub fn finish(self) -> PrivateCircuitPublicInputs {\n PrivateCircuitPublicInputs {\n call_context: self.inputs.call_context,\n args_hash: self.args_hash,\n returns_hash: self.return_hash,\n min_revertible_side_effect_counter: self.min_revertible_side_effect_counter,\n is_fee_payer: self.is_fee_payer,\n expiration_timestamp: self.expiration_timestamp,\n note_hash_read_requests: ClaimedLengthArray::from_bounded_vec(self.note_hash_read_requests),\n nullifier_read_requests: ClaimedLengthArray::from_bounded_vec(self.nullifier_read_requests),\n key_validation_requests_and_separators: ClaimedLengthArray::from_bounded_vec(\n self.key_validation_requests_and_separators,\n ),\n note_hashes: ClaimedLengthArray::from_bounded_vec(self.note_hashes),\n nullifiers: ClaimedLengthArray::from_bounded_vec(self.nullifiers),\n private_call_requests: ClaimedLengthArray::from_bounded_vec(self.private_call_requests),\n public_call_requests: ClaimedLengthArray::from_bounded_vec(self.public_call_requests),\n public_teardown_call_request: self.public_teardown_call_request,\n l2_to_l1_msgs: ClaimedLengthArray::from_bounded_vec(self.l2_to_l1_msgs),\n start_side_effect_counter: self.inputs.start_side_effect_counter,\n end_side_effect_counter: self.side_effect_counter,\n private_logs: ClaimedLengthArray::from_bounded_vec(self.private_logs),\n contract_class_logs_hashes: ClaimedLengthArray::from_bounded_vec(self.contract_class_logs_hashes),\n anchor_block_header: self.anchor_block_header,\n tx_context: self.inputs.tx_context,\n expected_non_revertible_side_effect_counter: self.expected_non_revertible_side_effect_counter,\n expected_revertible_side_effect_counter: self.expected_revertible_side_effect_counter,\n }\n }\n\n /// Designates this contract as the fee payer for the transaction.\n ///\n /// Unlike Ethereum, where the transaction sender always pays fees, Aztec allows any contract to voluntarily pay\n /// transaction fees. This enables patterns like sponsored transactions or fee abstraction where users don't need\n /// to hold fee-juice themselves. (Fee juice is a fee-paying asset for Aztec).\n ///\n /// Only one contract per transaction can declare itself as the fee payer, and it must have sufficient fee-juice\n /// balance (>= the gas limits specified in the TxContext) by the time we reach the public setup phase of the tx.\n ///\n pub fn set_as_fee_payer(&mut self) {\n aztecnr_trace_log_format!(\"Setting {0} as fee payer\")([self.this_address().to_field()]);\n self.is_fee_payer = true;\n }\n\n pub fn in_revertible_phase(&mut self) -> bool {\n let current_counter = self.side_effect_counter;\n\n // Safety: Kernel will validate that the claim is correct by validating the expected counters.\n let is_revertible = unsafe { is_execution_in_revertible_phase(current_counter) };\n\n if is_revertible {\n if (self.expected_revertible_side_effect_counter == 0)\n | (current_counter < self.expected_revertible_side_effect_counter) {\n self.expected_revertible_side_effect_counter = current_counter;\n }\n } else if current_counter > self.expected_non_revertible_side_effect_counter {\n self.expected_non_revertible_side_effect_counter = current_counter;\n }\n\n is_revertible\n }\n\n /// Declares the end of the \"setup phase\" of this tx.\n ///\n /// Only one function per tx can declare the end of the setup phase.\n ///\n /// Niche function: Only wallet developers and paymaster contract developers (aka Fee-payment contracts) will need\n /// to make use of this function.\n ///\n /// Aztec supports a three-phase execution model: setup, app logic, teardown. The phases exist to enable a fee\n /// payer to take on the risk of paying a transaction fee, safe in the knowledge that their payment (in whatever\n /// token or method the user chooses) will succeed, regardless of whether the app logic will succeed. The \"setup\"\n /// phase enables such a payment to be made, because the setup phase _cannot revert_: a reverting function within\n /// the setup phase would result in an invalid block which cannot be proven. Any side-effects generated during that\n /// phase are guaranteed to be inserted into Aztec's state trees (except for squashed notes & nullifiers, of\n /// course).\n ///\n /// Even though the end of the setup phase is declared within a private function, you might have noticed that\n /// _public_ functions can also execute within the setup phase. This is because any public function calls which\n /// were enqueued _within the setup phase_ by a private function are considered part of the setup phase.\n ///\n /// # Advanced\n /// * Sets the minimum revertible side effect counter of this tx to be the PrivateContext's _current_ side effect\n /// counter.\n ///\n pub fn end_setup(&mut self) {\n // Incrementing the side effect counter when ending setup ensures non ambiguity for the counter where we change\n // phases.\n self.side_effect_counter += 1;\n aztecnr_trace_log_format!(\"Ending setup at counter {0}\")([self.side_effect_counter as Field]);\n self.min_revertible_side_effect_counter = self.next_counter();\n notify_revertible_phase_start(self.min_revertible_side_effect_counter);\n }\n\n /// Sets a deadline (an \"include-by timestamp\") for when this transaction must be included in a block.\n ///\n /// Other functions in this tx might call this setter with differing values for the include-by timestamp. To ensure\n /// that all functions' deadlines are met, the _minimum_ of all these include-by timestamps will be exposed when\n /// this tx is submitted to the network.\n ///\n /// If the transaction is not included in a block by its include-by timestamp, it becomes invalid and it will never\n /// be included.\n ///\n /// This expiry timestamp is publicly visible. See the \"Advanced\" section for privacy concerns.\n ///\n /// # Arguments\n /// * `expiration_timestamp` - Unix timestamp (seconds) deadline for inclusion. The include-by timestamp of this tx\n /// will be _at most_ the timestamp specified.\n ///\n /// # Advanced\n /// * If multiple functions set differing `expiration_timestamp`s, the kernel circuits will set it to be the\n /// _minimum_ of the two. This ensures the tx expiry requirements of all functions in the tx are met.\n /// * Rollup circuits will reject expired txs.\n /// * The protocol enforces that all transactions must be included within 24 hours of their chosen anchor block's\n /// timestamp, to enable safe mempool pruning.\n /// * The DelayedPublicMutable design makes heavy use of this functionality, to enable private functions to read\n /// public state.\n /// * A sophisticated Wallet should cleverly set an include-by timestamp to improve the privacy of the user and the\n /// network as a whole. For example, if a contract interaction sets include-by to some publicly-known value (e.g.\n /// the time when a contract upgrades), then the wallet might wish to set an even lower one to avoid revealing that\n /// this tx is interacting with said contract. Ideally, all wallets should standardize on an approach in order to\n /// provide users with a large privacy set -- although the exact approach\n /// will need to be discussed. Wallets that deviate from a standard might accidentally reveal which wallet each\n /// transaction originates from.\n ///\n // docs:start:expiration-timestamp\n pub fn set_expiration_timestamp(&mut self, expiration_timestamp: u64) {\n // docs:end:expiration-timestamp\n self.expiration_timestamp = std::cmp::min(self.expiration_timestamp, expiration_timestamp);\n }\n\n /// Asserts that a note has been created.\n ///\n /// This function will cause the transaction to fail unless the requested note exists. This is the preferred\n /// mechanism for performing this check, and the only one that works for pending notes.\n ///\n /// ## Pending Notes\n ///\n /// Both settled notes (created in prior transactions) and pending notes (created in the current transaction) will\n /// be considered by this function. Pending notes must have been created **before** this call is made for the check\n /// to pass.\n ///\n /// ## Historical Notes\n ///\n /// If you need to assert that a note existed _by some specific block in the past_, instead of simply proving that\n /// it exists by the current anchor block, use [`crate::history::note::assert_note_existed_by`] instead.\n ///\n /// ## Cost\n ///\n /// This uses up one of the call's kernel note hash read requests, which are limited. Like all kernel requests,\n /// proving time costs are only incurred when the total number of requests exceeds the kernel's capacity, requiring\n /// an additional invocation of the kernel reset circuit.\n pub fn assert_note_exists(&mut self, note_existence_request: NoteExistenceRequest) {\n // Note that the `note_hash_read_requests` array does not hold `NoteExistenceRequest` objects, but rather a\n // custom kernel type. We convert from the aztec-nr type into it.\n\n let note_hash = note_existence_request.note_hash();\n let contract_address = note_existence_request.maybe_contract_address().unwrap_or(AztecAddress::zero());\n\n let side_effect = Scoped::new(\n Counted::new(note_hash, self.next_counter()),\n contract_address,\n );\n\n self.note_hash_read_requests.push(side_effect);\n }\n\n /// Asserts that a nullifier has been emitted.\n ///\n /// This function will cause the transaction to fail unless the requested nullifier exists. This is the preferred\n /// mechanism for performing this check, and the only one that works for pending nullifiers.\n ///\n /// ## Pending Nullifiers\n ///\n /// Both settled nullifiers (emitted in prior transactions) and pending nullifiers (emitted in the current\n /// transaction) will be considered by this function. Pending nullifiers must have been emitted **before** this\n /// call is made for the check to pass.\n ///\n /// ## Historical Nullifiers\n ///\n /// If you need to assert that a nullifier existed _by some specific block in the past_, instead of simply proving\n /// that it exists by the current anchor block, use [`crate::history::nullifier::assert_nullifier_existed_by`]\n /// instead.\n ///\n /// ## Public vs Private\n ///\n /// In general, it is unsafe to check for nullifier non-existence in private, as that will not consider the\n /// possibility of the nullifier having been emitted in any transaction between the anchor block and the inclusion\n /// block. Private functions instead prove existence via this function and 'prove' non-existence by _emitting_ the\n /// nullifer, which would cause the transaction to fail if the nullifier existed.\n ///\n /// This is not the case in public functions, which do have access to the tip of the blockchain and so can reliably\n /// prove whether a nullifier exists or not via\n /// [`crate::context::public_context::PublicContext::nullifier_exists_unsafe`].\n ///\n /// ## Cost\n ///\n /// This uses up one of the call's kernel nullifier read requests, which are limited. Like all kernel requests,\n /// proving time costs are only incurred when the total number of requests exceeds the kernel's capacity, requiring\n /// an additional invocation of the kernel reset circuit.\n pub fn assert_nullifier_exists(&mut self, nullifier_existence_request: NullifierExistenceRequest) {\n let nullifier = nullifier_existence_request.nullifier();\n let contract_address = nullifier_existence_request.maybe_contract_address().unwrap_or(AztecAddress::zero());\n\n let request = Scoped::new(\n Counted::new(nullifier, self.next_counter()),\n contract_address,\n );\n\n self.nullifier_read_requests.push(request);\n }\n\n /// Requests the app-siloed nullifier hiding key (nhk_app) for the given (hashed) master nullifier public key\n /// (npk_m), from the user's PXE.\n ///\n /// Advanced function: Only needed if you're designing your own notes and/or nullifiers.\n ///\n /// Contracts are not allowed to compute nullifiers for other contracts, as that would let them read parts of their\n /// private state. Because of this, a contract is only given an \"app-siloed key\", which is constructed by\n /// hashing the user's master nullifier hiding key with the contract's address. However, because contracts cannot\n /// be trusted with a user's master nullifier hiding key (because we don't know which contracts are honest or\n /// malicious), the PXE refuses to provide any master secret keys to any app smart contract function. This means\n /// app functions are unable to prove that the derivation of an app-siloed nullifier hiding key has been computed\n /// correctly. Instead, an app function can request to the kernel (via `request_nhk_app`) that it validates the\n /// siloed derivation, since the kernel has been vetted to not leak any master secret keys.\n ///\n /// A common nullification scheme is to inject a nullifier hiding key into the preimage of a nullifier, to make the\n /// nullifier deterministic but random-looking. This function enables that flow.\n ///\n /// # Arguments\n /// * `npk_m_hash` - A hash of the master nullifier public key of the user whose PXE is executing this function.\n ///\n /// # Returns\n /// * The app-siloed nullifier hiding key that corresponds to the given `npk_m_hash`.\n ///\n pub fn request_nhk_app(&mut self, npk_m_hash: Field) -> Field {\n self.request_sk_app(npk_m_hash, NULLIFIER_INDEX)\n }\n\n /// Requests the app-siloed nullifier secret key (nsk_app) for the given (hashed) master nullifier public key\n /// (npk_m), from the user's PXE.\n ///\n /// See `request_nsk_app` and `request_sk_app` for more info.\n ///\n /// The intention of the \"outgoing\" keypair is to provide a second secret key for all of a user's outgoing activity\n /// (i.e. for notes that a user creates, as opposed to notes that a user receives from others). The separation of\n /// incoming and outgoing data was a distinction made by zcash, with the intention of enabling a user to optionally\n /// share with a 3rd party a controlled view of only incoming or outgoing notes. Similar functionality of sharing\n /// select data can be achieved with offchain zero-knowledge proofs. It is up to an app developer whether they\n /// choose to make use of a user's outgoing keypair within their application logic, or instead simply use the same\n /// keypair (the address keypair (which is effectively the same as the \"incoming\" keypair)) for all incoming &\n /// outgoing messages to a user.\n ///\n /// Currently, all of the exposed encryption functions in aztec-nr ignore the outgoing viewing keys, and instead\n /// encrypt all note logs and event logs to a user's address public key.\n ///\n /// # Arguments\n /// * `ovpk_m_hash` - Hash of the outgoing viewing public key master\n ///\n /// # Returns\n /// * The application-specific outgoing viewing secret key\n ///\n pub fn request_ovsk_app(&mut self, ovpk_m_hash: Field) -> Field {\n self.request_sk_app(ovpk_m_hash, OUTGOING_INDEX)\n }\n\n /// Pushes a Key Validation Request to the kernel.\n ///\n /// Private functions are not allowed to see a user's master secret keys, because we do not trust them. They are\n /// instead given \"app-siloed\" secret keys with a claim that they relate to a master public key. They can then\n /// request validation of this claim, by making a \"key validation request\" to the protocol's kernel circuits (which\n /// _are_ allowed to see certain master secret keys).\n ///\n /// When a Key Validation Request tuple of (sk_app, Pk_m, app_address) is submitted to the kernel, it will perform\n /// the following derivations to validate the relationship between the claimed sk_app and the user's Pk_m:\n ///\n /// (sk_m) ----> * G ----> Pk_m\n /// | |\n /// v We use the kernel to prove this\n /// h(sk_m, app_address) | sk_app-Pk_m relationship, because app\n /// | circuits must not be trusted to see sk_m.\n /// v |\n /// sk_app - - - - - - - - -\n ///\n /// The function is named \"request_\" instead of \"get_\" to remind the user that a Key Validation Request will be\n /// emitted to the kernel.\n ///\n fn request_sk_app(&mut self, pk_m_hash: Field, key_index: Field) -> Field {\n let cached_request =\n self.last_key_validation_requests[key_index as u32].unwrap_or(KeyValidationRequest::empty());\n\n if cached_request.pk_m.hash() == pk_m_hash {\n // We get a match so the cached request is the latest one\n cached_request.sk_app\n } else {\n // We didn't get a match meaning the cached result is stale Typically we'd validate keys by showing that\n // they are the preimage of `pk_m_hash`, but that'd require the oracle returning the master secret keys,\n // which could cause malicious contracts to leak it or learn about secrets from other contracts. We\n // therefore silo secret keys, and rely on the private kernel to validate that we siloed secret key\n // corresponds to correct siloing of the master secret key that hashes to `pk_m_hash`.\n\n // Safety: Kernels verify that the key validation request is valid and below we verify that a request for\n // the correct public key has been received.\n let request = unsafe { get_key_validation_request(pk_m_hash, key_index) };\n assert(!request.pk_m.is_infinite, \"Infinite public key points are not allowed\");\n assert_eq(request.pk_m.hash(), pk_m_hash, \"Obtained invalid key validation request\");\n\n self.key_validation_requests_and_separators.push(\n KeyValidationRequestAndSeparator {\n request,\n key_type_domain_separator: public_key_domain_separators[key_index as u32],\n },\n );\n self.last_key_validation_requests[key_index as u32] = Option::some(request);\n request.sk_app\n }\n }\n\n /// Sends an \"L2 -> L1 message\" from this function (Aztec, L2) to a smart contract on Ethereum (L1). L1 contracts\n /// which are designed to send/receive messages to/from Aztec are called \"Portal Contracts\".\n ///\n /// Common use cases include withdrawals, cross-chain asset transfers, and triggering L1 actions based on L2 state\n /// changes.\n ///\n /// The message will be inserted into an Aztec \"Outbox\" contract on L1, when this transaction's block is proposed\n /// to L1. Sending the message will not result in any immediate state changes in the target portal contract. The\n /// message will need to be manually consumed from the Outbox through a separate Ethereum transaction: a user will\n /// need to call a function of the portal contract -- a function specifically designed to make a call to the Outbox\n /// to consume the message. The message will only be available for consumption once the _epoch_ proof has been\n /// submitted. Given that there are multiple Aztec blocks within an epoch, it might take some time for this epoch\n /// proof to be submitted -- especially if the block was near the start of an epoch.\n ///\n /// # Arguments\n /// * `recipient` - Ethereum address that will receive the message\n /// * `content` - Message content (32 bytes as a Field element). This content has a very\n /// specific layout. docs:start:context_message_portal\n pub fn message_portal(&mut self, recipient: EthAddress, content: Field) {\n let message = L2ToL1Message { recipient, content };\n self.l2_to_l1_msgs.push(message.count(self.next_counter()));\n }\n\n /// Consumes a message sent from Ethereum (L1) to Aztec (L2).\n ///\n /// Common use cases include token bridging, cross-chain governance, and triggering L2 actions based on L1 events.\n ///\n /// Use this function if you only want the message to ever be \"referred to\" once. Once consumed using this method,\n /// the message cannot be consumed again, because a nullifier is emitted. If your use case wants for the message to\n /// be read unlimited times, then you can always read any historic message from the L1-to-L2 messages tree;\n /// messages never technically get deleted from that tree.\n ///\n /// The message will first be inserted into an Aztec \"Inbox\" smart contract on L1. Sending the message will not\n /// result in any immediate state changes in the target L2 contract. The message will need to be manually consumed\n /// by the target contract through a separate Aztec transaction. The message will not be available for consumption\n /// immediately. Messages get copied over from the L1 Inbox to L2 by the next Proposer in batches. So you will need\n /// to wait until the messages are copied before you can consume them.\n ///\n /// # Arguments\n /// * `content` - The message content that was sent from L1\n /// * `secret` - Secret value used for message privacy (if needed)\n /// * `sender` - Ethereum address that sent the message\n /// * `leaf_index` - Index of the message in the L1-to-L2 message tree\n ///\n /// # Advanced\n /// Validates message existence in the L1-to-L2 message tree and nullifies the message to prevent\n /// double-consumption.\n pub fn consume_l1_to_l2_message(&mut self, content: Field, secret: Field, sender: EthAddress, leaf_index: Field) {\n let nullifier = process_l1_to_l2_message(\n self.anchor_block_header.state.l1_to_l2_message_tree.root,\n self.this_address(),\n sender,\n self.chain_id(),\n self.version(),\n content,\n secret,\n leaf_index,\n );\n\n // Push nullifier (and the \"commitment\" corresponding to this can be \"empty\")\n self.push_nullifier(nullifier)\n }\n\n /// Emits a private log (an array of Fields) that will be published to an Ethereum blob.\n ///\n /// Private logs are intended for the broadcasting of ciphertexts: that is, encrypted events or encrypted note\n /// contents. Since the data in the logs is meant to be _encrypted_, private_logs are broadcast to publicly-visible\n /// Ethereum blobs. The intended recipients of such encrypted messages can then discover and decrypt these\n /// encrypted logs using their viewing secret key. (See `../messages/discovery` for more details).\n ///\n /// Important note: This function DOES NOT _do_ any encryption of the input `log` fields. This function blindly\n /// publishes whatever input `log` data is fed into it, so the caller of this function should have already\n /// performed the encryption, and the `log` should be the result of that encryption.\n ///\n /// The protocol does not dictate what encryption scheme should be used: a smart contract developer can choose\n /// whatever encryption scheme they like. Aztec-nr includes some off-the-shelf encryption libraries that developers\n /// might wish to use, for convenience. These libraries not only encrypt a plaintext (to produce a ciphertext);\n /// they also prepend the ciphertext with a `tag` and `ephemeral public key` for easier message discovery. This is\n /// a very dense topic, and we will be writing more libraries and docs soon.\n ///\n /// > Currently, AES128 CBC encryption is the main scheme included in > aztec.nr. > We are currently making\n /// significant changes to the interfaces of the > encryption library.\n ///\n /// In some niche use cases, an app might be tempted to publish _un-encrypted_ data via a private log, because\n /// _public logs_ are not available to private functions. Be warned that emitting public data via private logs is\n /// strongly discouraged, and is considered a \"privacy anti-pattern\", because it reveals identifiable information\n /// about _which_ function has been executed. A tx which leaks such information does not contribute to the privacy\n /// set of the network.\n ///\n /// * Unlike `emit_raw_note_log_unsafe`, this log is not tied to any specific note\n ///\n /// # Arguments\n /// * `tag` - A tag placed at `fields[0]` of the emitted log. Used by recipients and nodes to identify and\n /// filter for relevant logs without scanning all of them.\n /// * `log` - The log data that will be publicly broadcast (so make sure it's already been encrypted before you\n /// call this function). Private logs are bounded in size (`PRIVATE_LOG_CIPHERTEXT_LEN`), to encourage all logs\n /// from all smart contracts look identical.\n /// * `length` - The actual length of `log` (measured in number of Fields). Although the input log has a max\n /// size of `PRIVATE_LOG_CIPHERTEXT_LEN`, the latter values of the array might all be 0's for small logs. This\n /// `length` should reflect the trimmed length of the array. The protocol's kernel circuits can then append\n /// random fields as \"padding\" after the `length`, so that the logs of this smart contract look\n /// indistinguishable from (the same length as) the logs of all other applications. It's up to wallets how much\n /// padding to apply, so ideally all wallets should agree on standards for this.\n ///\n /// ## Safety\n ///\n /// The `tag` should be domain-separated (e.g. via [`crate::protocol::hash::compute_log_tag`]) to prevent\n /// collisions between logs from different sources. Without domain separation, two unrelated log types that\n /// happen to share a raw tag value become indistinguishable. Prefer the higher-level APIs\n /// ([`crate::messages::message_delivery::MessageDelivery`] for messages, `self.emit(event)` for events) which\n /// handle tagging automatically.\n pub fn emit_private_log_unsafe(&mut self, tag: Field, log: [Field; PRIVATE_LOG_CIPHERTEXT_LEN], length: u32) {\n let counter = self.next_counter();\n let full_log = [tag].concat(log);\n self.private_logs.push(PrivateLogData { log: PrivateLog::new(full_log, length + 1), note_hash_counter: 0 }\n .count(counter));\n }\n\n // TODO: rename.\n /// Emits a private log that is explicitly tied to a newly-emitted note_hash, to convey to the kernel: \"this log\n /// relates to this note\".\n ///\n /// This linkage is important in case the note gets squashed (due to being read later in this same tx), since we\n /// can then squash the log as well.\n ///\n /// See `emit_private_log_unsafe` for more info about private log emission.\n ///\n /// # Arguments\n /// * `tag` - A tag placed at `fields[0]`. See `emit_private_log_unsafe`.\n /// * `log` - The log data as an array of Field elements\n /// * `length` - The actual length of the `log` (measured in number of Fields).\n /// * `note_hash_counter` - The side-effect counter that was assigned to the new note_hash when it was pushed to\n /// this `PrivateContext`.\n ///\n /// Important: If your application logic requires the log to always be emitted regardless of note squashing,\n /// consider using `emit_private_log_unsafe` instead, or emitting additional events.\n ///\n /// ## Safety\n ///\n /// Same as [`PrivateContext::emit_private_log_unsafe`]: the `tag` should be domain-separated.\n pub fn emit_raw_note_log_unsafe(\n &mut self,\n tag: Field,\n log: [Field; PRIVATE_LOG_CIPHERTEXT_LEN],\n length: u32,\n note_hash_counter: u32,\n ) {\n let counter = self.next_counter();\n let full_log = [tag].concat(log);\n let private_log = PrivateLogData { log: PrivateLog::new(full_log, length + 1), note_hash_counter };\n self.private_logs.push(private_log.count(counter));\n }\n\n pub fn emit_contract_class_log(&mut self, log: [Field; N]) {\n let contract_address = self.this_address();\n let counter = self.next_counter();\n\n let log_to_emit: [Field; CONTRACT_CLASS_LOG_SIZE_IN_FIELDS] =\n log.concat([0; CONTRACT_CLASS_LOG_SIZE_IN_FIELDS - N]);\n // Note: the length is not always N, it is the number of fields we want to broadcast, omitting trailing zeros\n // to save blob space.\n // Safety: The below length is constrained in the base rollup, which will make sure that all the fields beyond\n // length are zero. However, it won't be able to check that we didn't add extra padding (trailing zeroes) or\n // that we cut trailing zeroes from the end.\n let length = unsafe { trimmed_array_length_hint(log_to_emit) };\n // We hash the entire padded log to ensure a user cannot pass a shorter length and so emit incorrect shorter\n // bytecode.\n let log_hash = poseidon2_hash(log_to_emit);\n // Safety: the below only exists to broadcast the raw log, so we can provide it to the base rollup later to be\n // constrained.\n unsafe {\n notify_created_contract_class_log(contract_address, log_to_emit, length, counter);\n }\n\n self.contract_class_logs_hashes.push(LogHash { value: log_hash, length: length }.count(counter));\n }\n\n /// Calls a private function on another contract (or the same contract).\n ///\n /// Very low-level function.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args` - Array of arguments to pass to the called function\n ///\n /// # Returns\n /// * `ReturnsHash` - Hash of the called function's return values. Use `.get_preimage()` to extract the actual\n /// return values.\n ///\n /// This enables contracts to interact with each other while maintaining privacy. This \"composability\" of private\n /// contract functions is a key feature of the Aztec network.\n ///\n /// If a user's transaction includes multiple private function calls, then by the design of Aztec, the following\n /// information will remain private[1]:\n /// - The function selectors and contract addresses of all private function calls will remain private, so an\n /// observer of the public mempool will not be able to look at a tx and deduce which private functions have been\n /// executed.\n /// - The arguments and return values of all private function calls will remain private.\n /// - The person who initiated the tx will remain private.\n /// - The notes and nullifiers and private logs that are emitted by all private function calls will (if designed\n /// well) not leak any user secrets, nor leak which functions have been executed.\n ///\n /// [1] Caveats: Some of these privacy guarantees depend on how app developers design their smart contracts. Some\n /// actions _can_ leak information, such as:\n /// - Calling an internal public function.\n /// - Calling a public function and not setting msg_sender to Option::none (feature not built yet - see github).\n /// - Calling any public function will always leak details about the nature of the transaction, so devs should be\n /// careful in their contract designs. If it can be done in a private function, then that will give the best\n /// privacy.\n /// - Not padding the side-effects of a tx to some standardized, uniform size. The kernel circuits can take hints\n /// to pad side-effects, so a wallet should be able to request for a particular amount of padding. Wallets should\n /// ideally agree on some standard.\n /// - Padding should include:\n /// - Padding the lengths of note & nullifier arrays\n /// - Padding private logs with random fields, up to some standardized size. See also:\n /// https://docs.aztec.network/developers/resources/considerations/privacy_considerations\n ///\n /// # Advanced\n /// * The call is added to the private call stack and executed by kernel circuits after this function completes\n /// * The called function can modify its own contract's private state\n /// * Side effects from the called function are included in this transaction\n /// * The call inherits the current transaction's context and gas limits\n ///\n pub fn call_private_function(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; ArgsCount],\n ) -> ReturnsHash {\n let args_hash = hash_args(args);\n execution_cache::store(args, args_hash);\n self.call_private_function_with_args_hash(contract_address, function_selector, args_hash, false)\n }\n\n /// Makes a read-only call to a private function on another contract.\n ///\n /// This is similar to Solidity's `staticcall`. The called function cannot modify state, emit L2->L2 messages, nor\n /// emit events. Any nested calls are constrained to also be staticcalls.\n ///\n /// See `call_private_function` for more general info on private function calls.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract to call\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args` - Array of arguments to pass to the called function\n ///\n /// # Returns\n /// * `ReturnsHash` - Hash of the called function's return values. Use `.get_preimage()` to extract the actual\n /// return values.\n ///\n pub fn static_call_private_function(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; ArgsCount],\n ) -> ReturnsHash {\n let args_hash = hash_args(args);\n execution_cache::store(args, args_hash);\n self.call_private_function_with_args_hash(contract_address, function_selector, args_hash, true)\n }\n\n /// Calls a private function that takes no arguments.\n ///\n /// This is a convenience function for calling private functions that don't require any input parameters. It's\n /// equivalent to `call_private_function` but slightly more efficient to use when no arguments are needed.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n ///\n /// # Returns\n /// * `ReturnsHash` - Hash of the called function's return values. Use `.get_preimage()` to extract the actual\n /// return values.\n ///\n pub fn call_private_function_no_args(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n ) -> ReturnsHash {\n self.call_private_function_with_args_hash(contract_address, function_selector, 0, false)\n }\n\n /// Makes a read-only call to a private function which takes no arguments.\n ///\n /// This combines the optimisation of `call_private_function_no_args` with the safety of\n /// `static_call_private_function`.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n ///\n /// # Returns\n /// * `ReturnsHash` - Hash of the called function's return values. Use `.get_preimage()` to extract the actual\n /// return values.\n ///\n pub fn static_call_private_function_no_args(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n ) -> ReturnsHash {\n self.call_private_function_with_args_hash(contract_address, function_selector, 0, true)\n }\n\n /// Low-level private function call.\n ///\n /// This is the underlying implementation used by all other private function call methods. Instead of taking raw\n /// arguments, it accepts a hash of the arguments.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args_hash` - Pre-computed hash of the function arguments\n /// * `is_static_call` - Whether this should be a read-only call\n ///\n /// # Returns\n /// * `ReturnsHash` - Hash of the called function's return values\n ///\n pub fn call_private_function_with_args_hash(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args_hash: Field,\n is_static_call: bool,\n ) -> ReturnsHash {\n let mut is_static_call = is_static_call | self.inputs.call_context.is_static_call;\n let start_side_effect_counter = self.side_effect_counter;\n\n // Safety: The oracle simulates the private call and returns the value of the side effects counter after\n // execution of the call (which means that end_side_effect_counter - start_side_effect_counter is the number of\n // side effects that took place), along with the hash of the return values. We validate these by requesting a\n // private kernel iteration in which the return values are constrained to hash to `returns_hash` and the side\n // effects counter to increment from start to end.\n let (end_side_effect_counter, returns_hash) = unsafe {\n call_private_function_internal(\n contract_address,\n function_selector,\n args_hash,\n start_side_effect_counter,\n is_static_call,\n )\n };\n\n self.private_call_requests.push(\n PrivateCallRequest {\n call_context: CallContext {\n msg_sender: self.this_address(),\n contract_address,\n function_selector,\n is_static_call,\n },\n args_hash,\n returns_hash,\n start_side_effect_counter,\n end_side_effect_counter,\n },\n );\n\n // TODO (fees) figure out why this crashes the prover and enable it we need this in order to pay fees inside\n // child call contexts assert(\n // (item.public_inputs.min_revertible_side_effect_counter == 0 as u32)\n // | (item.public_inputs.min_revertible_side_effect_counter\n // > self.min_revertible_side_effect_counter)\n // ); if item.public_inputs.min_revertible_side_effect_counter\n // > self.min_revertible_side_effect_counter { self.min_revertible_side_effect_counter =\n // item.public_inputs.min_revertible_side_effect_counter; }\n self.side_effect_counter = end_side_effect_counter + 1; // TODO: call `next_counter`\n // instead, for consistency\n ReturnsHash::new(returns_hash)\n }\n\n /// Enqueues a call to a public function to be executed later.\n ///\n /// Unlike private functions which execute immediately on the user's device, public function calls are \"enqueued\"\n /// and executed some time later by a block proposer.\n ///\n /// This means a public function cannot return any values back to a private function, because by the time the\n /// public function is being executed, the private function which called it has already completed execution. (In\n /// fact, the private function has been executed and proven, along with all other private function calls of the\n /// user's tx. A single proof of the tx has been submitted to the Aztec network, and some time later a proposer has\n /// picked the tx up from the mempool and begun executing all of the enqueued public functions).\n ///\n /// # Privacy warning Enqueueing a public function call is an inherently leaky action. Many interesting applications will require some interaction with public state, but smart contract developers should try to use public function calls sparingly, and carefully. _Internal_ public function calls are especially leaky, because they completely leak which private contract made the call. See also: https://docs.aztec.network/developers/resources/considerations/privacy_considerations\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args` - Array of arguments to pass to the public function\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn call_public_function(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; ArgsCount],\n hide_msg_sender: bool,\n ) {\n let calldata = [function_selector.to_field()].concat(args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n self.call_public_function_with_calldata_hash(contract_address, calldata_hash, false, hide_msg_sender)\n }\n\n /// Enqueues a read-only call to a public function.\n ///\n /// This is similar to Solidity's `staticcall`. The called function cannot modify state or emit events. Any nested\n /// calls are constrained to also be staticcalls.\n ///\n /// See also `call_public_function` for more important information about making private -> public function calls.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args` - Array of arguments to pass to the public function\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn static_call_public_function(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; ArgsCount],\n hide_msg_sender: bool,\n ) {\n let calldata = [function_selector.to_field()].concat(args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n self.call_public_function_with_calldata_hash(contract_address, calldata_hash, true, hide_msg_sender)\n }\n\n /// Enqueues a call to a public function that takes no arguments.\n ///\n /// This is an optimisation for calling public functions that don't take any input parameters. It's otherwise\n /// equivalent to `call_public_function`.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn call_public_function_no_args(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n hide_msg_sender: bool,\n ) {\n let calldata_hash = hash_calldata_array([function_selector.to_field()]);\n self.call_public_function_with_calldata_hash(contract_address, calldata_hash, false, hide_msg_sender)\n }\n\n /// Enqueues a read-only call to a public function with no arguments.\n ///\n /// This combines the optimisation of `call_public_function_no_args` with the safety of\n /// `static_call_public_function`.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn static_call_public_function_no_args(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n hide_msg_sender: bool,\n ) {\n let calldata_hash = hash_calldata_array([function_selector.to_field()]);\n self.call_public_function_with_calldata_hash(contract_address, calldata_hash, true, hide_msg_sender)\n }\n\n /// Low-level public function call.\n ///\n /// This is the underlying implementation used by all other public function call methods. Instead of taking raw\n /// arguments, it accepts a hash of the arguments.\n ///\n /// Advanced function: Most developers should use `call_public_function` or `static_call_public_function` instead.\n /// This function is exposed for performance optimization and advanced use cases.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the function\n /// * `calldata_hash` - Hash of the function calldata\n /// * `is_static_call` - Whether this should be a read-only call\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn call_public_function_with_calldata_hash(\n &mut self,\n contract_address: AztecAddress,\n calldata_hash: Field,\n is_static_call: bool,\n hide_msg_sender: bool,\n ) {\n let counter = self.next_counter();\n\n let mut is_static_call = is_static_call | self.inputs.call_context.is_static_call;\n\n assert_valid_public_call_data(calldata_hash);\n\n let msg_sender = if hide_msg_sender {\n NULL_MSG_SENDER_CONTRACT_ADDRESS\n } else {\n self.this_address()\n };\n\n let call_request = PublicCallRequest { msg_sender, contract_address, is_static_call, calldata_hash };\n\n self.public_call_requests.push(Counted::new(call_request, counter));\n }\n\n /// Enqueues a public function call, and designates it to be the teardown function for this tx. Only one teardown\n /// function call can be made by a tx.\n ///\n /// Niche function: Only wallet developers and paymaster contract developers (aka Fee-payment contracts) will need\n /// to make use of this function.\n ///\n /// Aztec supports a three-phase execution model: setup, app logic, teardown. The phases exist to enable a fee\n /// payer to take on the risk of paying a transaction fee, safe in the knowledge that their payment (in whatever\n /// token or method the user chooses) will succeed, regardless of whether the app logic will succeed. The \"setup\"\n /// phase ensures the fee payer has sufficient balance to pay the proposer their fees. The teardown phase is\n /// primarily intended to: calculate exactly how much the user owes, based on gas consumption, and refund the user\n /// any change.\n ///\n /// Note: in some cases, the cost of refunding the user (i.e. DA costs of tx side-effects) might exceed the refund\n /// amount. For app logic with fairly stable and predictable gas consumption, a material refund amount is unlikely.\n /// For app logic with unpredictable gas consumption, a refund might be important to the user (e.g. if a hefty\n /// function reverts very early). Wallet/FPC/Paymaster developers should be mindful of this.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the teardown function\n /// * `function_selector` - 4-byte identifier of the function to call\n /// * `args` - An array of fields to pass to the function.\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n pub fn set_public_teardown_function(\n &mut self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; ArgsCount],\n hide_msg_sender: bool,\n ) {\n let calldata = [function_selector.to_field()].concat(args);\n let calldata_hash = hash_calldata_array(calldata);\n execution_cache::store(calldata, calldata_hash);\n self.set_public_teardown_function_with_calldata_hash(contract_address, calldata_hash, false, hide_msg_sender)\n }\n\n /// Low-level function to set the public teardown function.\n ///\n /// This is the underlying implementation for setting the teardown function call that will execute at the end of\n /// the transaction. Instead of taking raw arguments, it accepts a hash of the arguments.\n ///\n /// Advanced function: Most developers should use `set_public_teardown_function` instead.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract containing the teardown function\n /// * `calldata_hash` - Hash of the function calldata\n /// * `is_static_call` - Whether this should be a read-only call\n /// * `hide_msg_sender` - the called function will see a \"null\" value for `msg_sender` if set to `true`\n ///\n pub fn set_public_teardown_function_with_calldata_hash(\n &mut self,\n contract_address: AztecAddress,\n calldata_hash: Field,\n is_static_call: bool,\n hide_msg_sender: bool,\n ) {\n let is_static_call = is_static_call | self.inputs.call_context.is_static_call;\n\n assert_valid_public_call_data(calldata_hash);\n\n let msg_sender = if hide_msg_sender {\n NULL_MSG_SENDER_CONTRACT_ADDRESS\n } else {\n self.this_address()\n };\n\n self.public_teardown_call_request =\n PublicCallRequest { msg_sender, contract_address, is_static_call, calldata_hash };\n }\n\n /// Increments the side-effect counter.\n ///\n /// Very low-level function.\n ///\n /// # Advanced\n ///\n /// Every side-effect of a private function is given a \"side-effect counter\", based on when it is created. This\n /// PrivateContext is in charge of assigning the counters.\n ///\n /// The reason we have side-effect counters is complicated. Consider this illustrative pseudocode of inter-contract\n /// function calls:\n /// ```\n /// contract A {\n /// let x = 5; // pseudocode for storage var x.\n /// fn a1 {\n /// read x; // value: 5, counter: 1.\n /// x = x + 1;\n /// write x; // value: 6, counter: 2.\n ///\n /// B.b(); // start_counter: 2, end_counter: 4\n ///\n /// read x; // value: 36, counter: 5.\n /// x = x + 1;\n /// write x; // value: 37, counter: 6.\n /// }\n ///\n /// fn a2 {\n /// read x; // value: 6, counter: 3.\n /// x = x * x;\n /// write x; // value: 36, counter: 4.\n /// }\n /// }\n ///\n /// contract B {\n /// fn b() {\n /// A.a2();\n /// }\n /// }\n /// ```\n ///\n /// Suppose a1 is the first function called. The comments show the execution counter of each side-effect, and what\n /// the new value of `x` is.\n ///\n /// These (private) functions are processed by Aztec's kernel circuits in an order that is different from execution\n /// order: All of A.a1 is proven before B.b is proven, before A.a2 is proven. So when we're in the 2nd execution\n /// frame of A.a1 (after the call to B.b), the circuit needs to justify why x went from being `6` to `36`. But the\n /// circuit doesn't know why, and given the order of proving, the kernel hasn't _seen_ a value of 36 get written\n /// yet. The kernel needs to track big arrays of all side-effects of all private functions in a tx. Then, as it\n /// recurses and processes B.b(), it will eventually see a value of 36 get written.\n ///\n /// Suppose side-effect counters weren't exposed: The kernel would only see this ordering (in order of proof\n /// verification): [ A.a1.read, A.a1.write, A.a1.read, A.a1.write, A.a2.read, A.a2.write ]\n /// [ 5, 6, 36, 37, 6, 36 ]\n /// The kernel wouldn't know _when_ B.b() was called within A.a1(), because it can't see what's going on within an\n /// app circuit. So the kernel wouldn't know that the ordering of reads and writes should actually be: [ A.a1.read,\n /// A.a1.write, A.a2.read, A.a2.write, A.a1.read, A.a1.write ]\n /// [ 5, 6, 6, 36, 36, 37 ]\n ///\n /// And so, we introduced side-effect counters: every private function must assign side-effect counters alongside\n /// every side-effect that it emits, and also expose to the kernel the counters that it started and ended with.\n /// This gives the kernel enough information to arrange all side-effects in the correct order. It can then catch\n /// (for example) if a function tries to read state before it has been written (e.g. if A.a2() maliciously tried to\n /// read a value of x=37) (e.g. if A.a1() maliciously tried to read x=6).\n ///\n /// If a malicious app contract _lies_ and does not count correctly:\n /// - It cannot lie about its start and end counters because the kernel will catch this.\n /// - It _could_ lie about its intermediate counters:\n /// - 1. It could not increment its side-effects correctly\n /// - 2. It could label its side-effects with counters outside of its start and end counters' range. The kernel\n /// will catch 2. The kernel will not catch 1., but this would only cause corruption to the private state of the\n /// malicious contract, and not any other contracts (because a contract can only modify its own state). If a \"good\"\n /// contract is given _read access_ to a maliciously-counting contract (via an external getter function, or by\n /// reading historic state from the archive tree directly), and they then make state changes to their _own_ state\n /// accordingly, that could be dangerous. Developers should be mindful not to trust the claimed innards of external\n /// contracts unless they have audited/vetted the contracts including vetting the side-effect counter\n /// incrementation. This is a similar paradigm to Ethereum smart contract development: you must vet external\n /// contracts that your contract relies upon, and you must not make any presumptions about their claimed behaviour.\n /// (Hopefully if a contract imports a version of aztec-nr, we will get contract verification tooling that can\n /// validate the authenticity of the imported aztec-nr package, and hence infer that the side- effect counting will\n /// be correct, without having to re-audit such logic for every contract).\n ///\n fn next_counter(&mut self) -> u32 {\n let counter = self.side_effect_counter;\n self.side_effect_counter += 1;\n counter\n }\n}\n\nimpl Empty for PrivateContext {\n fn empty() -> Self {\n PrivateContext {\n inputs: PrivateContextInputs::empty(),\n side_effect_counter: 0 as u32,\n min_revertible_side_effect_counter: 0 as u32,\n is_fee_payer: false,\n args_hash: 0,\n return_hash: 0,\n expiration_timestamp: 0,\n note_hash_read_requests: BoundedVec::new(),\n nullifier_read_requests: BoundedVec::new(),\n key_validation_requests_and_separators: BoundedVec::new(),\n note_hashes: BoundedVec::new(),\n nullifiers: BoundedVec::new(),\n private_call_requests: BoundedVec::new(),\n public_call_requests: BoundedVec::new(),\n public_teardown_call_request: PublicCallRequest::empty(),\n l2_to_l1_msgs: BoundedVec::new(),\n anchor_block_header: BlockHeader::empty(),\n private_logs: BoundedVec::new(),\n contract_class_logs_hashes: BoundedVec::new(),\n last_key_validation_requests: [Option::none(); NUM_KEY_TYPES],\n expected_non_revertible_side_effect_counter: 0,\n expected_revertible_side_effect_counter: 0,\n }\n }\n}\n" }, "86": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/public_context.nr", "source": "use crate::{\n context::gas::GasOpts,\n hash::{\n compute_l1_to_l2_message_hash, compute_l1_to_l2_message_nullifier, compute_secret_hash,\n compute_siloed_nullifier,\n },\n oracle::avm,\n};\nuse crate::protocol::{\n abis::function_selector::FunctionSelector,\n address::{AztecAddress, EthAddress},\n constants::{MAX_U32_VALUE, NULL_MSG_SENDER_CONTRACT_ADDRESS},\n traits::{Empty, FromField, Packable, Serialize, ToField},\n utils::writer::Writer,\n};\n\n/// # PublicContext\n///\n/// The **main interface** between an #[external(\"public\")] function and the Aztec blockchain.\n///\n/// An instance of the PublicContext is initialized automatically at the outset of every public function, within the\n/// #[external(\"public\")] macro, so you'll never need to consciously instantiate this yourself.\n///\n/// The instance is always named `context`, and it will always be available within the body of every\n/// #[external(\"public\")] function in your smart contract.\n///\n/// Typical usage for a smart contract developer will be to call getter methods of the PublicContext.\n///\n/// _Pushing_ data and requests to the context is mostly handled within aztec-nr's own functions, so typically a smart\n/// contract developer won't need to call any setter methods directly.\n///\n/// ## Responsibilities\n/// - Exposes contextual data to a public function:\n/// - Data relating to how this public function was called:\n/// - msg_sender, this_address\n/// - Data relating to the current blockchain state:\n/// - timestamp, block_number, chain_id, version\n/// - Gas and fee information\n/// - Provides state access:\n/// - Read/write public storage (key-value mapping)\n/// - Check existence of notes and nullifiers (Some patterns use notes & nullifiers to store public (not private)\n/// information)\n/// - Enables consumption of L1->L2 messages.\n/// - Enables calls to other public smart contract functions:\n/// - Writes data to the blockchain:\n/// - Updates to public state variables\n/// - New public logs (for events)\n/// - New L2->L1 messages\n/// - New notes & nullifiers (E.g. pushing public info to notes/nullifiers, or for completing \"partial notes\")\n///\n/// ## Key Differences from Private Execution\n///\n/// Unlike private functions -- which are executed on the user's device and which can only reference historic state --\n/// public functions are executed by a block proposer and are executed \"live\" on the _current_ tip of the chain. This\n/// means public functions can:\n/// - Read and write _current_ public state\n/// - Immediately see the effects of earlier transactions in the same block\n///\n/// Also, public functions are executed within a zkVM (the \"AVM\"), so that they can _revert_ whilst still ensuring\n/// payment to the proposer and prover. (Private functions cannot revert: they either succeed, or they cannot be\n/// included).\n///\n/// ## Optimising Public Functions\n///\n/// Using the AVM to execute public functions means they compile down to \"AVM bytecode\" instead of the ACIR that\n/// private functions (standalone circuits) compile to. Therefore the approach to optimising a public function is\n/// fundamentally different from optimising a public function.\n///\npub struct PublicContext {\n pub args_hash: Option,\n pub compute_args_hash: fn() -> Field,\n}\n\nimpl Eq for PublicContext {\n fn eq(self, other: Self) -> bool {\n (self.args_hash == other.args_hash)\n // Can't compare the function compute_args_hash\n }\n}\n\nimpl PublicContext {\n /// Creates a new PublicContext instance.\n ///\n /// Low-level function: This is called automatically by the #[external(\"public\")] macro, so you shouldn't need to\n /// be called directly by smart contract developers.\n ///\n /// # Arguments\n /// * `compute_args_hash` - Function to compute the args_hash\n ///\n /// # Returns\n /// * A new PublicContext instance\n ///\n pub fn new(compute_args_hash: fn() -> Field) -> Self {\n PublicContext { args_hash: Option::none(), compute_args_hash }\n }\n\n /// Emits a _public_ log that will be visible onchain to everyone.\n ///\n /// # Arguments\n /// * `tag` - A tag placed at `fields[0]` of the emitted log. Nodes index logs by this value, allowing\n /// clients to efficiently query for matching logs without scanning all of them.\n /// * `log` - The data to log, must implement Serialize trait.\n ///\n /// ## Safety\n ///\n /// The `tag` should be domain-separated (e.g. via [`crate::protocol::hash::compute_log_tag`]) to prevent\n /// collisions between logs from different sources. Without domain separation, two unrelated log types that\n /// happen to share a raw tag value become indistinguishable. Prefer `self.emit(event)` for events, which\n /// handles tagging automatically.\n pub fn emit_public_log_unsafe(_self: Self, tag: Field, log: T)\n where\n T: Serialize,\n {\n // We use a Writer to serialize the log directly after the tag, avoiding an extra O(n) copy that would\n // result from serializing first and then prepending the tag.\n let mut writer: Writer<1 + ::N> = Writer::new();\n writer.write(tag);\n Serialize::stream_serialize(log, &mut writer);\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::emit_public_log(writer.finish().as_vector()) };\n }\n\n /// Checks if a given note hash exists in the note hash tree at a particular leaf_index.\n ///\n /// # Arguments\n /// * `note_hash` - The note hash to check for existence\n /// * `leaf_index` - The index where the note hash should be located\n ///\n /// # Returns\n /// * `bool` - True if the note hash exists at the specified index\n ///\n pub fn note_hash_exists(_self: Self, note_hash: Field, leaf_index: u64) -> bool {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::note_hash_exists(note_hash, leaf_index) } == 1\n }\n\n /// Checks if a specific L1-to-L2 message exists in the L1-to-L2 message tree at a particular leaf index.\n ///\n /// Common use cases include token bridging, cross-chain governance, and triggering L2 actions based on L1 events.\n ///\n /// This function should be called before attempting to consume an L1-to-L2 message.\n ///\n /// # Arguments\n /// * `msg_hash` - Hash of the L1-to-L2 message to check\n /// * `msg_leaf_index` - The index where the message should be located\n ///\n /// # Returns\n /// * `bool` - True if the message exists at the specified index\n ///\n /// # Advanced\n /// * Uses the AVM l1_to_l2_msg_exists opcode for tree lookup\n /// * Messages are copied from L1 Inbox to L2 by block proposers\n ///\n pub fn l1_to_l2_msg_exists(_self: Self, msg_hash: Field, msg_leaf_index: Field) -> bool {\n // Safety: AVM opcodes are constrained by the AVM itself TODO(alvaro): Make l1l2msg leaf index a u64 upstream\n unsafe { avm::l1_to_l2_msg_exists(msg_hash, msg_leaf_index as u64) } == 1\n }\n\n /// Returns `true` if an `unsiloed_nullifier` has been emitted by `contract_address`.\n ///\n /// Note that unsiloed nullifiers are not the actual values stored in the nullifier tree: they are first siloed via\n /// [`crate::hash::compute_siloed_nullifier`] with the emitting contract's address.\n ///\n /// ## Use Cases\n ///\n /// Nullifiers are typically used as a _privacy-preserving_ record of a one-time action, but they can also be used\n /// to efficiently record _public_ one-time actions as well. This is cheaper than using public storage, and has the\n /// added benefit of the nullifier being emittable from a private function.\n ///\n /// An example is to check whether a contract has been published: we emit a nullifier that is deterministic and\n /// which has a _public_ preimage.\n ///\n /// ## Public vs Private\n ///\n /// In general, one should not attempt to prove nullifier non-existence in private, as that will not consider the\n /// possibility of the nullifier having been emitted in any transaction between the anchor block and the inclusion\n /// block. Private functions instead prove existence via\n /// [`crate::context::PrivateContext::assert_nullifier_exists`]\n /// and 'prove' non-existence by _emitting_ the nullifer, which would cause the transaction to fail if the\n /// nullifier existed.\n ///\n /// This is not the case in public functions, which **do** have access to the tip of the blockchain and so can\n /// reliably prove whether a nullifier exists or not.\n ///\n /// ## Safety\n ///\n /// While it is safe to rely on this function's return value to determine if a nullifier exists or not, it is often\n /// **not** safe to infer additional information from that. In particular, it is **unsafe** to infer that the\n /// existence of a nullifier emitted from a private function implies that all other side-effects of said private\n /// execution have been completed, more concretely that any enqueued public calls have been executed.\n ///\n /// For example, if a function in contract `A` privately emits nullifier `X` and then enqueues public function `Y`,\n /// then it is **unsafe** for a contract `B` to infer that `Y` has alredy executed simply because `X` exists.\n ///\n /// This is because **all** private transaction effects are committed _before_ enqueued public functions are run\n /// (in\n /// order to not reveal detailed timing information about the transaction), so it is possible to observe a\n /// nullifier that was emitted alongside the enqueuing of a public call **before** said call has been completed.\n ///\n /// ## Cost\n ///\n /// This emits the `CHECKNULLIFIEREXISTS` opcode, which conceptually performs a merkle inclusion proof on the\n /// nullifier tree (both when the nullifier exists and when it doesn't).\n pub fn nullifier_exists_unsafe(_self: Self, unsiloed_nullifier: Field, contract_address: AztecAddress) -> bool {\n let siloed_nullifier = compute_siloed_nullifier(contract_address, unsiloed_nullifier);\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::nullifier_exists(siloed_nullifier) } == 1\n }\n\n /// Consumes a message sent from Ethereum (L1) to Aztec (L2) -- effectively marking it as \"read\".\n ///\n /// Use this function if you only want the message to ever be \"referred to\" once. Once consumed using this method,\n /// the message cannot be consumed again, because a nullifier is emitted. If your use case wants for the message to\n /// be read unlimited times, then you can always read any historic message from the L1-to-L2 messages tree, using\n /// the `l1_to_l2_msg_exists` method. Messages never technically get deleted from that tree.\n ///\n /// The message will first be inserted into an Aztec \"Inbox\" smart contract on L1. It will not be available for\n /// consumption immediately. Messages get copied-over from the L1 Inbox to L2 by the next Proposer in batches. So\n /// you will need to wait until the messages are copied before you can consume them.\n ///\n /// # Arguments\n /// * `content` - The message content that was sent from L1\n /// * `secret` - Secret value used for message privacy (if needed)\n /// * `sender` - Ethereum address that sent the message\n /// * `leaf_index` - Index of the message in the L1-to-L2 message tree\n ///\n /// # Advanced\n /// * Validates message existence in the L1-to-L2 message tree\n /// * Prevents double-consumption by emitting a nullifier\n /// * Message hash is computed from all parameters + chain context\n /// * Will revert if message doesn't exist or was already consumed\n ///\n pub fn consume_l1_to_l2_message(self: Self, content: Field, secret: Field, sender: EthAddress, leaf_index: Field) {\n let secret_hash = compute_secret_hash(secret);\n let message_hash = compute_l1_to_l2_message_hash(\n sender,\n self.chain_id(),\n /*recipient=*/\n self.this_address(),\n self.version(),\n content,\n secret_hash,\n leaf_index,\n );\n let nullifier = compute_l1_to_l2_message_nullifier(message_hash, secret);\n\n assert(!self.nullifier_exists_unsafe(nullifier, self.this_address()), \"L1-to-L2 message is already nullified\");\n assert(self.l1_to_l2_msg_exists(message_hash, leaf_index), \"Tried to consume nonexistent L1-to-L2 message\");\n\n self.push_nullifier(nullifier);\n }\n\n /// Sends an \"L2 -> L1 message\" from this function (Aztec, L2) to a smart contract on Ethereum (L1). L1 contracts\n /// which are designed to send/receive messages to/from Aztec are called \"Portal Contracts\".\n ///\n /// Common use cases include withdrawals, cross-chain asset transfers, and triggering L1 actions based on L2 state\n /// changes.\n ///\n /// The message will be inserted into an Aztec \"Outbox\" contract on L1, when this transaction's block is proposed\n /// to L1. Sending the message will not result in any immediate state changes in the target portal contract. The\n /// message will need to be manually consumed from the Outbox through a separate Ethereum transaction: a user will\n /// need to call a function of the portal contract -- a function specifically designed to make a call to the Outbox\n /// to consume the message. The message will only be available for consumption once the _epoch_ proof has been\n /// submitted. Given that there are multiple Aztec blocks within an epoch, it might take some time for this epoch\n /// proof to be submitted -- especially if the block was near the start of an epoch.\n ///\n /// # Arguments\n /// * `recipient` - Ethereum address that will receive the message\n /// * `content` - Message content (32 bytes as a Field element)\n ///\n pub fn message_portal(_self: Self, recipient: EthAddress, content: Field) {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::send_l2_to_l1_msg(recipient, content) };\n }\n\n /// Calls a public function on another contract.\n ///\n /// Will revert if the called function reverts or runs out of gas.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract to call\n /// * `function_selector` - Function to call on the target contract\n /// * `args` - Arguments to pass to the function\n /// * `gas_opts` - An optional allocation of gas to the called function.\n ///\n /// # Returns\n /// * `[Field]` - Return data from the called function\n ///\n pub unconstrained fn call_public_function(\n _self: Self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; N],\n gas_opts: GasOpts,\n ) -> [Field] {\n let calldata = [function_selector.to_field()].concat(args);\n\n avm::call(\n gas_opts.l2_gas.unwrap_or(MAX_U32_VALUE),\n gas_opts.da_gas.unwrap_or(MAX_U32_VALUE),\n contract_address,\n calldata,\n );\n // Use success_copy to determine whether the call succeeded\n let success = avm::success_copy();\n\n let result_data = avm::returndata_copy(0, avm::returndata_size());\n if !success {\n // Rethrow the revert data.\n avm::revert(result_data);\n }\n result_data\n }\n\n /// Makes a read-only call to a public function on another contract.\n ///\n /// This is similar to Solidity's `staticcall`. The called function cannot modify state or emit events. Any nested\n /// calls are constrained to also be staticcalls.\n ///\n /// Useful for querying data from other contracts safely.\n ///\n /// Will revert if the called function reverts or runs out of gas.\n ///\n /// # Arguments\n /// * `contract_address` - Address of the contract to call\n /// * `function_selector` - Function to call on the target contract\n /// * `args` - Array of arguments to pass to the called function\n /// * `gas_opts` - An optional allocation of gas to the called function.\n ///\n /// # Returns\n /// * `[Field]` - Return data from the called function\n ///\n pub unconstrained fn static_call_public_function(\n _self: Self,\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args: [Field; N],\n gas_opts: GasOpts,\n ) -> [Field] {\n let calldata = [function_selector.to_field()].concat(args);\n\n avm::call_static(\n gas_opts.l2_gas.unwrap_or(MAX_U32_VALUE),\n gas_opts.da_gas.unwrap_or(MAX_U32_VALUE),\n contract_address,\n calldata,\n );\n // Use success_copy to determine whether the call succeeded\n let success = avm::success_copy();\n\n let result_data = avm::returndata_copy(0, avm::returndata_size());\n if !success {\n // Rethrow the revert data.\n avm::revert(result_data);\n }\n result_data\n }\n\n /// Adds a new note hash to the Aztec blockchain's global Note Hash Tree.\n ///\n /// Notes are ordinarily constructed and emitted by _private_ functions, to ensure that both the content of the\n /// note, and the contract that emitted the note, stay private.\n ///\n /// There are however some useful patterns whereby a note needs to contain _public_ data. The ability to push a new\n /// note_hash from a _public_ function means that notes can be injected with public data immediately -- as soon as\n /// the public value is known. The slower alternative would be to submit a follow-up transaction so that a private\n /// function can inject the data. Both are possible on Aztec.\n ///\n /// Search \"Partial Note\" for a very common pattern which enables a note to be \"partially\" populated with some data\n /// in a _private_ function, and then later \"completed\" with some data in a public function.\n ///\n /// # Arguments\n /// * `note_hash` - The hash of the note to add to the tree\n ///\n /// # Advanced\n /// * The note hash will be siloed with the contract address by the protocol\n ///\n pub fn push_note_hash(_self: Self, note_hash: Field) {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::emit_note_hash(note_hash) };\n }\n\n /// Creates a new [nullifier](crate::nullifier).\n ///\n /// While nullifiers are primarily intended as a _privacy-preserving_ record of a one-time action, they can also\n /// be used to efficiently record _public_ one-time actions. This function allows creating nullifiers from public\n /// contract functions, which behave just like those created from private functions.\n ///\n /// ## Safety\n ///\n /// This is a low-level function that must be used with great care to avoid subtle corruption of contract state.\n ///\n /// In particular, callers must ensure all nullifiers created by a contract are properly domain-separated, so that\n /// unrelated components don't interfere with one another (e.g. a transaction nullifier accidentally marking a\n /// variable as initialized). Note nullifiers should only be created via\n /// [`crate::context::PrivateContext::push_nullifier_for_note_hash`].\n ///\n /// ## Advanced\n ///\n /// The raw `nullifier` is not what is inserted into the Aztec state tree: it will be first siloed by contract\n /// address via [`crate::protocol::hash::compute_siloed_nullifier`] in order to prevent accidental or malicious\n /// interference of nullifiers from different contracts.\n pub fn push_nullifier(_self: Self, nullifier: Field) {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::emit_nullifier(nullifier) };\n }\n\n /// Returns the address of the current contract being executed.\n ///\n /// This is equivalent to `address(this)` in Solidity (hence the name). Use this to identify the current contract's\n /// address, commonly needed for access control or when interacting with other contracts.\n ///\n /// # Returns\n /// * `AztecAddress` - The contract address of the current function being executed.\n ///\n pub fn this_address(_self: Self) -> AztecAddress {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::address()\n }\n }\n\n /// Returns the contract address that initiated this function call.\n ///\n /// This is similar to `msg.sender` in Solidity (hence the name).\n ///\n /// Important Note: If the calling function is a _private_ function, then it had the option of hiding its address\n /// when enqueuing this public function call. In such cases, this method will return `Option::none`.\n /// If the calling function is a _public_ function, it will always return an `Option::some` (i.e. a\n /// non-null value).\n ///\n /// # Returns\n /// * `Option` - The address of the smart contract that called this function (be it an app contract\n /// or a user's account contract).\n ///\n /// # Advanced\n /// * Value is provided by the AVM sender opcode\n /// * In nested calls, this is the immediate caller, not the original transaction sender\n ///\n pub fn maybe_msg_sender(_self: Self) -> Option {\n // Safety: AVM opcodes are constrained by the AVM itself\n let maybe_msg_sender = unsafe { avm::sender() };\n if maybe_msg_sender == NULL_MSG_SENDER_CONTRACT_ADDRESS {\n Option::none()\n } else {\n Option::some(maybe_msg_sender)\n }\n }\n\n /// Returns the function selector of the currently-executing function.\n ///\n /// This is similar to `msg.sig` in Solidity, returning the first 4 bytes of the function signature.\n ///\n /// # Returns\n /// * `FunctionSelector` - The 4-byte function identifier\n ///\n /// # Advanced\n /// * Extracted from the first element of calldata\n /// * Used internally for function dispatch in the AVM\n ///\n pub fn selector(_self: Self) -> FunctionSelector {\n // The selector is the first element of the calldata when calling a public function through dispatch.\n // Safety: AVM opcodes are constrained by the AVM itself.\n let raw_selector: [Field; 1] = unsafe { avm::calldata_copy(0, 1) };\n FunctionSelector::from_field(raw_selector[0])\n }\n\n /// Returns the hash of the arguments passed to the current function.\n ///\n /// Very low-level function: The #[external(\"public\")] macro uses this internally. Smart contract developers\n /// typically won't need to access this directly as arguments are automatically made available.\n ///\n /// # Returns\n /// * `Field` - Hash of the function arguments\n ///\n pub fn get_args_hash(mut self) -> Field {\n if !self.args_hash.is_some() {\n self.args_hash = Option::some((self.compute_args_hash)());\n }\n\n self.args_hash.unwrap_unchecked()\n }\n\n /// Returns the \"transaction fee\" for the current transaction. This is the final tx fee that will be deducted from\n /// the fee_payer's \"fee-juice\" balance (in the protocol's Base Rollup circuit).\n ///\n /// # Returns\n /// * `Field` - The actual, final cost of the transaction, taking into account: the actual gas used during the\n /// setup and app-logic phases, and the fixed amount of gas that's been allocated by the user for the teardown\n /// phase. I.e. effectiveL2FeePerGas * l2GasUsed + effectiveDAFeePerGas * daGasUsed\n ///\n /// This will return `0` during the \"setup\" and \"app-logic\" phases of tx execution (because the final tx fee is not\n /// known at that time). This will only return a nonzero value during the \"teardown\" phase of execution, where the\n /// final tx fee can actually be computed.\n ///\n /// Regardless of _when_ this function is called during the teardown phase, it will always return the same final tx\n /// fee value. The teardown phase does not consume a variable amount of gas: it always consumes a pre-allocated\n /// amount of gas, as specified by the user when they generate their tx.\n ///\n pub fn transaction_fee(_self: Self) -> Field {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::transaction_fee()\n }\n }\n\n /// Returns the chain ID of the current network.\n ///\n /// This is similar to `block.chainid` in Solidity. Returns the unique identifier for the blockchain network this\n /// transaction is executing on.\n ///\n /// Helps prevent cross-chain replay attacks. Useful if implementing multi-chain contract logic.\n ///\n /// # Returns\n /// * `Field` - The chain ID as a field element\n ///\n pub fn chain_id(_self: Self) -> Field {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::chain_id()\n }\n }\n\n /// Returns the Aztec protocol version that this transaction is executing under. Different versions may have\n /// different rules, opcodes, or cryptographic primitives.\n ///\n /// This is similar to how Ethereum has different EVM versions.\n ///\n /// Useful for forward/backward compatibility checks\n ///\n /// Not to be confused with contract versions; this is the protocol version.\n ///\n /// # Returns\n /// * `Field` - The protocol version as a field element\n ///\n pub fn version(_self: Self) -> Field {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::version()\n }\n }\n /// Returns the current block number.\n ///\n /// This is similar to `block.number` in Solidity.\n ///\n /// Note: the current block number is only available within a public function (as opposed to a private function).\n ///\n /// Note: the time intervals between blocks should not be relied upon as being consistent:\n /// - Timestamps of blocks fall within a range, rather than at exact regular intervals.\n /// - Slots can be missed.\n /// - Protocol upgrades can completely change the intervals between blocks (and indeed the current roadmap plans to\n /// reduce the time between blocks, eventually). Use `context.timestamp()` for more-reliable time-based logic.\n ///\n /// # Returns\n /// * `u32` - The current block number\n ///\n pub fn block_number(_self: Self) -> u32 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::block_number()\n }\n }\n\n /// Returns the timestamp of the current block.\n ///\n /// This is similar to `block.timestamp` in Solidity.\n ///\n /// All functions of all transactions in a block share the exact same timestamp (even though technically each\n /// transaction is executed one-after-the-other).\n ///\n /// Important note: Timestamps of Aztec blocks are not at reliably-fixed intervals. The proposer of the block has\n /// some flexibility to choose a timestamp which is in a valid _range_: Obviously the timestamp of this block must\n /// be strictly greater than that of the previous block, and must must be less than the timestamp of whichever\n /// ethereum block the aztec block is proposed to. Furthermore, if the timestamp is not deemed close enough to the\n /// actual current time, the committee of validators will not attest to the block.\n ///\n /// # Returns\n /// * `u64` - Unix timestamp in seconds\n ///\n pub fn timestamp(_self: Self) -> u64 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::timestamp()\n }\n }\n\n /// Returns the fee per unit of L2 gas for this transaction (aka the \"L2 gas price\"), as chosen by the user.\n ///\n /// L2 gas covers the cost of executing public functions and handling side-effects within the AVM.\n ///\n /// # Returns\n /// * `u128` - Fee per unit of L2 gas\n ///\n /// Wallet developers should be mindful that the choice of gas price (which is publicly visible) can leak\n /// information about the user, e.g.:\n /// - which wallet software the user is using;\n /// - the amount of time which has elapsed from the time the user's wallet chose a gas price (at the going rate),\n /// to the time of tx submission. This can give clues about the proving time, and hence the nature of the tx.\n /// - the urgency of the transaction (which is kind of unavoidable, if the tx is indeed urgent).\n /// - the wealth of the user.\n /// - the exact user (if the gas price is explicitly chosen by the user to be some unique number like 0.123456789,\n /// or their favorite number). Wallet devs might wish to consider fuzzing the choice of gas price.\n ///\n pub fn min_fee_per_l2_gas(_self: Self) -> u128 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::min_fee_per_l2_gas()\n }\n }\n\n /// Returns the fee per unit of DA (Data Availability) gas (aka the \"DA gas price\").\n ///\n /// DA gas covers the cost of making transaction data available on L1.\n ///\n /// See the warning in `min_fee_per_l2_gas` for how gas prices can be leaky.\n ///\n /// # Returns\n /// * `u128` - Fee per unit of DA gas\n ///\n pub fn min_fee_per_da_gas(_self: Self) -> u128 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::min_fee_per_da_gas()\n }\n }\n\n /// Returns the remaining L2 gas available for this transaction.\n ///\n /// Different AVM opcodes consume different amounts of gas.\n ///\n /// # Returns\n /// * `u32` - Remaining L2 gas units\n ///\n pub fn l2_gas_left(_self: Self) -> u32 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::l2_gas_left()\n }\n }\n\n /// Returns the remaining DA (Data Availability) gas available for this transaction.\n ///\n /// DA gas is consumed when emitting data that needs to be made available on L1, such as public logs or state\n /// updates. All of the side-effects from the private part of the tx also consume DA gas before execution of any\n /// public functions even begins.\n ///\n /// # Returns\n /// * `u32` - Remaining DA gas units\n ///\n pub fn da_gas_left(_self: Self) -> u32 {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe {\n avm::da_gas_left()\n }\n }\n\n /// Checks if the current execution is within a staticcall context, where no state changes or logs are allowed to\n /// be emitted (by this function or any nested function calls).\n ///\n /// # Returns\n /// * `bool` - True if in staticcall context, false otherwise\n ///\n pub fn is_static_call(_self: Self) -> bool {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::is_static_call() } == 1\n }\n\n /// Reads raw field values from public storage. Reads N consecutive storage slots starting from the given slot.\n ///\n /// Very low-level function. Users should typically use the public state variable abstractions to perform reads:\n /// PublicMutable & PublicImmutable.\n ///\n /// # Arguments\n /// * `storage_slot` - The starting storage slot to read from\n ///\n /// # Returns\n /// * `[Field; N]` - Array of N field values from consecutive storage slots\n ///\n /// # Generic Parameters\n /// * `N` - the number of consecutive slots to return, starting from the `storage_slot`.\n ///\n pub fn raw_storage_read(self: Self, storage_slot: Field) -> [Field; N] {\n let mut out = [0; N];\n for i in 0..N {\n // Safety: AVM opcodes are constrained by the AVM itself\n out[i] = unsafe { avm::storage_read(storage_slot + i as Field, self.this_address().to_field()) };\n }\n out\n }\n\n /// Reads a typed value from public storage.\n ///\n /// Low-level function. Users should typically use the public state variable abstractions to perform reads:\n /// PublicMutable & PublicImmutable.\n ///\n /// # Arguments\n /// * `storage_slot` - The storage slot to read from\n ///\n /// # Returns\n /// * `T` - The deserialized value from storage\n ///\n /// # Generic Parameters\n /// * `T` - The type that the caller expects to read from the `storage_slot`.\n ///\n pub fn storage_read(self, storage_slot: Field) -> T\n where\n T: Packable,\n {\n T::unpack(self.raw_storage_read(storage_slot))\n }\n\n /// Writes raw field values to public storage. Writes to N consecutive storage slots starting from the given slot.\n ///\n /// Very low-level function. Users should typically use the public state variable abstractions to perform writes:\n /// PublicMutable & PublicImmutable.\n ///\n /// Public storage writes take effect immediately.\n ///\n /// # Arguments\n /// * `storage_slot` - The starting storage slot to write to\n /// * `values` - Array of N Fields to write to storage\n ///\n pub fn raw_storage_write(_self: Self, storage_slot: Field, values: [Field; N]) {\n for i in 0..N {\n // Safety: AVM opcodes are constrained by the AVM itself\n unsafe { avm::storage_write(storage_slot + i as Field, values[i]) };\n }\n }\n\n /// Writes a typed value to public storage.\n ///\n /// Low-level function. Users should typically use the public state variable abstractions to perform writes:\n /// PublicMutable & PublicImmutable.\n ///\n /// # Arguments\n /// * `storage_slot` - The storage slot to write to\n /// * `value` - The typed value to write to storage\n ///\n /// # Generic Parameters\n /// * `T` - The type to write to storage.\n ///\n pub fn storage_write(self, storage_slot: Field, value: T)\n where\n T: Packable,\n {\n self.raw_storage_write(storage_slot, value.pack());\n }\n}\n\nimpl Empty for PublicContext {\n fn empty() -> Self {\n PublicContext::new(|| 0)\n }\n}\n" }, "87": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/returns_hash.nr", "source": "use crate::{hash::hash_args, oracle::execution_cache};\nuse crate::protocol::traits::Deserialize;\n\n/// The hash of a private contract function call's return value.\n///\n/// Use [`ReturnsHash::get_preimage`] to get the underlying value.\n///\n/// The kernels don't process the actual return values but instead their hashes, so it is up to contracts to populate\n/// oracles with the preimages of these hashes on return to make them available to their callers.\n///\n/// Public calls don't utilize this mechanism since the AVM does process the full return values.\npub struct ReturnsHash {\n hash: Field,\n}\n\nimpl ReturnsHash {\n pub fn new(hash: Field) -> Self {\n ReturnsHash { hash }\n }\n\n /// Fetches the underlying return value from an oracle, constraining that it corresponds to the return data hash.\n pub fn get_preimage(self) -> T\n where\n T: Deserialize,\n {\n // Safety: We verify that the value returned by `load` is the preimage of `hash`, fully constraining it. If `T`\n // is `()`, then `preimage` must be an array of length 0 (since that is `()`'s deserialization length).\n // `hash_args` handles empty arrays following the protocol rules (i.e. an empty args array is signaled with a\n // zero hash), correctly constraining `self.hash`.\n let preimage = unsafe { execution_cache::load(self.hash) };\n assert_eq(self.hash, hash_args(preimage), \"Preimage mismatch\");\n\n Deserialize::deserialize(preimage)\n }\n}\n\nmod test {\n use crate::{\n hash::hash_args,\n oracle::execution_cache,\n test::{helpers::test_environment::TestEnvironment, mocks::MockStruct},\n };\n use crate::protocol::traits::Serialize;\n use super::ReturnsHash;\n use std::test::OracleMock;\n\n #[test]\n unconstrained fn retrieves_preimage() {\n let env = TestEnvironment::new();\n env.private_context(|_| {\n let value = MockStruct::new(4, 7);\n let serialized = value.serialize();\n\n let hash = hash_args(serialized);\n execution_cache::store(serialized, hash);\n\n assert_eq(ReturnsHash::new(hash).get_preimage(), value);\n });\n }\n\n #[test]\n unconstrained fn retrieves_empty_preimage() {\n let env = TestEnvironment::new();\n env.private_context(|_| {\n let value = ();\n let serialized = [];\n\n let hash = hash_args(serialized);\n execution_cache::store(serialized, hash);\n\n assert_eq(ReturnsHash::new(hash).get_preimage(), value);\n });\n }\n\n #[test(should_fail_with = \"Preimage mismatch\")]\n unconstrained fn rejects_bad_preimage() {\n let value = MockStruct::new(4, 7);\n let serialized = value.serialize();\n\n let mut bad_serialized = serialized;\n bad_serialized[0] += 1;\n\n let hash = hash_args(serialized);\n\n let _ = OracleMock::mock(\"aztec_prv_getHashPreimage\").returns(bad_serialized);\n assert_eq(ReturnsHash::new(hash).get_preimage(), value);\n }\n}\n" }, "88": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/context/utility_context.nr", "source": "use crate::oracle::{execution::get_utility_context, storage::storage_read};\nuse crate::protocol::{abis::block_header::BlockHeader, address::AztecAddress, traits::Packable};\n\n// If you'll modify this struct don't forget to update utility_context.ts as well.\npub struct UtilityContext {\n block_header: BlockHeader,\n contract_address: AztecAddress,\n}\n\nimpl UtilityContext {\n pub unconstrained fn new() -> Self {\n get_utility_context()\n }\n\n pub unconstrained fn at(contract_address: AztecAddress) -> Self {\n // We get a context with default contract address, and then we construct the final context with the provided\n // contract address.\n let default_context = get_utility_context();\n\n Self { block_header: default_context.block_header, contract_address }\n }\n\n pub fn block_header(self) -> BlockHeader {\n self.block_header\n }\n\n pub fn block_number(self) -> u32 {\n self.block_header.block_number()\n }\n\n pub fn timestamp(self) -> u64 {\n self.block_header.timestamp()\n }\n\n pub fn this_address(self) -> AztecAddress {\n self.contract_address\n }\n\n pub fn version(self) -> Field {\n self.block_header.version()\n }\n\n pub fn chain_id(self) -> Field {\n self.block_header.chain_id()\n }\n\n pub unconstrained fn raw_storage_read(self: Self, storage_slot: Field) -> [Field; N] {\n storage_read(self.block_header, self.this_address(), storage_slot)\n }\n\n pub unconstrained fn storage_read(self, storage_slot: Field) -> T\n where\n T: Packable,\n {\n T::unpack(self.raw_storage_read(storage_slot))\n }\n}\n" }, "90": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/contract_self/contract_self_public.nr", "source": "//! The `self` contract value for public execution contexts.\n\nuse crate::{\n context::{calls::{PublicCall, PublicStaticCall}, PublicContext},\n event::{event_emission::emit_event_in_public, event_interface::EventInterface},\n};\nuse crate::protocol::{address::AztecAddress, traits::{Deserialize, Serialize}};\n\n/// Core interface for interacting with aztec-nr contract features in public execution contexts.\n///\n/// This struct is automatically injected into every [`external`](crate::macros::functions::external) and\n/// [`internal`](crate::macros::functions::internal) contract function marked with `\"public\"` by the Aztec macro\n/// system and is accessible through the `self` variable.\n///\n/// ## Type Parameters\n///\n/// - `Storage`: The contract's storage struct (defined with [`storage`](crate::macros::storage::storage), or `()` if\n/// the contract has no storage\n/// - `CallSelf`: Macro-generated type for calling contract's own non-view functions\n/// - `CallSelfStatic`: Macro-generated type for calling contract's own view functions\n/// - `CallInternal`: Macro-generated type for calling internal functions\npub struct ContractSelfPublic {\n /// The address of this contract\n pub address: AztecAddress,\n\n /// The contract's storage instance, representing the struct to which the\n /// [`storage`](crate::macros::storage::storage) macro was applied in your contract. If the contract has no\n /// storage, the type of this will be `()`.\n ///\n /// This storage instance is specialized for the current execution context (public) and\n /// provides access to the contract's state variables.\n ///\n /// ## Developer Note\n ///\n /// If you've arrived here while trying to access your contract's storage while the `Storage` generic type is set\n /// to unit type `()`, it means you haven't yet defined a Storage struct using the\n /// [`storage`](crate::macros::storage::storage) macro in your contract. For guidance on setting this up, please\n /// refer to our docs: https://docs.aztec.network/developers/docs/guides/smart_contracts/storage\n pub storage: Storage,\n\n /// The public execution context.\n pub context: PublicContext,\n\n /// Provides type-safe methods for calling this contract's own non-view functions.\n ///\n /// Example API:\n /// ```noir\n /// self.call_self.some_public_function(args)\n /// ```\n pub call_self: CallSelf,\n\n /// Provides type-safe methods for calling this contract's own view functions.\n ///\n /// Example API:\n /// ```noir\n /// self.call_self_static.some_view_function(args)\n /// ```\n pub call_self_static: CallSelfStatic,\n\n /// Provides type-safe methods for calling internal functions.\n ///\n /// Example API:\n /// ```noir\n /// self.internal.some_internal_function(args)\n /// ```\n pub internal: CallInternal,\n}\n\nimpl ContractSelfPublic {\n /// Creates a new `ContractSelfPublic` instance for a public function.\n ///\n /// This constructor is called automatically by the macro system and should not be called directly.\n pub fn new(\n context: PublicContext,\n storage: Storage,\n call_self: CallSelf,\n call_self_static: CallSelfStatic,\n internal: CallInternal,\n ) -> Self {\n Self { context, storage, address: context.this_address(), call_self, call_self_static, internal }\n }\n\n /// The address of the contract address that made this function call.\n ///\n /// This is similar to Solidity's `msg.sender` value.\n ///\n /// ## Incognito Calls\n ///\n /// Contracts can call public functions from private ones hiding their identity (see\n ///\n /// [`ContractSelfPrivate::enqueue_incognito`](crate::contract_self::ContractSelfPrivate::enqueue_incognito)).\n /// This function reverts when executed in such a context.\n ///\n /// If you need to handle these cases, use [`PublicContext::maybe_msg_sender`].\n pub fn msg_sender(self: Self) -> AztecAddress {\n self.context.maybe_msg_sender().unwrap()\n }\n\n /// Emits an event publicly.\n ///\n /// Public events are emitted as plaintext and are therefore visible to everyone. This is is the same as Solidity\n /// events on EVM chains.\n ///\n /// Unlike private events, they don't require delivery of an event message.\n ///\n /// # Example\n /// ```noir\n /// #[event]\n /// struct Update { value: Field }\n ///\n /// #[external(\"public\")]\n /// fn publish_update(value: Field) {\n /// self.emit(Update { value });\n /// }\n /// ```\n ///\n /// # Cost\n ///\n /// Public event emission is achieved by emitting public transaction logs. A total of `N+1` fields are emitted,\n /// where `N` is the serialization length of the event.\n pub fn emit(&mut self, event: Event)\n where\n Event: EventInterface + Serialize,\n {\n emit_event_in_public(self.context, event);\n }\n\n /// Makes a public contract call.\n ///\n /// Will revert if the called function reverts or runs out of gas.\n ///\n /// # Arguments\n /// * `call` - The object representing the public function to invoke.\n ///\n /// # Returns\n /// * `T` - Whatever data the called function has returned.\n ///\n /// # Example\n /// ```noir\n /// self.call(Token::at(address).transfer_in_public(recipient, amount));\n /// ```\n ///\n pub unconstrained fn call(self, call: PublicCall) -> T\n where\n T: Deserialize,\n {\n call.call(self.context)\n }\n\n /// Makes a public read-only contract call.\n ///\n /// This is similar to Solidity's `staticcall`. The called function cannot modify state or emit events. Any nested\n /// calls are constrained to also be static calls.\n ///\n /// Will revert if the called function reverts or runs out of gas.\n ///\n /// # Arguments\n /// * `call` - The object representing the read-only public function to invoke.\n ///\n /// # Returns\n /// * `T` - Whatever data the called function has returned.\n ///\n /// # Example\n /// ```noir\n /// self.view(Token::at(address).balance_of_public(recipient));\n /// ```\n ///\n pub unconstrained fn view(self, call: PublicStaticCall) -> T\n where\n T: Deserialize,\n {\n call.view(self.context)\n }\n}\n" }, "93": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/event/event_emission.nr", "source": "use crate::{\n context::{PrivateContext, PublicContext},\n event::{event_interface::{compute_private_event_commitment, EventInterface}, EventMessage},\n oracle::random::random,\n};\nuse crate::protocol::{constants::DOM_SEP__EVENT_LOG_TAG, hash::compute_log_tag, traits::{Serialize, ToField}};\n\n/// An event that was emitted in the current contract call.\npub struct NewEvent {\n pub(crate) event: Event,\n pub(crate) randomness: Field,\n}\n\n/// Equivalent to `self.emit(event)`: see [`crate::contract_self::ContractSelfPrivate::emit`].\npub fn emit_event_in_private(context: &mut PrivateContext, event: Event) -> EventMessage\nwhere\n Event: EventInterface + Serialize,\n{\n // In private events, we automatically inject randomness to prevent event commitment preimage attacks and event\n // commitment collisions (the commitments are included in the nullifier tree and duplicate nullifiers are by\n // definition not allowed).\n\n // Safety: We use the randomness to preserve the privacy of the event recipient by preventing brute-forcing, so a\n // malicious sender could use non-random values to make the event less private. But they already know the full\n // event pre-image anyway, and so the recipient already trusts them to not disclose this information. We can\n // therefore assume that the sender will cooperate in the random value generation.\n let randomness = unsafe { random() };\n\n // The event commitment is emitted as a nullifier instead of as a note because these are simpler: nullifiers cannot\n // be squashed, making kernel processing simpler, and they have no nonce that recipients need to discover.\n let commitment = compute_private_event_commitment(event, randomness);\n context.push_nullifier(commitment);\n\n EventMessage::new(NewEvent { event, randomness }, context)\n}\n\n/// Equivalent to `self.emit(event)`: see [`crate::contract_self::ContractSelfPublic::emit`].\npub fn emit_event_in_public(context: PublicContext, event: Event)\nwhere\n Event: EventInterface + Serialize,\n{\n // We prepend a domain-separated tag derived from the event type ID so that clients can filter for specific\n // events without scanning all public logs.\n let log_tag = compute_log_tag(\n Event::get_event_type_id().to_field(),\n DOM_SEP__EVENT_LOG_TAG,\n );\n context.emit_public_log_unsafe(log_tag, event);\n}\n" }, "94": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/event/event_interface.nr", "source": "use crate::{event::EventSelector, messages::logs::event::MAX_EVENT_SERIALIZED_LEN};\nuse crate::protocol::{\n constants::DOM_SEP__EVENT_COMMITMENT,\n hash::{poseidon2_hash_with_separator, poseidon2_hash_with_separator_bounded_vec},\n traits::{Serialize, ToField},\n};\n\npub trait EventInterface {\n fn get_event_type_id() -> EventSelector;\n}\n\n/// A private event's commitment is a value stored on-chain which is used to verify that the event was indeed emitted.\n///\n/// It requires a `randomness` value that must be produced alongside the event in order to perform said validation.\n/// This random value prevents attacks in which someone guesses plausible events (e.g. 'Alice transfers to Bob an\n/// amount of 10'), since they will not be able to test for existence of their guessed events without brute-forcing the\n/// entire `Field` space by guessing `randomness` values.\npub fn compute_private_event_commitment(event: Event, randomness: Field) -> Field\nwhere\n Event: EventInterface + Serialize,\n{\n poseidon2_hash_with_separator(\n [randomness, Event::get_event_type_id().to_field()].concat(event.serialize()),\n DOM_SEP__EVENT_COMMITMENT,\n )\n}\n\n/// Unconstrained variant of [`compute_private_event_commitment`] which takes the event in serialized form.\n///\n/// This function is unconstrained as the mechanism it uses to compute the commitment would be very inefficient in a\n/// constrained environment (due to the hashing of a dynamically sized array). This is not an issue as it is typically\n/// invoked when processing event messages, which is an unconstrained operation.\npub unconstrained fn compute_private_serialized_event_commitment(\n serialized_event: BoundedVec,\n randomness: Field,\n event_type_id: Field,\n) -> Field {\n let mut commitment_preimage =\n BoundedVec::<_, 2 + MAX_EVENT_SERIALIZED_LEN>::from_array([randomness, event_type_id]);\n commitment_preimage.extend_from_bounded_vec(serialized_event);\n\n poseidon2_hash_with_separator_bounded_vec(commitment_preimage, DOM_SEP__EVENT_COMMITMENT)\n}\n\nmod test {\n use crate::event::event_interface::{\n compute_private_event_commitment, compute_private_serialized_event_commitment, EventInterface,\n };\n use crate::messages::logs::event::MAX_EVENT_SERIALIZED_LEN;\n use crate::protocol::traits::{Serialize, ToField};\n use crate::test::mocks::mock_event::MockEvent;\n\n global VALUE: Field = 7;\n global RANDOMNESS: Field = 10;\n\n #[test]\n unconstrained fn max_size_serialized_event_commitment() {\n let serialized_event = BoundedVec::from_array([0; MAX_EVENT_SERIALIZED_LEN]);\n let _ = compute_private_serialized_event_commitment(serialized_event, 0, 0);\n }\n\n #[test]\n unconstrained fn event_commitment_equivalence() {\n let event = MockEvent::new(VALUE).build_event();\n\n assert_eq(\n compute_private_event_commitment(event, RANDOMNESS),\n compute_private_serialized_event_commitment(\n BoundedVec::from_array(event.serialize()),\n RANDOMNESS,\n MockEvent::get_event_type_id().to_field(),\n ),\n );\n }\n}\n" }, "95": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/event/event_message.nr", "source": "use crate::{\n context::PrivateContext,\n event::{event_emission::NewEvent, event_interface::EventInterface},\n messages::{logs::event::encode_private_event_message, message_delivery::do_private_message_delivery},\n};\nuse crate::protocol::{address::AztecAddress, traits::Serialize};\n\n/// A message with information about an event that was emitted in the current contract call. This message **MUST** be\n/// delivered to a recipient in order to not lose the private event information.\n///\n/// Use [`EventMessage::deliver_to`] to select a delivery mechanism.\n#[must_use = \"Unused EventMessage result - use the `deliver_to` function to prevent the event information from being lost forever\"]\npub struct EventMessage {\n pub(crate) new_event: NewEvent,\n\n // EventMessage is constructed when an event is emitted, which means that the `context` object will be in scope. By\n // storing a reference to it inside this object we remove the need for its methods to receive it, resulting in a\n // cleaner end-user API.\n context: &mut PrivateContext,\n}\n\nimpl EventMessage\nwhere\n Event: EventInterface + Serialize,\n{\n pub(crate) fn new(new_event: NewEvent, context: &mut PrivateContext) -> Self {\n Self { new_event, context }\n }\n\n /// Delivers the event message to a `recipient`, providing them access to the private event information.\n ///\n /// The same message can be delivered to multiple recipients, resulting in all of them learning about the event.\n /// Any recipient that receives the private event information will be able to prove its emission - events have no\n /// owner, and as such all recipients are treated equally.\n ///\n /// The message is first encrypted to the recipient's public key, ensuring no other actor can read it.\n ///\n /// The `delivery_mode` must be one of [`crate::messages::message_delivery::MessageDeliveryEnum`], and will inform\n /// costs (both proving time and TX fees) as well as delivery guarantees. This value must be a compile-time\n /// constant.\n ///\n /// # Invalid Recipients\n ///\n /// If `recipient` is an invalid address, then a random public key is selected and message delivery continues as\n /// normal. This prevents both 'king of the hill' attacks (where a sender would otherwise fail to deliver a message\n /// to an invalid recipient) and forced privacy leaks (where an invalid recipient results in a unique transaction\n /// fingerprint, e.g. one lacking the private logs that would correspond to message delivery).\n pub fn deliver_to(self, recipient: AztecAddress, delivery_mode: u8) {\n // Technical note: we're about to call a closure that needs access to `new_event`, but we can't pass `self` to\n // it because the closure might execute in unconstrained mode, and since `self` contains a mutable reference to\n // `context` this would cause for a mutable reference to cross the constrained-unconstrained barrier, which is\n // not allowed. As a workaround, we create a variable without the context and capture that instead.\n let new_event = self.new_event;\n\n do_private_message_delivery(\n self.context,\n || encode_private_event_message(new_event.event, new_event.randomness),\n Option::none(),\n recipient,\n delivery_mode,\n );\n }\n}\n" }, "96": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/event/event_selector.nr", "source": "use crate::protocol::{hash::poseidon2_hash_bytes, traits::{Deserialize, Empty, FromField, Serialize, ToField}};\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct EventSelector {\n // 1st 4-bytes (big-endian leftmost) of abi-encoding of an event.\n inner: u32,\n}\n\nimpl FromField for EventSelector {\n fn from_field(field: Field) -> Self {\n Self { inner: field as u32 }\n }\n}\n\nimpl ToField for EventSelector {\n fn to_field(self) -> Field {\n self.inner as Field\n }\n}\n\nimpl Empty for EventSelector {\n fn empty() -> Self {\n Self { inner: 0 as u32 }\n }\n}\n\nimpl EventSelector {\n pub fn from_u32(value: u32) -> Self {\n Self { inner: value }\n }\n\n pub fn from_signature(signature: str) -> Self {\n let bytes = signature.as_bytes();\n let hash = poseidon2_hash_bytes(bytes);\n\n // `hash` is automatically truncated to fit within 32 bits.\n EventSelector::from_field(hash)\n }\n\n pub fn zero() -> Self {\n Self { inner: 0 }\n }\n}\n" }, "98": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/hash.nr", "source": "//! Aztec hash functions.\n\nuse crate::protocol::{\n address::{AztecAddress, EthAddress},\n constants::{\n DOM_SEP__FUNCTION_ARGS, DOM_SEP__MESSAGE_NULLIFIER, DOM_SEP__PUBLIC_BYTECODE, DOM_SEP__PUBLIC_CALLDATA,\n DOM_SEP__SECRET_HASH, MAX_PACKED_PUBLIC_BYTECODE_SIZE_IN_FIELDS,\n },\n hash::{poseidon2_hash_subarray, poseidon2_hash_with_separator, sha256_to_field},\n traits::ToField,\n};\n\npub use crate::protocol::hash::compute_siloed_nullifier;\n\npub fn compute_secret_hash(secret: Field) -> Field {\n poseidon2_hash_with_separator([secret], DOM_SEP__SECRET_HASH)\n}\n\npub fn compute_l1_to_l2_message_hash(\n sender: EthAddress,\n chain_id: Field,\n recipient: AztecAddress,\n version: Field,\n content: Field,\n secret_hash: Field,\n leaf_index: Field,\n) -> Field {\n let mut hash_bytes = [0 as u8; 224];\n let sender_bytes: [u8; 32] = sender.to_field().to_be_bytes();\n let chain_id_bytes: [u8; 32] = chain_id.to_be_bytes();\n let recipient_bytes: [u8; 32] = recipient.to_field().to_be_bytes();\n let version_bytes: [u8; 32] = version.to_be_bytes();\n let content_bytes: [u8; 32] = content.to_be_bytes();\n let secret_hash_bytes: [u8; 32] = secret_hash.to_be_bytes();\n let leaf_index_bytes: [u8; 32] = leaf_index.to_be_bytes();\n\n for i in 0..32 {\n hash_bytes[i] = sender_bytes[i];\n hash_bytes[i + 32] = chain_id_bytes[i];\n hash_bytes[i + 64] = recipient_bytes[i];\n hash_bytes[i + 96] = version_bytes[i];\n hash_bytes[i + 128] = content_bytes[i];\n hash_bytes[i + 160] = secret_hash_bytes[i];\n hash_bytes[i + 192] = leaf_index_bytes[i];\n }\n\n sha256_to_field(hash_bytes)\n}\n\n// The nullifier of a l1 to l2 message is the hash of the message salted with the secret\npub fn compute_l1_to_l2_message_nullifier(message_hash: Field, secret: Field) -> Field {\n poseidon2_hash_with_separator([message_hash, secret], DOM_SEP__MESSAGE_NULLIFIER)\n}\n\n// Computes the hash of input arguments or return values for private functions, or for authwit creation.\npub fn hash_args(args: [Field; N]) -> Field {\n if args.len() == 0 {\n 0\n } else {\n poseidon2_hash_with_separator(args, DOM_SEP__FUNCTION_ARGS)\n }\n}\n\n// Computes the hash of calldata for public functions.\npub fn hash_calldata_array(calldata: [Field; N]) -> Field {\n poseidon2_hash_with_separator(calldata, DOM_SEP__PUBLIC_CALLDATA)\n}\n\n/// Computes the public bytecode commitment for a contract class. The commitment is `hash([(length | separator),\n/// ...bytecode])`.\n///\n/// @param packed_bytecode - The packed bytecode of the contract class. 0th word is the length in bytes.\n/// packed_bytecode is mutable so that we can avoid copying the array to construct one starting with first_field\n/// instead of length. @returns The public bytecode commitment.\npub fn compute_public_bytecode_commitment(\n mut packed_public_bytecode: [Field; MAX_PACKED_PUBLIC_BYTECODE_SIZE_IN_FIELDS],\n) -> Field {\n // First field element contains the length of the bytecode\n let bytecode_length_in_bytes: u32 = packed_public_bytecode[0] as u32;\n let bytecode_length_in_fields: u32 = (bytecode_length_in_bytes / 31) + (bytecode_length_in_bytes % 31 != 0) as u32;\n // Don't allow empty public bytecode. AVM doesn't handle execution of contracts that exist with empty bytecode.\n assert(bytecode_length_in_fields != 0);\n assert(bytecode_length_in_fields < MAX_PACKED_PUBLIC_BYTECODE_SIZE_IN_FIELDS);\n\n // Packed_bytecode's 0th entry is the length. Append it to the separator before hashing.\n let first_field = DOM_SEP__PUBLIC_BYTECODE.to_field() + (packed_public_bytecode[0] as u64 << 32) as Field;\n packed_public_bytecode[0] = first_field;\n\n // `fields_to_hash` is the number of fields from the start of `packed_public_bytecode` that should be included in\n // the hash. Fields after this length are ignored. +1 to account for the prepended field.\n let num_fields_to_hash = bytecode_length_in_fields + 1;\n\n poseidon2_hash_subarray(packed_public_bytecode, num_fields_to_hash)\n}\n\n#[test]\nunconstrained fn secret_hash_matches_typescript() {\n let secret = 8;\n let hash = compute_secret_hash(secret);\n\n // The following value was generated by `yarn-project/stdlib/src/hash/hash.test.ts`\n let secret_hash_from_ts = 0x1848b066724ab0ffb50ecb0ee3398eb839f162823d262bad959721a9c13d1e96;\n\n assert_eq(hash, secret_hash_from_ts);\n}\n\n#[test]\nunconstrained fn var_args_hash_matches_typescript() {\n let mut input = [0; 100];\n for i in 0..100 {\n input[i] = i as Field;\n }\n let hash = hash_args(input);\n\n // The following value was generated by `yarn-project/stdlib/src/hash/hash.test.ts`\n let var_args_hash_from_ts = 0x262e5e121a8efc0382566ab42f0ae2a78bd85db88484f83018fe07fc2552ba0c;\n\n assert_eq(hash, var_args_hash_from_ts);\n}\n\n#[test]\nunconstrained fn compute_calldata_hash() {\n let mut input = [0; 100];\n for i in 0..input.len() {\n input[i] = i as Field;\n }\n let hash = hash_calldata_array(input);\n\n // The following value was generated by `yarn-project/stdlib/src/hash/hash.test.ts`\n let calldata_hash_from_ts = 0x14a1539bdb1d26e03097cf4d40c87e02ca03f0bb50a3e617ace5a7bfd3943944;\n\n // Used in cpp vm2 tests:\n assert_eq(hash, calldata_hash_from_ts);\n}\n\n#[test]\nunconstrained fn public_bytecode_commitment() {\n let mut input = [0; MAX_PACKED_PUBLIC_BYTECODE_SIZE_IN_FIELDS];\n let len = 99;\n for i in 1..len + 1 {\n input[i] = i as Field;\n }\n input[0] = (len as Field) * 31;\n let hash = compute_public_bytecode_commitment(input);\n // Used in cpp vm2 tests:\n assert_eq(hash, 0x09348974e76c3602893d7a4b4bb52c2ec746f1ade5004ac471d0fbb4587a81a6);\n}\n" }, "106": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/history/storage.nr", "source": "//! Historical storage accesses.\n\nuse crate::protocol::{\n abis::block_header::BlockHeader, address::AztecAddress, constants::DOM_SEP__PUBLIC_LEAF_SLOT,\n data::public_data_storage_read, hash::poseidon2_hash_with_separator, merkle_tree::MembershipWitness,\n traits::ToField,\n};\n\nuse crate::oracle::get_public_data_witness::get_public_data_witness;\n\nmod test;\n\npub fn public_storage_historical_read(\n block_header: BlockHeader,\n storage_slot: Field,\n contract_address: AztecAddress,\n) -> Field {\n // 1) Compute the leaf index by siloing the storage slot with the contract address\n let public_data_tree_index = poseidon2_hash_with_separator(\n [contract_address.to_field(), storage_slot],\n DOM_SEP__PUBLIC_LEAF_SLOT,\n );\n\n // 2) Get the membership witness for the tree index.\n // Safety: The witness is only used as a \"magical value\" that makes the proof below pass. Hence it's safe.\n let witness = unsafe { get_public_data_witness(block_header, public_data_tree_index) };\n\n public_data_storage_read(\n block_header.state.partial.public_data_tree.root,\n public_data_tree_index,\n MembershipWitness { leaf_index: witness.index, sibling_path: witness.path },\n witness.leaf_preimage,\n )\n}\n" }, "109": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/keys/ecdh_shared_secret.nr", "source": "use crate::protocol::{\n address::aztec_address::AztecAddress,\n constants::{DOM_SEP__APP_SILOED_ECDH_SHARED_SECRET, DOM_SEP__ECDH_FIELD_MASK, DOM_SEP__ECDH_SUBKEY},\n hash::poseidon2_hash_with_separator,\n point::Point,\n scalar::Scalar,\n traits::{FromField, ToField},\n};\nuse std::{embedded_curve_ops::multi_scalar_mul, ops::Neg};\n\n/// Computes a standard ECDH shared secret: secret * public_key = shared_secret.\n///\n/// The input secret is known only to one party. The output shared secret can be derived given knowledge of\n/// `public_key`'s key-pair and the public ephemeral secret, using this same function (with reversed inputs).\n///\n/// E.g.: Epk = esk * G // ephemeral key-pair\n/// Pk = sk * G // recipient key-pair\n/// Shared secret S = esk * Pk = sk * Epk\n///\n/// See also: https://en.wikipedia.org/wiki/Elliptic-curve_Diffie%E2%80%93Hellman\npub fn derive_ecdh_shared_secret(secret: Scalar, public_key: Point) -> Point {\n multi_scalar_mul([public_key], [secret])\n}\n\n/// Computes an app-siloed shared secret from a raw ECDH shared secret point and a contract address.\n///\n/// `s_app = h(DOM_SEP__APP_SILOED_ECDH_SHARED_SECRET, S.x, S.y, contract_address)`\npub(crate) fn compute_app_siloed_shared_secret(shared_secret: Point, contract_address: AztecAddress) -> Field {\n poseidon2_hash_with_separator(\n [shared_secret.x, shared_secret.y, contract_address.to_field()],\n DOM_SEP__APP_SILOED_ECDH_SHARED_SECRET,\n )\n}\n\n/// Derives an indexed subkey from an app-siloed shared secret, used for AES key/IV derivation.\n///\n/// `s_i = h(DOM_SEP__ECDH_SUBKEY + i, s_app)`\npub(crate) fn derive_shared_secret_subkey(s_app: Field, index: u32) -> Field {\n poseidon2_hash_with_separator([s_app], DOM_SEP__ECDH_SUBKEY + index)\n}\n\n/// Derives an indexed field mask from an app-siloed shared secret, used for masking ciphertext fields.\n///\n/// `m_i = h(DOM_SEP__ECDH_FIELD_MASK + i, s_app)`\npub(crate) fn derive_shared_secret_field_mask(s_app: Field, index: u32) -> Field {\n poseidon2_hash_with_separator([s_app], DOM_SEP__ECDH_FIELD_MASK + index)\n}\n\n#[test]\nunconstrained fn test_consistency_with_typescript() {\n let secret = Scalar {\n lo: 0x00000000000000000000000000000000649e7ca01d9de27b21624098b897babd,\n hi: 0x0000000000000000000000000000000023b3127c127b1f29a7adff5cccf8fb06,\n };\n let point = Point {\n x: 0x2688431c705a5ff3e6c6f2573c9e3ba1c1026d2251d0dbbf2d810aa53fd1d186,\n y: 0x1e96887b117afca01c00468264f4f80b5bb16d94c1808a448595f115556e5c8e,\n is_infinite: false,\n };\n\n let shared_secret = derive_ecdh_shared_secret(secret, point);\n\n // This is just pasted from a test run. The original typescript code from which this could be generated seems to\n // have been deleted by someone, and soon the typescript code for encryption and decryption won't be needed, so\n // this will have to do.\n let hard_coded_shared_secret = Point {\n x: 0x15d55a5b3b2caa6a6207f313f05c5113deba5da9927d6421bcaa164822b911bc,\n y: 0x0974c3d0825031ae933243d653ebb1a0b08b90ee7f228f94c5c74739ea3c871e,\n is_infinite: false,\n };\n assert_eq(shared_secret, hard_coded_shared_secret);\n}\n\n#[test]\nunconstrained fn test_shared_secret_computation_in_both_directions() {\n let secret_a = Scalar { lo: 0x1234, hi: 0x2345 };\n let secret_b = Scalar { lo: 0x3456, hi: 0x4567 };\n\n let pk_a = std::embedded_curve_ops::fixed_base_scalar_mul(secret_a);\n let pk_b = std::embedded_curve_ops::fixed_base_scalar_mul(secret_b);\n\n let shared_secret = derive_ecdh_shared_secret(secret_a, pk_b);\n let shared_secret_alt = derive_ecdh_shared_secret(secret_b, pk_a);\n\n assert_eq(shared_secret, shared_secret_alt);\n}\n\n#[test]\nunconstrained fn test_shared_secret_computation_from_address_in_both_directions() {\n let secret_a = Scalar { lo: 0x1234, hi: 0x2345 };\n let secret_b = Scalar { lo: 0x3456, hi: 0x4567 };\n\n let mut pk_a = std::embedded_curve_ops::fixed_base_scalar_mul(secret_a);\n let mut pk_b = std::embedded_curve_ops::fixed_base_scalar_mul(secret_b);\n\n let address_b = AztecAddress::from_field(pk_b.x);\n\n // We were lazy in deriving the secret keys, and didn't check the resulting y-coordinates of the pk_a or pk_b to be\n // less than half the field modulus. If needed, we negate the pk's so that they yield valid address points. (We\n // could also have negated the secrets, but there's no negate method for EmbeddedCurvesScalar).\n pk_a = if (AztecAddress::from_field(pk_a.x).to_address_point().unwrap().inner == pk_a) {\n pk_a\n } else {\n pk_a.neg()\n };\n pk_b = if (address_b.to_address_point().unwrap().inner == pk_b) {\n pk_b\n } else {\n pk_b.neg()\n };\n\n let shared_secret = derive_ecdh_shared_secret(secret_a, address_b.to_address_point().unwrap().inner);\n let shared_secret_alt = derive_ecdh_shared_secret(secret_b, pk_a);\n\n assert_eq(shared_secret, shared_secret_alt);\n}\n\n#[test]\nunconstrained fn test_app_siloed_shared_secret_differs_per_contract() {\n let secret_a = Scalar { lo: 0x1234, hi: 0x2345 };\n let pk_b = std::embedded_curve_ops::fixed_base_scalar_mul(Scalar { lo: 0x3456, hi: 0x4567 });\n\n let shared_secret = derive_ecdh_shared_secret(secret_a, pk_b);\n\n let contract_a = AztecAddress::from_field(0xAAAA);\n let contract_b = AztecAddress::from_field(0xBBBB);\n\n let s_app_a = compute_app_siloed_shared_secret(shared_secret, contract_a);\n let s_app_b = compute_app_siloed_shared_secret(shared_secret, contract_b);\n\n assert(s_app_a != s_app_b, \"app-siloed secrets must differ for different contracts\");\n}\n" }, "110": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/keys/ephemeral.nr", "source": "use std::embedded_curve_ops::{EmbeddedCurveScalar, fixed_base_scalar_mul};\n\nuse crate::protocol::{point::Point, scalar::Scalar};\n\nuse crate::{oracle::random::random, utils::point::get_sign_of_point};\n\n/// Generates a random ephemeral key pair.\npub fn generate_ephemeral_key_pair() -> (Scalar, Point) {\n // @todo Need to draw randomness from the full domain of Fq not only Fr\n\n // Safety: we use the randomness to preserve the privacy of both the sender and recipient via encryption, so a\n // malicious sender could use non-random values to reveal the plaintext. But they already know it themselves\n // anyway, and so the recipient already trusts them to not disclose this information. We can therefore assume that\n // the sender will cooperate in the random value generation.\n let randomness = unsafe { random() };\n\n // TODO(#12757): compute the key pair without constraining eph_sk twice (once in from_field, once in the black box\n // called by fixed_base_scalar_mul).\n let eph_sk = EmbeddedCurveScalar::from_field(randomness);\n let eph_pk = fixed_base_scalar_mul(eph_sk);\n\n (eph_sk, eph_pk)\n}\n\n/// Generates a random ephemeral key pair with a positive y-coordinate.\n///\n/// Unlike [`generate_ephemeral_key_pair`], the y-coordinate of the public key is guaranteed to be a positive value\n/// (i.e. [`crate::utils::point::get_sign_of_point`] will return `true`).\n///\n/// This is useful as it means it is possible to just broadcast the x-coordinate as a single `Field` and then\n/// reconstruct the original public key using [`crate::utils::point::point_from_x_coord_and_sign`] with `sign: true`.\npub fn generate_positive_ephemeral_key_pair() -> (Scalar, Point) {\n // Safety: we use the randomness to preserve the privacy of both the sender and recipient via encryption, so a\n // malicious sender could use non-random values to reveal the plaintext. But they already know it themselves\n // anyway, and so the recipient already trusts them to not disclose this information. We can therefore assume that\n // the sender will cooperate in the random value generation.\n let eph_sk = unsafe { generate_secret_key_for_positive_public_key() };\n let eph_pk = fixed_base_scalar_mul(eph_sk);\n\n assert(get_sign_of_point(eph_pk), \"Got an ephemeral public key with a negative y coordinate\");\n\n (eph_sk, eph_pk)\n}\n\nunconstrained fn generate_secret_key_for_positive_public_key() -> EmbeddedCurveScalar {\n let mut sk = std::mem::zeroed();\n\n loop {\n // We simply produce random secret keys until we find one that has results in a positive public key. About half\n // of all public keys fulfill this condition, so this should only take a few iterations at most.\n\n // @todo Need to draw randomness from the full domain of Fq not only Fr\n sk = EmbeddedCurveScalar::from_field(random());\n let pk = fixed_base_scalar_mul(sk);\n if get_sign_of_point(pk) {\n break;\n }\n }\n\n sk\n}\n\nmod test {\n use crate::utils::point::get_sign_of_point;\n use super::generate_positive_ephemeral_key_pair;\n\n #[test]\n fn generate_positive_ephemeral_key_pair_produces_positive_keys() {\n // About half of random points are negative, so testing just a couple gives us high confidence that\n // `generate_positive_ephemeral_key_pair` is indeed producing positive ones.\n for _ in 0..10 {\n let (_, pk) = generate_positive_ephemeral_key_pair();\n assert(get_sign_of_point(pk));\n }\n }\n}\n" }, "111": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/keys/getters/mod.nr", "source": "use crate::{\n keys::constants::{NULLIFIER_INDEX, OUTGOING_INDEX},\n oracle::{\n key_validation_request::get_key_validation_request,\n keys::{get_public_keys_and_partial_address, try_get_public_keys_and_partial_address},\n },\n};\nuse crate::protocol::{address::AztecAddress, public_keys::PublicKeys};\n\npub unconstrained fn get_nhk_app(npk_m_hash: Field) -> Field {\n get_key_validation_request(npk_m_hash, NULLIFIER_INDEX).sk_app\n}\n\n// A helper function that gets app-siloed outgoing viewing key for a given `ovpk_m_hash`. This function is used in\n// unconstrained contexts only - when computing unconstrained note logs. The safe alternative is `request_ovsk_app`\n// function defined on `PrivateContext`.\npub unconstrained fn get_ovsk_app(ovpk_m_hash: Field) -> Field {\n get_key_validation_request(ovpk_m_hash, OUTGOING_INDEX).sk_app\n}\n\n// Returns all public keys for a given account, applying proper constraints to the context. We read all keys at once\n// since the constraints for reading them all are actually fewer than if we read them one at a time - any read keys\n// that are not required by the caller can simply be discarded.\n// Fails if the public keys are not registered\npub fn get_public_keys(account: AztecAddress) -> PublicKeys {\n // Safety: Public keys are constrained by showing their inclusion in the address's preimage.\n let (public_keys, partial_address) = unsafe { get_public_keys_and_partial_address(account) };\n assert_eq(account, AztecAddress::compute(public_keys, partial_address), \"Invalid public keys hint for address\");\n\n public_keys\n}\n\n/// Returns all public keys for a given account, or `None` if the public keys are not registered in the PXE.\npub unconstrained fn try_get_public_keys(account: AztecAddress) -> Option {\n try_get_public_keys_and_partial_address(account).map(|(public_keys, _)| public_keys)\n}\n\nmod test {\n use super::get_public_keys;\n\n use crate::test::helpers::test_environment::TestEnvironment;\n use std::test::OracleMock;\n\n global KEY_ORACLE_RESPONSE_LENGTH: u32 = 13; // 12 fields for the keys, one field for the partial address\n\n #[test(should_fail_with = \"Invalid public keys hint for address\")]\n unconstrained fn get_public_keys_fails_with_bad_hint() {\n let mut env = TestEnvironment::new();\n let account = env.create_light_account();\n\n // Instead of querying for some unknown account, which would result in the oracle erroring out, we mock a bad\n // oracle response to check that the circuit properly checks the address derivation.\n let mut random_keys_and_partial_address = [0; KEY_ORACLE_RESPONSE_LENGTH];\n // We use randomly generated points on the curve, and a random partial address to ensure that this combination\n // does not derive the address and we should see the assertion fail. npk_m\n random_keys_and_partial_address[0] = 0x292364b852c6c6f01472951e76a39cbcf074591fd0e063a81965e7b51ad868a5;\n random_keys_and_partial_address[1] = 0x0a687b46cdc9238f1c311f126aaaa4acbd7a737bff2efd7aeabdb8d805843a27;\n random_keys_and_partial_address[2] = 0x0000000000000000000000000000000000000000000000000000000000000000;\n // ivpk_m\n random_keys_and_partial_address[3] = 0x173c5229a00c5425255680dd6edc27e278c48883991f348fe6985de43b4ec25f;\n random_keys_and_partial_address[4] = 0x1698608e23b5f6c2f43c49a559108bb64e2247b8fc2da842296a416817f40b7f;\n random_keys_and_partial_address[5] = 0x0000000000000000000000000000000000000000000000000000000000000000;\n // ovpk_m\n random_keys_and_partial_address[6] = 0x1bad2f7d1ad960a1bd0fe4d2c8d17f5ab4a86ef8b103e0a9e7f67ec0d3b4795e;\n random_keys_and_partial_address[7] = 0x206db87110abbecc9fbaef2c865189d94ef2c106202f734ee4eba9257fd28bf1;\n random_keys_and_partial_address[8] = 0x0000000000000000000000000000000000000000000000000000000000000000;\n // tpk_m\n random_keys_and_partial_address[9] = 0x05e3bd9cfe6b47daa139613619cf7d7fd8bb0112b6f2908caa6d9b536ed948ed;\n random_keys_and_partial_address[10] = 0x051066f877c9df47552d02e7dc32127ff4edefc8498e813bca1cbd3f5d1be429;\n random_keys_and_partial_address[11] = 0x0000000000000000000000000000000000000000000000000000000000000000;\n // partial address\n random_keys_and_partial_address[12] = 0x236703e2cb00a182e024e98e9f759231b556d25ff19f98896cebb69e9e678cc9;\n\n let _ = OracleMock::mock(\"aztec_utl_getPublicKeysAndPartialAddress\").returns(Option::some(\n random_keys_and_partial_address,\n ));\n let _ = get_public_keys(account);\n }\n}\n" }, "114": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/logging.nr", "source": "// Not all log levels are currently used, but we provide the full set so that new call sites can use any level. Because\n// of that we tag all with `#[allow(dead_code)]` to prevent warnings.\n//\n// All wrappers resolve function paths at comptime via `resolve_fn` so that the emitted `Quoted` code works both inside\n// aztec-nr (where `crate::` = aztec) and inside macro-generated contract code (where `crate::` = the contract).\n\nuse std::meta::ctstring::AsCtString;\n\ncomptime fn log_prefix(msg: str) -> CtString {\n \"[aztec-nr] \".as_ctstring().append_str(msg)\n}\n\n// --- No-args variants (direct call) ---\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_fatal_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::fatal_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_error_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::error_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_warn_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::warn_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_info_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::info_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_verbose_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::verbose_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_debug_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::debug_log });\n quote { $f($msg) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_trace_log(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::trace_log });\n quote { $f($msg) }\n}\n\n// --- Format variants (return lambda for runtime args) ---\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_fatal_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::fatal_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_error_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::error_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_warn_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::warn_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_info_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::info_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_verbose_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::verbose_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_debug_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::debug_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n#[allow(dead_code)]\npub(crate) comptime fn aztecnr_trace_log_format(msg: str) -> Quoted {\n let msg = log_prefix(msg);\n let f = resolve_fn(quote { crate::protocol::logging::trace_log_format });\n quote { (|args| $f($msg, args)) }\n}\n\n// See module-level comment for why this is needed.\ncomptime fn resolve_fn(path: Quoted) -> TypedExpr {\n path.as_expr().unwrap().resolve(Option::none())\n}\n" }, "116": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/aztec/compute_note_hash_and_nullifier.nr", "source": "use crate::logging;\nuse crate::macros::{notes::NOTES, utils::get_trait_impl_method};\n\n/// Generates two contract library methods called `_compute_note_hash` and `_compute_note_nullifier`, plus a\n/// (deprecated) wrapper called `_compute_note_hash_and_nullifier`, which are used for note discovery (i.e. these are\n/// of the `aztec::messages::discovery::ComputeNoteHash` and `aztec::messages::discovery::ComputeNoteNullifier` types).\npub(crate) comptime fn generate_contract_library_methods_compute_note_hash_and_nullifier() -> Quoted {\n let compute_note_hash = generate_contract_library_method_compute_note_hash();\n let compute_note_nullifier = generate_contract_library_method_compute_note_nullifier();\n\n quote {\n $compute_note_hash\n $compute_note_nullifier\n\n /// Unpacks an array into a note corresponding to `note_type_id` and then computes its note hash (non-siloed) and inner nullifier (non-siloed) assuming the note has been inserted into the note hash tree with `note_nonce`.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[contract_library_method]\n #[allow(dead_code)]\n unconstrained fn _compute_note_hash_and_nullifier(\n packed_note: BoundedVec,\n owner: aztec::protocol::address::AztecAddress,\n storage_slot: Field,\n note_type_id: Field,\n contract_address: aztec::protocol::address::AztecAddress,\n randomness: Field,\n note_nonce: Field,\n ) -> Option {\n _compute_note_hash(packed_note, owner, storage_slot, note_type_id, contract_address, randomness).map(|note_hash| {\n\n let siloed_note_hash = aztec::protocol::hash::compute_siloed_note_hash(contract_address, note_hash);\n let unique_note_hash = aztec::protocol::hash::compute_unique_note_hash(note_nonce, siloed_note_hash);\n \n let inner_nullifier = _compute_note_nullifier(unique_note_hash, packed_note, owner, storage_slot, note_type_id, contract_address, randomness);\n\n aztec::messages::discovery::NoteHashAndNullifier {\n note_hash,\n inner_nullifier,\n }\n })\n }\n }\n}\n\ncomptime fn generate_contract_library_method_compute_note_hash() -> Quoted {\n if NOTES.len() == 0 {\n // Contracts with no notes still implement this function to avoid having special-casing, the implementation\n // simply throws immediately.\n quote {\n /// This contract does not use private notes, so this function should never be called as it will unconditionally fail.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[contract_library_method]\n unconstrained fn _compute_note_hash(\n _packed_note: BoundedVec,\n _owner: aztec::protocol::address::AztecAddress,\n _storage_slot: Field,\n _note_type_id: Field,\n _contract_address: aztec::protocol::address::AztecAddress,\n _randomness: Field,\n ) -> Option {\n panic(f\"This contract does not use private notes\")\n }\n }\n } else {\n // Contracts that do define notes produce an if-else chain where `note_type_id` is matched against the\n // `get_note_type_id()` function of each note type that we know of, in order to identify the note type. Once we\n // know it we call the correct `unpack` method from the `Packable` trait to obtain the underlying note type,\n // and\n // compute the note hash (non-siloed).\n\n // We resolve the log format calls here so that the resulting Quoted values can be spliced into the quote\n // block below.\n let warn_length_mismatch = logging::aztecnr_warn_log_format(\n \"Packed note length mismatch for note type id {0}: expected {1} fields, got {2}. Skipping note.\",\n );\n let warn_unknown_note_type = logging::aztecnr_warn_log_format(\"Unknown note type id {0}. Skipping note.\");\n\n let mut if_note_type_id_match_statements_list = @[];\n for i in 0..NOTES.len() {\n let typ = NOTES.get(i);\n\n let get_note_type_id = get_trait_impl_method(\n typ,\n quote { crate::note::note_interface::NoteType },\n quote { get_id },\n );\n let unpack = get_trait_impl_method(\n typ,\n quote { crate::protocol::traits::Packable },\n quote { unpack },\n );\n\n let compute_note_hash = get_trait_impl_method(\n typ,\n quote { crate::note::note_interface::NoteHash },\n quote { compute_note_hash },\n );\n\n let if_or_else_if = if i == 0 {\n quote { if }\n } else {\n quote { else if }\n };\n\n if_note_type_id_match_statements_list = if_note_type_id_match_statements_list.push_back(\n quote {\n $if_or_else_if note_type_id == $get_note_type_id() {\n // As an extra safety check we make sure that the packed_note BoundedVec has the expected\n // length, since we're about to interpret its raw storage as a fixed-size array by calling the\n // unpack function on it.\n let expected_len = <$typ as $crate::protocol::traits::Packable>::N;\n let actual_len = packed_note.len();\n if actual_len != expected_len {\n $warn_length_mismatch([note_type_id, expected_len as Field, actual_len as Field]);\n Option::none()\n } else {\n let note = $unpack(aztec::utils::array::subarray(packed_note.storage(), 0));\n\n Option::some($compute_note_hash(note, owner, storage_slot, randomness))\n }\n }\n },\n );\n }\n\n let if_note_type_id_match_statements = if_note_type_id_match_statements_list.join(quote {});\n\n quote {\n /// Unpacks an array into a note corresponding to `note_type_id` and then computes its note hash (non-siloed).\n ///\n /// The signature of this function notably matches the `aztec::messages::discovery::ComputeNoteHash` type, and so it can be used to call functions from that module such as `do_sync_state` and `attempt_note_discovery`.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[contract_library_method]\n unconstrained fn _compute_note_hash(\n packed_note: BoundedVec,\n owner: aztec::protocol::address::AztecAddress,\n storage_slot: Field,\n note_type_id: Field,\n _contract_address: aztec::protocol::address::AztecAddress,\n randomness: Field,\n ) -> Option {\n $if_note_type_id_match_statements\n else {\n $warn_unknown_note_type([note_type_id]);\n Option::none()\n }\n }\n }\n }\n}\n\ncomptime fn generate_contract_library_method_compute_note_nullifier() -> Quoted {\n if NOTES.len() == 0 {\n // Contracts with no notes still implement this function to avoid having special-casing, the implementation\n // simply throws immediately.\n quote {\n /// This contract does not use private notes, so this function should never be called as it will unconditionally fail.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[contract_library_method]\n unconstrained fn _compute_note_nullifier(\n _unique_note_hash: Field,\n _packed_note: BoundedVec,\n _owner: aztec::protocol::address::AztecAddress,\n _storage_slot: Field,\n _note_type_id: Field,\n _contract_address: aztec::protocol::address::AztecAddress,\n _randomness: Field,\n ) -> Option {\n panic(f\"This contract does not use private notes\")\n }\n }\n } else {\n // Contracts that do define notes produce an if-else chain where `note_type_id` is matched against the\n // `get_note_type_id()` function of each note type that we know of, in order to identify the note type. Once we\n // know it we call the correct `unpack` method from the `Packable` trait to obtain the underlying note type,\n // and\n // compute the inner nullifier (non-siloed).\n\n // We resolve the log format calls here so that the resulting Quoted values can be spliced into the quote\n // block below.\n let warn_length_mismatch = logging::aztecnr_warn_log_format(\n \"Packed note length mismatch for note type id {0}: expected {1} fields, got {2}. Skipping note.\",\n );\n let warn_unknown_note_type = logging::aztecnr_warn_log_format(\"Unknown note type id {0}. Skipping note.\");\n\n let mut if_note_type_id_match_statements_list = @[];\n for i in 0..NOTES.len() {\n let typ = NOTES.get(i);\n\n let get_note_type_id = get_trait_impl_method(\n typ,\n quote { crate::note::note_interface::NoteType },\n quote { get_id },\n );\n let unpack = get_trait_impl_method(\n typ,\n quote { crate::protocol::traits::Packable },\n quote { unpack },\n );\n\n let compute_nullifier_unconstrained = get_trait_impl_method(\n typ,\n quote { crate::note::note_interface::NoteHash },\n quote { compute_nullifier_unconstrained },\n );\n\n let if_or_else_if = if i == 0 {\n quote { if }\n } else {\n quote { else if }\n };\n\n if_note_type_id_match_statements_list = if_note_type_id_match_statements_list.push_back(\n quote {\n $if_or_else_if note_type_id == $get_note_type_id() {\n // As an extra safety check we make sure that the packed_note BoundedVec has the expected\n // length, since we're about to interpret its raw storage as a fixed-size array by calling the\n // unpack function on it.\n let expected_len = <$typ as $crate::protocol::traits::Packable>::N;\n let actual_len = packed_note.len();\n if actual_len != expected_len {\n $warn_length_mismatch([note_type_id, expected_len as Field, actual_len as Field]);\n Option::none()\n } else {\n let note = $unpack(aztec::utils::array::subarray(packed_note.storage(), 0));\n\n // The message discovery process finds settled notes, that is, notes that were created in\n // prior transactions and are therefore already part of the note hash tree. The note hash\n // for nullification is hence the unique note hash.\n $compute_nullifier_unconstrained(note, owner, unique_note_hash)\n }\n }\n },\n );\n }\n\n let if_note_type_id_match_statements = if_note_type_id_match_statements_list.join(quote {});\n\n quote {\n /// Computes a note's inner nullifier (non-siloed) given its unique note hash, preimage and extra data.\n ///\n /// The signature of this function notably matches the `aztec::messages::discovery::ComputeNoteNullifier` type, and so it can be used to call functions from that module such as `do_sync_state` and `attempt_note_discovery`.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[contract_library_method]\n unconstrained fn _compute_note_nullifier(\n unique_note_hash: Field,\n packed_note: BoundedVec,\n owner: aztec::protocol::address::AztecAddress,\n _storage_slot: Field,\n note_type_id: Field,\n _contract_address: aztec::protocol::address::AztecAddress,\n _randomness: Field,\n ) -> Option {\n $if_note_type_id_match_statements\n else {\n $warn_unknown_note_type([note_type_id]);\n Option::none()\n }\n }\n }\n }\n}\n" }, "117": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/aztec.nr", "source": "mod compute_note_hash_and_nullifier;\n\nuse crate::{\n macros::{\n calls_generation::{\n external_functions::{generate_external_function_calls, generate_external_function_self_calls_structs},\n internal_functions::generate_call_internal_struct,\n },\n dispatch::generate_public_dispatch,\n emit_public_init_nullifier::generate_emit_public_init_nullifier,\n internals_functions_generation::{create_fn_abi_exports, process_functions},\n storage::STORAGE_LAYOUT_NAME,\n utils::{is_fn_contract_library_method, is_fn_external, is_fn_internal, is_fn_test, module_has_storage},\n },\n messages::discovery::CustomMessageHandler,\n};\n\nuse compute_note_hash_and_nullifier::generate_contract_library_methods_compute_note_hash_and_nullifier;\n\n/// Configuration for the [`aztec`] macro.\n///\n/// This type lets users override different parts of the default aztec-nr contract behavior, such\n/// as message handling. These are advanced features that require careful understanding of\n/// the behavior of these systems.\n///\n/// ## Examples\n///\n/// ```noir\n/// #[aztec(aztec::macros::AztecConfig::new().custom_message_handler(my_handler))]\n/// contract MyContract { ... }\n/// ```\npub struct AztecConfig {\n custom_message_handler: Option>,\n}\n\nimpl AztecConfig {\n /// Creates a new `AztecConfig` with default values.\n ///\n /// Calling `new` is equivalent to invoking the [`aztec`] macro with no parameters. The different methods\n /// (e.g. [`AztecConfig::custom_message_handler`]) can then be used to change the default behavior.\n pub comptime fn new() -> Self {\n Self { custom_message_handler: Option::none() }\n }\n\n /// Sets a handler for custom messages.\n ///\n /// This enables contracts to process non-standard messages (i.e. any with a message type that is not in\n /// [`crate::messages::msg_type`]).\n ///\n /// `handler` must be a `#[contract_library_method]` function that conforms to the\n /// [`crate::messages::discovery::CustomMessageHandler`] type signature.\n pub comptime fn custom_message_handler(_self: Self, handler: CustomMessageHandler<()>) -> Self {\n Self { custom_message_handler: Option::some(handler) }\n }\n}\n\n/// Enables aztec-nr features on a `contract`.\n///\n/// All aztec-nr contracts should have this macro invoked on them, as it is the one that processes all contract\n/// functions, notes, storage, generates interfaces for external calls, and creates the message processing\n/// boilerplate.\n///\n/// ## Examples\n///\n/// Most contracts can simply invoke the macro with no parameters, resulting in default aztec-nr behavior:\n/// ```noir\n/// #[aztec]\n/// contract MyContract { ... }\n/// ```\n///\n/// Advanced contracts can use [`AztecConfig`] to customize parts of its behavior, such as message\n/// processing.\n/// ```noir\n/// #[aztec(aztec::macros::AztecConfig::new().custom_message_handler(my_handler))]\n/// contract MyAdvancedContract { ... }\n/// ```\n#[varargs]\npub comptime fn aztec(m: Module, args: [AztecConfig]) -> Quoted {\n let num_args = args.len();\n let config = if num_args == 0 {\n AztecConfig::new()\n } else if num_args == 1 {\n args[0]\n } else {\n panic(f\"#[aztec] expects 0 or 1 arguments, got {num_args}\")\n };\n\n // Functions that don't have #[external(...)], #[contract_library_method], or #[test] are not allowed in contracts.\n check_each_fn_macroified(m);\n\n // We generate new functions prefixed with `__aztec_nr_internals__` and we replace the original functions' bodies\n // with `static_assert(false, ...)` to prevent them from being called directly from within the contract.\n let functions = process_functions(m);\n\n // We generate structs and their implementations necessary for convenient functions calls.\n let interface = generate_contract_interface(m);\n let self_call_structs = generate_external_function_self_calls_structs(m);\n let call_internal_struct = generate_call_internal_struct(m);\n\n // We generate ABI exports for all the external functions in the contract.\n let fn_abi_exports = create_fn_abi_exports(m);\n\n // We generate `_compute_note_hash`, `_compute_note_nullifier` (and the deprecated\n // `_compute_note_hash_and_nullifier` wrapper) and `sync_state` functions only if they are not already implemented.\n // If they are implemented we just insert empty quotes.\n let contract_library_method_compute_note_hash_and_nullifier = if !m.functions().any(|f| {\n // Note that we don't test for `_compute_note_hash` or `_compute_note_nullifier` in order to make this simpler\n // - users must either implement all three or none.\n // Down the line we'll remove this check and use `AztecConfig`.\n f.name() == quote { _compute_note_hash_and_nullifier }\n }) {\n generate_contract_library_methods_compute_note_hash_and_nullifier()\n } else {\n quote {}\n };\n let process_custom_message_option = if config.custom_message_handler.is_some() {\n let handler = config.custom_message_handler.unwrap();\n quote { Option::some($handler) }\n } else {\n quote { Option::>::none() }\n };\n\n let offchain_inbox_sync_option = quote {\n Option::some(aztec::messages::processing::offchain::sync_inbox)\n };\n\n let sync_state_fn_and_abi_export = if !m.functions().any(|f| f.name() == quote { sync_state }) {\n generate_sync_state(process_custom_message_option, offchain_inbox_sync_option)\n } else {\n quote {}\n };\n\n if m.functions().any(|f| f.name() == quote { offchain_receive }) {\n panic(\n \"User-defined 'offchain_receive' is not allowed. The function is auto-injected by the #[aztec] macro. See https://docs.aztec.network/errors/7\",\n );\n }\n let offchain_receive_fn_and_abi_export = generate_offchain_receive();\n\n let (has_public_init_nullifier_fn, emit_public_init_nullifier_fn_body) = generate_emit_public_init_nullifier(m);\n let public_dispatch = generate_public_dispatch(m, has_public_init_nullifier_fn);\n\n quote {\n $interface\n $self_call_structs\n $call_internal_struct\n $functions\n $fn_abi_exports\n $contract_library_method_compute_note_hash_and_nullifier\n $public_dispatch\n $sync_state_fn_and_abi_export\n $emit_public_init_nullifier_fn_body\n $offchain_receive_fn_and_abi_export\n }\n}\n\ncomptime fn generate_contract_interface(m: Module) -> Quoted {\n let calls = generate_external_function_calls(m);\n\n let module_name = m.name();\n\n let has_storage_layout = module_has_storage(m) & STORAGE_LAYOUT_NAME.get(m).is_some();\n let storage_layout_getter = if has_storage_layout {\n let storage_layout_name = STORAGE_LAYOUT_NAME.get(m).unwrap();\n quote {\n pub fn storage_layout() -> StorageLayoutFields {\n $storage_layout_name.fields\n }\n }\n } else {\n quote {}\n };\n\n let library_storage_layout_getter = if has_storage_layout {\n quote {\n #[contract_library_method]\n $storage_layout_getter\n }\n } else {\n quote {}\n };\n\n quote {\n pub struct $module_name {\n pub target_contract: aztec::protocol::address::AztecAddress\n }\n\n impl $module_name {\n $calls\n\n pub fn at(\n addr: aztec::protocol::address::AztecAddress\n ) -> Self {\n Self { target_contract: addr }\n }\n\n pub fn interface() -> Self {\n Self { target_contract: aztec::protocol::address::AztecAddress::zero() }\n }\n\n $storage_layout_getter\n }\n\n #[contract_library_method]\n pub fn at(\n addr: aztec::protocol::address::AztecAddress\n ) -> $module_name {\n $module_name { target_contract: addr }\n }\n\n #[contract_library_method]\n pub fn interface() -> $module_name {\n $module_name { target_contract: aztec::protocol::address::AztecAddress::zero() }\n }\n\n $library_storage_layout_getter\n\n }\n}\n\n/// Generates the `sync_state` utility function that performs message discovery.\ncomptime fn generate_sync_state(process_custom_message_option: Quoted, offchain_inbox_sync_option: Quoted) -> Quoted {\n quote {\n pub struct sync_state_parameters {\n pub scope: aztec::protocol::address::AztecAddress,\n }\n\n #[abi(functions)]\n pub struct sync_state_abi {\n parameters: sync_state_parameters,\n }\n\n #[aztec::macros::internals_functions_generation::abi_attributes::abi_utility]\n unconstrained fn sync_state(scope: aztec::protocol::address::AztecAddress) {\n let address = aztec::context::UtilityContext::new().this_address();\n aztec::messages::discovery::do_sync_state(\n address,\n _compute_note_hash,\n _compute_note_nullifier,\n $process_custom_message_option,\n $offchain_inbox_sync_option,\n scope,\n );\n }\n }\n}\n\n/// Generates an `offchain_receive` utility function that lets callers add messages to the offchain message inbox.\n///\n/// For more details, see `aztec::messages::processing::offchain::receive`.\ncomptime fn generate_offchain_receive() -> Quoted {\n quote {\n pub struct offchain_receive_parameters {\n pub messages: BoundedVec<\n aztec::messages::processing::offchain::OffchainMessage,\n aztec::messages::processing::offchain::MAX_OFFCHAIN_MESSAGES_PER_RECEIVE_CALL,\n >,\n }\n\n #[abi(functions)]\n pub struct offchain_receive_abi {\n parameters: offchain_receive_parameters,\n }\n\n /// Receives offchain messages into this contract's offchain inbox for subsequent processing.\n ///\n /// Each message is routed to the inbox scoped to its `recipient` field.\n ///\n /// For more details, see `aztec::messages::processing::offchain::receive`.\n ///\n /// This function is automatically injected by the `#[aztec]` macro.\n #[aztec::macros::internals_functions_generation::abi_attributes::abi_utility]\n unconstrained fn offchain_receive(\n messages: BoundedVec<\n aztec::messages::processing::offchain::OffchainMessage,\n aztec::messages::processing::offchain::MAX_OFFCHAIN_MESSAGES_PER_RECEIVE_CALL,\n >,\n ) {\n let address = aztec::context::UtilityContext::new().this_address();\n aztec::messages::processing::offchain::receive(address, messages);\n }\n }\n}\n\n/// Checks that all functions in the module have a context macro applied.\n///\n/// Non-macroified functions are not allowed in contracts. They must all be one of\n/// [`crate::macros::functions::external`], [`crate::macros::functions::internal`] or `test`.\ncomptime fn check_each_fn_macroified(m: Module) {\n for f in m.functions() {\n let name = f.name();\n if !is_fn_external(f) & !is_fn_contract_library_method(f) & !is_fn_internal(f) & !is_fn_test(f) {\n // We don't suggest that #[contract_library_method] is allowed because we don't want to introduce another\n // concept\n panic(\n f\"Function {name} must be marked as either #[external(...)], #[internal(...)], or #[test]\",\n );\n }\n }\n}\n" }, "119": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/calls_generation/external_functions_stubs.nr", "source": "//! Stubs are auto-generated wrapper functions that provide an ergonomic interface for cross-contract calls. ! Instead\n// of manually serializing arguments and creating call interfaces, stubs allow natural syntax, e.g. for ! enqueuing\n// calls to public functions: ! ! ExternalContract.at(address).some_method(arg1, arg2).enqueue()\n\nuse crate::macros::utils::compute_fn_selector;\nuse crate::protocol::meta::utils::derive_serialization_quotes;\nuse std::meta::unquote;\n\ncomptime global FROM_FIELD: TypedExpr = {\n let from_field_trait = quote { crate::protocol::traits::FromField }.as_trait_constraint();\n let function_selector_typ = quote { crate::protocol::abis::function_selector::FunctionSelector }.as_type();\n function_selector_typ.get_trait_impl(from_field_trait).unwrap().methods().filter(|m| {\n m.name() == quote { from_field }\n })[0]\n .as_typed_expr()\n};\n\n/// Utility function creating stubs used by all the stub functions in this file.\ncomptime fn create_stub_base(f: FunctionDefinition) -> (Quoted, Quoted, Quoted, Quoted, u32, Quoted, u32, Field) {\n // Dear privacy adventurer, chances are, you've command+clicked on the name of an external function call -- seeking\n // to view that function -- only to end up here. Here's an explanation: The external contract that you're calling\n // was likely annotated with the `#[aztec]` annotation -- as all good aztec contracts are. This triggers a macro\n // which generates a \"contract interface\" for that contract, which is effectively a pretty interface that gives\n // natural contract calling semantics:\n //\n // `MyImportedContract.at(some_address).my_method(arg1, arg2).enqueue();\n //\n // Unfortunately, the usage of macros makes it a bit of a black box. To actually view the target function, you\n // could instead command+click on `MyImportedContract`, or you can just manually search it. If you want to view the\n // noir code that gets generated by this macro, you can use `nargo expand` on your contract.\n let fn_name = f.name();\n let fn_parameters = f.parameters();\n let fn_parameters_list = fn_parameters.map(|(name, typ): (Quoted, Type)| quote { $name: $typ }).join(quote {,});\n\n let (serialized_args_array_construction, serialized_args_array_len_quote, serialized_args_array_name) =\n derive_serialization_quotes(fn_parameters, false);\n let serialized_args_array_len: u32 = unquote!(quote { ($serialized_args_array_len_quote) as u32 });\n\n let fn_name_str = f\"\\\"{fn_name}\\\"\".quoted_contents();\n let fn_name_len: u32 = unquote!(quote { $fn_name_str.as_bytes().len()});\n let fn_selector: Field = compute_fn_selector(f.name(), f.parameters());\n\n (\n fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name,\n serialized_args_array_len, fn_name_str, fn_name_len, fn_selector,\n )\n}\n\npub(crate) comptime fn create_private_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, serialized_args_array_len, fn_name_str, fn_name_len, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> aztec::context::calls::PrivateCall<$fn_name_len, $serialized_args_array_len, $fn_return_type> {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n aztec::context::calls::PrivateCall::new(\n self.target_contract,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n )\n }\n }\n}\n\npub(crate) comptime fn create_private_static_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, serialized_args_array_len, fn_name_str, fn_name_len, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> aztec::context::calls::PrivateStaticCall<$fn_name_len, $serialized_args_array_len, $fn_return_type> {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n aztec::context::calls::PrivateStaticCall::new(\n self.target_contract,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n )\n }\n }\n}\n\npub(crate) comptime fn create_public_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, serialized_args_array_len, fn_name_str, fn_name_len, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> aztec::context::calls::PublicCall<$fn_name_len, $serialized_args_array_len, $fn_return_type> {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n aztec::context::calls::PublicCall::new(\n self.target_contract,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n )\n }\n }\n}\n\npub(crate) comptime fn create_public_static_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, serialized_args_array_len, fn_name_str, fn_name_len, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> aztec::context::calls::PublicStaticCall<$fn_name_len, $serialized_args_array_len, $fn_return_type> {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n aztec::context::calls::PublicStaticCall::new(\n self.target_contract,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n )\n }\n }\n}\n\npub(crate) comptime fn create_utility_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, serialized_args_array_len, fn_name_str, fn_name_len, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> aztec::context::calls::UtilityCall<$fn_name_len, $serialized_args_array_len, $fn_return_type> {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n aztec::context::calls::UtilityCall::new(\n self.target_contract,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n )\n }\n }\n}\n\n// Self-call stub generation functions for CallSelf, CallSelfStatic, EnqueueSelf, and EnqueueSelfStatic structs. Unlike\n// the stubs above, the self-call stubs directly perform the call instead of returning a struct that can be later used\n// to perform the call.\n\n/// Creates a stub for calling a private function (or static private function if `is_static` is true) from private\n/// context (for CallSelf<&mut PrivateContext> and CallSelfStatic<&mut PrivateContext>).\npub comptime fn create_private_self_call_stub(f: FunctionDefinition, is_static: bool) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, _, _, _, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> $fn_return_type {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n let args_hash = aztec::hash::hash_args($serialized_args_array_name);\n aztec::oracle::execution_cache::store($serialized_args_array_name, args_hash);\n let returns_hash = self.context.call_private_function_with_args_hash(\n self.address,\n selector,\n args_hash,\n $is_static\n );\n returns_hash.get_preimage()\n }\n }\n}\n\n/// Creates a stub for calling a public function from public context (for CallSelf)\npub comptime fn create_public_self_call_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, _, fn_name_str, _, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n // TODO: It makes sense to drop the use of PublicStaticCall struct here and just perform the call directly but\n // before doing that we need to drop the use of slices from return values because we cannot return slices from an\n // unconstrained function. This is not a priority right now.\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> $fn_return_type {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n unsafe {\n aztec::context::calls::PublicCall::new(\n self.address,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n ).call(self.context)\n }\n }\n }\n}\n\n/// Creates a static stub for calling a public view function from public context (for CallSelfStatic)\npub comptime fn create_public_self_call_static_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, _, fn_name_str, _, fn_selector) =\n create_stub_base(f);\n let fn_return_type = f.return_type();\n\n // TODO: It makes sense to drop the use of PublicStaticCall struct here and just perform the call directly but\n // before doing that we need to drop the use of slices from return values because we cannot return slices from an\n // unconstrained function. This is not a priority right now.\n quote {\n pub fn $fn_name(self, $fn_parameters_list) -> $fn_return_type {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n unsafe {\n aztec::context::calls::PublicStaticCall::new(\n self.address,\n selector,\n $fn_name_str,\n $serialized_args_array_name,\n ).view(self.context)\n }\n }\n }\n}\n\n/// Creates a static stub for enqueuing a public view function from private context (for EnqueueSelfStatic<&mut\n/// PrivateContext>)\npub comptime fn create_public_self_enqueue_static_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, _serialized_args_array_len, _fn_name_str, _fn_name_len, fn_selector) =\n create_stub_base(f);\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n let calldata = [aztec::protocol::traits::ToField::to_field(selector)].concat($serialized_args_array_name);\n let calldata_hash = aztec::hash::hash_calldata_array(calldata);\n aztec::oracle::execution_cache::store(calldata, calldata_hash);\n self.context.call_public_function_with_calldata_hash(\n self.address,\n calldata_hash,\n /*is_static_call=*/ true,\n /*hide_msg_sender=*/ false,\n );\n }\n }\n}\n\n/// Creates a stub for enqueuing a public function from private context (for EnqueueSelf<&mut PrivateContext>)\npub comptime fn create_public_self_enqueue_stub(f: FunctionDefinition) -> Quoted {\n let (fn_name, fn_parameters_list, serialized_args_array_construction, serialized_args_array_name, _serialized_args_array_len, _fn_name_str, _fn_name_len, fn_selector) =\n create_stub_base(f);\n\n quote {\n pub fn $fn_name(self, $fn_parameters_list) {\n $serialized_args_array_construction\n let selector = $FROM_FIELD($fn_selector);\n let calldata = [aztec::protocol::traits::ToField::to_field(selector)].concat($serialized_args_array_name);\n let calldata_hash = aztec::hash::hash_calldata_array(calldata);\n aztec::oracle::execution_cache::store(calldata, calldata_hash);\n self.context.call_public_function_with_calldata_hash(\n self.address,\n calldata_hash,\n /*is_static_call=*/ false,\n /*hide_msg_sender=*/ false,\n );\n }\n }\n}\n" }, "122": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/dispatch.nr", "source": "use crate::macros::internals_functions_generation::external_functions_registry::get_public_functions;\nuse crate::protocol::meta::utils::get_params_len_quote;\nuse crate::utils::cmap::CHashMap;\nuse super::functions::initialization_utils::EMIT_PUBLIC_INIT_NULLIFIER_FN_NAME;\nuse super::utils::compute_fn_selector;\nuse std::panic;\n\n/// Generates a `public_dispatch` function for an Aztec contract module `m`.\n///\n/// The generated function dispatches public calls based on selector to the appropriate contract function. If\n/// `generate_emit_public_init_nullifier` is true, it also handles dispatch to the macro-generated\n/// `__emit_public_init_nullifier` function.\npub comptime fn generate_public_dispatch(m: Module, generate_emit_public_init_nullifier: bool) -> Quoted {\n let functions = get_public_functions(m);\n\n let unit = get_type::<()>();\n\n let seen_selectors = &mut CHashMap::::new();\n\n let mut ifs = functions.map(|function: FunctionDefinition| {\n let parameters = function.parameters();\n let return_type = function.return_type();\n\n let fn_name = function.name();\n let selector: Field = compute_fn_selector(fn_name, parameters);\n\n // Since function selectors are computed as the first 4 bytes of the hash of the function signature, it's\n // possible to have collisions. With the following check, we ensure it doesn't happen within the same contract.\n let existing_fn = seen_selectors.get(selector);\n if existing_fn.is_some() {\n let existing_fn = existing_fn.unwrap();\n panic(\n f\"Public function selector collision detected between functions '{fn_name}' and '{existing_fn}'\",\n );\n }\n seen_selectors.insert(selector, fn_name);\n\n let params_len_quote = get_params_len_quote(parameters);\n\n let initial_read = if parameters.len() == 0 {\n quote {}\n } else {\n // The initial calldata_copy offset is 1 to skip the Field selector The expected calldata is the\n // serialization of\n // - FunctionSelector: the selector of the function intended to dispatch\n // - Parameters: the parameters of the function intended to dispatch That is, exactly what is expected for\n // a call to the target function, but with a selector added at the beginning.\n quote {\n let input_calldata: [Field; $params_len_quote] = aztec::oracle::avm::calldata_copy(1, $params_len_quote);\n let mut reader = aztec::protocol::utils::reader::Reader::new(input_calldata);\n }\n };\n\n let parameter_index: &mut u32 = &mut 0;\n let reads = parameters.map(|param: (Quoted, Type)| {\n let parameter_index_value = *parameter_index;\n let param_name = f\"arg{parameter_index_value}\".quoted_contents();\n let param_type = param.1;\n let read = quote {\n let $param_name: $param_type = aztec::protocol::traits::Deserialize::stream_deserialize(&mut reader);\n };\n *parameter_index += 1;\n quote { $read }\n });\n let read = reads.join(quote { });\n\n let mut args = @[];\n for parameter_index in 0..parameters.len() {\n let param_name = f\"arg{parameter_index}\".quoted_contents();\n args = args.push_back(quote { $param_name });\n }\n\n // We call a function whose name is prefixed with `__aztec_nr_internals__`. This is necessary because the\n // original function is intentionally made uncallable, preventing direct invocation within the contract.\n // Instead, a new function with the same name, but prefixed by `__aztec_nr_internals__`, has been generated to\n // be called here. For more details see the `process_functions` function.\n let name = f\"__aztec_nr_internals__{fn_name}\".quoted_contents();\n let args = args.join(quote { , });\n let call = quote { $name($args) };\n\n let return_code = if return_type == unit {\n quote {\n $call;\n // Force early return.\n aztec::oracle::avm::avm_return([]);\n }\n } else {\n quote {\n let return_value = aztec::protocol::traits::Serialize::serialize($call);\n aztec::oracle::avm::avm_return(return_value.as_vector());\n }\n };\n\n let if_ = quote {\n if selector == $selector {\n $initial_read\n $read\n $return_code\n }\n };\n if_\n });\n\n // If we injected the auto-generated public function to emit the public initialization nullifier, then\n // we'll also need to handle its dispatch.\n if generate_emit_public_init_nullifier {\n let name = EMIT_PUBLIC_INIT_NULLIFIER_FN_NAME;\n let init_nullifier_selector: Field = compute_fn_selector(name, @[]);\n\n ifs = ifs.push_back(\n quote {\n if selector == $init_nullifier_selector {\n $name();\n aztec::oracle::avm::avm_return([]);\n }\n },\n );\n }\n\n if ifs.len() == 0 {\n // No dispatch function if there are no public functions\n quote {}\n } else {\n let ifs = ifs.push_back(quote { panic(f\"Unknown selector {selector}\") });\n let dispatch = ifs.join(quote { });\n\n let body = quote {\n // We mark this as public because our whole system depends on public functions having this attribute.\n #[aztec::macros::internals_functions_generation::abi_attributes::abi_public]\n pub unconstrained fn public_dispatch(selector: Field) {\n $dispatch\n }\n };\n\n body\n }\n}\n\ncomptime fn get_type() -> Type {\n let t: T = std::mem::zeroed();\n std::meta::type_of(t)\n}\n" }, "126": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/functions/initialization_utils.nr", "source": "use crate::protocol::{\n abis::function_selector::FunctionSelector,\n address::AztecAddress,\n constants::{\n DOM_SEP__INITIALIZER, DOM_SEP__PRIVATE_INITIALIZATION_NULLIFIER, DOM_SEP__PUBLIC_INITIALIZATION_NULLIFIER,\n },\n hash::poseidon2_hash_with_separator,\n traits::ToField,\n};\nuse std::meta::{ctstring::AsCtString, unquote};\n\nuse crate::{\n context::{PrivateContext, PublicContext, UtilityContext},\n macros::{\n internals_functions_generation::external_functions_registry::get_public_functions, utils::fn_has_noinitcheck,\n },\n nullifier::utils::compute_nullifier_existence_request,\n oracle::{\n get_contract_instance::{\n get_contract_instance, get_contract_instance_deployer_avm, get_contract_instance_initialization_hash_avm,\n },\n nullifiers::check_nullifier_exists,\n },\n};\n\n/// The name of the auto-generated function that emits the public initialization nullifier.\n///\n/// This function is injected into the public dispatch table for contracts with initializers.\npub(crate) comptime global EMIT_PUBLIC_INIT_NULLIFIER_FN_NAME: Quoted = quote { __emit_public_init_nullifier };\n\n/// Returns `true` if the module has any public functions that require initialization checks (i.e. that don't have\n/// `#[noinitcheck]`). If all public functions skip initialization checks, there's no point emitting the public\n/// initialization nullifier since nothing will check it.\npub(crate) comptime fn has_public_init_checked_functions(m: Module) -> bool {\n get_public_functions(m).any(|f: FunctionDefinition| !fn_has_noinitcheck(f))\n}\n\n/// Selector for `EMIT_PUBLIC_INIT_NULLIFIER_FN_NAME`, derived at comptime so it stays in sync.\nglobal EMIT_PUBLIC_INIT_NULLIFIER_SELECTOR: FunctionSelector = comptime {\n let name = EMIT_PUBLIC_INIT_NULLIFIER_FN_NAME;\n let sig = f\"{name}()\".as_ctstring();\n unquote!(quote { FunctionSelector::from_signature($sig) })\n};\n\n/// Emits (only) the public initialization nullifier.\n///\n/// This function is called by the aztec-nr auto-generated external public contract function (enqueued by private\n/// [`initializer`](crate::macros::functions::initializer) functions), and also by\n/// [`mark_as_initialized_from_public_initializer`] for public initializers.\n///\n/// # Warning\n///\n/// This should not be called manually. Incorrect use can leave the contract in a broken initialization state (e.g.\n/// emitting the public nullifier without the private one). The macro-generated code handles this automatically.\npub fn mark_as_initialized_public(context: PublicContext) {\n let init_nullifier = compute_public_initialization_nullifier(context.this_address());\n context.push_nullifier(init_nullifier);\n}\n\nfn mark_as_initialized_private(context: &mut PrivateContext) {\n let address = (*context).this_address();\n let instance = get_contract_instance(address);\n let init_nullifier = compute_private_initialization_nullifier(address, instance.initialization_hash);\n context.push_nullifier(init_nullifier);\n}\n\n/// Emits the private initialization nullifier and, if relevant, enqueues the emission of the public one.\n///\n/// If the contract has public functions that perform initialization checks (i.e. that don't have `#[noinitcheck]`),\n/// this also enqueues a call to the auto-generated `__emit_public_init_nullifier` function so the public\n/// initialization nullifier is emitted in public. Called by private\n/// [`initializer`](crate::macros::functions::initializer) macros.\npub fn mark_as_initialized_from_private_initializer(context: &mut PrivateContext, emit_public_init_nullifier: bool) {\n mark_as_initialized_private(context);\n if emit_public_init_nullifier {\n context.call_public_function((*context).this_address(), EMIT_PUBLIC_INIT_NULLIFIER_SELECTOR, [], false);\n }\n}\n\n/// Emits both initialization nullifiers (private and public).\n///\n/// Called by public [`initializer`](crate::macros::functions::initializer) macros, since public initializers must set\n/// both so that both private and public functions see the contract as initialized.\npub fn mark_as_initialized_from_public_initializer(context: PublicContext) {\n let address = context.this_address();\n // `get_contract_instance_initialization_hash_avm` returns None when there is no deployed contract instance at the\n // given address. This cannot happen here because we're querying `this_address()`, i.e. the contract that is\n // currently executing, which by definition must have been deployed.\n let init_hash = get_contract_instance_initialization_hash_avm(address).unwrap();\n let private_nullifier = compute_private_initialization_nullifier(address, init_hash);\n context.push_nullifier(private_nullifier);\n mark_as_initialized_public(context);\n}\n\n/// Asserts that the contract has been initialized, from public's perspective.\n///\n/// Checks that the public initialization nullifier exists.\npub fn assert_is_initialized_public(context: PublicContext) {\n let init_nullifier = compute_public_initialization_nullifier(context.this_address());\n // Safety: the public initialization nullifier is only ever emitted by public functions, and so the timing\n // concerns from nullifier_exists_unsafe do not apply. Additionally, it is emitted after all initializer\n // functions have run, so initialization is guaranteed to be complete by the time it exists.\n assert(context.nullifier_exists_unsafe(init_nullifier, context.this_address()), \"Not initialized\");\n}\n\n/// Asserts that the contract has been initialized, from private's perspective.\n///\n/// Checks that the private initialization nullifier exists.\npub fn assert_is_initialized_private(context: &mut PrivateContext) {\n let address = context.this_address();\n let instance = get_contract_instance(address);\n let init_nullifier = compute_private_initialization_nullifier(address, instance.initialization_hash);\n let nullifier_existence_request = compute_nullifier_existence_request(init_nullifier, address);\n context.assert_nullifier_exists(nullifier_existence_request);\n}\n\n/// Asserts that the contract has been initialized, from a utility function's perspective.\n///\n/// Only checks the private initialization nullifier in the settled nullifier tree. Since both nullifiers are emitted\n/// in the same transaction, the private nullifier's presence in settled state guarantees the public one is also\n/// settled.\npub unconstrained fn assert_is_initialized_utility(context: UtilityContext) {\n let address = context.this_address();\n let instance = get_contract_instance(address);\n let initialization_nullifier = compute_private_initialization_nullifier(address, instance.initialization_hash);\n assert(check_nullifier_exists(initialization_nullifier), \"Not initialized\");\n}\n\n/// Computes the private initialization nullifier for a contract.\n///\n/// Including `init_hash` ensures that an observer who knows only the contract address cannot reconstruct this value\n/// and scan the nullifier tree to determine initialization status. `init_hash` is only known to parties that hold\n/// the contract instance.\npub fn compute_private_initialization_nullifier(address: AztecAddress, init_hash: Field) -> Field {\n poseidon2_hash_with_separator(\n [address.to_field(), init_hash],\n DOM_SEP__PRIVATE_INITIALIZATION_NULLIFIER,\n )\n}\n\nfn compute_public_initialization_nullifier(address: AztecAddress) -> Field {\n poseidon2_hash_with_separator(\n [address.to_field()],\n DOM_SEP__PUBLIC_INITIALIZATION_NULLIFIER,\n )\n}\n\n// Used by `create_assert_correct_initializer_args` (you won't find it through searching)\npub fn assert_initialization_matches_address_preimage_public(context: PublicContext) {\n let address = context.this_address();\n let deployer = get_contract_instance_deployer_avm(address).unwrap();\n let initialization_hash = get_contract_instance_initialization_hash_avm(address).unwrap();\n let expected_init = compute_initialization_hash(context.selector(), context.get_args_hash());\n assert(initialization_hash == expected_init, \"Initialization hash does not match\");\n assert(\n (deployer.is_zero()) | (deployer == context.maybe_msg_sender().unwrap()),\n \"Initializer address is not the contract deployer\",\n );\n}\n\n// Used by `create_assert_correct_initializer_args` (you won't find it through searching)\npub fn assert_initialization_matches_address_preimage_private(context: PrivateContext) {\n let address = context.this_address();\n let instance = get_contract_instance(address);\n let expected_init = compute_initialization_hash(context.selector(), context.get_args_hash());\n assert(instance.initialization_hash == expected_init, \"Initialization hash does not match\");\n assert(\n (instance.deployer.is_zero()) | (instance.deployer == context.maybe_msg_sender().unwrap()),\n \"Initializer address is not the contract deployer\",\n );\n}\n\n/// This function is not only used in macros but it's also used by external people to check that an instance has been\n/// initialized with the correct constructor arguments. Don't hide this unless you implement factory functionality.\npub fn compute_initialization_hash(init_selector: FunctionSelector, init_args_hash: Field) -> Field {\n poseidon2_hash_with_separator(\n [init_selector.to_field(), init_args_hash],\n DOM_SEP__INITIALIZER,\n )\n}\n" }, "132": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/internals_functions_generation/external/private.nr", "source": "use crate::macros::{\n functions::initialization_utils::has_public_init_checked_functions,\n internals_functions_generation::external::helpers::{create_authorize_once_check, get_abi_relevant_attributes},\n utils::{\n fn_has_allow_phase_change, fn_has_authorize_once, fn_has_noinitcheck, is_fn_initializer, is_fn_only_self,\n is_fn_view, module_has_initializer, module_has_storage,\n },\n};\nuse crate::protocol::meta::utils::derive_serialization_quotes;\nuse std::meta::type_of;\n\npub(crate) comptime fn generate_private_external(f: FunctionDefinition) -> Quoted {\n let module_has_initializer = module_has_initializer(f.module());\n let module_has_storage = module_has_storage(f.module());\n\n // Private functions undergo a lot of transformations from their Aztec.nr form into a circuit that can be fed to\n // the Private Kernel Circuit. First we change the function signature so that it also receives\n // `PrivateContextInputs`, which contain information about the execution context (e.g. the caller).\n let original_params = f.parameters();\n\n let original_params_quotes =\n original_params.map(|(param_name, param_type)| quote { $param_name: $param_type }).join(quote {, });\n\n let params = quote { inputs: aztec::context::inputs::PrivateContextInputs, $original_params_quotes };\n\n let mut body = f.body().as_block().unwrap();\n\n // The original params are hashed and passed to the `context` object, so that the kernel can verify we've received\n // the correct values.\n let (args_serialization, _, serialized_args_name) = derive_serialization_quotes(original_params, false);\n\n let storage_init = if module_has_storage {\n // Contract has Storage defined so we initialize it.\n quote {\n let storage = Storage::init(&mut context);\n }\n } else {\n // Contract does not have Storage defined, so we set storage to the unit type `()`. ContractSelfPrivate\n // requires a storage struct in its constructor. Using an Option type would lead to worse developer experience\n // and higher constraint counts so we use the unit type `()` instead.\n quote {\n let storage = ();\n }\n };\n\n let contract_self_creation = quote {\n #[allow(unused_variables)]\n let mut self = {\n $args_serialization\n let args_hash = aztec::hash::hash_args($serialized_args_name);\n let mut context = aztec::context::PrivateContext::new(inputs, args_hash);\n $storage_init\n let self_address = context.this_address();\n let call_self: CallSelf<&mut aztec::context::PrivateContext> = CallSelf { address: self_address, context: &mut context };\n let enqueue_self: EnqueueSelf<&mut aztec::context::PrivateContext> = EnqueueSelf { address: self_address, context: &mut context };\n let call_self_static: CallSelfStatic<&mut aztec::context::PrivateContext> = CallSelfStatic { address: self_address, context: &mut context };\n let enqueue_self_static: EnqueueSelfStatic<&mut aztec::context::PrivateContext> = EnqueueSelfStatic { address: self_address, context: &mut context };\n let internal: CallInternal<&mut aztec::context::PrivateContext> = CallInternal { context: &mut context };\n aztec::contract_self::ContractSelfPrivate::new(&mut context, storage, call_self, enqueue_self, call_self_static, enqueue_self_static, internal)\n };\n };\n\n let original_function_name = f.name();\n\n // Modifications introduced by the different marker attributes.\n let internal_check = if is_fn_only_self(f) {\n let assertion_message = f\"Function {original_function_name} can only be called by the same contract\";\n quote { assert(self.msg_sender() == self.address, $assertion_message); }\n } else {\n quote {}\n };\n\n let view_check = if is_fn_view(f) {\n let assertion_message = f\"Function {original_function_name} can only be called statically\".as_quoted_str();\n quote { assert(self.context.inputs.call_context.is_static_call, $assertion_message); }\n } else {\n quote {}\n };\n\n let (assert_initializer, mark_as_initialized) = if is_fn_initializer(f) {\n let has_public_fns_with_init_check = has_public_init_checked_functions(f.module());\n (\n quote { aztec::macros::functions::initialization_utils::assert_initialization_matches_address_preimage_private(*self.context); },\n quote { aztec::macros::functions::initialization_utils::mark_as_initialized_from_private_initializer(self.context, $has_public_fns_with_init_check); },\n )\n } else {\n (quote {}, quote {})\n };\n\n // Initialization checks are not included in contracts that don't have initializers.\n let init_check = if module_has_initializer & !is_fn_initializer(f) & !fn_has_noinitcheck(f) {\n quote { aztec::macros::functions::initialization_utils::assert_is_initialized_private(self.context); }\n } else {\n quote {}\n };\n\n // Phase checks are skipped in functions that request to manually handle phases\n let initial_phase_store = if fn_has_allow_phase_change(f) {\n quote {}\n } else {\n quote { let within_revertible_phase: bool = self.context.in_revertible_phase(); }\n };\n\n let no_phase_change_check = if fn_has_allow_phase_change(f) {\n quote {}\n } else {\n quote { \n assert_eq(\n within_revertible_phase,\n self.context.in_revertible_phase(),\n f\"Phase change detected on function with phase check. If this is expected, use #[allow_phase_change]\",\n ); \n }\n };\n\n // Inject the authwit check if the function is marked with #[authorize_once].\n let authorize_once_check = if fn_has_authorize_once(f) {\n create_authorize_once_check(f, true)\n } else {\n quote {}\n };\n\n // Finally, we need to change the return type to be `PrivateCircuitPublicInputs`, which is what the Private Kernel\n // circuit expects.\n let return_value_var_name = quote { macro__returned__values };\n\n let return_value_type = f.return_type();\n let return_value = if body.len() == 0 {\n quote {}\n } else if return_value_type != type_of(()) {\n // The original return value is serialized and hashed before being passed to the context.\n let (body_without_return, last_body_expr) = body.pop_back();\n let return_value = last_body_expr.quoted();\n let return_value_assignment = quote { let $return_value_var_name: $return_value_type = $return_value; };\n\n let (return_serialization, _, serialized_return_name) =\n derive_serialization_quotes([(return_value_var_name, return_value_type)], false);\n\n body = body_without_return;\n\n quote {\n $return_value_assignment\n $return_serialization\n self.context.set_return_hash($serialized_return_name);\n }\n } else {\n let (body_without_return, last_body_expr) = body.pop_back();\n if !last_body_expr.has_semicolon()\n & last_body_expr.as_for().is_none()\n & last_body_expr.as_assert().is_none()\n & last_body_expr.as_for_range().is_none()\n & last_body_expr.as_assert_eq().is_none()\n & last_body_expr.as_let().is_none() {\n let unused_return_value_name = f\"_{return_value_var_name}\".quoted_contents();\n body = body_without_return.push_back(quote { let $unused_return_value_name = $last_body_expr; }\n .as_expr()\n .unwrap());\n }\n quote {}\n };\n\n let context_finish = quote { self.context.finish() };\n\n // Preserve all attributes that are relevant to the function's ABI.\n let abi_relevant_attributes = get_abi_relevant_attributes(f);\n\n let fn_name = f\"__aztec_nr_internals__{original_function_name}\".quoted_contents();\n\n let to_prepend = quote {\n aztec::oracle::version::assert_compatible_oracle_version();\n $contract_self_creation\n $initial_phase_store\n $assert_initializer\n $init_check\n $internal_check\n $view_check\n $authorize_once_check\n };\n\n let body_quote = body.map(|expr| expr.quoted()).join(quote { });\n\n // `mark_as_initialized` is placed after the user's function body. If it ran at the beginning, the contract\n // would appear initialized while the initializer is still running, allowing contracts called by the initializer\n // to re-enter into a half-initialized contract.\n let to_append = quote {\n $return_value\n $mark_as_initialized\n $no_phase_change_check\n $context_finish\n };\n\n quote {\n #[aztec::macros::internals_functions_generation::abi_attributes::abi_private]\n $abi_relevant_attributes\n fn $fn_name($params) -> return_data aztec::protocol::abis::private_circuit_public_inputs::PrivateCircuitPublicInputs {\n $to_prepend\n $body_quote\n $to_append\n }\n }\n}\n" }, "133": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/macros/internals_functions_generation/external/public.nr", "source": "use crate::macros::{\n internals_functions_generation::external::helpers::{create_authorize_once_check, get_abi_relevant_attributes},\n utils::{\n fn_has_authorize_once, fn_has_noinitcheck, is_fn_initializer, is_fn_only_self, is_fn_view,\n module_has_initializer, module_has_storage,\n },\n};\n\npub(crate) comptime fn generate_public_external(f: FunctionDefinition) -> Quoted {\n let module_has_initializer = module_has_initializer(f.module());\n let module_has_storage = module_has_storage(f.module());\n\n // Public functions undergo a lot of transformations from their Aztec.nr form.\n let original_params = f.parameters();\n\n let args_len_quote = if original_params.len() == 0 {\n // If the function has no parameters, we set the args_len to 0.\n quote { 0 }\n } else {\n // The following will give us ::N + ::N + ...\n original_params\n .map(|(_, param_type): (Quoted, Type)| {\n quote {\n <$param_type as $crate::protocol::traits::Serialize>::N\n }\n })\n .join(quote {+})\n };\n\n let storage_init = if module_has_storage {\n quote {\n let storage = Storage::init(context);\n }\n } else {\n // Contract does not have Storage defined, so we set storage to the unit type `()`. ContractSelfPublic requires\n // a storage struct in its constructor. Using an Option type would lead to worse developer experience and\n // higher constraint counts so we use the unit type `()` instead.\n quote {\n let storage = ();\n }\n };\n\n // Unlike in the private case, in public the `context` does not need to receive the hash of the original params.\n let contract_self_creation = quote {\n #[allow(unused_variables)]\n let mut self = {\n let context = aztec::context::PublicContext::new(|| {\n // We start from 1 because we skip the selector for the dispatch function.\n let serialized_args : [Field; $args_len_quote] = aztec::oracle::avm::calldata_copy(1, $args_len_quote);\n aztec::hash::hash_args(serialized_args)\n });\n $storage_init\n let self_address = context.this_address();\n let call_self: CallSelf = CallSelf { address: self_address, context };\n let call_self_static: CallSelfStatic = CallSelfStatic { address: self_address, context };\n let internal: CallInternal = CallInternal { context };\n aztec::contract_self::ContractSelfPublic::new(context, storage, call_self, call_self_static, internal)\n };\n };\n\n let original_function_name = f.name();\n\n // Modifications introduced by the different marker attributes.\n let internal_check = if is_fn_only_self(f) {\n let assertion_message = f\"Function {original_function_name} can only be called by the same contract\";\n quote { assert(self.msg_sender() == self.address, $assertion_message); }\n } else {\n quote {}\n };\n\n let view_check = if is_fn_view(f) {\n let assertion_message = f\"Function {original_function_name} can only be called statically\".as_quoted_str();\n quote { assert(self.context.is_static_call(), $assertion_message); }\n } else {\n quote {}\n };\n\n let (assert_initializer, mark_as_initialized) = if is_fn_initializer(f) {\n (\n quote { aztec::macros::functions::initialization_utils::assert_initialization_matches_address_preimage_public(self.context); },\n quote { aztec::macros::functions::initialization_utils::mark_as_initialized_from_public_initializer(self.context); },\n )\n } else {\n (quote {}, quote {})\n };\n\n // Initialization checks are not included in contracts that don't have initializers.\n let init_check = if module_has_initializer & !fn_has_noinitcheck(f) & !is_fn_initializer(f) {\n quote { aztec::macros::functions::initialization_utils::assert_is_initialized_public(self.context); }\n } else {\n quote {}\n };\n\n // Inject the authwit check if the function is marked with #[authorize_once].\n let authorize_once_check = if fn_has_authorize_once(f) {\n create_authorize_once_check(f, false)\n } else {\n quote {}\n };\n\n let to_prepend = quote {\n $contract_self_creation\n $assert_initializer\n $init_check\n $internal_check\n $view_check\n $authorize_once_check\n };\n\n // `mark_as_initialized` is placed after the user's function body. If it ran at the beginning, the contract\n // would appear initialized while the initializer is still running, allowing contracts called by the initializer\n // to re-enter into a half-initialized contract.\n let to_append = quote {\n $mark_as_initialized\n };\n\n let fn_name = f\"__aztec_nr_internals__{original_function_name}\".quoted_contents();\n let body = f.body();\n let return_type = f.return_type();\n\n // New function parameters are the same as the original function's ones.\n let params = original_params.map(|(param_name, param_type)| quote { $param_name: $param_type }).join(quote {, });\n\n // Preserve all attributes that are relevant to the function's ABI.\n let abi_relevant_attributes = get_abi_relevant_attributes(f);\n\n // All public functions are automatically made unconstrained, even if they were not marked as such. This is because\n // instead of compiling into a circuit, they will compile to bytecode that will be later transpiled into AVM\n // bytecode.\n quote {\n #[aztec::macros::internals_functions_generation::abi_attributes::abi_public]\n $abi_relevant_attributes\n unconstrained fn $fn_name($params) -> pub $return_type {\n $to_prepend\n $body\n $to_append\n }\n }\n}\n" }, "143": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/mod.nr", "source": "use crate::logging::{aztecnr_debug_log, aztecnr_debug_log_format, aztecnr_warn_log_format};\nuse crate::protocol::address::AztecAddress;\n\npub(crate) mod nonce_discovery;\npub(crate) mod partial_notes;\npub(crate) mod private_events;\npub mod private_notes;\npub mod process_message;\n\nuse crate::{\n capsules::CapsuleArray,\n messages::{\n discovery::process_message::process_message_ciphertext,\n encoding::MAX_MESSAGE_CONTENT_LEN,\n logs::note::MAX_NOTE_PACKED_LEN,\n processing::{\n get_private_logs, MessageContext, offchain::OffchainInboxSync, OffchainMessageWithContext,\n pending_tagged_log::PendingTaggedLog, validate_and_store_enqueued_notes_and_events,\n },\n },\n utils::array,\n};\n\npub struct NoteHashAndNullifier {\n /// The result of [`crate::note::note_interface::NoteHash::compute_note_hash`].\n pub note_hash: Field,\n /// The result of [`crate::note::note_interface::NoteHash::compute_nullifier_unconstrained`].\n ///\n /// This value is unconstrained, as all of message discovery is unconstrained. It is `None` if the nullifier\n /// cannot be computed (e.g. because the nullifier hiding key is not available).\n pub inner_nullifier: Option,\n}\n\n/// A contract's way of computing note hashes.\n///\n/// Each contract in the network is free to compute their note's hash as they see fit - the hash function itself is not\n/// enshrined or standardized. Some aztec-nr functions however do need to know the details of this computation (e.g.\n/// when finding new notes), which is what this type represents.\n///\n/// This function takes a note's packed content, storage slot, note type ID, address of the emitting contract and\n/// randomness, and attempts to compute its inner note hash (not siloed by address nor uniqued by nonce).\n///\n/// ## Transient Notes\n///\n/// This function is meant to always be used on **settled** notes, i.e. those that have been inserted into the trees\n/// and for which the nonce is known. It is never invoked in the context of a transient note, as those are not involved\n/// in message processing.\n///\n/// ## Automatic Implementation\n///\n/// The [`[#aztec]`](crate::macros::aztec::aztec) macro automatically creates a correct implementation of this function\n/// for each contract by inspecting all note types in use and the storage layout. This injected function is a\n/// `#[contract_library_method]` called `_compute_note_hash`, and it looks something like this:\n///\n/// ```noir\n/// |packed_note, owner, storage_slot, note_type_id, _contract_address, randomness| {\n/// if note_type_id == MyNoteType::get_id() {\n/// if packed_note.len() != MY_NOTE_TYPE_SERIALIZATION_LENGTH {\n/// Option::none()\n/// } else {\n/// let note = MyNoteType::unpack(aztec::utils::array::subarray(packed_note.storage(), 0));\n/// Option::some(note.compute_note_hash(owner, storage_slot, randomness))\n/// }\n/// } else if note_type_id == MyOtherNoteType::get_id() {\n/// ... // Similar to above but calling MyOtherNoteType::unpack\n/// } else {\n/// Option::none() // Unknown note type ID\n/// };\n/// }\n/// ```\npub type ComputeNoteHash = unconstrained fn(/* packed_note */BoundedVec, /*\n owner */ AztecAddress, /* storage_slot */ Field, /* note_type_id */ Field, /* contract_address */ AztecAddress, /*\nrandomness */ Field) -> Option;\n\n/// A contract's way of computing note nullifiers.\n///\n/// Like [`ComputeNoteHash`], each contract is free to derive nullifiers as they see fit. This function takes the\n/// unique note hash (used as the note hash for nullification for settled notes), plus the note's packed content and\n/// metadata, and attempts to compute the inner nullifier (not siloed by address).\n///\n/// ## Automatic Implementation\n///\n/// The [`[#aztec]`](crate::macros::aztec::aztec) macro automatically creates a correct implementation of this function\n/// for each contract called `_compute_note_nullifier`. It dispatches on `note_type_id` similarly to\n/// [`ComputeNoteHash`], then calls the note's\n/// [`compute_nullifier_unconstrained`](crate::note::note_interface::NoteHash::compute_nullifier_unconstrained) method.\npub type ComputeNoteNullifier = unconstrained fn(/* unique_note_hash */Field, /* packed_note */ BoundedVec,\n/* owner */ AztecAddress, /* storage_slot */ Field, /* note_type_id */ Field, /* contract_address */ AztecAddress,\n/* randomness */ Field) -> Option;\n\n/// Deprecated: use [`ComputeNoteHash`] and [`ComputeNoteNullifier`] instead.\npub type ComputeNoteHashAndNullifier = unconstrained fn[Env](/* packed_note */BoundedVec,\n/* owner */ AztecAddress, /* storage_slot */ Field, /* note_type_id */ Field, /* contract_address */ AztecAddress,\n/*randomness */ Field, /* note nonce */ Field) -> Option;\n\n/// A handler for custom messages.\n///\n/// Contracts that emit custom messages (i.e. any with a message type that is not in [`crate::messages::msg_type`])\n/// need to use [`crate::macros::AztecConfig::custom_message_handler`] with a function of this type in order to\n/// process them. They will otherwise be **silently ignored**.\npub type CustomMessageHandler = unconstrained fn[Env](\n/* contract_address */AztecAddress,\n/* msg_type_id */ u64,\n/* msg_metadata */ u64,\n/* msg_content */ BoundedVec,\n/* message_context */ MessageContext,\n/* scope */ AztecAddress);\n\n/// Synchronizes the contract's private state with the network.\n///\n/// As blocks are mined, it is possible for a contract's private state to change (e.g. with new notes being created),\n/// but because these changes are private they will be invisible to most actors. This is the function that processes\n/// new transactions in order to discover new notes, events, and other kinds of private state changes.\n///\n/// The private state will be synchronized up to the block that will be used for private transactions (i.e. the anchor\n/// block. This will typically be close to the tip of the chain.\npub unconstrained fn do_sync_state(\n contract_address: AztecAddress,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n process_custom_message: Option>,\n offchain_inbox_sync: Option>,\n scope: AztecAddress,\n) {\n aztecnr_debug_log!(\"Performing state synchronization\");\n\n // First we process all private logs, which can contain different kinds of messages e.g. private notes, partial\n // notes, private events, etc.\n let logs = get_private_logs(contract_address, scope);\n logs.for_each(|i, pending_tagged_log: PendingTaggedLog| {\n if pending_tagged_log.log.len() == 0 {\n aztecnr_warn_log_format!(\"Skipping empty log from tx {0}\")([pending_tagged_log.context.tx_hash]);\n } else {\n aztecnr_debug_log_format!(\"Processing log with tag {0}\")([pending_tagged_log.log.get(0)]);\n\n // We remove the tag from the pending tagged log and process the message ciphertext contained in it.\n let message_ciphertext = array::subbvec(pending_tagged_log.log, 1);\n\n process_message_ciphertext(\n contract_address,\n compute_note_hash,\n compute_note_nullifier,\n process_custom_message,\n message_ciphertext,\n pending_tagged_log.context,\n scope,\n );\n }\n\n // We need to delete each log from the array so that we won't process them again. `CapsuleArray::for_each`\n // allows deletion of the current element during iteration, so this is safe.\n // Note that this (and all other database changes) will only be committed if contract execution succeeds,\n // including any enqueued validation requests.\n logs.remove(i);\n });\n\n if offchain_inbox_sync.is_some() {\n let msgs: CapsuleArray = offchain_inbox_sync.unwrap()(contract_address, scope);\n msgs.for_each(|i, msg| {\n process_message_ciphertext(\n contract_address,\n compute_note_hash,\n compute_note_nullifier,\n process_custom_message,\n msg.message_ciphertext,\n msg.message_context,\n scope,\n );\n // The inbox sync returns _a copy_ of messages to process, so we clear them as we do so. This is a\n // volatile array with the to-process message, not the actual persistent storage of them.\n msgs.remove(i);\n });\n }\n\n // Then we process all pending partial notes, regardless of whether they were found in the current or previous\n // executions.\n partial_notes::fetch_and_process_partial_note_completion_logs(\n contract_address,\n compute_note_hash,\n compute_note_nullifier,\n scope,\n );\n\n // Finally we validate all notes and events that were found as part of the previous processes, resulting in them\n // being added to PXE's database and retrievable via oracles (get_notes) and our TS API (PXE::getPrivateEvents).\n validate_and_store_enqueued_notes_and_events(contract_address, scope);\n}\n\nmod test {\n use crate::{\n capsules::CapsuleArray,\n messages::{\n discovery::{CustomMessageHandler, do_sync_state},\n logs::note::MAX_NOTE_PACKED_LEN,\n processing::{\n offchain::OffchainInboxSync, pending_tagged_log::PendingTaggedLog, PENDING_TAGGED_LOG_ARRAY_BASE_SLOT,\n },\n },\n test::helpers::test_environment::TestEnvironment,\n };\n use crate::protocol::address::AztecAddress;\n\n global SCOPE: AztecAddress = AztecAddress { inner: 0xcafe };\n\n #[test]\n unconstrained fn do_sync_state_does_not_panic_on_empty_logs() {\n let env = TestEnvironment::new();\n\n let contract_address = AztecAddress { inner: 0xdeadbeef };\n\n env.utility_context_at(contract_address, |_| {\n let base_slot = PENDING_TAGGED_LOG_ARRAY_BASE_SLOT;\n\n let logs: CapsuleArray = CapsuleArray::at(contract_address, base_slot, SCOPE);\n logs.push(PendingTaggedLog { log: BoundedVec::new(), context: std::mem::zeroed() });\n assert_eq(logs.len(), 1);\n\n let no_inbox_sync: Option> = Option::none();\n let no_handler: Option> = Option::none();\n do_sync_state(\n contract_address,\n dummy_compute_note_hash,\n dummy_compute_note_nullifier,\n no_handler,\n no_inbox_sync,\n SCOPE,\n );\n\n assert_eq(logs.len(), 0);\n });\n }\n\n unconstrained fn dummy_compute_note_hash(\n _packed_note: BoundedVec,\n _owner: AztecAddress,\n _storage_slot: Field,\n _note_type_id: Field,\n _contract_address: AztecAddress,\n _randomness: Field,\n ) -> Option {\n Option::none()\n }\n\n unconstrained fn dummy_compute_note_nullifier(\n _unique_note_hash: Field,\n _packed_note: BoundedVec,\n _owner: AztecAddress,\n _storage_slot: Field,\n _note_type_id: Field,\n _contract_address: AztecAddress,\n _randomness: Field,\n ) -> Option {\n Option::none()\n }\n}\n" }, "144": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/nonce_discovery.nr", "source": "use crate::messages::{discovery::{ComputeNoteHash, ComputeNoteNullifier}, logs::note::MAX_NOTE_PACKED_LEN};\n\nuse crate::logging::{aztecnr_debug_log_format, aztecnr_warn_log_format};\nuse crate::protocol::{\n address::AztecAddress,\n constants::MAX_NOTE_HASHES_PER_TX,\n hash::{compute_note_hash_nonce, compute_siloed_note_hash, compute_unique_note_hash},\n traits::ToField,\n};\n\n/// A struct with the discovered information of a complete note, required for delivery to PXE. Note that this is *not*\n/// the complete note information, since it does not include content, storage slot, etc.\npub(crate) struct DiscoveredNoteInfo {\n pub(crate) note_nonce: Field,\n pub(crate) note_hash: Field,\n pub(crate) inner_nullifier: Field,\n}\n\n/// Searches for note nonces that will result in a note that was emitted in a transaction. While rare, it is possible\n/// for multiple notes to have the exact same packed content and storage slot but different nonces, resulting in\n/// different unique note hashes. Because of this this function returns a *vector* of discovered notes, though in most\n/// cases it will contain a single element.\n///\n/// Due to how nonces are computed, this function requires knowledge of the transaction in which the note was created,\n/// more specifically the list of all unique note hashes in it plus the value of its first nullifier.\npub(crate) unconstrained fn attempt_note_nonce_discovery(\n unique_note_hashes_in_tx: BoundedVec,\n first_nullifier_in_tx: Field,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n contract_address: AztecAddress,\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n note_type_id: Field,\n packed_note: BoundedVec,\n) -> BoundedVec {\n let discovered_notes = &mut BoundedVec::new();\n\n aztecnr_debug_log_format!(\n \"Attempting nonce discovery on {0} potential notes on contract {1} for storage slot {2}\",\n )(\n [unique_note_hashes_in_tx.len() as Field, contract_address.to_field(), storage_slot],\n );\n\n let maybe_note_hash = compute_note_hash(\n packed_note,\n owner,\n storage_slot,\n note_type_id,\n contract_address,\n randomness,\n );\n\n if maybe_note_hash.is_none() {\n aztecnr_warn_log_format!(\n \"Unable to compute note hash for note of id {0} with packed length {1}, skipping nonce discovery\",\n )(\n [note_type_id, packed_note.len() as Field],\n );\n } else {\n let note_hash = maybe_note_hash.unwrap();\n let siloed_note_hash = compute_siloed_note_hash(contract_address, note_hash);\n\n // We need to find nonces (typically just one) that result in the siloed note hash that being uniqued into one\n // of the transaction's effects.\n // The nonce is meant to be derived from the index of the note hash in the transaction effects array. However,\n // due to an issue in the kernels the nonce might actually use any of the possible note hash indices - not\n // necessarily the one that corresponds to the note hash. Hence, we need to try them all.\n for i in 0..MAX_NOTE_HASHES_PER_TX {\n let nonce_for_i = compute_note_hash_nonce(first_nullifier_in_tx, i);\n let unique_note_hash_for_i = compute_unique_note_hash(nonce_for_i, siloed_note_hash);\n\n let matching_notes = bvec_filter(\n unique_note_hashes_in_tx,\n |unique_note_hash_in_tx| unique_note_hash_in_tx == unique_note_hash_for_i,\n );\n if matching_notes.len() > 1 {\n let identical_note_hashes = matching_notes.len();\n // Note that we don't actually check that the note hashes array contains unique values, only that the\n // note we found is unique. We don't expect for this to ever happen (it'd indicate a malicious node or\n // PXE, which are both assumed to be cooperative) so testing for it just in case is unnecessary, but we\n // _do_ need to handle it if we find a duplicate.\n panic(\n f\"Received {identical_note_hashes} identical note hashes for a transaction - these should all be unique\",\n )\n } else if matching_notes.len() == 1 {\n let maybe_inner_nullifier_for_i = compute_note_nullifier(\n unique_note_hash_for_i,\n packed_note,\n owner,\n storage_slot,\n note_type_id,\n contract_address,\n randomness,\n );\n\n if maybe_inner_nullifier_for_i.is_none() {\n // TODO: down the line we want to be able to store notes for which we don't know their nullifier,\n // e.g. notes that belong to someone that is not us (and for which we therefore don't know their\n // associated app-siloed nullifer hiding secret key).\n // https://linear.app/aztec-labs/issue/F-265/store-external-notes\n aztecnr_warn_log_format!(\n \"Unable to compute nullifier of unique note {0} with note type id {1} and owner {2}, skipping PXE insertion\",\n )(\n [unique_note_hash_for_i, note_type_id, owner.to_field()],\n );\n } else {\n // Note that while we did check that the note hash is the preimage of a unique note hash, we\n // perform no validations on the nullifier - we fundamentally cannot, since only the application\n // knows how to compute nullifiers. We simply trust it to have provided the correct one: if it\n // hasn't, then PXE may fail to realize that a given note has been nullified already, and calls to\n // the application could result in invalid transactions (with duplicate nullifiers). This is not a\n // concern because an application already has more direct means of making a call to it fail the\n // transaction.\n discovered_notes.push(\n DiscoveredNoteInfo {\n note_nonce: nonce_for_i,\n note_hash,\n inner_nullifier: maybe_inner_nullifier_for_i.unwrap(),\n },\n );\n }\n // We don't exit the loop - it is possible (though rare) for the exact same note content to be present\n // multiple times in the same transaction with different nonces. This typically doesn't happen due to\n // notes containing random values in order to hide their contents.\n }\n }\n }\n\n *discovered_notes\n}\n\n// There is no BoundedVec::filter in the stdlib, so we use this until that is implemented.\nunconstrained fn bvec_filter(\n bvec: BoundedVec,\n filter: fn[Env](T) -> bool,\n) -> BoundedVec {\n let filtered = &mut BoundedVec::new();\n\n bvec.for_each(|value| {\n if filter(value) {\n filtered.push(value);\n }\n });\n\n *filtered\n}\n\nmod test {\n use crate::{\n messages::logs::note::MAX_NOTE_PACKED_LEN,\n note::{\n note_interface::{NoteHash, NoteType},\n note_metadata::SettledNoteMetadata,\n utils::compute_note_hash_for_nullification,\n },\n oracle::random::random,\n test::mocks::mock_note::MockNote,\n utils::array,\n };\n\n use crate::protocol::{\n address::AztecAddress,\n hash::{compute_note_hash_nonce, compute_siloed_note_hash, compute_unique_note_hash},\n traits::{FromField, Packable},\n };\n\n use super::attempt_note_nonce_discovery;\n\n // This implementation could be simpler, but this serves as a nice example of the expected flow in a real\n // implementation, and as a sanity check that the interface is sufficient.\n\n unconstrained fn compute_note_hash(\n packed_note: BoundedVec,\n owner: AztecAddress,\n storage_slot: Field,\n note_type_id: Field,\n _contract_address: AztecAddress,\n randomness: Field,\n ) -> Option {\n if (note_type_id == MockNote::get_id()) & (packed_note.len() == ::N) {\n let note = MockNote::unpack(array::subarray(packed_note.storage(), 0));\n Option::some(note.compute_note_hash(owner, storage_slot, randomness))\n } else {\n Option::none()\n }\n }\n\n unconstrained fn compute_note_nullifier(\n unique_note_hash: Field,\n packed_note: BoundedVec,\n owner: AztecAddress,\n _storage_slot: Field,\n note_type_id: Field,\n _contract_address: AztecAddress,\n _randomness: Field,\n ) -> Option {\n if (note_type_id == MockNote::get_id()) & (packed_note.len() == ::N) {\n let note = MockNote::unpack(array::subarray(packed_note.storage(), 0));\n note.compute_nullifier_unconstrained(owner, unique_note_hash)\n } else {\n Option::none()\n }\n }\n\n global VALUE: Field = 7;\n global FIRST_NULLIFIER_IN_TX: Field = 47;\n global CONTRACT_ADDRESS: AztecAddress = AztecAddress::from_field(13);\n global OWNER: AztecAddress = AztecAddress::from_field(14);\n global STORAGE_SLOT: Field = 99;\n global RANDOMNESS: Field = 99;\n\n #[test]\n unconstrained fn no_note_hashes() {\n let unique_note_hashes_in_tx = BoundedVec::new();\n let packed_note = BoundedVec::new();\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n packed_note,\n );\n\n assert_eq(discovered_notes.len(), 0);\n }\n\n #[test]\n unconstrained fn failed_hash_computation_is_ignored() {\n let unique_note_hashes_in_tx = BoundedVec::from_array([random()]);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n |_, _, _, _, _, _| Option::none(),\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::new(),\n );\n\n assert_eq(discovered_notes.len(), 0);\n }\n\n #[test]\n unconstrained fn failed_nullifier_computation_is_ignored() {\n let note_index_in_tx = 2;\n let note_and_data = construct_note(VALUE, note_index_in_tx);\n\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n unique_note_hashes_in_tx.set(note_index_in_tx, note_and_data.unique_note_hash);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n |_, _, _, _, _, _, _| Option::none(),\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(note_and_data.note.pack()),\n );\n\n assert_eq(discovered_notes.len(), 0);\n }\n\n struct NoteAndData {\n note: MockNote,\n note_nonce: Field,\n note_hash: Field,\n unique_note_hash: Field,\n inner_nullifier: Field,\n }\n\n unconstrained fn construct_note(value: Field, note_index_in_tx: u32) -> NoteAndData {\n let note_nonce = compute_note_hash_nonce(FIRST_NULLIFIER_IN_TX, note_index_in_tx);\n\n let hinted_note = MockNote::new(value)\n .contract_address(CONTRACT_ADDRESS)\n .owner(OWNER)\n .randomness(RANDOMNESS)\n .storage_slot(STORAGE_SLOT)\n .note_metadata(SettledNoteMetadata::new(note_nonce).into())\n .build_hinted_note();\n let note = hinted_note.note;\n\n let note_hash = note.compute_note_hash(OWNER, STORAGE_SLOT, RANDOMNESS);\n let unique_note_hash = compute_unique_note_hash(\n note_nonce,\n compute_siloed_note_hash(CONTRACT_ADDRESS, note_hash),\n );\n let inner_nullifier = note\n .compute_nullifier_unconstrained(OWNER, compute_note_hash_for_nullification(hinted_note))\n .expect(f\"Could not compute nullifier for note owned by {OWNER}\");\n\n NoteAndData { note, note_nonce, note_hash, unique_note_hash, inner_nullifier }\n }\n\n #[test]\n unconstrained fn single_note() {\n let note_index_in_tx = 2;\n let note_and_data = construct_note(VALUE, note_index_in_tx);\n\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n unique_note_hashes_in_tx.set(note_index_in_tx, note_and_data.unique_note_hash);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(note_and_data.note.pack()),\n );\n\n assert_eq(discovered_notes.len(), 1);\n let discovered_note = discovered_notes.get(0);\n\n assert_eq(discovered_note.note_nonce, note_and_data.note_nonce);\n assert_eq(discovered_note.note_hash, note_and_data.note_hash);\n assert_eq(discovered_note.inner_nullifier, note_and_data.inner_nullifier);\n }\n\n #[test]\n unconstrained fn multiple_notes_same_preimage() {\n let first_note_index_in_tx = 3;\n let first_note_and_data = construct_note(VALUE, first_note_index_in_tx);\n\n let second_note_index_in_tx = 5;\n let second_note_and_data = construct_note(VALUE, second_note_index_in_tx);\n\n // Both notes have the same preimage (and therefore packed representation), so both should be found in the same\n // call.\n assert_eq(first_note_and_data.note, second_note_and_data.note);\n let packed_note = first_note_and_data.note.pack();\n\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n unique_note_hashes_in_tx.set(first_note_index_in_tx, first_note_and_data.unique_note_hash);\n unique_note_hashes_in_tx.set(second_note_index_in_tx, second_note_and_data.unique_note_hash);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(packed_note),\n );\n\n assert_eq(discovered_notes.len(), 2);\n\n assert(discovered_notes.any(|discovered_note| {\n (discovered_note.note_nonce == first_note_and_data.note_nonce)\n & (discovered_note.note_hash == first_note_and_data.note_hash)\n & (discovered_note.inner_nullifier == first_note_and_data.inner_nullifier)\n }));\n\n assert(discovered_notes.any(|discovered_note| {\n (discovered_note.note_nonce == second_note_and_data.note_nonce)\n & (discovered_note.note_hash == second_note_and_data.note_hash)\n & (discovered_note.inner_nullifier == second_note_and_data.inner_nullifier)\n }));\n }\n\n #[test]\n unconstrained fn single_note_misaligned_nonce() {\n let note_index_in_tx = 2;\n let note_and_data = construct_note(VALUE, note_index_in_tx);\n\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n\n // The note is not at the correct index\n unique_note_hashes_in_tx.set(note_index_in_tx + 1, note_and_data.unique_note_hash);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(note_and_data.note.pack()),\n );\n\n assert_eq(discovered_notes.len(), 1);\n let discovered_note = discovered_notes.get(0);\n\n assert_eq(discovered_note.note_nonce, note_and_data.note_nonce);\n assert_eq(discovered_note.note_hash, note_and_data.note_hash);\n assert_eq(discovered_note.inner_nullifier, note_and_data.inner_nullifier);\n }\n\n #[test]\n unconstrained fn single_note_nonce_with_index_past_note_hashes_in_tx() {\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n\n // The nonce is computed with an index that does not exist in the tx\n let note_index_in_tx = unique_note_hashes_in_tx.len() + 5;\n let note_and_data = construct_note(VALUE, note_index_in_tx);\n\n // The note is inserted at an arbitrary index - its true index is out of the array's bounds\n unique_note_hashes_in_tx.set(2, note_and_data.unique_note_hash);\n\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(note_and_data.note.pack()),\n );\n\n assert_eq(discovered_notes.len(), 1);\n let discovered_note = discovered_notes.get(0);\n\n assert_eq(discovered_note.note_nonce, note_and_data.note_nonce);\n assert_eq(discovered_note.note_hash, note_and_data.note_hash);\n assert_eq(discovered_note.inner_nullifier, note_and_data.inner_nullifier);\n }\n\n #[test(should_fail_with = \"identical note hashes for a transaction\")]\n unconstrained fn duplicate_unique_note_hashes() {\n let note_index_in_tx = 2;\n let note_and_data = construct_note(VALUE, note_index_in_tx);\n\n let mut unique_note_hashes_in_tx = BoundedVec::from_array([\n random(), random(), random(), random(), random(), random(), random(),\n ]);\n\n // The same unique note hash is present in two indices in the array, which is not allowed. Note that we don't\n // test all note hashes for uniqueness, only those that we actually find.\n unique_note_hashes_in_tx.set(note_index_in_tx, note_and_data.unique_note_hash);\n unique_note_hashes_in_tx.set(note_index_in_tx + 1, note_and_data.unique_note_hash);\n\n let _ = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n FIRST_NULLIFIER_IN_TX,\n compute_note_hash,\n compute_note_nullifier,\n CONTRACT_ADDRESS,\n OWNER,\n STORAGE_SLOT,\n RANDOMNESS,\n MockNote::get_id(),\n BoundedVec::from_array(note_and_data.note.pack()),\n );\n }\n}\n" }, "145": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/partial_notes.nr", "source": "use crate::{\n capsules::CapsuleArray,\n messages::{\n discovery::{ComputeNoteHash, ComputeNoteNullifier, nonce_discovery::attempt_note_nonce_discovery},\n encoding::MAX_MESSAGE_CONTENT_LEN,\n logs::partial_note::{decode_partial_note_private_message, MAX_PARTIAL_NOTE_PRIVATE_PACKED_LEN},\n processing::{\n enqueue_note_for_validation,\n get_pending_partial_notes_completion_logs,\n log_retrieval_response::{LogRetrievalResponse, MAX_LOG_CONTENT_LEN},\n },\n },\n utils::array,\n};\n\nuse crate::logging::aztecnr_debug_log_format;\nuse crate::protocol::{address::AztecAddress, hash::sha256_to_field, traits::{Deserialize, Serialize}};\n\n/// The slot in the PXE capsules where we store a `CapsuleArray` of `DeliveredPendingPartialNote`.\npub(crate) global DELIVERED_PENDING_PARTIAL_NOTE_ARRAY_LENGTH_CAPSULES_SLOT: Field = sha256_to_field(\n \"AZTEC_NR::DELIVERED_PENDING_PARTIAL_NOTE_ARRAY_LENGTH_CAPSULES_SLOT\".as_bytes(),\n);\n\n/// A partial note that was delivered but is still pending completion. Contains the information necessary to find the\n/// log that will complete it and lead to a note being discovered and delivered.\n#[derive(Serialize, Deserialize)]\npub(crate) struct DeliveredPendingPartialNote {\n pub(crate) owner: AztecAddress,\n pub(crate) randomness: Field,\n pub(crate) note_completion_log_tag: Field,\n pub(crate) note_type_id: Field,\n pub(crate) packed_private_note_content: BoundedVec,\n}\n\npub(crate) unconstrained fn process_partial_note_private_msg(\n contract_address: AztecAddress,\n msg_metadata: u64,\n msg_content: BoundedVec,\n tx_hash: Field,\n scope: AztecAddress,\n) {\n let decoded = decode_partial_note_private_message(msg_metadata, msg_content);\n\n if decoded.is_some() {\n // We store the information of the partial note we found in a persistent capsule in PXE, so that we can later\n // search for the public log that will complete it.\n let (owner, randomness, note_completion_log_tag, note_type_id, packed_private_note_content) = decoded.unwrap();\n\n let pending = DeliveredPendingPartialNote {\n owner,\n randomness,\n note_completion_log_tag,\n note_type_id,\n packed_private_note_content,\n };\n\n CapsuleArray::at(\n contract_address,\n DELIVERED_PENDING_PARTIAL_NOTE_ARRAY_LENGTH_CAPSULES_SLOT,\n scope,\n )\n .push(pending);\n } else {\n aztecnr_debug_log_format!(\n \"Could not decode partial note private message from tx {0}, ignoring\",\n )(\n [tx_hash],\n );\n }\n}\n\n/// Searches for logs that would result in the completion of pending partial notes, ultimately resulting in the notes\n/// being delivered to PXE if completed.\npub(crate) unconstrained fn fetch_and_process_partial_note_completion_logs(\n contract_address: AztecAddress,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n scope: AztecAddress,\n) {\n let pending_partial_notes = CapsuleArray::at(\n contract_address,\n DELIVERED_PENDING_PARTIAL_NOTE_ARRAY_LENGTH_CAPSULES_SLOT,\n scope,\n );\n\n aztecnr_debug_log_format!(\"{} pending partial notes\")([pending_partial_notes.len() as Field]);\n\n // Each of the pending partial notes might get completed by a log containing its public values. For performance\n // reasons, we fetch all of these logs concurrently and then process them one by one, minimizing the amount of time\n // waiting for the node roundtrip.\n let maybe_completion_logs =\n get_pending_partial_notes_completion_logs(contract_address, pending_partial_notes, scope);\n\n // Each entry in the maybe completion logs array corresponds to the entry in the pending partial notes array at the\n // same index. This means we can use the same index as we iterate through the responses to get both the partial\n // note and the log that might complete it.\n assert_eq(maybe_completion_logs.len(), pending_partial_notes.len());\n\n maybe_completion_logs.for_each(|i, maybe_log: Option| {\n // We clear the completion logs as we read them so that the array is empty by the time we next query it.\n // TODO(#14943): use volatile arrays to avoid having to manually clear this.\n maybe_completion_logs.remove(i);\n\n let pending_partial_note = pending_partial_notes.get(i);\n\n if maybe_log.is_none() {\n aztecnr_debug_log_format!(\"Found no completion logs for partial note with tag {}\")(\n [pending_partial_note.note_completion_log_tag],\n );\n\n // Note that we're not removing the pending partial note from the capsule array, so we will continue\n // searching for this tagged log when performing message discovery in the future until we either find it or\n // the entry is somehow removed from the array.\n } else {\n aztecnr_debug_log_format!(\"Completion log found for partial note with tag {}\")([\n pending_partial_note.note_completion_log_tag,\n ]);\n let log = maybe_log.unwrap();\n\n // The first field in the completion log payload is the storage slot, followed by the public note\n // content fields.\n let storage_slot = log.log_payload.get(0);\n let public_note_content: BoundedVec = array::subbvec(log.log_payload, 1);\n\n // Public fields are assumed to all be placed at the end of the packed representation, so we combine\n // the private and public packed fields (i.e. the contents of the private message and public log\n // plaintext) to get the complete packed content.\n let complete_packed_note = array::append(\n pending_partial_note.packed_private_note_content,\n public_note_content,\n );\n\n let discovered_notes = attempt_note_nonce_discovery(\n log.unique_note_hashes_in_tx,\n log.first_nullifier_in_tx,\n compute_note_hash,\n compute_note_nullifier,\n contract_address,\n pending_partial_note.owner,\n storage_slot,\n pending_partial_note.randomness,\n pending_partial_note.note_type_id,\n complete_packed_note,\n );\n\n // TODO(#11627): is there anything reasonable we can do if we get a log but it doesn't result in a note\n // being found?\n if discovered_notes.len() == 0 {\n panic(\n f\"A partial note's completion log did not result in any notes being found - this should never happen\",\n );\n }\n\n aztecnr_debug_log_format!(\"Discovered {0} notes for partial note with tag {1}\")([\n discovered_notes.len() as Field,\n pending_partial_note.note_completion_log_tag,\n ]);\n\n discovered_notes.for_each(|discovered_note| {\n enqueue_note_for_validation(\n contract_address,\n pending_partial_note.owner,\n storage_slot,\n pending_partial_note.randomness,\n discovered_note.note_nonce,\n complete_packed_note,\n discovered_note.note_hash,\n discovered_note.inner_nullifier,\n log.tx_hash,\n scope,\n );\n });\n\n // Because there is only a single log for a given tag, once we've processed the tagged log then we simply\n // delete the pending work entry, regardless of whether it was actually completed or not.\n pending_partial_notes.remove(i);\n }\n });\n}\n" }, "146": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/private_events.nr", "source": "use crate::{\n event::event_interface::compute_private_serialized_event_commitment,\n messages::{\n encoding::MAX_MESSAGE_CONTENT_LEN, logs::event::decode_private_event_message,\n processing::enqueue_event_for_validation,\n },\n};\nuse crate::protocol::{address::AztecAddress, logging::debug_log_format, traits::ToField};\n\npub(crate) unconstrained fn process_private_event_msg(\n contract_address: AztecAddress,\n msg_metadata: u64,\n msg_content: BoundedVec,\n tx_hash: Field,\n recipient: AztecAddress,\n) {\n let decoded = decode_private_event_message(msg_metadata, msg_content);\n\n if decoded.is_some() {\n let (event_type_id, randomness, serialized_event) = decoded.unwrap();\n\n let event_commitment =\n compute_private_serialized_event_commitment(serialized_event, randomness, event_type_id.to_field());\n\n enqueue_event_for_validation(\n contract_address,\n event_type_id,\n randomness,\n serialized_event,\n event_commitment,\n tx_hash,\n recipient,\n );\n } else {\n debug_log_format(\n \"Could not decode private event message from tx {0}, ignoring\",\n [tx_hash],\n );\n }\n}\n" }, "147": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/private_notes.nr", "source": "use crate::{\n logging::{aztecnr_debug_log_format, aztecnr_warn_log_format},\n messages::{\n discovery::{ComputeNoteHash, ComputeNoteNullifier, nonce_discovery::attempt_note_nonce_discovery},\n encoding::MAX_MESSAGE_CONTENT_LEN,\n logs::note::{decode_private_note_message, MAX_NOTE_PACKED_LEN},\n processing::enqueue_note_for_validation,\n },\n protocol::{address::AztecAddress, constants::MAX_NOTE_HASHES_PER_TX, traits::ToField},\n};\n\npub(crate) unconstrained fn process_private_note_msg(\n contract_address: AztecAddress,\n tx_hash: Field,\n unique_note_hashes_in_tx: BoundedVec,\n first_nullifier_in_tx: Field,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n msg_metadata: u64,\n msg_content: BoundedVec,\n recipient: AztecAddress,\n) {\n let decoded = decode_private_note_message(msg_metadata, msg_content);\n\n if decoded.is_some() {\n let (note_type_id, owner, storage_slot, randomness, packed_note) = decoded.unwrap();\n\n attempt_note_discovery(\n contract_address,\n tx_hash,\n unique_note_hashes_in_tx,\n first_nullifier_in_tx,\n compute_note_hash,\n compute_note_nullifier,\n owner,\n storage_slot,\n randomness,\n note_type_id,\n packed_note,\n recipient,\n );\n } else {\n aztecnr_debug_log_format!(\n \"Could not decode private note message from tx {0}, ignoring\",\n )(\n [tx_hash],\n );\n }\n}\n\n/// Attempts discovery of a note given information about its contents and the transaction in which it is suspected the\n/// note was created.\npub unconstrained fn attempt_note_discovery(\n contract_address: AztecAddress,\n tx_hash: Field,\n unique_note_hashes_in_tx: BoundedVec,\n first_nullifier_in_tx: Field,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n note_type_id: Field,\n packed_note: BoundedVec,\n recipient: AztecAddress,\n) {\n let discovered_notes = attempt_note_nonce_discovery(\n unique_note_hashes_in_tx,\n first_nullifier_in_tx,\n compute_note_hash,\n compute_note_nullifier,\n contract_address,\n owner,\n storage_slot,\n randomness,\n note_type_id,\n packed_note,\n );\n\n if discovered_notes.len() == 0 {\n // A private note message that results in no discovered notes means none of the computed note hashes matched\n // any unique note hash in the transaction. This could indicate a malformed or malicious message (e.g. a sender\n // providing bogus note content).\n aztecnr_warn_log_format!(\n \"Discarding private note message from tx {0} for contract {1}: no matching note hash found in the tx\",\n )(\n [tx_hash, contract_address.to_field()],\n );\n } else {\n aztecnr_debug_log_format!(\n \"Discovered {0} notes from a private message for contract {1}\",\n )(\n [discovered_notes.len() as Field, contract_address.to_field()],\n );\n }\n\n discovered_notes.for_each(|discovered_note| {\n enqueue_note_for_validation(\n contract_address,\n owner,\n storage_slot,\n randomness,\n discovered_note.note_nonce,\n packed_note,\n discovered_note.note_hash,\n discovered_note.inner_nullifier,\n tx_hash,\n recipient,\n );\n });\n}\n" }, "148": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/discovery/process_message.nr", "source": "use crate::messages::{\n discovery::{\n ComputeNoteHash, ComputeNoteNullifier, CustomMessageHandler, partial_notes::process_partial_note_private_msg,\n private_events::process_private_event_msg, private_notes::process_private_note_msg,\n },\n encoding::{decode_message, MESSAGE_CIPHERTEXT_LEN, MESSAGE_PLAINTEXT_LEN},\n encryption::{aes128::AES128, message_encryption::MessageEncryption},\n msg_type::{\n MIN_CUSTOM_MSG_TYPE_ID, PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID, PRIVATE_EVENT_MSG_TYPE_ID, PRIVATE_NOTE_MSG_TYPE_ID,\n },\n processing::MessageContext,\n};\n\nuse crate::logging::{aztecnr_debug_log, aztecnr_warn_log_format};\nuse crate::protocol::address::AztecAddress;\n\n/// Processes a message that can contain notes, partial notes, or events.\n///\n/// Notes result in nonce discovery being performed prior to delivery, which requires knowledge of the transaction hash\n/// in which the notes would've been created (typically the same transaction in which the log was emitted), along with\n/// the list of unique note hashes in said transaction and the `compute_note_hash` and `compute_note_nullifier`\n/// functions. Once discovered, the notes are enqueued for validation.\n///\n/// Partial notes result in a pending partial note entry being stored in a PXE capsule, which will later be retrieved\n/// to search for the note's completion public log.\n///\n/// Events are processed by computing an event commitment from the serialized event data and its randomness field, then\n/// enqueueing the event data and commitment for validation.\npub unconstrained fn process_message_ciphertext(\n contract_address: AztecAddress,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n process_custom_message: Option>,\n message_ciphertext: BoundedVec,\n message_context: MessageContext,\n recipient: AztecAddress,\n) {\n let message_plaintext_option = AES128::decrypt(message_ciphertext, recipient, contract_address);\n\n if message_plaintext_option.is_some() {\n process_message_plaintext(\n contract_address,\n compute_note_hash,\n compute_note_nullifier,\n process_custom_message,\n message_plaintext_option.unwrap(),\n message_context,\n recipient,\n );\n } else {\n aztecnr_warn_log_format!(\"Could not decrypt message ciphertext from tx {0}, ignoring\")([message_context.tx_hash]);\n }\n}\n\npub(crate) unconstrained fn process_message_plaintext(\n contract_address: AztecAddress,\n compute_note_hash: ComputeNoteHash,\n compute_note_nullifier: ComputeNoteNullifier,\n process_custom_message: Option>,\n message_plaintext: BoundedVec,\n message_context: MessageContext,\n recipient: AztecAddress,\n) {\n // The first thing to do after decrypting the message is to determine what type of message we're processing. We\n // have 3 message types: private notes, partial notes and events.\n\n // We decode the message to obtain the message type id, metadata and content.\n let decoded = decode_message(message_plaintext);\n\n if decoded.is_some() {\n let (msg_type_id, msg_metadata, msg_content) = decoded.unwrap();\n\n if msg_type_id == PRIVATE_NOTE_MSG_TYPE_ID {\n aztecnr_debug_log!(\"Processing private note msg\");\n\n process_private_note_msg(\n contract_address,\n message_context.tx_hash,\n message_context.unique_note_hashes_in_tx,\n message_context.first_nullifier_in_tx,\n compute_note_hash,\n compute_note_nullifier,\n msg_metadata,\n msg_content,\n recipient,\n );\n } else if msg_type_id == PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID {\n aztecnr_debug_log!(\"Processing partial note private msg\");\n\n process_partial_note_private_msg(\n contract_address,\n msg_metadata,\n msg_content,\n message_context.tx_hash,\n recipient,\n );\n } else if msg_type_id == PRIVATE_EVENT_MSG_TYPE_ID {\n aztecnr_debug_log!(\"Processing private event msg\");\n\n process_private_event_msg(\n contract_address,\n msg_metadata,\n msg_content,\n message_context.tx_hash,\n recipient,\n );\n } else if msg_type_id < MIN_CUSTOM_MSG_TYPE_ID {\n // The message type ID falls in the range reserved for aztec.nr built-in types but wasn't matched above.\n // This most likely means the message is malformed or a custom message was incorrectly assigned a reserved\n // ID. Custom message types must use IDs allocated via `custom_msg_type_id`.\n aztecnr_warn_log_format!(\n \"Message type ID {0} is in the reserved range but is not recognized, ignoring. See https://docs.aztec.network/errors/3\",\n )(\n [msg_type_id as Field],\n );\n } else if process_custom_message.is_some() {\n process_custom_message.unwrap()(\n contract_address,\n msg_type_id,\n msg_metadata,\n msg_content,\n message_context,\n recipient,\n );\n } else {\n // A custom message was received but no handler is configured. This likely means the contract emits custom\n // messages but forgot to register a handler via `AztecConfig::custom_message_handler`.\n aztecnr_warn_log_format!(\n \"Received custom message with type id {0} but no handler is configured, ignoring. See https://docs.aztec.network/errors/2\",\n )(\n [msg_type_id as Field],\n );\n }\n } else {\n aztecnr_warn_log_format!(\"Could not decode message plaintext from tx {0}, ignoring\")([message_context.tx_hash]);\n }\n}\n" }, "149": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/encoding.nr", "source": "// TODO(#12750): don't make these values assume we're using AES.\nuse crate::protocol::constants::PRIVATE_LOG_CIPHERTEXT_LEN;\nuse crate::utils::array;\n\n// We reassign to the constant here to communicate the distinction between a log and a message. In Aztec.nr, unlike in\n// protocol circuits, we have a concept of a message that can be emitted either as a private log or as an offchain\n// message. Message is a piece of data that is to be eventually delivered to a contract via the `process_message(...)`\n// utility function function that is injected by the #[aztec] macro. Note: PRIVATE_LOG_CIPHERTEXT_LEN is an amount of\n// fields, so MESSAGE_CIPHERTEXT_LEN is the size of the message in fields.\npub global MESSAGE_CIPHERTEXT_LEN: u32 = PRIVATE_LOG_CIPHERTEXT_LEN;\n\n// TODO(#12750): The global variables below should not be here as they are AES128 specific.\n// The header plaintext is 2 bytes (ciphertext length), padded to the 16-byte AES block size by PKCS#7.\npub(crate) global HEADER_CIPHERTEXT_SIZE_IN_BYTES: u32 = 16;\n// AES PKCS#7 always adds at least one byte of padding. Since each plaintext field is 32 bytes (a multiple of the\n// 16-byte AES block size), a full 16-byte padding block is always appended.\npub(crate) global AES128_PKCS7_EXPANSION_IN_BYTES: u32 = 16;\n\npub global EPH_PK_X_SIZE_IN_FIELDS: u32 = 1;\n\n// (15 - 1) * 31 - 16 - 16 = 402. Note: We multiply by 31 because ciphertext bytes are stored in fields using\n// bytes_to_fields, which packs 31 bytes per field (since a Field is ~254 bits and can safely store 31 whole bytes).\npub(crate) global MESSAGE_PLAINTEXT_SIZE_IN_BYTES: u32 = (MESSAGE_CIPHERTEXT_LEN - EPH_PK_X_SIZE_IN_FIELDS) * 31\n - HEADER_CIPHERTEXT_SIZE_IN_BYTES\n - AES128_PKCS7_EXPANSION_IN_BYTES;\n// The plaintext bytes represent Field values that were originally serialized using fields_to_bytes, which converts\n// each Field to 32 bytes. To convert the plaintext bytes back to fields, we divide by 32. 402 / 32 = 12\npub global MESSAGE_PLAINTEXT_LEN: u32 = MESSAGE_PLAINTEXT_SIZE_IN_BYTES / 32;\n\npub global MESSAGE_EXPANDED_METADATA_LEN: u32 = 1;\n\n// The standard message layout is composed of:\n// - an initial field called the 'expanded metadata'\n// - an arbitrary number of fields following that called the 'message content'\n//\n// ```\n// message: [ msg_expanded_metadata, ...msg_content ]\n// ```\n//\n// The expanded metadata itself is interpreted as a u128, of which:\n// - the upper 64 bits are the message type id\n// - the lower 64 bits are called the 'message metadata'\n//\n// ```\n// msg_expanded_metadata: [ msg_type_id | msg_metadata ]\n// <--- 64 bits --->|<--- 64 bits --->\n// ```\n//\n// The meaning of the message metadata and message content depend on the value of the message type id. Note that there\n// is nothing special about the message metadata, it _can_ be considered part of the content. It just has a different\n// name to make it distinct from the message content given that it is not a full field.\n\n/// The maximum length of a message's content, i.e. not including the expanded message metadata.\npub global MAX_MESSAGE_CONTENT_LEN: u32 = MESSAGE_PLAINTEXT_LEN - MESSAGE_EXPANDED_METADATA_LEN;\n\n/// Encodes a message following aztec-nr's standard message encoding. This message can later be decoded with\n/// `decode_message` to retrieve the original values.\n///\n/// - The `msg_type` is an identifier that groups types of messages that are all processed the same way, e.g. private\n/// notes or events. Possible values are defined in `aztec::messages::msg_type`.\n/// - The `msg_metadata` and `msg_content` are the values stored in the message, whose meaning depends on the\n/// `msg_type`. The only special thing about `msg_metadata` that separates it from `msg_content` is that it is a u64\n/// instead of a full Field (due to details of how messages are encoded), allowing applications that can fit values\n/// into this smaller variable to achieve higher data efficiency.\npub fn encode_message(\n msg_type: u64,\n msg_metadata: u64,\n msg_content: [Field; N],\n) -> [Field; (N + MESSAGE_EXPANDED_METADATA_LEN)] {\n std::static_assert(\n msg_content.len() <= MAX_MESSAGE_CONTENT_LEN,\n \"Invalid message content: it must have a length of at most MAX_MESSAGE_CONTENT_LEN\",\n );\n\n // If MESSAGE_EXPANDED_METADATA_LEN is changed, causing the assertion below to fail, then the destructuring of the\n // message encoding below must be updated as well.\n std::static_assert(\n MESSAGE_EXPANDED_METADATA_LEN == 1,\n \"unexpected value for MESSAGE_EXPANDED_METADATA_LEN\",\n );\n let mut message: [Field; (N + MESSAGE_EXPANDED_METADATA_LEN)] = std::mem::zeroed();\n\n message[0] = to_expanded_metadata(msg_type, msg_metadata);\n for i in 0..msg_content.len() {\n message[MESSAGE_EXPANDED_METADATA_LEN + i] = msg_content[i];\n }\n\n message\n}\n\n/// Decodes a standard aztec-nr message, i.e. one created via `encode_message`, returning the original encoded values.\n///\n/// Returns `None` if the message is empty or has invalid (>128 bit) expanded metadata.\n///\n/// Note that `encode_message` returns a fixed size array while this function takes a `BoundedVec`: this is because\n/// prior to decoding the message type is unknown, and consequentially not known at compile time. If working with\n/// fixed-size messages, consider using `BoundedVec::from_array` to convert them.\npub unconstrained fn decode_message(\n message: BoundedVec,\n) -> Option<(u64, u64, BoundedVec)> {\n Option::some(message)\n .and_then(|message| {\n // If MESSAGE_EXPANDED_METADATA_LEN is changed, causing the assertion below to fail, then the destructuring\n // of the\n // message encoding below must be updated as well.\n std::static_assert(\n MESSAGE_EXPANDED_METADATA_LEN == 1,\n \"unexpected value for MESSAGE_EXPANDED_METADATA_LEN\",\n );\n if message.len() < MESSAGE_EXPANDED_METADATA_LEN {\n Option::none()\n } else {\n Option::some(message.get(0))\n }\n })\n .and_then(|msg_expanded_metadata| from_expanded_metadata(msg_expanded_metadata))\n .map(|(msg_type_id, msg_metadata)| {\n let msg_content = array::subbvec(message, MESSAGE_EXPANDED_METADATA_LEN);\n (msg_type_id, msg_metadata, msg_content)\n })\n}\n\nglobal U64_SHIFT_MULTIPLIER: Field = 2.pow_32(64);\n\nfn to_expanded_metadata(msg_type: u64, msg_metadata: u64) -> Field {\n // We use multiplication instead of bit shifting operations to shift the type bits as bit shift operations are\n // expensive in circuits.\n let type_field: Field = (msg_type as Field) * U64_SHIFT_MULTIPLIER;\n let msg_metadata_field = msg_metadata as Field;\n\n type_field + msg_metadata_field\n}\n\nglobal TWO_POW_128: Field = 2.pow_32(128);\n\n/// Unpacks expanded metadata into (msg_type, msg_metadata). Returns `None` if `input >= 2^128`.\nfn from_expanded_metadata(input: Field) -> Option<(u64, u64)> {\n if input.lt(TWO_POW_128) {\n let msg_metadata = (input as u64);\n let msg_type = ((input - (msg_metadata as Field)) / U64_SHIFT_MULTIPLIER) as u64;\n // Use division instead of bit shift since bit shifts are expensive in circuits\n Option::some((msg_type, msg_metadata))\n } else {\n Option::none()\n }\n}\n\nmod tests {\n use crate::utils::array::subarray::subarray;\n use super::{\n decode_message, encode_message, from_expanded_metadata, MAX_MESSAGE_CONTENT_LEN, to_expanded_metadata,\n TWO_POW_128,\n };\n\n global U64_MAX: u64 = (2.pow_32(64) - 1) as u64;\n global U128_MAX: Field = (2.pow_32(128) - 1);\n\n #[test]\n unconstrained fn encode_decode_empty_message(msg_type: u64, msg_metadata: u64) {\n let encoded = encode_message(msg_type, msg_metadata, []);\n let (decoded_msg_type, decoded_msg_metadata, decoded_msg_content) =\n decode_message(BoundedVec::from_array(encoded)).unwrap();\n\n assert_eq(decoded_msg_type, msg_type);\n assert_eq(decoded_msg_metadata, msg_metadata);\n assert_eq(decoded_msg_content.len(), 0);\n }\n\n #[test]\n unconstrained fn encode_decode_short_message(\n msg_type: u64,\n msg_metadata: u64,\n msg_content: [Field; MAX_MESSAGE_CONTENT_LEN / 2],\n ) {\n let encoded = encode_message(msg_type, msg_metadata, msg_content);\n let (decoded_msg_type, decoded_msg_metadata, decoded_msg_content) =\n decode_message(BoundedVec::from_array(encoded)).unwrap();\n\n assert_eq(decoded_msg_type, msg_type);\n assert_eq(decoded_msg_metadata, msg_metadata);\n assert_eq(decoded_msg_content.len(), msg_content.len());\n assert_eq(subarray(decoded_msg_content.storage(), 0), msg_content);\n }\n\n #[test]\n unconstrained fn encode_decode_full_message(\n msg_type: u64,\n msg_metadata: u64,\n msg_content: [Field; MAX_MESSAGE_CONTENT_LEN],\n ) {\n let encoded = encode_message(msg_type, msg_metadata, msg_content);\n let (decoded_msg_type, decoded_msg_metadata, decoded_msg_content) =\n decode_message(BoundedVec::from_array(encoded)).unwrap();\n\n assert_eq(decoded_msg_type, msg_type);\n assert_eq(decoded_msg_metadata, msg_metadata);\n assert_eq(decoded_msg_content.len(), msg_content.len());\n assert_eq(subarray(decoded_msg_content.storage(), 0), msg_content);\n }\n\n #[test]\n unconstrained fn to_expanded_metadata_packing() {\n // Test case 1: All bits set\n let packed = to_expanded_metadata(U64_MAX, U64_MAX);\n let (msg_type, msg_metadata) = from_expanded_metadata(packed).unwrap();\n assert_eq(msg_type, U64_MAX);\n assert_eq(msg_metadata, U64_MAX);\n\n // Test case 2: Only log type bits set\n let packed = to_expanded_metadata(U64_MAX, 0);\n let (msg_type, msg_metadata) = from_expanded_metadata(packed).unwrap();\n assert_eq(msg_type, U64_MAX);\n assert_eq(msg_metadata, 0);\n\n // Test case 3: Only msg_metadata bits set\n let packed = to_expanded_metadata(0, U64_MAX);\n let (msg_type, msg_metadata) = from_expanded_metadata(packed).unwrap();\n assert_eq(msg_type, 0);\n assert_eq(msg_metadata, U64_MAX);\n\n // Test case 4: No bits set\n let packed = to_expanded_metadata(0, 0);\n let (msg_type, msg_metadata) = from_expanded_metadata(packed).unwrap();\n assert_eq(msg_type, 0);\n assert_eq(msg_metadata, 0);\n }\n\n #[test]\n unconstrained fn from_expanded_metadata_packing() {\n // Test case 1: All bits set\n let input = U128_MAX as Field;\n let (msg_type, msg_metadata) = from_expanded_metadata(input).unwrap();\n assert_eq(msg_type, U64_MAX);\n assert_eq(msg_metadata, U64_MAX);\n\n // Test case 2: Only log type bits set\n let input = (U128_MAX - U64_MAX as Field);\n let (msg_type, msg_metadata) = from_expanded_metadata(input).unwrap();\n assert_eq(msg_type, U64_MAX);\n assert_eq(msg_metadata, 0);\n\n // Test case 3: Only msg_metadata bits set\n let input = U64_MAX as Field;\n let (msg_type, msg_metadata) = from_expanded_metadata(input).unwrap();\n assert_eq(msg_type, 0);\n assert_eq(msg_metadata, U64_MAX);\n\n // Test case 4: No bits set\n let input = 0;\n let (msg_type, msg_metadata) = from_expanded_metadata(input).unwrap();\n assert_eq(msg_type, 0);\n assert_eq(msg_metadata, 0);\n }\n\n #[test]\n unconstrained fn to_from_expanded_metadata(original_msg_type: u64, original_msg_metadata: u64) {\n let packed = to_expanded_metadata(original_msg_type, original_msg_metadata);\n let (unpacked_msg_type, unpacked_msg_metadata) = from_expanded_metadata(packed).unwrap();\n\n assert_eq(original_msg_type, unpacked_msg_type);\n assert_eq(original_msg_metadata, unpacked_msg_metadata);\n }\n\n #[test]\n unconstrained fn encode_decode_max_size_message() {\n let msg_type_id: u64 = 42;\n let msg_metadata: u64 = 99;\n let mut msg_content = [0; MAX_MESSAGE_CONTENT_LEN];\n for i in 0..MAX_MESSAGE_CONTENT_LEN {\n msg_content[i] = i as Field;\n }\n\n let encoded = encode_message(msg_type_id, msg_metadata, msg_content);\n let (decoded_type_id, decoded_metadata, decoded_content) =\n decode_message(BoundedVec::from_array(encoded)).unwrap();\n\n assert_eq(decoded_type_id, msg_type_id);\n assert_eq(decoded_metadata, msg_metadata);\n assert_eq(decoded_content, BoundedVec::from_array(msg_content));\n }\n\n #[test(should_fail_with = \"Invalid message content: it must have a length of at most MAX_MESSAGE_CONTENT_LEN\")]\n fn encode_oversized_message_fails() {\n let msg_content = [0; MAX_MESSAGE_CONTENT_LEN + 1];\n let _ = encode_message(0, 0, msg_content);\n }\n\n #[test]\n unconstrained fn decode_empty_message_returns_none() {\n assert(decode_message(BoundedVec::new()).is_none());\n }\n\n #[test]\n unconstrained fn decode_message_with_oversized_metadata_returns_none() {\n let message = BoundedVec::from_array([TWO_POW_128]);\n assert(decode_message(message).is_none());\n }\n}\n" }, "150": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/encryption/aes128.nr", "source": "use crate::protocol::{address::AztecAddress, public_keys::AddressPoint, traits::ToField};\n\nuse crate::{\n keys::{\n ecdh_shared_secret::{\n compute_app_siloed_shared_secret, derive_ecdh_shared_secret, derive_shared_secret_field_mask,\n derive_shared_secret_subkey,\n },\n ephemeral::generate_positive_ephemeral_key_pair,\n },\n logging::aztecnr_warn_log_format,\n messages::{\n encoding::{\n EPH_PK_X_SIZE_IN_FIELDS, HEADER_CIPHERTEXT_SIZE_IN_BYTES, MESSAGE_CIPHERTEXT_LEN, MESSAGE_PLAINTEXT_LEN,\n MESSAGE_PLAINTEXT_SIZE_IN_BYTES,\n },\n encryption::message_encryption::MessageEncryption,\n logs::arithmetic_generics_utils::{\n get_arr_of_size__message_bytes__from_PT, get_arr_of_size__message_bytes_padding__from_PT,\n },\n },\n oracle::{aes128_decrypt::try_aes128_decrypt, random::random, shared_secret::get_shared_secret},\n utils::{\n array,\n conversion::{\n bytes_to_fields::{bytes_from_fields, bytes_to_fields},\n fields_to_bytes::{fields_to_bytes, try_fields_from_bytes},\n },\n point::point_from_x_coord_and_sign,\n },\n};\n\nuse std::aes128::aes128_encrypt;\n\n/// Computes N close-to-uniformly-random 256 bits from a given app-siloed shared secret.\n///\n/// NEVER re-use the same iv and sym_key. DO NOT call this function more than once with the same s_app.\n///\n/// This function is only known to be safe if s_app is derived from combining a random ephemeral key with an\n/// address point and a contract address. See big comment within the body of the function.\nfn extract_many_close_to_uniformly_random_256_bits_using_poseidon2(s_app: Field) -> [[u8; 32]; N] {\n /*\n * Unsafe because of https://eprint.iacr.org/2010/264.pdf Page 13, Lemma 2 (and the two paragraphs below it).\n *\n * If you call this function, you need to be careful and aware of how the arg `s_app` has been derived.\n *\n * The paper says that the way you derive aes keys and IVs should be fine with poseidon2 (modelled as a RO),\n * as long as you _don't_ use Poseidon2 as a PRG to generate the two exponents x & y which multiply to the\n * shared secret S:\n *\n * S = [x*y]*G.\n *\n * (Otherwise, you would have to \"key\" poseidon2, i.e. generate a uniformly string K which can be public and\n * compute Hash(x) as poseidon(K,x)).\n * In that lemma, k would be 2*254=508, and m would be the number of points on the grumpkin curve (which is\n * close to r according to the Hasse bound).\n *\n * Our shared secret S is [esk * address_sk] * G, and the question is: Can we compute hash(S) using poseidon2\n * instead of sha256?\n *\n * Well, esk is random and not generated with poseidon2, so that's good.\n * What about address_sk?\n * Well, address_sk = poseidon2(stuff) + ivsk, so there was some discussion about whether address_sk is\n * independent of poseidon2. Given that ivsk is random and independent of poseidon2, the address_sk is also\n * independent of poseidon2.\n *\n * Tl;dr: we believe it's safe to hash S = [esk * address_sk] * G using poseidon2, in order to derive a\n * symmetric key.\n *\n * If you're calling this function for a differently-derived `s_app`, be careful.\n */\n \n\n /* The output of this function needs to be 32 random bytes.\n * A single field won't give us 32 bytes of entropy. So we compute two \"random\" fields, by poseidon-hashing\n * with two different indices. We then extract the last 16 (big endian) bytes of each \"random\" field.\n * Note: we use to_be_bytes because it's slightly more efficient. But we have to be careful not to take bytes\n * from the \"big end\", because the \"big\" byte is not uniformly random over the byte: it only has < 6 bits of\n * randomness, because it's the big end of a 254-bit field element.\n */\n\n let mut all_bytes: [[u8; 32]; N] = std::mem::zeroed();\n std::static_assert(N < 256, \"N too large\");\n for k in 0..N {\n let rand1: Field = derive_shared_secret_subkey(s_app, 2 * k);\n let rand2: Field = derive_shared_secret_subkey(s_app, 2 * k + 1);\n\n let rand1_bytes: [u8; 32] = rand1.to_be_bytes();\n let rand2_bytes: [u8; 32] = rand2.to_be_bytes();\n\n let mut bytes: [u8; 32] = [0; 32];\n for i in 0..16 {\n // We take bytes from the \"little end\" of the be-bytes arrays:\n let j = 32 - i - 1;\n bytes[i] = rand1_bytes[j];\n bytes[16 + i] = rand2_bytes[j];\n }\n\n all_bytes[k] = bytes;\n }\n\n all_bytes\n}\n\nfn derive_aes_symmetric_key_and_iv_from_uniformly_random_256_bits(\n many_random_256_bits: [[u8; 32]; N],\n) -> [([u8; 16], [u8; 16]); N] {\n // Many (sym_key, iv) pairs:\n let mut many_pairs: [([u8; 16], [u8; 16]); N] = std::mem::zeroed();\n for k in 0..N {\n let random_256_bits = many_random_256_bits[k];\n let mut sym_key = [0; 16];\n let mut iv = [0; 16];\n for i in 0..16 {\n sym_key[i] = random_256_bits[i];\n iv[i] = random_256_bits[i + 16];\n }\n many_pairs[k] = (sym_key, iv);\n }\n\n many_pairs\n}\n\npub fn derive_aes_symmetric_key_and_iv_from_shared_secret(s_app: Field) -> [([u8; 16], [u8; 16]); N] {\n let many_random_256_bits: [[u8; 32]; N] = extract_many_close_to_uniformly_random_256_bits_using_poseidon2(s_app);\n\n derive_aes_symmetric_key_and_iv_from_uniformly_random_256_bits(many_random_256_bits)\n}\n\npub struct AES128 {}\n\nimpl MessageEncryption for AES128 {\n\n /// AES128-CBC encryption for Aztec protocol messages.\n ///\n /// ## Overview\n ///\n /// The plaintext is an array of up to `MESSAGE_PLAINTEXT_LEN` (12) fields. The output is always exactly\n /// `MESSAGE_CIPHERTEXT_LEN` (15) fields, regardless of plaintext size. All output fields except the\n /// ephemeral public key are uniformly random `Field` values to any observer without knowledge of the\n /// shared secret, making all encrypted messages indistinguishable by size or content.\n ///\n /// ## PKCS#7 Padding\n ///\n /// AES operates on 16-byte blocks, so the plaintext must be padded to a multiple of 16. PKCS#7 padding always\n /// adds at least 1 byte (so the receiver can always detect and strip it), which means:\n /// - 1 B plaintext -> 15 B padding -> 16 B total\n /// - 15 B plaintext -> 1 B padding -> 16 B total\n /// - 16 B plaintext -> 16 B padding -> 32 B total (full extra block)\n ///\n /// In general: if the plaintext is already a multiple of 16, a full 16-byte padding block is appended.\n ///\n /// ## Encryption Steps\n ///\n /// **1. Body encryption.** The plaintext fields are serialized to bytes (32 bytes per field) and AES-128-CBC\n /// encrypted. Since 32 is a multiple of 16, PKCS#7 always adds a full 16-byte padding block (see above):\n ///\n /// ```text\n /// +---------------------------------------------+\n /// | body ct |\n /// | PlaintextLen*32 + 16 B |\n /// +-------------------------------+--------------+\n /// | encrypted plaintext fields | PKCS#7 (16B) |\n /// | (serialized at 32 B each) | |\n /// +-------------------------------+--------------+\n /// ```\n ///\n /// **2. Header encryption.** The byte length of `body_ct` is stored as a 2-byte big-endian integer. This 2-byte\n /// header plaintext is then AES-encrypted; PKCS#7 pads the remaining 14 bytes to fill one 16-byte AES block,\n /// producing a 16-byte header ciphertext:\n ///\n /// ```text\n /// +---------------------------+\n /// | header ct |\n /// | 16 B |\n /// +--------+------------------+\n /// | body ct| PKCS#7 (14B) |\n /// | length | |\n /// | (2 B) | |\n /// +--------+------------------+\n /// ```\n ///\n /// ## Wire Format\n ///\n /// Messages are transmitted as fields, not bytes. A field is ~254 bits and can safely store 31 whole bytes, so\n /// we need to pack our byte data into 31-byte chunks. This packing drives the wire format.\n ///\n /// **Step 1 -- Assemble bytes.** The ciphertexts are laid out in a byte array, padded with zero bytes to a\n /// multiple of 31 so it divides evenly into fields:\n ///\n /// ```text\n /// +------------+-------------------------+---------+\n /// | header ct | body ct | byte pad|\n /// | 16 B | PlaintextLen*32 + 16 B | (zeros) |\n /// +------------+-------------------------+---------+\n /// |<-------- padded to a multiple of 31 B -------->|\n /// ```\n ///\n /// **Step 2 -- Pack and mask.** The byte array is split into 31-byte chunks, each stored in one field. A\n /// Poseidon2-derived mask (see `derive_shared_secret_field_mask`) is added to each so that the resulting\n /// fields appear as uniformly random `Field` values to any observer without knowledge of the shared secret,\n /// hiding the fact that the underlying ciphertext consists of 128-bit AES blocks.\n ///\n /// **Step 3 -- Assemble ciphertext.** The ephemeral public key x-coordinate is prepended and random field padding\n /// is appended to fill to 15 fields:\n ///\n /// ```text\n /// +----------+-------------------------+-------------------+\n /// | eph_pk.x | masked message fields | random field pad |\n /// | | (packed 31 B per field) | (fills to 15) |\n /// +----------+-------------------------+-------------------+\n /// |<---------- MESSAGE_CIPHERTEXT_LEN = 15 fields -------->|\n /// ```\n ///\n /// ## Key Derivation\n ///\n /// The raw ECDH shared secret point is first app-siloed into a scalar `s_app` by hashing with the contract\n /// address (see\n /// [`compute_app_siloed_shared_secret`](crate::keys::ecdh_shared_secret::compute_app_siloed_shared_secret)).\n /// Two (key, IV) pairs are then derived from `s_app` via indexed Poseidon2 hashing: one pair for the body\n /// ciphertext and one for the header ciphertext.\n fn encrypt(\n plaintext: [Field; PlaintextLen],\n recipient: AztecAddress,\n contract_address: AztecAddress,\n ) -> [Field; MESSAGE_CIPHERTEXT_LEN] {\n std::static_assert(\n PlaintextLen <= MESSAGE_PLAINTEXT_LEN,\n \"Plaintext length exceeds MESSAGE_PLAINTEXT_LEN\",\n );\n\n // AES 128 operates on bytes, not fields, so we need to convert the fields to bytes. (This process is then\n // reversed when processing the message in `process_message_ciphertext`)\n let plaintext_bytes = fields_to_bytes(plaintext);\n\n // Derive ECDH shared secret with recipient using a fresh ephemeral keypair.\n let (eph_sk, eph_pk) = generate_positive_ephemeral_key_pair();\n\n let raw_shared_secret = derive_ecdh_shared_secret(\n eph_sk,\n recipient\n .to_address_point()\n .unwrap_or_else(|| {\n aztecnr_warn_log_format!(\n \"Attempted to encrypt message for an invalid recipient ({0})\",\n )(\n [recipient.to_field()],\n );\n\n // Safety: if the recipient is an invalid address, then it is not possible to encrypt a message for\n // them because we cannot establish a shared secret. This is never expected to occur during normal\n // operation. However, it is technically possible for us to receive an invalid address, and we must\n // therefore handle it. We could simply fail, but that'd introduce a potential security issue in\n // which an attacker forces a contract to encrypt a message for an invalid address, resulting in an\n // impossible transaction - this is sometimes called a 'king of the hill' attack. We choose instead\n // to not fail and encrypt the plaintext regardless using the shared secret that results from a\n // random valid address. The sender is free to choose this address and hence shared secret, but\n // this has no security implications as they already know not only the full plaintext but also the\n // ephemeral private key anyway.\n unsafe {\n random_address_point()\n }\n })\n .inner,\n );\n\n let s_app = compute_app_siloed_shared_secret(raw_shared_secret, contract_address);\n\n // It is safe to derive AES keys from `s_app` using Poseidon2 because `s_app` was derived from an ECDH shared\n // secret using an AztecAddress (the recipient). See the block comment in\n // `extract_many_close_to_uniformly_random_256_bits_using_poseidon2` for more info.\n let pairs = derive_aes_symmetric_key_and_iv_from_shared_secret::<2>(s_app);\n let (body_sym_key, body_iv) = pairs[0];\n let (header_sym_key, header_iv) = pairs[1];\n\n let ciphertext_bytes = aes128_encrypt(plaintext_bytes, body_iv, body_sym_key);\n\n // Each plaintext field is 32 bytes (a multiple of the 16-byte AES block\n // size), so PKCS#7 always appends a full 16-byte padding block:\n // |ciphertext| = PlaintextLen*32 + 16 = 16 * (1 + PlaintextLen*32 / 16)\n std::static_assert(\n ciphertext_bytes.len() == 16 * (1 + (PlaintextLen * 32) / 16),\n \"unexpected ciphertext length\",\n );\n\n // Encrypt a 2-byte header containing the body ciphertext length.\n let header_plaintext = encode_header(ciphertext_bytes.len());\n\n // Note: the aes128_encrypt builtin fn automatically appends bytes to the input, according to pkcs#7; hence why\n // the output `header_ciphertext_bytes` is 16 bytes larger than the input in this case.\n let header_ciphertext_bytes = aes128_encrypt(header_plaintext, header_iv, header_sym_key);\n // Verify expected header ciphertext size at compile time.\n std::static_assert(\n header_ciphertext_bytes.len() == HEADER_CIPHERTEXT_SIZE_IN_BYTES,\n \"unexpected ciphertext header length\",\n );\n\n // Assemble the message byte array:\n // [header_ct (16B)] [body_ct] [padding to mult of 31]\n let message_bytes_padding_to_mult_31 = get_arr_of_size__message_bytes_padding__from_PT::();\n\n let mut message_bytes = get_arr_of_size__message_bytes__from_PT::();\n\n std::static_assert(\n message_bytes.len() % 31 == 0,\n \"Unexpected error: message_bytes.len() should be divisible by 31, by construction.\",\n );\n\n let mut offset = 0;\n for i in 0..header_ciphertext_bytes.len() {\n message_bytes[offset + i] = header_ciphertext_bytes[i];\n }\n offset += header_ciphertext_bytes.len();\n\n for i in 0..ciphertext_bytes.len() {\n message_bytes[offset + i] = ciphertext_bytes[i];\n }\n offset += ciphertext_bytes.len();\n\n for i in 0..message_bytes_padding_to_mult_31.len() {\n message_bytes[offset + i] = message_bytes_padding_to_mult_31[i];\n }\n offset += message_bytes_padding_to_mult_31.len();\n\n // Ideally we would be able to have a static assert where we check that the offset would be such that we've\n // written to the entire log_bytes array, but we cannot since Noir does not treat the offset as a comptime\n // value (despite the values that it goes through being known at each stage). We instead check that the\n // computation used to obtain the offset computes the expected value (which we _can_ do in a static check), and\n // then add a cheap runtime check to also validate that the offset matches this.\n std::static_assert(\n header_ciphertext_bytes.len() + ciphertext_bytes.len() + message_bytes_padding_to_mult_31.len()\n == message_bytes.len(),\n \"unexpected message length\",\n );\n assert(offset == message_bytes.len(), \"unexpected encrypted message length\");\n\n // Pack message bytes into fields (31 bytes per field) and prepend eph_pk.x.\n let message_bytes_as_fields = bytes_to_fields(message_bytes);\n\n let mut ciphertext: [Field; MESSAGE_CIPHERTEXT_LEN] = [0; MESSAGE_CIPHERTEXT_LEN];\n\n ciphertext[0] = eph_pk.x;\n\n // Mask each content field with a Poseidon2-derived value, so that they appear as uniformly random `Field`\n // values\n let mut offset = 1;\n for i in 0..message_bytes_as_fields.len() {\n let mask = derive_shared_secret_field_mask(s_app, i as u32);\n ciphertext[offset + i] = message_bytes_as_fields[i] + mask;\n }\n offset += message_bytes_as_fields.len();\n\n // Pad with random fields so that padding is indistinguishable from masked data fields.\n for i in offset..MESSAGE_CIPHERTEXT_LEN {\n // Safety: we assume that the sender wants for the message to be private - a malicious one could simply\n // reveal its contents publicly. It is therefore fine to trust the sender to provide random padding.\n ciphertext[i] = unsafe { random() };\n }\n\n ciphertext\n }\n\n unconstrained fn decrypt(\n ciphertext: BoundedVec,\n recipient: AztecAddress,\n contract_address: AztecAddress,\n ) -> Option> {\n // Extract the ephemeral public key x-coordinate and masked fields, returning None for empty ciphertext.\n if ciphertext.len() > 0 {\n let masked_fields: BoundedVec =\n array::subbvec(ciphertext, EPH_PK_X_SIZE_IN_FIELDS);\n Option::some((ciphertext.get(0), masked_fields))\n } else {\n Option::none()\n }\n .and_then(|(eph_pk_x, masked_fields)| {\n // With the x-coordinate of the ephemeral public key we can reconstruct the point as we know that the\n // y-coordinate must be positive. This may fail however, as not all x-coordinates are on the curve. In\n // that case, we simply return `Option::none`.\n point_from_x_coord_and_sign(eph_pk_x, true).and_then(|eph_pk| {\n let s_app = get_shared_secret(recipient, eph_pk, contract_address);\n\n let unmasked_fields = masked_fields.mapi(|i, field| {\n let unmasked = unmask_field(s_app, i, field);\n // If we failed to unmask the field, we are dealing with the random padding. We'll ignore it\n // later, so we can simply set it to 0\n unmasked.unwrap_or(0)\n });\n let ciphertext_without_eph_pk_x = bytes_from_fields(unmasked_fields);\n\n // Derive symmetric keys:\n let pairs = derive_aes_symmetric_key_and_iv_from_shared_secret::<2>(s_app);\n let (body_sym_key, body_iv) = pairs[0];\n let (header_sym_key, header_iv) = pairs[1];\n\n // Extract the header ciphertext\n let header_start = 0;\n let header_ciphertext: [u8; HEADER_CIPHERTEXT_SIZE_IN_BYTES] =\n array::subarray(ciphertext_without_eph_pk_x.storage(), header_start);\n // We need to convert the array to a BoundedVec because the oracle expects a BoundedVec as it's\n // designed to work with messages with unknown length at compile time. This would not be necessary\n // here as the header ciphertext length is fixed. But we do it anyway to not have to have duplicate\n // oracles.\n let header_ciphertext_bvec =\n BoundedVec::::from_array(header_ciphertext);\n\n try_aes128_decrypt(header_ciphertext_bvec, header_iv, header_sym_key)\n // Extract ciphertext length from header (2 bytes, big-endian)\n .and_then(|header_plaintext| extract_ciphertext_length(header_plaintext))\n .filter(|ciphertext_length| ciphertext_length <= MESSAGE_PLAINTEXT_SIZE_IN_BYTES)\n .map(|ciphertext_length| {\n // Extract and decrypt main ciphertext\n let ciphertext_start = header_start + HEADER_CIPHERTEXT_SIZE_IN_BYTES;\n let ciphertext_with_padding: [u8; MESSAGE_PLAINTEXT_SIZE_IN_BYTES] =\n array::subarray(ciphertext_without_eph_pk_x.storage(), ciphertext_start);\n BoundedVec::from_parts(ciphertext_with_padding, ciphertext_length)\n })\n // Decrypt main ciphertext and return it\n .and_then(|ciphertext| try_aes128_decrypt(ciphertext, body_iv, body_sym_key))\n // Convert bytes back to fields (32 bytes per field). Returns None if the actual bytes are\n // not valid.\n .and_then(|plaintext_bytes| try_fields_from_bytes(plaintext_bytes))\n })\n })\n }\n}\n\n/// Encodes the body ciphertext length into a 2-byte big-endian header.\nfn encode_header(ciphertext_length: u32) -> [u8; 2] {\n [(ciphertext_length >> 8) as u8, ciphertext_length as u8]\n}\n\n/// Extracts the body ciphertext length from a decrypted header as a 2-byte big-endian integer.\n///\n/// Returns `Option::none()` if the header has fewer than 2 bytes.\nunconstrained fn extract_ciphertext_length(header: BoundedVec) -> Option {\n if header.len() >= 2 {\n Option::some(((header.get(0) as u32) << 8) | (header.get(1) as u32))\n } else {\n Option::none()\n }\n}\n\n/// 2^248: upper bound for values that fit in 31 bytes\nglobal TWO_POW_248: Field = 2.pow_32(248);\n\n/// Removes the Poseidon2-derived mask from a ciphertext field. Returns the unmasked value if it fits in 31 bytes\n/// (a content field), or `None` if it doesn't (random padding). Unconstrained to prevent accidental use in\n/// constrained context.\nunconstrained fn unmask_field(s_app: Field, index: u32, masked: Field) -> Option {\n let unmasked = masked - derive_shared_secret_field_mask(s_app, index);\n if unmasked.lt(TWO_POW_248) {\n Option::some(unmasked)\n } else {\n Option::none()\n }\n}\n\n/// Produces a random valid address point, i.e. one that is on the curve. This is equivalent to calling\n/// [`AztecAddress::to_address_point`] on a random valid address.\nunconstrained fn random_address_point() -> AddressPoint {\n let mut result = std::mem::zeroed();\n\n loop {\n // We simply produce random x coordinates until we find one that is on the curve. About half of the x\n // coordinates fulfill this condition, so this should only take a few iterations at most.\n let x_coord = random();\n let point = point_from_x_coord_and_sign(x_coord, true);\n if point.is_some() {\n result = AddressPoint { inner: point.unwrap() };\n break;\n }\n }\n\n result\n}\n\nmod test {\n use crate::{\n keys::ecdh_shared_secret::{compute_app_siloed_shared_secret, derive_ecdh_shared_secret},\n messages::{\n encoding::{HEADER_CIPHERTEXT_SIZE_IN_BYTES, MESSAGE_PLAINTEXT_LEN, MESSAGE_PLAINTEXT_SIZE_IN_BYTES},\n encryption::message_encryption::MessageEncryption,\n },\n test::helpers::test_environment::TestEnvironment,\n };\n use crate::protocol::{address::AztecAddress, traits::FromField};\n use super::{AES128, encode_header, random_address_point};\n use std::{embedded_curve_ops::EmbeddedCurveScalar, test::OracleMock};\n\n #[test]\n unconstrained fn encrypt_decrypt_deterministic() {\n let env = TestEnvironment::new();\n\n // Message decryption requires oracles that are only available during private execution\n env.private_context(|context| {\n let contract_address = context.this_address();\n let plaintext = [1, 2, 3];\n\n let recipient = AztecAddress::from_field(\n 0x25afb798ea6d0b8c1618e50fdeafa463059415013d3b7c75d46abf5e242be70c,\n );\n\n // Mock random values for deterministic test\n let eph_sk = 0x1358d15019d4639393d62b97e1588c095957ce74a1c32d6ec7d62fe6705d9538;\n let _ = OracleMock::mock(\"aztec_utl_getRandomField\").returns(eph_sk).times(1);\n\n let randomness = 0x0101010101010101010101010101010101010101010101010101010101010101;\n let _ = OracleMock::mock(\"aztec_utl_getRandomField\").returns(randomness).times(1000000);\n\n let _ = OracleMock::mock(\"aztec_prv_getNextAppTagAsSender\").returns(42);\n\n // Encrypt the message\n let encrypted_message = BoundedVec::from_array(AES128::encrypt(plaintext, recipient, contract_address));\n\n // Compute the same app-siloed shared secret that the oracle would return\n let raw_shared_secret = derive_ecdh_shared_secret(\n EmbeddedCurveScalar::from_field(eph_sk),\n recipient.to_address_point().unwrap().inner,\n );\n let s_app = compute_app_siloed_shared_secret(raw_shared_secret, contract_address);\n\n let _ = OracleMock::mock(\"aztec_utl_getSharedSecret\").returns(s_app);\n\n // Decrypt the message\n let decrypted = AES128::decrypt(encrypted_message, recipient, contract_address).unwrap();\n\n // The decryption function spits out a BoundedVec because it's designed to work with messages with unknown\n // length at compile time. For this reason we need to convert the original input to a BoundedVec.\n let plaintext_bvec = BoundedVec::::from_array(plaintext);\n\n // Verify decryption matches original plaintext\n assert_eq(decrypted, plaintext_bvec, \"Decrypted bytes should match original plaintext\");\n\n // The following is a workaround of \"struct is never constructed\" Noir compilation error (we only ever use\n // static methods of the struct).\n let _ = AES128 {};\n });\n }\n\n #[test]\n unconstrained fn encrypt_decrypt_random() {\n // Same as `encrypt_decrypt_deterministic`, except we don't mock any of the oracles and rely on\n // `TestEnvironment` instead.\n let mut env = TestEnvironment::new();\n\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n let plaintext = [1, 2, 3];\n let ciphertext = AES128::encrypt(plaintext, recipient, contract_address);\n\n assert_eq(\n AES128::decrypt(\n BoundedVec::from_array(ciphertext),\n recipient,\n contract_address,\n )\n .unwrap(),\n BoundedVec::from_array(plaintext),\n );\n });\n }\n\n #[test]\n unconstrained fn encrypt_to_invalid_address() {\n // x = 3 is a non-residue for this curve, resulting in an invalid address\n let invalid_address = AztecAddress { inner: 3 };\n let contract_address = AztecAddress { inner: 42 };\n\n let _ = AES128::encrypt([1, 2, 3, 4], invalid_address, contract_address);\n }\n\n // Documents the PKCS#7 padding behavior that `encrypt` relies on (see its static_assert).\n #[test]\n fn pkcs7_padding_always_adds_at_least_one_byte() {\n let key = [0 as u8; 16];\n let iv = [0 as u8; 16];\n\n // 1 byte input + 15 bytes padding = 16 bytes\n assert_eq(std::aes128::aes128_encrypt([0; 1], iv, key).len(), 16);\n\n // 15 bytes input + 1 byte padding = 16 bytes\n assert_eq(std::aes128::aes128_encrypt([0; 15], iv, key).len(), 16);\n\n // 16 bytes input (block-aligned) + full 16-byte padding block = 32 bytes\n assert_eq(std::aes128::aes128_encrypt([0; 16], iv, key).len(), 32);\n }\n\n #[test]\n unconstrained fn encrypt_decrypt_max_size_plaintext() {\n let mut env = TestEnvironment::new();\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n let mut plaintext = [0; MESSAGE_PLAINTEXT_LEN];\n for i in 0..MESSAGE_PLAINTEXT_LEN {\n plaintext[i] = i as Field;\n }\n let ciphertext = AES128::encrypt(plaintext, recipient, contract_address);\n\n assert_eq(\n AES128::decrypt(\n BoundedVec::from_array(ciphertext),\n recipient,\n contract_address,\n )\n .unwrap(),\n BoundedVec::from_array(plaintext),\n );\n });\n }\n\n #[test(should_fail_with = \"Plaintext length exceeds MESSAGE_PLAINTEXT_LEN\")]\n unconstrained fn encrypt_oversized_plaintext() {\n let address = AztecAddress { inner: 3 };\n let contract_address = AztecAddress { inner: 42 };\n let plaintext: [Field; MESSAGE_PLAINTEXT_LEN + 1] = [0; MESSAGE_PLAINTEXT_LEN + 1];\n let _ = AES128::encrypt(plaintext, address, contract_address);\n }\n\n #[test]\n unconstrained fn random_address_point_produces_valid_points() {\n // About half of random addresses are invalid, so testing just a couple gives us high confidence that\n // `random_address_point` is indeed producing valid addresses.\n for _ in 0..10 {\n let random_address = AztecAddress { inner: random_address_point().inner.x };\n assert(random_address.to_address_point().is_some());\n }\n }\n\n #[test]\n unconstrained fn decrypt_invalid_ephemeral_public_key() {\n let mut env = TestEnvironment::new();\n\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n let plaintext = [1, 2, 3, 4];\n let ciphertext = AES128::encrypt(plaintext, recipient, contract_address);\n\n // The first field of the ciphertext is the x-coordinate of the ephemeral public key. We set it to a known\n // non-residue (3), causing `decrypt` to fail to produce a decryption shared secret.\n let mut bad_ciphertext = BoundedVec::from_array(ciphertext);\n bad_ciphertext.set(0, 3);\n\n assert(AES128::decrypt(bad_ciphertext, recipient, contract_address).is_none());\n });\n }\n\n #[test]\n unconstrained fn decrypt_returns_none_on_empty_ciphertext() {\n let mut env = TestEnvironment::new();\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n assert(AES128::decrypt(BoundedVec::new(), recipient, contract_address).is_none());\n });\n }\n\n // Mocks the header AES decrypt oracle to return an empty result. The TS oracle never throws on invalid\n // input: it decrypts to garbage bytes or returns empty\n #[test]\n unconstrained fn decrypt_returns_none_on_empty_header() {\n let mut env = TestEnvironment::new();\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n let plaintext = [1, 2, 3];\n let ciphertext = BoundedVec::from_array(AES128::encrypt(plaintext, recipient, contract_address));\n\n let empty_header = BoundedVec::::new();\n let _ = OracleMock::mock(\"aztec_utl_decryptAes128\").returns(Option::some(empty_header)).times(1);\n\n assert(AES128::decrypt(ciphertext, recipient, contract_address).is_none());\n });\n }\n\n // Mocks the header oracle to return a 2-byte header that decodes to a ciphertext_length one past the maximum\n // allowed value, verifying the edge case is handled correctly.\n #[test]\n unconstrained fn decrypt_returns_none_on_oversized_ciphertext_length() {\n let mut env = TestEnvironment::new();\n let recipient = env.create_light_account();\n\n env.private_context(|context| {\n let contract_address = context.this_address();\n let plaintext = [1, 2, 3];\n let ciphertext = BoundedVec::from_array(AES128::encrypt(plaintext, recipient, contract_address));\n\n let bad_header = BoundedVec::::from_array(encode_header(\n MESSAGE_PLAINTEXT_SIZE_IN_BYTES + 1,\n ));\n let _ = OracleMock::mock(\"aztec_utl_decryptAes128\").returns(Option::some(bad_header)).times(1);\n\n assert(AES128::decrypt(ciphertext, recipient, contract_address).is_none());\n });\n }\n\n}\n" }, "155": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/logs/event.nr", "source": "use crate::{\n event::{event_interface::EventInterface, EventSelector},\n messages::{\n encoding::{encode_message, MAX_MESSAGE_CONTENT_LEN, MESSAGE_EXPANDED_METADATA_LEN},\n msg_type::PRIVATE_EVENT_MSG_TYPE_ID,\n },\n utils::array,\n};\nuse crate::protocol::traits::{FromField, Serialize, ToField};\n\n/// The number of fields in a private event message content that are not the event's serialized representation (1 field\n/// for randomness).\npub(crate) global PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN: u32 = 1;\npub(crate) global PRIVATE_EVENT_MSG_PLAINTEXT_RANDOMNESS_INDEX: u32 = 0;\n\n/// The maximum length of the packed representation of an event's contents. This is limited by private log size,\n/// encryption overhead and extra fields in the message (e.g. message type id, randomness, etc.).\npub global MAX_EVENT_SERIALIZED_LEN: u32 = MAX_MESSAGE_CONTENT_LEN - PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN;\n\n/// Creates the plaintext for a private event message (i.e. one of type [`PRIVATE_EVENT_MSG_TYPE_ID`]).\n///\n/// This plaintext is meant to be decoded via [`decode_private_event_message`].\npub fn encode_private_event_message(\n event: Event,\n randomness: Field,\n) -> [Field; PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N + MESSAGE_EXPANDED_METADATA_LEN]\nwhere\n Event: EventInterface + Serialize,\n{\n // We use `Serialize` because we want for events to be processable by off-chain actors, e.g. block explorers,\n // wallets and apps, without having to rely on contract invocation. If we used `Packable` we'd need to call utility\n // functions in order to unpack events, which would introduce a level of complexity we don't currently think is\n // worth the savings in DA (for public events) and proving time (when encrypting private event messages).\n let serialized_event = event.serialize();\n\n // If PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN is changed, causing the assertion below to fail, then the\n // encoding below must be updated as well.\n std::static_assert(\n PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 1,\n \"unexpected value for PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN\",\n );\n\n let mut msg_plaintext = [0; PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N];\n msg_plaintext[PRIVATE_EVENT_MSG_PLAINTEXT_RANDOMNESS_INDEX] = randomness;\n\n for i in 0..serialized_event.len() {\n msg_plaintext[PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + i] = serialized_event[i];\n }\n\n // The event type id is stored in the message metadata\n encode_message(\n PRIVATE_EVENT_MSG_TYPE_ID,\n Event::get_event_type_id().to_field() as u64,\n msg_plaintext,\n )\n}\n\n/// Decodes the plaintext from a private event message (i.e. one of type [`PRIVATE_EVENT_MSG_TYPE_ID`]).\n///\n/// Returns `None` if `msg_content` has too few fields. This plaintext is meant to have originated\n/// from [`encode_private_event_message`].\n///\n/// Note that while [`encode_private_event_message`] returns a fixed-size array, this function takes a [`BoundedVec`]\n/// instead. This is because when decoding we're typically processing runtime-sized plaintexts, more specifically,\n/// those that originate from [`crate::messages::encryption::message_encryption::MessageEncryption::decrypt`].\npub(crate) unconstrained fn decode_private_event_message(\n msg_metadata: u64,\n msg_content: BoundedVec,\n) -> Option<(EventSelector, Field, BoundedVec)> {\n if msg_content.len() <= PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN {\n Option::none()\n } else {\n let event_type_id = EventSelector::from_field(msg_metadata as Field);\n\n // If PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN is changed, causing the assertion below to fail, then the\n // destructuring of the private event message encoding below must be updated as well.\n std::static_assert(\n PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 1,\n \"unexpected value for PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN\",\n );\n\n let randomness = msg_content.get(PRIVATE_EVENT_MSG_PLAINTEXT_RANDOMNESS_INDEX);\n let serialized_event = array::subbvec(msg_content, PRIVATE_EVENT_MSG_PLAINTEXT_RESERVED_FIELDS_LEN);\n\n Option::some((event_type_id, randomness, serialized_event))\n }\n}\n\nmod test {\n use crate::{\n event::event_interface::EventInterface,\n messages::{\n encoding::decode_message,\n logs::event::{decode_private_event_message, encode_private_event_message},\n msg_type::PRIVATE_EVENT_MSG_TYPE_ID,\n },\n };\n use crate::protocol::traits::Serialize;\n use crate::test::mocks::mock_event::MockEvent;\n\n global VALUE: Field = 7;\n global RANDOMNESS: Field = 10;\n\n #[test]\n unconstrained fn encode_decode() {\n let event = MockEvent::new(VALUE).build_event();\n\n let message_plaintext = encode_private_event_message(event, RANDOMNESS);\n\n let (msg_type_id, msg_metadata, msg_content) =\n decode_message(BoundedVec::from_array(message_plaintext)).unwrap();\n\n assert_eq(msg_type_id, PRIVATE_EVENT_MSG_TYPE_ID);\n\n let (event_type_id, randomness, serialized_event) =\n decode_private_event_message(msg_metadata, msg_content).unwrap();\n\n assert_eq(event_type_id, MockEvent::get_event_type_id());\n assert_eq(randomness, RANDOMNESS);\n assert_eq(serialized_event, BoundedVec::from_array(event.serialize()));\n }\n\n #[test]\n unconstrained fn decode_empty_content_returns_none() {\n let empty = BoundedVec::new();\n assert(decode_private_event_message(0, empty).is_none());\n }\n\n #[test]\n unconstrained fn decode_with_only_reserved_fields_returns_none() {\n let content = BoundedVec::from_array([0]);\n assert(decode_private_event_message(0, content).is_none());\n }\n}\n" }, "157": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/logs/note.nr", "source": "use crate::{\n messages::{\n encoding::{encode_message, MAX_MESSAGE_CONTENT_LEN, MESSAGE_EXPANDED_METADATA_LEN},\n msg_type::PRIVATE_NOTE_MSG_TYPE_ID,\n },\n note::note_interface::NoteType,\n utils::array,\n};\nuse crate::protocol::{address::AztecAddress, traits::{FromField, Packable, ToField}};\n\n/// The number of fields in a private note message content that are not the note's packed representation.\npub(crate) global PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN: u32 = 3;\n\npub(crate) global PRIVATE_NOTE_MSG_PLAINTEXT_OWNER_INDEX: u32 = 0;\npub(crate) global PRIVATE_NOTE_MSG_PLAINTEXT_STORAGE_SLOT_INDEX: u32 = 1;\npub(crate) global PRIVATE_NOTE_MSG_PLAINTEXT_RANDOMNESS_INDEX: u32 = 2;\n\n/// The maximum length of the packed representation of a note's contents. This is limited by private log size,\n/// encryption overhead and extra fields in the message (e.g. message type id, storage slot, randomness, etc.).\npub global MAX_NOTE_PACKED_LEN: u32 = MAX_MESSAGE_CONTENT_LEN - PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN;\n\n/// Creates the plaintext for a private note message (i.e. one of type [`PRIVATE_NOTE_MSG_TYPE_ID`]).\n///\n/// This plaintext is meant to be decoded via [`decode_private_note_message`].\npub fn encode_private_note_message(\n note: Note,\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n) -> [Field; PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N + MESSAGE_EXPANDED_METADATA_LEN]\nwhere\n Note: NoteType + Packable,\n{\n let packed_note = note.pack();\n\n // If PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN is changed, causing the assertion below to fail, then the\n // encoding below must be updated as well.\n std::static_assert(\n PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 3,\n \"unexpected value for PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN\",\n );\n\n let mut msg_content = [0; PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N];\n msg_content[PRIVATE_NOTE_MSG_PLAINTEXT_OWNER_INDEX] = owner.to_field();\n msg_content[PRIVATE_NOTE_MSG_PLAINTEXT_STORAGE_SLOT_INDEX] = storage_slot;\n msg_content[PRIVATE_NOTE_MSG_PLAINTEXT_RANDOMNESS_INDEX] = randomness;\n for i in 0..packed_note.len() {\n msg_content[PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + i] = packed_note[i];\n }\n\n // Notes use the note type id for metadata\n encode_message(PRIVATE_NOTE_MSG_TYPE_ID, Note::get_id() as u64, msg_content)\n}\n\n/// Decodes the plaintext from a private note message (i.e. one of type [`PRIVATE_NOTE_MSG_TYPE_ID`]).\n///\n/// Returns `None` if `msg_content` has too few fields. This plaintext is meant to have originated\n/// from [`encode_private_note_message`].\n///\n/// Note that while [`encode_private_note_message`] returns a fixed-size array, this function takes a [`BoundedVec`]\n/// instead. This is because when decoding we're typically processing runtime-sized plaintexts, more specifically,\n/// those that originate from [`crate::messages::encryption::message_encryption::MessageEncryption::decrypt`].\npub(crate) unconstrained fn decode_private_note_message(\n msg_metadata: u64,\n msg_content: BoundedVec,\n) -> Option<(Field, AztecAddress, Field, Field, BoundedVec)> {\n if msg_content.len() <= PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN {\n Option::none()\n } else {\n let note_type_id = msg_metadata as Field; // TODO: make note type id not be a full field\n\n // If PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN is changed, causing the assertion below to fail, then the\n // decoding below must be updated as well.\n std::static_assert(\n PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 3,\n \"unexpected value for PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN\",\n );\n\n let owner = AztecAddress::from_field(msg_content.get(PRIVATE_NOTE_MSG_PLAINTEXT_OWNER_INDEX));\n let storage_slot = msg_content.get(PRIVATE_NOTE_MSG_PLAINTEXT_STORAGE_SLOT_INDEX);\n let randomness = msg_content.get(PRIVATE_NOTE_MSG_PLAINTEXT_RANDOMNESS_INDEX);\n let packed_note = array::subbvec(msg_content, PRIVATE_NOTE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN);\n\n Option::some((note_type_id, owner, storage_slot, randomness, packed_note))\n }\n}\n\nmod test {\n use crate::{\n messages::{\n encoding::decode_message,\n logs::note::{decode_private_note_message, encode_private_note_message, MAX_NOTE_PACKED_LEN},\n msg_type::PRIVATE_NOTE_MSG_TYPE_ID,\n },\n note::note_interface::NoteType,\n };\n use crate::protocol::{address::AztecAddress, traits::{FromField, Packable}};\n use crate::test::mocks::mock_note::MockNote;\n\n global VALUE: Field = 7;\n global OWNER: AztecAddress = AztecAddress::from_field(8);\n global STORAGE_SLOT: Field = 9;\n global RANDOMNESS: Field = 10;\n\n #[test]\n unconstrained fn encode_decode() {\n let note = MockNote::new(VALUE).build_note();\n\n let message_plaintext = encode_private_note_message(note, OWNER, STORAGE_SLOT, RANDOMNESS);\n\n let (msg_type_id, msg_metadata, msg_content) =\n decode_message(BoundedVec::from_array(message_plaintext)).unwrap();\n\n assert_eq(msg_type_id, PRIVATE_NOTE_MSG_TYPE_ID);\n\n let (note_type_id, owner, storage_slot, randomness, packed_note) =\n decode_private_note_message(msg_metadata, msg_content).unwrap();\n\n assert_eq(note_type_id, MockNote::get_id());\n assert_eq(owner, OWNER);\n assert_eq(storage_slot, STORAGE_SLOT);\n assert_eq(randomness, RANDOMNESS);\n assert_eq(packed_note, BoundedVec::from_array(note.pack()));\n }\n\n #[derive(Packable)]\n struct MaxSizeNote {\n data: [Field; MAX_NOTE_PACKED_LEN],\n }\n\n impl NoteType for MaxSizeNote {\n fn get_id() -> Field {\n 0\n }\n }\n\n #[test]\n unconstrained fn encode_decode_max_size_note() {\n let mut data = [0; MAX_NOTE_PACKED_LEN];\n for i in 0..MAX_NOTE_PACKED_LEN {\n data[i] = i as Field;\n }\n let note = MaxSizeNote { data };\n\n let encoded = encode_private_note_message(note, OWNER, STORAGE_SLOT, RANDOMNESS);\n let (msg_type_id, msg_metadata, msg_content) = decode_message(BoundedVec::from_array(encoded)).unwrap();\n\n assert_eq(msg_type_id, PRIVATE_NOTE_MSG_TYPE_ID);\n\n let (note_type_id, owner, storage_slot, randomness, packed_note) =\n decode_private_note_message(msg_metadata, msg_content).unwrap();\n\n assert_eq(note_type_id, MaxSizeNote::get_id());\n assert_eq(owner, OWNER);\n assert_eq(storage_slot, STORAGE_SLOT);\n assert_eq(randomness, RANDOMNESS);\n assert_eq(packed_note, BoundedVec::from_array(data));\n }\n\n #[derive(Packable)]\n struct OversizedNote {\n data: [Field; MAX_NOTE_PACKED_LEN + 1],\n }\n\n impl NoteType for OversizedNote {\n fn get_id() -> Field {\n 0\n }\n }\n\n #[test(should_fail_with = \"Invalid message content: it must have a length of at most MAX_MESSAGE_CONTENT_LEN\")]\n fn encode_oversized_note_fails() {\n let note = OversizedNote { data: [0; MAX_NOTE_PACKED_LEN + 1] };\n let _ = encode_private_note_message(note, OWNER, STORAGE_SLOT, RANDOMNESS);\n }\n\n #[test]\n unconstrained fn decode_empty_content_returns_none() {\n let empty = BoundedVec::new();\n assert(decode_private_note_message(0, empty).is_none());\n }\n\n #[test]\n unconstrained fn decode_with_only_reserved_fields_returns_none() {\n let content = BoundedVec::from_array([0, 0, 0]);\n assert(decode_private_note_message(0, content).is_none());\n }\n}\n" }, "158": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/logs/partial_note.nr", "source": "use crate::{\n messages::{\n encoding::{encode_message, MAX_MESSAGE_CONTENT_LEN, MESSAGE_EXPANDED_METADATA_LEN},\n msg_type::PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID,\n },\n note::note_interface::NoteType,\n utils::array,\n};\nuse crate::protocol::{address::AztecAddress, traits::{FromField, Packable, ToField}};\n\n/// The number of fields in a private note message content that are not the note's packed representation.\npub(crate) global PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN: u32 = 3;\npub(crate) global PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_OWNER_INDEX: u32 = 0;\npub(crate) global PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RANDOMNESS_INDEX: u32 = 1;\npub(crate) global PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NOTE_COMPLETION_LOG_TAG_INDEX: u32 = 2;\n\n/// Partial notes have a maximum packed length of their private fields bound by extra content in their private message\n/// (e.g. the storage slot, note completion log tag, etc.).\npub global MAX_PARTIAL_NOTE_PRIVATE_PACKED_LEN: u32 =\n MAX_MESSAGE_CONTENT_LEN - PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN;\n\n/// Creates the plaintext for a partial note private message (i.e. one of type [`PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID`]).\n///\n/// This plaintext is meant to be decoded via [`decode_partial_note_private_message`].\npub fn encode_partial_note_private_message(\n partial_note_private_content: PartialNotePrivateContent,\n owner: AztecAddress,\n randomness: Field,\n note_completion_log_tag: Field,\n ) -> [Field; PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N + MESSAGE_EXPANDED_METADATA_LEN]\nwhere\n PartialNotePrivateContent: NoteType + Packable,\n{\n let packed_private_content = partial_note_private_content.pack();\n\n // If PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NON_NOTE_FIELDS_LEN is changed, causing the assertion below to fail, then\n // the encoding below must be updated as well.\n std::static_assert(\n PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 3,\n \"unexpected value for PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NON_NOTE_FIELDS_LEN\",\n );\n\n let mut msg_content =\n [0; PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + ::N];\n msg_content[PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_OWNER_INDEX] = owner.to_field();\n msg_content[PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RANDOMNESS_INDEX] = randomness;\n msg_content[PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NOTE_COMPLETION_LOG_TAG_INDEX] = note_completion_log_tag;\n\n for i in 0..packed_private_content.len() {\n msg_content[PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN + i] = packed_private_content[i];\n }\n\n encode_message(\n PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID,\n // Notes use the note type id for metadata\n PartialNotePrivateContent::get_id() as u64,\n msg_content,\n )\n}\n\n/// Decodes the plaintext from a partial note private message (i.e. one of type\n/// [`PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID`]).\n///\n/// Returns `None` if `msg_content` has too few fields. This plaintext is meant to have originated\n/// from [`encode_partial_note_private_message`].\n///\n/// Note that while [`encode_partial_note_private_message`] returns a fixed-size array, this function takes a\n/// [`BoundedVec`] instead. This is because when decoding we're typically processing runtime-sized plaintexts, more\n/// specifically, those that originate from\n/// [`crate::messages::encryption::message_encryption::MessageEncryption::decrypt`].\npub(crate) unconstrained fn decode_partial_note_private_message(\n msg_metadata: u64,\n msg_content: BoundedVec,\n) -> Option<(AztecAddress, Field, Field, Field, BoundedVec)> {\n if msg_content.len() < PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN {\n Option::none()\n } else {\n let note_type_id: Field = msg_metadata as Field; // TODO: make note type id not be a full field\n\n // If PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NON_NOTE_FIELDS_LEN is changed, causing the assertion below to fail,\n // then the destructuring of the partial note private message encoding below must be updated as well.\n std::static_assert(\n PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN == 3,\n \"unexpected value for PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NON_NOTE_FIELDS_LEN\",\n );\n\n // We currently have three fields that are not the partial note's packed representation, which are the owner,\n // the randomness, and the note completion log tag.\n let owner = AztecAddress::from_field(\n msg_content.get(PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_OWNER_INDEX),\n );\n let randomness = msg_content.get(PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RANDOMNESS_INDEX);\n let note_completion_log_tag = msg_content.get(PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_NOTE_COMPLETION_LOG_TAG_INDEX);\n\n let packed_private_note_content: BoundedVec = array::subbvec(\n msg_content,\n PARTIAL_NOTE_PRIVATE_MSG_PLAINTEXT_RESERVED_FIELDS_LEN,\n );\n\n Option::some(\n (owner, randomness, note_completion_log_tag, note_type_id, packed_private_note_content),\n )\n }\n}\n\nmod test {\n use crate::{\n messages::{\n encoding::decode_message,\n logs::partial_note::{decode_partial_note_private_message, encode_partial_note_private_message},\n msg_type::PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID,\n },\n note::note_interface::NoteType,\n };\n use crate::protocol::{address::AztecAddress, traits::{FromField, Packable}};\n use crate::test::mocks::mock_note::MockNote;\n\n global VALUE: Field = 7;\n global OWNER: AztecAddress = AztecAddress::from_field(8);\n global RANDOMNESS: Field = 10;\n global NOTE_COMPLETION_LOG_TAG: Field = 11;\n\n #[test]\n unconstrained fn encode_decode() {\n // Note that here we use MockNote as the private fields of a partial note\n let note = MockNote::new(VALUE).build_note();\n\n let message_plaintext = encode_partial_note_private_message(note, OWNER, RANDOMNESS, NOTE_COMPLETION_LOG_TAG);\n\n let (msg_type_id, msg_metadata, msg_content) =\n decode_message(BoundedVec::from_array(message_plaintext)).unwrap();\n\n assert_eq(msg_type_id, PARTIAL_NOTE_PRIVATE_MSG_TYPE_ID);\n\n let (owner, randomness, note_completion_log_tag, note_type_id, packed_note) =\n decode_partial_note_private_message(msg_metadata, msg_content).unwrap();\n\n assert_eq(note_type_id, MockNote::get_id());\n assert_eq(owner, OWNER);\n assert_eq(randomness, RANDOMNESS);\n assert_eq(note_completion_log_tag, NOTE_COMPLETION_LOG_TAG);\n assert_eq(packed_note, BoundedVec::from_array(note.pack()));\n }\n\n #[test]\n unconstrained fn decode_empty_content_returns_none() {\n let empty = BoundedVec::new();\n assert(decode_partial_note_private_message(0, empty).is_none());\n }\n\n #[test]\n unconstrained fn decode_succeeds_with_only_reserved_fields() {\n let content = BoundedVec::from_array([0, 0, 0]);\n let (_, _, _, _, packed_note) = decode_partial_note_private_message(0, content).unwrap();\n assert_eq(packed_note.len(), 0);\n }\n}\n" }, "159": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/logs/utils.nr", "source": "use crate::oracle::notes::{get_next_app_tag_as_sender, get_sender_for_tags};\nuse crate::protocol::address::AztecAddress;\n\n// TODO(#14565): Add constrained tagging\n/// Returns the next discovery tag for a private log sent to `recipient`.\n///\n/// Private logs are encrypted, so the recipient cannot tell which logs are meant for it just by looking at them.\n/// To solve this, sender and recipient derive a shared secret from their keys, and from that secret they produce a\n/// sequence of one-time tags (tag_0, tag_1, ...). The recipient scans for these tags because it can compute the same\n/// sequence. This function returns the next raw (not domain-separated) tag in the sequence.\npub(crate) fn compute_discovery_tag(recipient: AztecAddress) -> Field {\n // Safety: we assume that the sender wants for the recipient to find the tagged note, and therefore that they will\n // cooperate and use the correct tag. Usage of a bad tag will result in the recipient not being able to find the\n // note automatically.\n unsafe {\n let sender = get_sender_for_tags().expect(\n f\"Sender for tags is not set when emitting a private log. Set it by calling `set_sender_for_tags(...)`.\",\n );\n get_next_app_tag_as_sender(sender, recipient)\n }\n}\n\nmod test {\n use crate::protocol::{address::AztecAddress, traits::FromField};\n use super::compute_discovery_tag;\n use std::test::OracleMock;\n\n #[test(should_fail_with = \"Sender for tags is not set\")]\n unconstrained fn no_tag_sender() {\n let recipient = AztecAddress::from_field(2);\n let _ = OracleMock::mock(\"aztec_prv_getSenderForTags\").returns(Option::::none());\n let _ = OracleMock::mock(\"aztec_prv_getNextAppTagAsSender\").returns(42);\n let _ = compute_discovery_tag(recipient);\n }\n\n #[test]\n unconstrained fn returns_oracle_tag() {\n let sender = AztecAddress::from_field(1);\n let recipient = AztecAddress::from_field(2);\n let expected_tag = 42;\n let _ = OracleMock::mock(\"aztec_prv_getSenderForTags\").returns(Option::some(sender));\n let _ = OracleMock::mock(\"aztec_prv_getNextAppTagAsSender\").returns(expected_tag);\n assert_eq(compute_discovery_tag(recipient), expected_tag);\n }\n}\n" }, "160": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/message_delivery.nr", "source": "use crate::{\n context::PrivateContext,\n messages::{\n encryption::{aes128::AES128, message_encryption::MessageEncryption},\n logs::utils::compute_discovery_tag,\n offchain_messages::deliver_offchain_message,\n },\n utils::remove_constraints::remove_constraints_if,\n};\nuse crate::protocol::{address::AztecAddress, constants::DOM_SEP__UNCONSTRAINED_MSG_LOG_TAG, hash::compute_log_tag};\n\n/// Placeholder struct until Noir adds `enum` support.\n///\n/// See [`MessageDelivery`] instead.\npub struct MessageDeliveryEnum {\n /// Delivers the message fully off-chain, with no guarantees whatsoever.\n ///\n /// ## Use Cases\n ///\n /// This delivery method is suitable when the sender is required to send the message to the recipient because of\n /// some external reason, and where the sender is able to directly contact the recipient off-chain. In these cases,\n /// it might be unnecessary to force the sender to spend proving time guaranteeing message correctness, or to pay\n /// transaction fees in order to use the chain as a medium.\n ///\n /// For example, if performing a payment in exchange for some good or service, the recipient will only accept the\n /// payment once they receive note and event messages, allowing them to observe the balance increase. The sender\n /// has no reason not to deliver the message correctly to the recipient, and in all likelihood has a way to send it\n /// to them.\n ///\n /// Similarly, in games and other applications that might rely on some server processing state, players might be\n /// required to update the server with their current state.\n ///\n /// Finally, any messages for which the recipient is a local account (e.g.: the message for the change note in a\n /// token transfer) work well with this delivery option, since the sender would only be harming themselves by not\n /// delivering correctly.\n ///\n /// ## Guarantees\n ///\n /// The sender of the message is free to both not deliver the message to the recipient at all (since no delivery\n /// occurs on-chain), and to alter the message contents (possibly resulting in an undecryptable message, or one\n /// with incorrect content).\n ///\n /// An undecryptable or otherwise invalid note or event message will however simply be ignored by the recipient,\n /// who can always validate the existence of the note or event on-chain.\n ///\n /// Because the message is not stored on-chain, it is the sender's (and eventually recipient's) responsability to\n /// back it up and make sure it is not lost.\n ///\n /// ## Costs\n ///\n /// Because no data is emitted on-chain, this delivery option is the cheapest one in terms of transaction fees:\n /// these are zero.\n ///\n /// Additionally, no circuit gates are introduced when the message is encrypted, since its provenance cannot be\n /// authenticated anyway. Therefore, off-chain messages do not affect proving time at all.\n ///\n /// ## Privacy\n ///\n /// No information is revelead on-chain about sender, recipient, or the message contents. The message itself\n /// reveals no information about the sender or recipient, and requires knowledge of the recipient's private address\n /// keys in order to obtain the plaintext.\n pub OFFCHAIN: u8,\n\n /// Delivers the message on-chain, but with no guarantees on the content.\n ///\n /// ## Use Cases\n ///\n /// This delivery method is suitable when the sender is required to send the message to the recipient because of\n /// some external reason, but might not have a way to contact them off-chain, or does not wish to bear the\n /// responsability of keeping backups. In these cases, it might be unnecessary to force the sender to spend proving\n /// time guaranteeing message correctness.\n ///\n /// For example, when depositing funds into an escrow or sale contract the sender may not have an off-chain channel\n /// through which they could send the recipient a message. But since the recipient will not acknowledge receipt and\n /// proceed with the exchange unless they obtain the message, the sender has no reason not to deliver the message\n /// correctly.\n ///\n /// ## Guarantees\n ///\n /// The message will be stored on-chain in a private log, as part of the transaction's effects, and will be\n /// retrievable in the future without requiring any backups. However, the sender is free to alter the message\n /// contents (possibly resulting in an undecryptable message, or one with incorrect content), including making it\n /// so that the recipient cannot find it.\n ///\n /// An undecryptable or otherwise invalid note or event message will however simply be ignored by the recipient,\n /// who can always validate the existence of the note or event on-chain.\n ///\n /// These guarantees make this delivery mechanism be quite similar to [`MessageDeliveryEnum::OFFCHAIN`], except the\n /// sender does not need to establish an off-chain communication channel with the recipient, and neither party\n /// needs to worry about backups.\n ///\n /// ## Costs\n ///\n /// Because the encrypted message is emitted on-chain as transaction private logs, this delivery option results in\n /// transaction fees associated with DA gas. The length of the original message is irrelevant to this cost, since\n /// all private logs are padded to the same length with random data to enhance privacy.\n ///\n /// However, no circuit gates are introduced when the message is encrypted. Therefore, on-chain unconstrained\n /// messages do not affect proving time at all.\n ///\n /// ## Privacy\n ///\n /// No information is revealed on-chain about sender, recipient, or the message contents. The message itself\n /// reveals no information about the sender or recipient, and requires knowledge of the recipient's private address\n /// keys in order to obtain the plaintext.\n ///\n /// Delivering the message does produce on-chain information in the form of private logs, so transactions that\n /// deliver many messages this way might be identifiable by the large number of logs.\n ///\n /// Identifying that a log corresponds to a message between a given sender and recipient requires, among other\n /// things, knowledge of both of their addresses **and** either the sender's or recipient's private address key.\n pub ONCHAIN_UNCONSTRAINED: u8,\n\n /// Delivers the message on-chain, guaranteeing the recipient will receive the correct content.\n ///\n /// >**WARNING**: this delivery mode is [currently NOT fully\n /// constrained](https://github.com/AztecProtocol/aztec-packages/issues/14565). The log's tag is unconstrained,\n /// meaning a malicious sender could manipulate it to prevent the recipient from finding the message.\n ///\n /// ## Use Cases\n ///\n /// This delivery method is suitable for all use cases, since it always works as expected. It is however the most\n /// costly method, and there are multiple scenarios where alternatives such as [`MessageDeliveryEnum::OFFCHAIN`] or\n /// [`MessageDeliveryEnum::ONCHAIN_UNCONSTRAINED`] will suffice.\n ///\n /// If the sender cannot be relied on to correctly send the message to the recipient (e.g. because they have no\n /// incentive to do so, such as when paying a fee to a protocol, creating the change note after spending a third\n /// party's tokens, or updating the configuration of a shared system like a multisig) then this is the only\n /// suitable delivery option.\n ///\n /// ## Guarantees\n ///\n /// The message will be stored on-chain in a private log, as part of the transaction's effects, and will be\n /// retrievable in the future without requiring any backups. The ciphertext will be decryptable by the recipient\n /// using their address private key and the ephemeral public key that accompanies the message.\n ///\n /// The log will be tagged in such a way that the recipient will be able to efficiently find it after querying for\n /// handshakes.\n ///\n /// ## Costs\n ///\n /// Because the encrypted message is emitted on-chain as transaction private logs, this delivery option results in\n /// transaction fees associated with DA gas. The length of the original message is irrelevant to this cost, since\n /// all private logs are padded to the same length with random data to enhance privacy.\n ///\n /// Additionally, the constraining of the log's tag results in additional DA usage and hence transaction fees due\n /// to the emission of nullifiers.\n ///\n /// Proving time is also increased as circuit gates are introduced to guarantee both the correct encryption of the\n /// message, and selection of log tag.\n ///\n /// ## Privacy\n ///\n /// No information is revelead on-chain about sender, recipient, or the message contents. The message itself\n /// reveals no information about the sender or recipient, and requires knowledge of the recipient's private address\n /// keys in order to obtain the plaintext.\n ///\n /// Delivering the message does produce on-chain information in the form of private logs and nullifiers, so\n /// transactions that deliver many messages this way might be identifiable by these markers.\n ///\n /// Identifying that a log corresponds to a message between a given sender and recipient requires, among other\n /// things, knowledge of both of their addresses **and** either the sender's or recipient's private address key.\n pub ONCHAIN_CONSTRAINED: u8,\n}\n\n/// Specifies how to deliver a message to a recipient.\n///\n/// All messages are delivered encrypted to their recipient's public address key, so no other account will be able to\n/// read their contents. This enum instead configures which **guarantees** exist regarding delivery.\n///\n/// There are two aspects to delivery guarantees:\n/// - the medium on which the message is sent (off-chain or on-chain)\n/// - whether the contract constrains the message to be constructed correctly\n///\n/// For scenarios where the sender is incentivized to deliver the message correctly, use\n/// [`MessageDeliveryEnum::OFFCHAIN`] (the cheapest delivery option, but requiring that sender and recipient can\n/// communicate off-chain) or [`MessageDeliveryEnum::ONCHAIN_UNCONSTRAINED`]. If the sender cannot be trusted to send\n/// the\n/// message to the recipient, use [`MessageDeliveryEnum::ONCHAIN_CONSTRAINED`].\npub global MessageDelivery: MessageDeliveryEnum =\n MessageDeliveryEnum { OFFCHAIN: 1, ONCHAIN_UNCONSTRAINED: 2, ONCHAIN_CONSTRAINED: 3 };\n\n/// Performs private delivery of a message to `recipient` according to `delivery_mode`.\n///\n/// The message is encoded into plaintext and then encrypted for `recipient`. This function takes a _function_ that\n/// returns the plaintext instead of taking the plaintext directly in order to not waste constraints encoding the\n/// message in scenarios where the plaintext will be encrypted with unconstrained encryption.\n///\n/// `maybe_note_hash_counter` is only relevant for on-chain delivery modes (i.e. via protocol logs): if a newly created\n/// note hash's side effect counter is passed, then the log will be squashed alongside the note should its nullifier be\n/// emitted in the current transaction. This is typically only used for note messages: since the note will not actually\n/// be created, there is no point in delivering the message.\n///\n/// `delivery_mode` must be one of [`MessageDeliveryEnum`].\npub fn do_private_message_delivery(\n context: &mut PrivateContext,\n encode_into_message_plaintext: fn[Env]() -> [Field; MESSAGE_PLAINTEXT_LEN],\n maybe_note_hash_counter: Option,\n recipient: AztecAddress,\n delivery_mode: u8,\n) {\n // This function relies on `delivery_mode` being a constant in order to reduce circuit constraints when\n // unconstrained usage is requested. If `delivery_mode` were a runtime value the compiler would be unable to\n // perform dead-code elimination.\n assert_constant(delivery_mode);\n\n // The following maps out the 3 dimensions across which we configure message delivery.\n let constrained_encryption = delivery_mode == MessageDelivery.ONCHAIN_CONSTRAINED;\n let deliver_as_offchain_message = delivery_mode == MessageDelivery.OFFCHAIN;\n // TODO(#14565): Add constrained tagging\n let _constrained_tagging = delivery_mode == MessageDelivery.ONCHAIN_CONSTRAINED;\n\n let contract_address = context.this_address();\n let ciphertext = remove_constraints_if(\n !constrained_encryption,\n || AES128::encrypt(encode_into_message_plaintext(), recipient, contract_address),\n );\n\n if deliver_as_offchain_message {\n deliver_offchain_message(ciphertext, recipient);\n } else {\n // TODO(#14565): constrained tagging is not yet implemented. Both modes currently use the unconstrained\n // domain separator because the discovery tag always comes from an oracle. Once constrained tagging lands,\n // this should branch on `constrained_tagging` to select the appropriate separator.\n let discovery_tag = compute_discovery_tag(recipient);\n let log_tag = compute_log_tag(discovery_tag, DOM_SEP__UNCONSTRAINED_MSG_LOG_TAG);\n\n // We forbid this value not being constant to avoid predicating the context calls below, which might result in\n // the context's arrays having unknown compile time write indices and hence dramatically increasing constraints\n // when accessing them. In practice this restriction is not a problem as we always know at compile time whether\n // we're emitting a note or non-note message.\n assert_constant(maybe_note_hash_counter.is_some());\n\n if maybe_note_hash_counter.is_some() {\n // We associate the log with the note's side effect counter, so that if the note ends up being squashed in\n // the current transaction, the log will be removed as well.\n //\n // Note that the log always has the same length regardless of `MESSAGE_PLAINTEXT_LEN`, because all message\n // ciphertexts also have the same length. This prevents accidental privacy leakage via the log length.\n context.emit_raw_note_log_unsafe(log_tag, ciphertext, ciphertext.len(), maybe_note_hash_counter.unwrap());\n } else {\n context.emit_private_log_unsafe(log_tag, ciphertext, ciphertext.len());\n }\n }\n}\n" }, "168": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/processing/mod.nr", "source": "pub(crate) mod event_validation_request;\npub mod offchain;\n\nmod message_context;\npub use message_context::MessageContext;\n\npub(crate) mod note_validation_request;\npub(crate) mod log_retrieval_request;\npub(crate) mod log_retrieval_response;\npub(crate) mod pending_tagged_log;\n\nuse crate::{\n capsules::CapsuleArray,\n event::EventSelector,\n messages::{\n discovery::partial_notes::DeliveredPendingPartialNote,\n encoding::MESSAGE_CIPHERTEXT_LEN,\n logs::{event::MAX_EVENT_SERIALIZED_LEN, note::MAX_NOTE_PACKED_LEN},\n processing::{\n log_retrieval_request::LogRetrievalRequest, log_retrieval_response::LogRetrievalResponse,\n note_validation_request::NoteValidationRequest, pending_tagged_log::PendingTaggedLog,\n },\n },\n oracle,\n};\nuse crate::protocol::{\n address::AztecAddress,\n constants::DOM_SEP__NOTE_COMPLETION_LOG_TAG,\n hash::{compute_log_tag, sha256_to_field},\n traits::{Deserialize, Serialize},\n};\nuse event_validation_request::EventValidationRequest;\n\n// Base slot for the pending tagged log array to which the fetch_tagged_logs oracle inserts found private logs.\npub(crate) global PENDING_TAGGED_LOG_ARRAY_BASE_SLOT: Field =\n sha256_to_field(\"AZTEC_NR::PENDING_TAGGED_LOG_ARRAY_BASE_SLOT\".as_bytes());\n\nglobal NOTE_VALIDATION_REQUESTS_ARRAY_BASE_SLOT: Field = sha256_to_field(\n \"AZTEC_NR::NOTE_VALIDATION_REQUESTS_ARRAY_BASE_SLOT\".as_bytes(),\n);\n\nglobal EVENT_VALIDATION_REQUESTS_ARRAY_BASE_SLOT: Field = sha256_to_field(\n \"AZTEC_NR::EVENT_VALIDATION_REQUESTS_ARRAY_BASE_SLOT\".as_bytes(),\n);\n\nglobal LOG_RETRIEVAL_REQUESTS_ARRAY_BASE_SLOT: Field = sha256_to_field(\n \"AZTEC_NR::LOG_RETRIEVAL_REQUESTS_ARRAY_BASE_SLOT\".as_bytes(),\n);\n\nglobal LOG_RETRIEVAL_RESPONSES_ARRAY_BASE_SLOT: Field = sha256_to_field(\n \"AZTEC_NR::LOG_RETRIEVAL_RESPONSES_ARRAY_BASE_SLOT\".as_bytes(),\n);\n\n/// An offchain-delivered message with resolved context, ready for processing during sync.\n#[derive(Serialize, Deserialize)]\npub struct OffchainMessageWithContext {\n pub message_ciphertext: BoundedVec,\n pub message_context: MessageContext,\n}\n\n/// Searches for private logs emitted by `contract_address` that might contain messages for the given `scope`.\npub(crate) unconstrained fn get_private_logs(\n contract_address: AztecAddress,\n scope: AztecAddress,\n) -> CapsuleArray {\n // We will eventually perform log discovery via tagging here, but for now we simply call the `fetchTaggedLogs`\n // oracle. This makes PXE synchronize tags, download logs and store the pending tagged logs in a capsule array.\n oracle::message_processing::fetch_tagged_logs(PENDING_TAGGED_LOG_ARRAY_BASE_SLOT, scope);\n\n CapsuleArray::at(contract_address, PENDING_TAGGED_LOG_ARRAY_BASE_SLOT, scope)\n}\n\n/// Enqueues a note for validation and storage by PXE.\n///\n/// Once validated, the note becomes retrievable via the `get_notes` oracle. The note will be scoped to\n/// `contract_address`, meaning other contracts will not be able to access it unless authorized.\n///\n/// In order for the note validation and insertion to occur, `validate_and_store_enqueued_notes_and_events` must be\n/// later called. For optimal performance, accumulate as many note validation requests as possible and then validate\n/// them all at the end (which results in PXE minimizing the number of network round-trips).\n///\n/// The `packed_note` is what `getNotes` will later return. PXE indexes notes by `storage_slot`, so this value is\n/// typically used to filter notes that correspond to different state variables. `note_hash` and `nullifier` are the\n/// inner hashes, i.e. the raw hashes returned by `NoteHash::compute_note_hash` and `NoteHash::compute_nullifier`. PXE\n/// will verify that the siloed unique note hash was inserted into the tree at `tx_hash`, and will store the nullifier\n/// to later check for nullification.\n///\n/// `owner` is the address used in note hash and nullifier computation, often requiring knowledge of their nullifier\n/// secret key.\n///\n/// `scope` is the account to which the note message was delivered (i.e. the address the message was encrypted to).\n/// This determines which PXE account can see the note - other accounts will not be able to access it (e.g. other\n/// accounts will not be able to see one another's token balance notes, even in the same PXE) unless authorized. In\n/// most cases `recipient` equals `owner`, but they can differ in scenarios like delegated discovery.\npub unconstrained fn enqueue_note_for_validation(\n contract_address: AztecAddress,\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n note_nonce: Field,\n packed_note: BoundedVec,\n note_hash: Field,\n nullifier: Field,\n tx_hash: Field,\n scope: AztecAddress,\n) {\n // We store requests in a `CapsuleArray`, which PXE will later read from and deserialize into its version of the\n // Noir `NoteValidationRequest`\n CapsuleArray::at(\n contract_address,\n NOTE_VALIDATION_REQUESTS_ARRAY_BASE_SLOT,\n scope,\n )\n .push(\n NoteValidationRequest {\n contract_address,\n owner,\n storage_slot,\n randomness,\n note_nonce,\n packed_note,\n note_hash,\n nullifier,\n tx_hash,\n },\n )\n}\n\n/// Enqueues an event for validation and storage by PXE.\n///\n/// This is the primary way for custom message handlers (registered via\n/// [`crate::macros::AztecConfig::custom_message_handler`]) to deliver reassembled events back to PXE after processing\n/// application-specific message formats.\n///\n/// In order for the event validation and insertion to occur, `validate_and_store_enqueued_notes_and_events` must be\n/// later called. For optimal performance, accumulate as many event validation requests as possible and then validate\n/// them all at the end (which results in PXE minimizing the number of network round-trips).\n///\n/// Note that `validate_and_store_enqueued_notes_and_events` is called by Aztec.nr after processing messages, so custom\n/// message processors do not need to be concerned with this.\npub unconstrained fn enqueue_event_for_validation(\n contract_address: AztecAddress,\n event_type_id: EventSelector,\n randomness: Field,\n serialized_event: BoundedVec,\n event_commitment: Field,\n tx_hash: Field,\n scope: AztecAddress,\n) {\n // We store requests in a `CapsuleArray`, which PXE will later read from and deserialize into its version of the\n // Noir `EventValidationRequest`\n CapsuleArray::at(\n contract_address,\n EVENT_VALIDATION_REQUESTS_ARRAY_BASE_SLOT,\n scope,\n )\n .push(\n EventValidationRequest {\n contract_address,\n event_type_id,\n randomness,\n serialized_event,\n event_commitment,\n tx_hash,\n },\n )\n}\n\n/// Validates and stores all enqueued notes and events.\n///\n/// Processes all requests enqueued via [`enqueue_note_for_validation`] and [`enqueue_event_for_validation`], inserting\n/// them into the note database and event store respectively, making them queryable via `get_notes` oracle and our TS\n/// API (PXE::getPrivateEvents).\n///\n/// This automatically clears both validation request queues, so no further work needs to be done by the caller.\npub unconstrained fn validate_and_store_enqueued_notes_and_events(contract_address: AztecAddress, scope: AztecAddress) {\n oracle::message_processing::validate_and_store_enqueued_notes_and_events(\n contract_address,\n NOTE_VALIDATION_REQUESTS_ARRAY_BASE_SLOT,\n EVENT_VALIDATION_REQUESTS_ARRAY_BASE_SLOT,\n MAX_NOTE_PACKED_LEN as Field,\n MAX_EVENT_SERIALIZED_LEN as Field,\n scope,\n );\n}\n\n/// Resolves message contexts for a list of tx hashes stored in a CapsuleArray.\n///\n/// The `message_context_requests_array_base_slot` must point to a CapsuleArray containing tx hashes.\n/// PXE will store `Option` values into the responses array at\n/// `message_context_responses_array_base_slot`.\npub unconstrained fn get_message_contexts_by_tx_hash(\n contract_address: AztecAddress,\n message_context_requests_array_base_slot: Field,\n message_context_responses_array_base_slot: Field,\n scope: AztecAddress,\n) {\n oracle::message_processing::get_message_contexts_by_tx_hash(\n contract_address,\n message_context_requests_array_base_slot,\n message_context_responses_array_base_slot,\n scope,\n );\n}\n\n/// Efficiently queries the node for logs that result in the completion of all `DeliveredPendingPartialNote`s stored in\n/// a `CapsuleArray` by performing all node communication concurrently. Returns a second `CapsuleArray` with Options\n/// for the responses that correspond to the pending partial notes at the same index.\n///\n/// For example, given an array with pending partial notes `[ p1, p2, p3 ]`, where `p1` and `p3` have corresponding\n/// completion logs but `p2` does not, the returned `CapsuleArray` will have contents `[some(p1_log), none(),\n/// some(p3_log)]`.\npub(crate) unconstrained fn get_pending_partial_notes_completion_logs(\n contract_address: AztecAddress,\n pending_partial_notes: CapsuleArray,\n scope: AztecAddress,\n) -> CapsuleArray> {\n let log_retrieval_requests = CapsuleArray::at(\n contract_address,\n LOG_RETRIEVAL_REQUESTS_ARRAY_BASE_SLOT,\n scope,\n );\n\n // We create a LogRetrievalRequest for each PendingPartialNote in the CapsuleArray. Because we need the indices in\n // the request array to match the indices in the partial note array, we can't use CapsuleArray::for_each, as that\n // function has arbitrary iteration order. Instead, we manually iterate the array from the beginning and push into\n // the requests array, which we expect to be empty.\n let mut i = 0;\n let pending_partial_notes_count = pending_partial_notes.len();\n while i < pending_partial_notes_count {\n let pending_partial_note = pending_partial_notes.get(i);\n // Partial note completion logs are emitted with a domain-separated tag. To find matching logs, we apply the\n // same domain separation to the stored raw tag.\n let log_tag = compute_log_tag(\n pending_partial_note.note_completion_log_tag,\n DOM_SEP__NOTE_COMPLETION_LOG_TAG,\n );\n log_retrieval_requests.push(LogRetrievalRequest { contract_address, unsiloed_tag: log_tag });\n i += 1;\n }\n\n oracle::message_processing::get_logs_by_tag(\n contract_address,\n LOG_RETRIEVAL_REQUESTS_ARRAY_BASE_SLOT,\n LOG_RETRIEVAL_RESPONSES_ARRAY_BASE_SLOT,\n scope,\n );\n\n CapsuleArray::at(\n contract_address,\n LOG_RETRIEVAL_RESPONSES_ARRAY_BASE_SLOT,\n scope,\n )\n}\n" }, "170": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/messages/processing/offchain.nr", "source": "use crate::{\n capsules::CapsuleArray,\n context::UtilityContext,\n messages::{\n encoding::MESSAGE_CIPHERTEXT_LEN,\n processing::{get_message_contexts_by_tx_hash, MessageContext, OffchainMessageWithContext},\n },\n oracle::contract_sync::set_contract_sync_cache_invalid,\n protocol::{\n address::AztecAddress,\n constants::MAX_TX_LIFETIME,\n hash::sha256_to_field,\n traits::{Deserialize, Serialize},\n },\n};\n\n/// Base capsule slot for the persistent inbox of [`PendingOffchainMsg`] entries.\n///\n/// This is the slot where we accumulate messages received through [`receive`].\nglobal OFFCHAIN_INBOX_SLOT: Field = sha256_to_field(\"AZTEC_NR::OFFCHAIN_INBOX_SLOT\".as_bytes());\n\n/// Capsule array slot used by [`sync_inbox`] to pass tx hash resolution requests to PXE.\nglobal OFFCHAIN_CONTEXT_REQUESTS_SLOT: Field = sha256_to_field(\"AZTEC_NR::OFFCHAIN_CONTEXT_REQUESTS_SLOT\".as_bytes());\n\n/// Capsule array slot used by [`sync_inbox`] to read tx context responses from PXE.\nglobal OFFCHAIN_CONTEXT_RESPONSES_SLOT: Field = sha256_to_field(\"AZTEC_NR::OFFCHAIN_CONTEXT_RESPONSES_SLOT\".as_bytes());\n\n/// Capsule array slot used by [`sync_inbox`] to collect messages ready for processing.\nglobal OFFCHAIN_READY_MESSAGES_SLOT: Field = sha256_to_field(\"AZTEC_NR::OFFCHAIN_READY_MESSAGES_SLOT\".as_bytes());\n\n/// Maximum number of offchain messages accepted by `offchain_receive` in a single call.\npub global MAX_OFFCHAIN_MESSAGES_PER_RECEIVE_CALL: u32 = 16;\n\n/// Tolerance added to the `MAX_TX_LIFETIME` cap for message expiration.\nglobal TX_EXPIRATION_TOLERANCE: u64 = 7200; // 2 hours\n\n/// Maximum time-to-live for a tx-bound offchain message.\n///\n/// After `anchor_block_timestamp + MAX_MSG_TTL`, the message is evicted from the inbox.\nglobal MAX_MSG_TTL: u64 = MAX_TX_LIFETIME + TX_EXPIRATION_TOLERANCE;\n\n/// A function that manages offchain-delivered messages for processing during sync.\n///\n/// Offchain messages are messages that are not broadcasted via onchain logs. They are instead delivered to the\n/// recipient by calling the `offchain_receive` utility function (injected by the `#[aztec]` macro). Message transport\n/// is the app's responsibility. Typical examples of transport methods are: messaging apps, email, QR codes, etc.\n///\n/// Once offchain messages are delivered to the recipient's private environment via `offchain_receive`, messages are\n/// locally stored in a persistent inbox.\n///\n/// This function determines when each message in said inbox is ready for processing, when it can be safely disposed\n/// of, etc.\n///\n/// The only current implementation of an `OffchainInboxSync` is [`sync_inbox`], which manages an inbox with expiration\n/// based eviction and automatic transaction context resolution.\npub(crate) type OffchainInboxSync = unconstrained fn[Env](\n/* contract_address */AztecAddress, /* scope */ AztecAddress) -> CapsuleArray;\n\n/// A message delivered via the `offchain_receive` utility function.\npub struct OffchainMessage {\n /// The encrypted message payload.\n pub ciphertext: BoundedVec,\n /// The intended recipient of the message.\n pub recipient: AztecAddress,\n /// The hash of the transaction that produced this message. `Option::none` indicates a tx-less message.\n pub tx_hash: Option,\n /// Anchor block timestamp at message emission.\n pub anchor_block_timestamp: u64,\n}\n\n/// An offchain message awaiting processing (or re-processing) in the inbox.\n///\n/// Messages remain in the inbox until they expire, even if they have already been processed. This is necessary to\n/// handle reorgs: a processed message may need to be re-processed if the transaction that provided its context is\n/// reverted. On each sync, resolved messages are promoted to [`OffchainMessageWithContext`] for processing.\n#[derive(Serialize, Deserialize)]\nstruct PendingOffchainMsg {\n /// The encrypted message payload.\n ciphertext: BoundedVec,\n /// The intended recipient of the message.\n recipient: AztecAddress,\n /// The hash of the transaction that produced this message. A value of 0 indicates a tx-less message.\n tx_hash: Field,\n /// Anchor block timestamp at message emission. Used to compute the effective expiration: messages are evicted\n /// after `anchor_block_timestamp + MAX_MSG_TTL`.\n anchor_block_timestamp: u64,\n}\n\n/// Delivers offchain messages to the given contract's offchain inbox for subsequent processing.\n///\n/// Offchain messages are transaction effects that are not broadcasted via onchain logs. Instead, the sender shares the\n/// message to the recipient through an external channel (e.g. a URL accessible by the recipient). The recipient then\n/// calls this function to hand the messages to the contract so they can be processed through the same mechanisms as\n/// onchain messages.\n///\n/// Each message is routed to the inbox scoped to its `recipient` field, so messages for different accounts are\n/// automatically isolated.\n///\n/// Messages are processed when their originating transaction is found onchain (providing the context needed to\n/// validate resulting notes and events).\n///\n/// Messages are kept in the inbox until they expire. The effective expiration is\n/// `anchor_block_timestamp + MAX_MSG_TTL`.\n///\n/// Processing order is not guaranteed.\npub unconstrained fn receive(\n contract_address: AztecAddress,\n messages: BoundedVec,\n) {\n // May contain duplicates if multiple messages target the same recipient. This is harmless since\n // cache invalidation on the TS side is idempotent (deleting an already-deleted key is a no-op).\n let mut scopes: BoundedVec = BoundedVec::new();\n let mut i = 0;\n let messages_len = messages.len();\n while i < messages_len {\n let msg = messages.get(i);\n let tx_hash = if msg.tx_hash.is_some() {\n msg.tx_hash.unwrap()\n } else {\n 0\n };\n let inbox: CapsuleArray =\n CapsuleArray::at(contract_address, OFFCHAIN_INBOX_SLOT, msg.recipient);\n inbox.push(\n PendingOffchainMsg {\n ciphertext: msg.ciphertext,\n recipient: msg.recipient,\n tx_hash,\n anchor_block_timestamp: msg.anchor_block_timestamp,\n },\n );\n scopes.push(msg.recipient);\n i += 1;\n }\n\n set_contract_sync_cache_invalid(contract_address, scopes);\n}\n\n/// Returns offchain-delivered messages to process during sync.\n///\n/// Messages remain in the inbox and are reprocessed on each sync until their originating transaction is no longer at\n/// risk of being dropped by a reorg.\npub unconstrained fn sync_inbox(\n contract_address: AztecAddress,\n scope: AztecAddress,\n) -> CapsuleArray {\n let inbox: CapsuleArray = CapsuleArray::at(contract_address, OFFCHAIN_INBOX_SLOT, scope);\n let context_resolution_requests: CapsuleArray =\n CapsuleArray::at(contract_address, OFFCHAIN_CONTEXT_REQUESTS_SLOT, scope);\n let resolved_contexts: CapsuleArray> =\n CapsuleArray::at(contract_address, OFFCHAIN_CONTEXT_RESPONSES_SLOT, scope);\n let ready_to_process: CapsuleArray =\n CapsuleArray::at(contract_address, OFFCHAIN_READY_MESSAGES_SLOT, scope);\n\n // Clear any stale ready messages from a previous run.\n ready_to_process.for_each(|i, _| { ready_to_process.remove(i); });\n\n // Clear any stale context resolution requests/responses from a previous run.\n context_resolution_requests.for_each(|i, _| { context_resolution_requests.remove(i); });\n resolved_contexts.for_each(|i, _| { resolved_contexts.remove(i); });\n\n // Build a request list aligned with the inbox indices.\n let mut i = 0;\n let inbox_len = inbox.len();\n while i < inbox_len {\n let msg = inbox.get(i);\n context_resolution_requests.push(msg.tx_hash);\n i += 1;\n }\n\n // Ask PXE to resolve contexts for all requested tx hashes.\n get_message_contexts_by_tx_hash(\n contract_address,\n OFFCHAIN_CONTEXT_REQUESTS_SLOT,\n OFFCHAIN_CONTEXT_RESPONSES_SLOT,\n scope,\n );\n\n assert_eq(resolved_contexts.len(), inbox_len);\n\n let now = UtilityContext::new().timestamp();\n\n let mut j = inbox_len;\n while j > 0 {\n // This loop decides what to do with each message in the offchain message inbox. We need to handle 3\n // different scenarios for each message.\n //\n // 1. The TX that emitted this message is still not known to PXE: in this case we can't yet process this\n // message, as any notes or events discovered will fail to be validated. So we leave the message in the inbox,\n // awaiting for future syncs to detect that the TX became available.\n //\n // 2. The message is not associated to a TX to begin with. The current version of offchain message processing\n // does not support this case, but in the future it will. Right now, a message without an associated TX will\n // sit in the inbox until it expires.\n //\n // 3. The TX that emitted this message has been found by PXE. That gives us all the information needed to\n // process the message. We add the message to the `ready_to_process` CapsuleArray so that the `sync_state` loop\n // processes it.\n //\n // In all cases, if the message has expired (i.e. `now > anchor_block_timestamp + MAX_MSG_TTL`), we remove it\n // from the inbox.\n //\n // Note: the loop runs backwards because it might call `inbox.remove(j)` to purge expired messages and we also\n // need to align it with `resolved_contexts.get(j)`. Going from last to first simplifies the algorithm as\n // not yet visited element indexes remain stable.\n j -= 1;\n let maybe_ctx = resolved_contexts.get(j);\n let msg = inbox.get(j);\n\n // Compute the message's effective expiration timestamp to determine if we can purge it from the inbox.\n let effective_expiration = msg.anchor_block_timestamp + MAX_MSG_TTL;\n\n // Message expired. We remove it from the inbox.\n if now > effective_expiration {\n inbox.remove(j);\n }\n\n // Scenario 1: associated TX not yet available. We keep the message in the inbox, as it might become\n // processable as new blocks get mined.\n // Scenario 2: no TX associated to message. The message will sit in the inbox until it expires.\n if maybe_ctx.is_none() {\n continue;\n }\n\n // Scenario 3: Message is ready to process, add to result array. Note we still keep it in the inbox unless we\n // consider it has expired: this is because we need to account for reorgs. If reorg occurs after we processed\n // a message, the effects of processing the message get rewind. However, the associated TX can be included in\n // a subsequent block. Should that happen, the message must be re-processed to ensure consistency.\n let message_context = maybe_ctx.unwrap();\n ready_to_process.push(OffchainMessageWithContext { message_ciphertext: msg.ciphertext, message_context });\n }\n\n ready_to_process\n}\n\nmod test {\n use crate::{\n capsules::CapsuleArray, oracle::random::random, protocol::address::AztecAddress,\n test::helpers::test_environment::TestEnvironment,\n };\n use super::{\n MAX_MSG_TTL, MAX_OFFCHAIN_MESSAGES_PER_RECEIVE_CALL, OFFCHAIN_INBOX_SLOT, OffchainMessage, PendingOffchainMsg,\n receive, sync_inbox,\n };\n\n global SCOPE: AztecAddress = AztecAddress { inner: 0xcafe };\n\n /// Creates an `OffchainMessage` with dummy ciphertext and `SCOPE` as recipient.\n fn make_msg(tx_hash: Option, anchor_block_timestamp: u64) -> OffchainMessage {\n OffchainMessage { ciphertext: BoundedVec::new(), recipient: SCOPE, tx_hash, anchor_block_timestamp }\n }\n\n /// Advances the TXE block timestamp by `offset` seconds and returns the resulting timestamp.\n unconstrained fn advance_by(env: TestEnvironment, offset: u64) -> u64 {\n env.advance_next_block_timestamp_by(offset);\n env.mine_block();\n env.last_block_timestamp()\n }\n\n #[test]\n unconstrained fn empty_inbox_returns_empty_result() {\n let env = TestEnvironment::new();\n env.utility_context(|context| {\n let result = sync_inbox(context.this_address(), SCOPE);\n let inbox: CapsuleArray =\n CapsuleArray::at(context.this_address(), OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0);\n assert_eq(inbox.len(), 0);\n });\n }\n\n #[test]\n unconstrained fn tx_bound_msg_expires_after_max_msg_ttl() {\n let env = TestEnvironment::new();\n let anchor_ts = advance_by(env, 10);\n\n env.utility_context(|context| {\n let mut msgs: BoundedVec = BoundedVec::new();\n msgs.push(make_msg(Option::some(random()), anchor_ts));\n receive(context.this_address(), msgs);\n });\n\n // Advance past anchor_ts + MAX_MSG_TTL.\n let _now = advance_by(env, MAX_MSG_TTL + 1);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0); // context is None, not ready\n assert_eq(inbox.len(), 0); // expired, removed\n });\n }\n\n #[test]\n unconstrained fn tx_bound_msg_not_expired_before_max_msg_ttl() {\n let env = TestEnvironment::new();\n let anchor_ts = advance_by(env, 10);\n\n env.utility_context(|context| {\n let mut msgs: BoundedVec = BoundedVec::new();\n msgs.push(make_msg(Option::some(random()), anchor_ts));\n receive(context.this_address(), msgs);\n });\n\n // Advance, but not past anchor_ts + MAX_MSG_TTL.\n let _now = advance_by(env, 100);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0); // context is None, not ready\n assert_eq(inbox.len(), 1); // not expired, stays\n });\n }\n\n #[test]\n unconstrained fn tx_less_msg_expires_after_max_msg_ttl() {\n let env = TestEnvironment::new();\n let anchor_ts = advance_by(env, 10);\n\n env.utility_context(|context| {\n let mut msgs: BoundedVec = BoundedVec::new();\n msgs.push(make_msg(Option::none(), anchor_ts));\n receive(context.this_address(), msgs);\n });\n\n // Advance past anchor_ts + MAX_MSG_TTL.\n let _now = advance_by(env, MAX_MSG_TTL + 1);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0); // context is None, not ready\n assert_eq(inbox.len(), 0); // expired, removed\n });\n }\n\n #[test]\n unconstrained fn unresolved_tx_stays_in_inbox() {\n let env = TestEnvironment::new();\n let anchor_ts = advance_by(env, 10);\n\n env.utility_context(|context| {\n let mut msgs: BoundedVec = BoundedVec::new();\n msgs.push(make_msg(Option::some(random()), anchor_ts));\n receive(context.this_address(), msgs);\n });\n\n let _now = advance_by(env, 100);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0); // not resolved, not ready\n assert_eq(inbox.len(), 1); // not expired, stays\n });\n }\n\n #[test]\n unconstrained fn multiple_messages_mixed_expiration() {\n let env = TestEnvironment::new();\n let anchor_ts = advance_by(env, 10);\n\n let survivor_tx_hash = random();\n\n env.utility_context(|context| {\n let address = context.this_address();\n let mut msgs: BoundedVec = BoundedVec::new();\n // Message 0: tx-bound, anchor_ts in the past so it expires at\n // anchor_ts + MAX_MSG_TTL. We set anchor to 0 so it expires quickly.\n msgs.push(make_msg(Option::some(random()), 0));\n // Message 1: tx-bound, anchor_ts is recent so it survives.\n msgs.push(make_msg(Option::some(survivor_tx_hash), anchor_ts));\n // Message 2: tx-less, anchor_ts=0 so it also expires.\n msgs.push(make_msg(Option::none(), 0));\n receive(address, msgs);\n });\n\n // Advance past MAX_MSG_TTL for anchor_ts=0, but not for anchor_ts=anchor_ts.\n let _now = advance_by(env, MAX_MSG_TTL);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n assert_eq(result.len(), 0); // all contexts are None\n // Message 0 expired (anchor=0), message 1 survived (anchor=anchor_ts),\n // Message 2 expired (anchor=0).\n assert_eq(inbox.len(), 1);\n assert_eq(inbox.get(0).tx_hash, survivor_tx_hash);\n });\n }\n\n // -- Resolved context (ready to process) ------------------------------\n\n #[test]\n unconstrained fn resolved_msg_is_ready_to_process() {\n let env = TestEnvironment::new();\n // TestEnvironment::new() deploys protocol contracts, creating blocks with tx effects.\n // In TXE, tx hashes equal Fr(blockNumber), so Fr(1) is the tx effect from block 1.\n // We use this as a \"known resolvable\" tx hash.\n let known_tx_hash: Field = 1;\n let anchor_ts = advance_by(env, 10);\n\n env.utility_context(|context| {\n let mut msgs: BoundedVec = BoundedVec::new();\n msgs.push(make_msg(Option::some(known_tx_hash), anchor_ts));\n receive(context.this_address(), msgs);\n });\n\n let _now = advance_by(env, 100);\n\n env.utility_context(|context| {\n let address = context.this_address();\n let result = sync_inbox(address, SCOPE);\n let inbox: CapsuleArray = CapsuleArray::at(address, OFFCHAIN_INBOX_SLOT, SCOPE);\n\n // The message should be ready to process since its tx context was resolved.\n assert_eq(result.len(), 1);\n\n let ctx = result.get(0).message_context;\n assert_eq(ctx.tx_hash, known_tx_hash);\n assert(ctx.first_nullifier_in_tx != 0, \"resolved context must have a first nullifier\");\n\n // Message stays in inbox (not expired) for potential reorg reprocessing.\n assert_eq(inbox.len(), 1);\n });\n }\n}\n" }, "185": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/note/utils.nr", "source": "use crate::{context::NoteExistenceRequest, note::{ConfirmedNote, HintedNote, note_interface::NoteHash}};\n\nuse crate::protocol::{\n constants::{DOM_SEP__NOTE_HASH, DOM_SEP__NOTE_NULLIFIER},\n hash::{compute_siloed_note_hash, compute_unique_note_hash, poseidon2_hash_with_separator},\n};\n\n/// Computes a domain-separated note hash.\n///\n/// Receives the `storage_slot` of the [`crate::state_vars::StateVariable`] that holds the note, plus any arbitrary\n/// note `data`. This typically includes randomness, owner, and domain specific values (e.g. numeric amount, address,\n/// id, etc.).\n///\n/// Usage of this function guarantees that different state variables will never produce colliding note hashes, even if\n/// their underlying notes have different implementations.\npub fn compute_note_hash(storage_slot: Field, data: [Field; N]) -> Field {\n // All state variables have different storage slots, so by placing this at a fixed first position in the preimage\n // we prevent collisions.\n poseidon2_hash_with_separator([storage_slot].concat(data), DOM_SEP__NOTE_HASH)\n}\n\n/// Computes a domain-separated note nullifier.\n///\n/// Receives the `note_hash_for_nullification` of the note (usually returned by\n/// [`compute_confirmed_note_hash_for_nullification`]), plus any arbitrary note `data`. This typically includes\n/// secrets, such as the app-siloed nullifier hiding key of the note's owner.\n///\n/// Usage of this function guarantees that different state variables will never produce colliding note nullifiers, even\n/// if their underlying notes have different implementations.\npub fn compute_note_nullifier(note_hash_for_nullification: Field, data: [Field; N]) -> Field {\n // All notes have different note hashes for nullification (i.e. transient or settled), so by placing this at a\n // fixed first position in the preimage we prevent collisions.\n poseidon2_hash_with_separator(\n [note_hash_for_nullification].concat(data),\n DOM_SEP__NOTE_NULLIFIER,\n )\n}\n\n/// Returns the [`NoteExistenceRequest`] used to prove a note exists.\npub fn compute_note_existence_request(hinted_note: HintedNote) -> NoteExistenceRequest\nwhere\n Note: NoteHash,\n{\n let note_hash =\n hinted_note.note.compute_note_hash(hinted_note.owner, hinted_note.storage_slot, hinted_note.randomness);\n\n if hinted_note.metadata.is_settled() {\n // Settled notes are read by siloing with contract address and nonce (resulting in the final unique note hash,\n // which is already in the note hash tree).\n let siloed_note_hash = compute_siloed_note_hash(hinted_note.contract_address, note_hash);\n NoteExistenceRequest::for_settled(compute_unique_note_hash(\n hinted_note.metadata.to_settled().note_nonce(),\n siloed_note_hash,\n ))\n } else {\n // Pending notes (both same phase and previous phase ones) are read by their non-siloed hash (not even by\n // contract address), which is what is stored in the new note hashes array (at the position hinted by note hash\n // counter).\n NoteExistenceRequest::for_pending(note_hash, hinted_note.contract_address)\n }\n}\n\n/// Unconstrained variant of [`compute_confirmed_note_hash_for_nullification`].\npub unconstrained fn compute_note_hash_for_nullification(hinted_note: HintedNote) -> Field\nwhere\n Note: NoteHash,\n{\n // Creating a ConfirmedNote like we do here is typically unsafe, as we've not confirmed existence. We can do it\n // here because this is an unconstrained function, so the returned value should not make its way to a constrained\n // function. This lets us reuse the `compute_confirmed_note_hash_for_nullification` implementation.\n compute_confirmed_note_hash_for_nullification(ConfirmedNote::new(\n hinted_note,\n compute_note_existence_request(hinted_note).note_hash(),\n ))\n}\n\n/// Returns the note hash to use when computing its nullifier.\n///\n/// The `note_hash_for_nullification` parameter [`NoteHash::compute_nullifier`] takes depends on the note's stage, e.g.\n/// settled notes use the unique note hash, but pending notes cannot as they have no nonce. This function returns the\n/// correct note hash to use.\n///\n/// Use [`compute_note_hash_for_nullification`] when computing this value in unconstrained functions.\npub fn compute_confirmed_note_hash_for_nullification(confirmed_note: ConfirmedNote) -> Field {\n // There is just one instance in which the note hash for nullification does not match the note hash used for a read\n // request, which is when dealing with pending previous phase notes. These had their existence proven using their\n // non-siloed note hash along with the note hash counter (like all pending notes), but since they will be\n // unconditionally inserted in the note hash tree (since they cannot be squashed) they must be nullified using the\n // *unique* note hash. If we didn't, it'd be possible to emit a second different nullifier for the same note in a\n // follow up transaction, once the note is settled, resulting in a double spend.\n\n if confirmed_note.metadata.is_pending_previous_phase() {\n let siloed_note_hash = compute_siloed_note_hash(\n confirmed_note.contract_address,\n confirmed_note.proven_note_hash,\n );\n let note_nonce = confirmed_note.metadata.to_pending_previous_phase().note_nonce();\n\n compute_unique_note_hash(note_nonce, siloed_note_hash)\n } else {\n confirmed_note.proven_note_hash\n }\n}\n" }, "187": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/nullifier/utils.nr", "source": "use crate::{context::NullifierExistenceRequest, oracle::nullifiers::is_nullifier_pending};\n\nuse crate::protocol::{address::aztec_address::AztecAddress, hash::compute_siloed_nullifier};\n\n/// Returns the [`NullifierExistenceRequest`] used to prove a nullifier exists.\npub fn compute_nullifier_existence_request(\n unsiloed_nullifier: Field,\n contract_address: AztecAddress,\n) -> NullifierExistenceRequest {\n let pending_read_request = NullifierExistenceRequest::for_pending(unsiloed_nullifier, contract_address);\n\n let siloed_nullifier = compute_siloed_nullifier(contract_address, unsiloed_nullifier);\n let settled_read_request = NullifierExistenceRequest::for_settled(siloed_nullifier);\n\n // Safety: This is a hint to check whether we are reading a pending or settled nullifier. The chosen read request\n // will be validated by the kernel. Failure to provide a correct hint will cause the read request validation to\n // fail.\n let should_use_pending_read_request = unsafe { is_nullifier_pending(unsiloed_nullifier, contract_address) };\n\n if should_use_pending_read_request {\n pending_read_request\n } else {\n settled_read_request\n }\n}\n" }, "188": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/aes128_decrypt.nr", "source": "#[oracle(aztec_utl_decryptAes128)]\nunconstrained fn aes128_decrypt_oracle(\n ciphertext: BoundedVec,\n iv: [u8; 16],\n sym_key: [u8; 16],\n) -> Option> {}\n\n/// Attempts to decrypt a ciphertext using AES128.\n///\n/// Returns `Option::some(plaintext)` on success, or `Option::none()` if decryption fails (e.g. due to malformed\n/// ciphertext). Note that decryption with the wrong key will still return `Some` with garbage data, it's up to\n/// the calling function to verify correctness (e.g. via a MAC check).\n///\n/// Note that we accept ciphertext as a BoundedVec, not as an array. This is because this function is typically used\n/// when processing logs and at that point we don't have comptime information about the length of the ciphertext as\n/// the log is not specific to any individual note.\n// TODO(F-498): review naming consistency\npub unconstrained fn try_aes128_decrypt(\n ciphertext: BoundedVec,\n iv: [u8; 16],\n sym_key: [u8; 16],\n) -> Option> {\n aes128_decrypt_oracle(ciphertext, iv, sym_key)\n}\n\nmod test {\n use crate::{\n keys::ecdh_shared_secret::compute_app_siloed_shared_secret,\n messages::encryption::aes128::derive_aes_symmetric_key_and_iv_from_shared_secret,\n utils::{array::subarray::subarray, point::point_from_x_coord},\n };\n use crate::protocol::address::AztecAddress;\n use crate::test::helpers::test_environment::TestEnvironment;\n use super::try_aes128_decrypt;\n use poseidon::poseidon2::Poseidon2;\n use std::aes128::aes128_encrypt;\n\n global CONTRACT_ADDRESS: AztecAddress = AztecAddress { inner: 42 };\n global TEST_PLAINTEXT_LENGTH: u32 = 10;\n global TEST_CIPHERTEXT_LENGTH: u32 = 16;\n global TEST_PADDING_LENGTH: u32 = TEST_CIPHERTEXT_LENGTH - TEST_PLAINTEXT_LENGTH;\n\n #[test]\n unconstrained fn aes_encrypt_then_decrypt() {\n let env = TestEnvironment::new();\n\n env.utility_context(|_| {\n let shared_secret_point = point_from_x_coord(1).unwrap();\n let s_app = compute_app_siloed_shared_secret(shared_secret_point, CONTRACT_ADDRESS);\n\n let (sym_key, iv) = derive_aes_symmetric_key_and_iv_from_shared_secret::<1>(s_app)[0];\n\n let plaintext: [u8; TEST_PLAINTEXT_LENGTH] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];\n\n let ciphertext: [u8; TEST_CIPHERTEXT_LENGTH] = aes128_encrypt(plaintext, iv, sym_key);\n\n let ciphertext_bvec = BoundedVec::::from_array(ciphertext);\n\n let received_plaintext = try_aes128_decrypt(ciphertext_bvec, iv, sym_key).unwrap();\n assert_eq(received_plaintext.len(), TEST_PLAINTEXT_LENGTH);\n assert_eq(received_plaintext.max_len(), TEST_CIPHERTEXT_LENGTH);\n assert_eq(subarray::<_, _, TEST_PLAINTEXT_LENGTH>(received_plaintext.storage(), 0), plaintext);\n assert_eq(\n subarray::<_, _, TEST_PADDING_LENGTH>(received_plaintext.storage(), TEST_PLAINTEXT_LENGTH),\n [0 as u8; TEST_PADDING_LENGTH],\n );\n })\n }\n\n global TEST_MAC_LENGTH: u32 = 32;\n\n #[test(should_fail_with = \"mac does not match\")]\n unconstrained fn aes_encrypt_then_decrypt_with_bad_sym_key_is_caught() {\n let env = TestEnvironment::new();\n\n env.utility_context(|_| {\n // The AES decryption oracle will not fail for any valid ciphertext; it will always return\n // `Some` with some data. Whether the decryption was successful is up to the app to check in a\n // custom way.\n //\n // E.g. if it's a note that's been encrypted, upon decryption the app can check whether the\n // note hash exists onchain. If it doesn't, that's a strong indicator that decryption failed.\n //\n // E.g. for non-note messages, the plaintext could include a MAC\n // (https://en.wikipedia.org/wiki/Message_authentication_code). We demonstrate this approach in\n // this test: we compute a MAC, include it in the plaintext, encrypt, and then verify that\n // decryption with a bad key produces a MAC mismatch.\n let shared_secret_point = point_from_x_coord(1).unwrap();\n let s_app = compute_app_siloed_shared_secret(shared_secret_point, CONTRACT_ADDRESS);\n\n let (sym_key, iv) = derive_aes_symmetric_key_and_iv_from_shared_secret::<1>(s_app)[0];\n\n let mac_preimage = 0x42;\n let mac = Poseidon2::hash([mac_preimage], 1);\n let mac_as_bytes = mac.to_be_bytes::();\n\n let plaintext: [u8; TEST_PLAINTEXT_LENGTH] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];\n\n // We append the mac to the plaintext. It doesn't necessarily have to be 32 bytes; that's quite an extreme\n // length. 16 bytes or 8 bytes might be sufficient, and would save on data broadcasting costs. The shorter\n // the mac, the more possibility of false positive decryptions (decryption seemingly succeeding, but the\n // decrypted plaintext being garbage). Some projects use the `iv` (all 16 bytes or the first 8 bytes) as a\n // mac.\n let mut plaintext_with_mac = [0 as u8; TEST_PLAINTEXT_LENGTH + TEST_MAC_LENGTH];\n for i in 0..TEST_PLAINTEXT_LENGTH {\n plaintext_with_mac[i] = plaintext[i];\n }\n for i in 0..TEST_MAC_LENGTH {\n plaintext_with_mac[TEST_PLAINTEXT_LENGTH + i] = mac_as_bytes[i];\n }\n\n let ciphertext = aes128_encrypt(plaintext_with_mac, iv, sym_key);\n\n // We now would broadcast the tuple [ciphertext, mac] to the network. The recipient will then decrypt the\n // ciphertext, and if the mac inside the received plaintext matches the mac that was broadcast, then the\n // recipient knows that decryption was successful.\n\n // For this test, we intentionally mutate the sym_key to a bad one, so that decryption fails. This allows\n // us to explore how the recipient can detect failed decryption by checking the decrypted mac against the\n // broadcasted mac.\n let mut bad_sym_key = sym_key;\n bad_sym_key[0] = 0;\n\n // We need to convert the array to a BoundedVec because the oracle expects a BoundedVec as it's designed to\n // work with logs of unknown length.\n let ciphertext_bvec = BoundedVec::::from_array(ciphertext);\n // Decryption with wrong key still returns Some (with garbage).\n let received_plaintext = try_aes128_decrypt(ciphertext_bvec, iv, bad_sym_key).unwrap();\n\n let extracted_mac_as_bytes: [u8; TEST_MAC_LENGTH] =\n subarray(received_plaintext.storage(), TEST_PLAINTEXT_LENGTH);\n\n assert_eq(mac_as_bytes, extracted_mac_as_bytes, \"mac does not match\");\n });\n }\n}\n" }, "190": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/avm.nr", "source": "//! AVM oracles.\n//!\n//! There are only available during public execution. Calling any of them from a private or utility function will\n//! result in runtime errors.\n\nuse crate::protocol::address::{AztecAddress, EthAddress};\n\npub unconstrained fn address() -> AztecAddress {\n address_opcode()\n}\npub unconstrained fn sender() -> AztecAddress {\n sender_opcode()\n}\npub unconstrained fn transaction_fee() -> Field {\n transaction_fee_opcode()\n}\npub unconstrained fn chain_id() -> Field {\n chain_id_opcode()\n}\npub unconstrained fn version() -> Field {\n version_opcode()\n}\npub unconstrained fn block_number() -> u32 {\n block_number_opcode()\n}\npub unconstrained fn timestamp() -> u64 {\n timestamp_opcode()\n}\npub unconstrained fn min_fee_per_l2_gas() -> u128 {\n min_fee_per_l2_gas_opcode()\n}\npub unconstrained fn min_fee_per_da_gas() -> u128 {\n min_fee_per_da_gas_opcode()\n}\npub unconstrained fn l2_gas_left() -> u32 {\n l2_gas_left_opcode()\n}\npub unconstrained fn da_gas_left() -> u32 {\n da_gas_left_opcode()\n}\npub unconstrained fn is_static_call() -> u1 {\n is_static_call_opcode()\n}\npub unconstrained fn note_hash_exists(note_hash: Field, leaf_index: u64) -> u1 {\n note_hash_exists_opcode(note_hash, leaf_index)\n}\npub unconstrained fn emit_note_hash(note_hash: Field) {\n emit_note_hash_opcode(note_hash)\n}\npub unconstrained fn nullifier_exists(siloed_nullifier: Field) -> u1 {\n nullifier_exists_opcode(siloed_nullifier)\n}\npub unconstrained fn emit_nullifier(nullifier: Field) {\n emit_nullifier_opcode(nullifier)\n}\npub unconstrained fn emit_public_log(message: [Field]) {\n emit_public_log_opcode(message)\n}\npub unconstrained fn l1_to_l2_msg_exists(msg_hash: Field, msg_leaf_index: u64) -> u1 {\n l1_to_l2_msg_exists_opcode(msg_hash, msg_leaf_index)\n}\npub unconstrained fn send_l2_to_l1_msg(recipient: EthAddress, content: Field) {\n send_l2_to_l1_msg_opcode(recipient, content)\n}\n\npub unconstrained fn call(\n l2_gas_allocation: u32,\n da_gas_allocation: u32,\n address: AztecAddress,\n args: [Field; N],\n) {\n call_opcode(l2_gas_allocation, da_gas_allocation, address, N, args)\n}\n\npub unconstrained fn call_static(\n l2_gas_allocation: u32,\n da_gas_allocation: u32,\n address: AztecAddress,\n args: [Field; N],\n) {\n call_static_opcode(l2_gas_allocation, da_gas_allocation, address, N, args)\n}\n\npub unconstrained fn calldata_copy(cdoffset: u32, copy_size: u32) -> [Field; N] {\n calldata_copy_opcode(cdoffset, copy_size)\n}\n\n/// `success_copy` is placed immediately after the CALL opcode to get the success value\npub unconstrained fn success_copy() -> bool {\n success_copy_opcode()\n}\n\npub unconstrained fn returndata_size() -> u32 {\n returndata_size_opcode()\n}\n\npub unconstrained fn returndata_copy(rdoffset: u32, copy_size: u32) -> [Field] {\n returndata_copy_opcode(rdoffset, copy_size)\n}\n\n/// The additional prefix is to avoid clashing with the `return` Noir keyword.\npub unconstrained fn avm_return(returndata: [Field]) {\n return_opcode(returndata)\n}\n\n/// This opcode reverts using the exact data given. In general it should only be used to do rethrows, where the revert\n/// data is the same as the original revert data. For normal reverts, use Noir's `assert` which, on top of reverting,\n/// will also add an error selector to the revert data.\npub unconstrained fn revert(revertdata: [Field]) {\n revert_opcode(revertdata)\n}\n\npub unconstrained fn storage_read(storage_slot: Field, contract_address: Field) -> Field {\n storage_read_opcode(storage_slot, contract_address)\n}\n\npub unconstrained fn storage_write(storage_slot: Field, value: Field) {\n storage_write_opcode(storage_slot, value);\n}\n\n#[oracle(aztec_avm_address)]\nunconstrained fn address_opcode() -> AztecAddress {}\n\n#[oracle(aztec_avm_sender)]\nunconstrained fn sender_opcode() -> AztecAddress {}\n\n#[oracle(aztec_avm_transactionFee)]\nunconstrained fn transaction_fee_opcode() -> Field {}\n\n#[oracle(aztec_avm_chainId)]\nunconstrained fn chain_id_opcode() -> Field {}\n\n#[oracle(aztec_avm_version)]\nunconstrained fn version_opcode() -> Field {}\n\n#[oracle(aztec_avm_blockNumber)]\nunconstrained fn block_number_opcode() -> u32 {}\n\n#[oracle(aztec_avm_timestamp)]\nunconstrained fn timestamp_opcode() -> u64 {}\n\n#[oracle(aztec_avm_minFeePerL2Gas)]\nunconstrained fn min_fee_per_l2_gas_opcode() -> u128 {}\n\n#[oracle(aztec_avm_minFeePerDaGas)]\nunconstrained fn min_fee_per_da_gas_opcode() -> u128 {}\n\n#[oracle(aztec_avm_l2GasLeft)]\nunconstrained fn l2_gas_left_opcode() -> u32 {}\n\n#[oracle(aztec_avm_daGasLeft)]\nunconstrained fn da_gas_left_opcode() -> u32 {}\n\n#[oracle(aztec_avm_isStaticCall)]\nunconstrained fn is_static_call_opcode() -> u1 {}\n\n#[oracle(aztec_avm_noteHashExists)]\nunconstrained fn note_hash_exists_opcode(note_hash: Field, leaf_index: u64) -> u1 {}\n\n#[oracle(aztec_avm_emitNoteHash)]\nunconstrained fn emit_note_hash_opcode(note_hash: Field) {}\n\n#[oracle(aztec_avm_nullifierExists)]\nunconstrained fn nullifier_exists_opcode(siloed_nullifier: Field) -> u1 {}\n\n#[oracle(aztec_avm_emitNullifier)]\nunconstrained fn emit_nullifier_opcode(nullifier: Field) {}\n\n#[oracle(aztec_avm_emitPublicLog)]\nunconstrained fn emit_public_log_opcode(message: [Field]) {}\n\n#[oracle(aztec_avm_l1ToL2MsgExists)]\nunconstrained fn l1_to_l2_msg_exists_opcode(msg_hash: Field, msg_leaf_index: u64) -> u1 {}\n\n#[oracle(aztec_avm_sendL2ToL1Msg)]\nunconstrained fn send_l2_to_l1_msg_opcode(recipient: EthAddress, content: Field) {}\n\n#[oracle(aztec_avm_calldataCopy)]\nunconstrained fn calldata_copy_opcode(cdoffset: u32, copy_size: u32) -> [Field; N] {}\n\n#[oracle(aztec_avm_returndataSize)]\nunconstrained fn returndata_size_opcode() -> u32 {}\n\n#[oracle(aztec_avm_returndataCopy)]\nunconstrained fn returndata_copy_opcode(rdoffset: u32, copy_size: u32) -> [Field] {}\n\n#[oracle(aztec_avm_return)]\nunconstrained fn return_opcode(returndata: [Field]) {}\n\n#[oracle(aztec_avm_revert)]\nunconstrained fn revert_opcode(revertdata: [Field]) {}\n\n// While the length parameter might seem unnecessary given that we have N we keep it around because at the AVM bytecode\n// level, we want to support non-comptime-known lengths for such opcodes, even if Noir code will not generally take\n// that route.\n#[oracle(aztec_avm_call)]\nunconstrained fn call_opcode(\n l2_gas_allocation: u32,\n da_gas_allocation: u32,\n address: AztecAddress,\n length: u32,\n args: [Field; N],\n) {}\n\n// While the length parameter might seem unnecessary given that we have N we keep it around because at the AVM bytecode\n// level, we want to support non-comptime-known lengths for such opcodes, even if Noir code will not generally take\n// that route.\n#[oracle(aztec_avm_staticCall)]\nunconstrained fn call_static_opcode(\n l2_gas_allocation: u32,\n da_gas_allocation: u32,\n address: AztecAddress,\n length: u32,\n args: [Field; N],\n) {}\n\n#[oracle(aztec_avm_successCopy)]\nunconstrained fn success_copy_opcode() -> bool {}\n\n#[oracle(aztec_avm_storageRead)]\nunconstrained fn storage_read_opcode(storage_slot: Field, contract_address: Field) -> Field {}\n\n#[oracle(aztec_avm_storageWrite)]\nunconstrained fn storage_write_opcode(storage_slot: Field, value: Field) {}\n" }, "192": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/call_private_function.nr", "source": "use crate::protocol::{abis::function_selector::FunctionSelector, address::AztecAddress, utils::reader::Reader};\n\n#[oracle(aztec_prv_callPrivateFunction)]\nunconstrained fn call_private_function_oracle(\n _contract_address: AztecAddress,\n _function_selector: FunctionSelector,\n _args_hash: Field,\n _start_side_effect_counter: u32,\n _is_static_call: bool,\n) -> [Field; 2] {}\n\npub unconstrained fn call_private_function_internal(\n contract_address: AztecAddress,\n function_selector: FunctionSelector,\n args_hash: Field,\n start_side_effect_counter: u32,\n is_static_call: bool,\n) -> (u32, Field) {\n let fields = call_private_function_oracle(\n contract_address,\n function_selector,\n args_hash,\n start_side_effect_counter,\n is_static_call,\n );\n\n let mut reader = Reader::new(fields);\n let end_side_effect_counter = reader.read_u32();\n let returns_hash = reader.read();\n\n (end_side_effect_counter, returns_hash)\n}\n" }, "193": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/capsules.nr", "source": "use crate::protocol::{address::AztecAddress, traits::{Deserialize, Serialize}};\n\n/// Stores arbitrary information in a per-contract non-volatile database, which can later be retrieved with `load`. If\n/// data was already stored at this slot, it is overwritten.\n// TODO(F-498): review naming consistency\npub unconstrained fn store(contract_address: AztecAddress, slot: Field, value: T, scope: AztecAddress)\nwhere\n T: Serialize,\n{\n let serialized = value.serialize();\n set_capsule_oracle(contract_address, slot, serialized, scope);\n}\n\n/// Returns data previously stored via `storeCapsule` in the per-contract non-volatile database. Returns\n/// Option::none() if nothing was stored at the given slot.\n// TODO(F-498): review naming consistency\npub unconstrained fn load(contract_address: AztecAddress, slot: Field, scope: AztecAddress) -> Option\nwhere\n T: Deserialize,\n{\n let serialized_option = get_capsule_oracle(contract_address, slot, ::N, scope);\n serialized_option.map(|arr| Deserialize::deserialize(arr))\n}\n\n/// Deletes data in the per-contract non-volatile database. Does nothing if no data was present.\npub unconstrained fn delete(contract_address: AztecAddress, slot: Field, scope: AztecAddress) {\n delete_oracle(contract_address, slot, scope);\n}\n\n/// Copies a number of contiguous entries in the per-contract non-volatile database. This allows for efficient data\n/// structures by avoiding repeated calls to `loadCapsule` and `storeCapsule`. Supports overlapping source and\n/// destination regions (which will result in the overlapped source values being overwritten). All copied slots must\n/// exist in the database (i.e. have been stored and not deleted)\npub unconstrained fn copy(\n contract_address: AztecAddress,\n src_slot: Field,\n dst_slot: Field,\n num_entries: u32,\n scope: AztecAddress,\n) {\n copy_oracle(contract_address, src_slot, dst_slot, num_entries, scope);\n}\n\n#[oracle(aztec_utl_setCapsule)]\nunconstrained fn set_capsule_oracle(\n contract_address: AztecAddress,\n slot: Field,\n values: [Field; N],\n scope: AztecAddress,\n) {}\n\n/// We need to pass in `array_len` (the value of N) as a parameter to tell the oracle how many fields the response must\n/// have.\n///\n/// Note that the oracle returns an Option<[Field; N]> because we cannot return an Option directly. That would\n/// require for the oracle resolver to know the shape of T (e.g. if T were a struct of 3 u32 values then the expected\n/// response shape would be 3 single items, whereas it were a struct containing `u32, [Field;10], u32` then the\n/// expected shape would be single, array, single.). Instead, we return the serialization and deserialize in Noir.\n#[oracle(aztec_utl_getCapsule)]\nunconstrained fn get_capsule_oracle(\n contract_address: AztecAddress,\n slot: Field,\n array_len: u32,\n scope: AztecAddress,\n) -> Option<[Field; N]> {}\n\n#[oracle(aztec_utl_deleteCapsule)]\nunconstrained fn delete_oracle(contract_address: AztecAddress, slot: Field, scope: AztecAddress) {}\n\n#[oracle(aztec_utl_copyCapsule)]\nunconstrained fn copy_oracle(\n contract_address: AztecAddress,\n src_slot: Field,\n dst_slot: Field,\n num_entries: u32,\n scope: AztecAddress,\n) {}\n\nmod test {\n // These tests are sort of redundant since we already test the oracle implementation directly in TypeScript, but\n // they are cheap regardless and help ensure both that the TXE implementation works accordingly and that the Noir\n // oracles are hooked up correctly.\n\n use crate::{\n oracle::capsules::{copy, delete, load, store},\n test::{helpers::test_environment::TestEnvironment, mocks::MockStruct},\n };\n use crate::protocol::{address::AztecAddress, traits::{FromField, ToField}};\n\n global SLOT: Field = 1;\n global SCOPE: AztecAddress = AztecAddress { inner: 0xcafe };\n\n #[test]\n unconstrained fn stores_and_loads() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let value = MockStruct::new(5, 6);\n store(contract_address, SLOT, value, SCOPE);\n\n assert_eq(load(contract_address, SLOT, SCOPE).unwrap(), value);\n });\n }\n\n #[test]\n unconstrained fn store_overwrites() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let value = MockStruct::new(5, 6);\n store(contract_address, SLOT, value, SCOPE);\n\n let new_value = MockStruct::new(7, 8);\n store(contract_address, SLOT, new_value, SCOPE);\n\n assert_eq(load(contract_address, SLOT, SCOPE).unwrap(), new_value);\n });\n }\n\n #[test]\n unconstrained fn loads_empty_slot() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let loaded_value: Option = load(contract_address, SLOT, SCOPE);\n assert_eq(loaded_value, Option::none());\n });\n }\n\n #[test]\n unconstrained fn deletes_stored_value() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let value = MockStruct::new(5, 6);\n store(contract_address, SLOT, value, SCOPE);\n delete(contract_address, SLOT, SCOPE);\n\n let loaded_value: Option = load(contract_address, SLOT, SCOPE);\n assert_eq(loaded_value, Option::none());\n });\n }\n\n #[test]\n unconstrained fn deletes_empty_slot() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n delete(contract_address, SLOT, SCOPE);\n let loaded_value: Option = load(contract_address, SLOT, SCOPE);\n assert_eq(loaded_value, Option::none());\n });\n }\n\n #[test]\n unconstrained fn copies_non_overlapping_values() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let src = 5;\n\n let values = [MockStruct::new(5, 6), MockStruct::new(7, 8), MockStruct::new(9, 10)];\n store(contract_address, src, values[0], SCOPE);\n store(contract_address, src + 1, values[1], SCOPE);\n store(contract_address, src + 2, values[2], SCOPE);\n\n let dst = 10;\n copy(contract_address, src, dst, 3, SCOPE);\n\n assert_eq(load(contract_address, dst, SCOPE).unwrap(), values[0]);\n assert_eq(load(contract_address, dst + 1, SCOPE).unwrap(), values[1]);\n assert_eq(load(contract_address, dst + 2, SCOPE).unwrap(), values[2]);\n });\n }\n\n #[test]\n unconstrained fn copies_overlapping_values_with_src_ahead() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let src = 1;\n\n let values = [MockStruct::new(5, 6), MockStruct::new(7, 8), MockStruct::new(9, 10)];\n store(contract_address, src, values[0], SCOPE);\n store(contract_address, src + 1, values[1], SCOPE);\n store(contract_address, src + 2, values[2], SCOPE);\n\n let dst = 2;\n copy(contract_address, src, dst, 3, SCOPE);\n\n assert_eq(load(contract_address, dst, SCOPE).unwrap(), values[0]);\n assert_eq(load(contract_address, dst + 1, SCOPE).unwrap(), values[1]);\n assert_eq(load(contract_address, dst + 2, SCOPE).unwrap(), values[2]);\n\n // src[1] and src[2] should have been overwritten since they are also dst[0] and dst[1]\n assert_eq(load(contract_address, src, SCOPE).unwrap(), values[0]); // src[0] (unchanged)\n assert_eq(load(contract_address, src + 1, SCOPE).unwrap(), values[0]); // dst[0]\n assert_eq(load(contract_address, src + 2, SCOPE).unwrap(), values[1]); // dst[1]\n });\n }\n\n #[test]\n unconstrained fn copies_overlapping_values_with_dst_ahead() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n let src = 2;\n\n let values = [MockStruct::new(5, 6), MockStruct::new(7, 8), MockStruct::new(9, 10)];\n store(contract_address, src, values[0], SCOPE);\n store(contract_address, src + 1, values[1], SCOPE);\n store(contract_address, src + 2, values[2], SCOPE);\n\n let dst = 1;\n copy(contract_address, src, dst, 3, SCOPE);\n\n assert_eq(load(contract_address, dst, SCOPE).unwrap(), values[0]);\n assert_eq(load(contract_address, dst + 1, SCOPE).unwrap(), values[1]);\n assert_eq(load(contract_address, dst + 2, SCOPE).unwrap(), values[2]);\n\n // src[0] and src[1] should have been overwritten since they are also dst[1] and dst[2]\n assert_eq(load(contract_address, src, SCOPE).unwrap(), values[1]); // dst[1]\n assert_eq(load(contract_address, src + 1, SCOPE).unwrap(), values[2]); // dst[2]\n assert_eq(load(contract_address, src + 2, SCOPE).unwrap(), values[2]); // src[2] (unchanged)\n });\n }\n\n #[test(should_fail_with = \"copy empty slot\")]\n unconstrained fn cannot_copy_empty_values() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n\n copy(contract_address, SLOT, SLOT, 1, SCOPE);\n });\n }\n\n #[test(should_fail_with = \"not allowed to access\")]\n unconstrained fn cannot_store_other_contract() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let other_contract_address = AztecAddress::from_field(contract_address.to_field() + 1);\n\n let value = MockStruct::new(5, 6);\n store(other_contract_address, SLOT, value, SCOPE);\n });\n }\n\n #[test(should_fail_with = \"not allowed to access\")]\n unconstrained fn cannot_load_other_contract() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let other_contract_address = AztecAddress::from_field(contract_address.to_field() + 1);\n\n let _: Option = load(other_contract_address, SLOT, SCOPE);\n });\n }\n\n #[test(should_fail_with = \"not allowed to access\")]\n unconstrained fn cannot_delete_other_contract() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let other_contract_address = AztecAddress::from_field(contract_address.to_field() + 1);\n\n delete(other_contract_address, SLOT, SCOPE);\n });\n }\n\n #[test(should_fail_with = \"not allowed to access\")]\n unconstrained fn cannot_copy_other_contract() {\n let env = TestEnvironment::new();\n env.private_context(|context| {\n let contract_address = context.this_address();\n let other_contract_address = AztecAddress::from_field(contract_address.to_field() + 1);\n\n copy(other_contract_address, SLOT, SLOT, 0, SCOPE);\n });\n }\n}\n" }, "194": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/contract_sync.nr", "source": "use crate::protocol::address::AztecAddress;\n\n#[oracle(aztec_utl_setContractSyncCacheInvalid)]\nunconstrained fn set_contract_sync_cache_invalid_oracle(\n contract_address: AztecAddress,\n scopes: BoundedVec,\n) {}\n\n/// Forces the PXE to re-sync the given contract for a set of scopes on the next query.\n///\n/// Call this after writing data (e.g. offchain messages) that the contract's `sync_state` function needs to discover.\n/// Without invalidation, the sync cache would skip re-running `sync_state` until the next block.\npub unconstrained fn set_contract_sync_cache_invalid(\n contract_address: AztecAddress,\n scopes: BoundedVec,\n) {\n set_contract_sync_cache_invalid_oracle(contract_address, scopes);\n}\n" }, "195": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/execution.nr", "source": "use crate::context::UtilityContext;\n\n#[oracle(aztec_utl_getUtilityContext)]\nunconstrained fn get_utility_context_oracle() -> UtilityContext {}\n\n/// Returns a utility context built from the global variables of anchor block and the contract address of the function\n/// being executed.\npub unconstrained fn get_utility_context() -> UtilityContext {\n get_utility_context_oracle()\n}\n" }, "196": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/execution_cache.nr", "source": "/// Stores values represented as slice in execution cache to be later obtained by its hash.\n// TODO(F-498): review naming consistency\npub fn store(values: [Field; N], hash: Field) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe to\n // call. When loading the values, however, the caller must check that the values are indeed the preimage.\n unsafe { set_hash_preimage_oracle_wrapper(values, hash) };\n}\n\nunconstrained fn set_hash_preimage_oracle_wrapper(values: [Field; N], hash: Field) {\n set_hash_preimage_oracle(values, hash);\n}\n\n// TODO(F-498): review naming consistency\npub unconstrained fn load(hash: Field) -> [Field; N] {\n get_hash_preimage_oracle(hash)\n}\n\n#[oracle(aztec_prv_setHashPreimage)]\nunconstrained fn set_hash_preimage_oracle(_values: [Field; N], _hash: Field) {}\n\n#[oracle(aztec_prv_getHashPreimage)]\nunconstrained fn get_hash_preimage_oracle(_hash: Field) -> [Field; N] {}\n" }, "197": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/get_contract_instance.nr", "source": "use crate::protocol::{\n address::AztecAddress, contract_class_id::ContractClassId, contract_instance::ContractInstance, traits::FromField,\n};\n\n// NOTE: this is for use in private only\n#[oracle(aztec_utl_getContractInstance)]\nunconstrained fn get_contract_instance_oracle(_address: AztecAddress) -> ContractInstance {}\n\n// NOTE: this is for use in private only\nunconstrained fn get_contract_instance_internal(address: AztecAddress) -> ContractInstance {\n get_contract_instance_oracle(address)\n}\n\n// NOTE: this is for use in private only\npub fn get_contract_instance(address: AztecAddress) -> ContractInstance {\n // Safety: The to_address function combines all values in the instance object to produce an address, so by checking\n // that we get the expected address we validate the entire struct.\n let instance = unsafe { get_contract_instance_internal(address) };\n assert_eq(instance.to_address(), address);\n\n instance\n}\n\nstruct GetContractInstanceResult {\n exists: bool,\n member: Field,\n}\n\n// These oracles each return a ContractInstance member plus a boolean indicating whether the instance was found.\n#[oracle(aztec_avm_getContractInstanceDeployer)]\nunconstrained fn get_contract_instance_deployer_oracle_avm(_address: AztecAddress) -> [GetContractInstanceResult; 1] {}\n#[oracle(aztec_avm_getContractInstanceClassId)]\nunconstrained fn get_contract_instance_class_id_oracle_avm(_address: AztecAddress) -> [GetContractInstanceResult; 1] {}\n#[oracle(aztec_avm_getContractInstanceInitializationHash)]\nunconstrained fn get_contract_instance_initialization_hash_oracle_avm(\n _address: AztecAddress,\n) -> [GetContractInstanceResult; 1] {}\n\nunconstrained fn get_contract_instance_deployer_internal_avm(address: AztecAddress) -> [GetContractInstanceResult; 1] {\n get_contract_instance_deployer_oracle_avm(address)\n}\nunconstrained fn get_contract_instance_class_id_internal_avm(address: AztecAddress) -> [GetContractInstanceResult; 1] {\n get_contract_instance_class_id_oracle_avm(address)\n}\nunconstrained fn get_contract_instance_initialization_hash_internal_avm(\n address: AztecAddress,\n) -> [GetContractInstanceResult; 1] {\n get_contract_instance_initialization_hash_oracle_avm(address)\n}\n\npub fn get_contract_instance_deployer_avm(address: AztecAddress) -> Option {\n // Safety: AVM opcodes are constrained by the AVM itself\n let GetContractInstanceResult { exists, member } =\n unsafe { get_contract_instance_deployer_internal_avm(address)[0] };\n if exists {\n Option::some(AztecAddress::from_field(member))\n } else {\n Option::none()\n }\n}\npub fn get_contract_instance_class_id_avm(address: AztecAddress) -> Option {\n // Safety: AVM opcodes are constrained by the AVM itself\n let GetContractInstanceResult { exists, member } =\n unsafe { get_contract_instance_class_id_internal_avm(address)[0] };\n if exists {\n Option::some(ContractClassId::from_field(member))\n } else {\n Option::none()\n }\n}\npub fn get_contract_instance_initialization_hash_avm(address: AztecAddress) -> Option {\n // Safety: AVM opcodes are constrained by the AVM itself\n let GetContractInstanceResult { exists, member } =\n unsafe { get_contract_instance_initialization_hash_internal_avm(address)[0] };\n if exists {\n Option::some(member)\n } else {\n Option::none()\n }\n}\n" }, "201": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/get_public_data_witness.nr", "source": "use crate::protocol::{\n abis::block_header::BlockHeader,\n constants::PUBLIC_DATA_TREE_HEIGHT,\n data::PublicDataTreeLeafPreimage,\n traits::{Deserialize, Hash, Serialize},\n};\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct PublicDataWitness {\n pub index: Field,\n pub leaf_preimage: PublicDataTreeLeafPreimage,\n pub path: [Field; PUBLIC_DATA_TREE_HEIGHT],\n}\n\n#[oracle(aztec_utl_getPublicDataWitness)]\nunconstrained fn get_public_data_witness_oracle(\n _block_hash: Field,\n _public_data_tree_index: Field,\n) -> PublicDataWitness {}\n\npub unconstrained fn get_public_data_witness(\n block_header: BlockHeader,\n public_data_tree_index: Field,\n) -> PublicDataWitness {\n let block_hash = block_header.hash();\n get_public_data_witness_oracle(block_hash, public_data_tree_index)\n}\n" }, "202": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/key_validation_request.nr", "source": "use crate::protocol::abis::validation_requests::KeyValidationRequest;\n\n#[oracle(aztec_utl_getKeyValidationRequest)]\nunconstrained fn get_key_validation_request_oracle(_pk_m_hash: Field, _key_index: Field) -> KeyValidationRequest {}\n\npub unconstrained fn get_key_validation_request(pk_m_hash: Field, key_index: Field) -> KeyValidationRequest {\n get_key_validation_request_oracle(pk_m_hash, key_index)\n}\n" }, "203": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/keys.nr", "source": "use crate::protocol::{\n address::{AztecAddress, PartialAddress},\n point::Point,\n public_keys::{IvpkM, NpkM, OvpkM, PublicKeys, TpkM},\n};\n\n// TODO(F-498): review naming consistency\npub unconstrained fn get_public_keys_and_partial_address(address: AztecAddress) -> (PublicKeys, PartialAddress) {\n try_get_public_keys_and_partial_address(address).expect(f\"Public keys not registered for account {address}\")\n}\n\n#[oracle(aztec_utl_getPublicKeysAndPartialAddress)]\nunconstrained fn get_public_keys_and_partial_address_oracle(_address: AztecAddress) -> Option<[Field; 13]> {}\n\n// TODO(F-498): review naming consistency\npub unconstrained fn try_get_public_keys_and_partial_address(\n address: AztecAddress,\n) -> Option<(PublicKeys, PartialAddress)> {\n get_public_keys_and_partial_address_oracle(address).map(|result: [Field; 13]| {\n let keys = PublicKeys {\n npk_m: NpkM { inner: Point { x: result[0], y: result[1], is_infinite: result[2] != 0 } },\n ivpk_m: IvpkM { inner: Point { x: result[3], y: result[4], is_infinite: result[5] != 0 } },\n ovpk_m: OvpkM { inner: Point { x: result[6], y: result[7], is_infinite: result[8] != 0 } },\n tpk_m: TpkM { inner: Point { x: result[9], y: result[10], is_infinite: result[11] != 0 } },\n };\n\n let partial_address = PartialAddress::from_field(result[12]);\n\n (keys, partial_address)\n })\n}\n" }, "205": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/message_processing.nr", "source": "use crate::protocol::address::AztecAddress;\n\n/// Finds new private logs that may have been sent to all registered accounts in PXE in the current contract and makes\n/// them available for later processing in Noir by storing them in a capsule array.\n// TODO(F-498): review naming consistency\npub unconstrained fn fetch_tagged_logs(pending_tagged_log_array_base_slot: Field, scope: AztecAddress) {\n get_pending_tagged_logs_oracle(pending_tagged_log_array_base_slot, scope);\n}\n\n#[oracle(aztec_utl_getPendingTaggedLogs)]\nunconstrained fn get_pending_tagged_logs_oracle(pending_tagged_log_array_base_slot: Field, scope: AztecAddress) {}\n\n// This must be a single oracle and not one for notes and one for events because the entire point is to validate all\n// notes and events in one go, minimizing node round-trips.\npub(crate) unconstrained fn validate_and_store_enqueued_notes_and_events(\n contract_address: AztecAddress,\n note_validation_requests_array_base_slot: Field,\n event_validation_requests_array_base_slot: Field,\n max_note_packed_len: Field,\n max_event_serialized_len: Field,\n scope: AztecAddress,\n) {\n validate_and_store_enqueued_notes_and_events_oracle(\n contract_address,\n note_validation_requests_array_base_slot,\n event_validation_requests_array_base_slot,\n max_note_packed_len,\n max_event_serialized_len,\n scope,\n );\n}\n\n#[oracle(aztec_utl_validateAndStoreEnqueuedNotesAndEvents)]\nunconstrained fn validate_and_store_enqueued_notes_and_events_oracle(\n contract_address: AztecAddress,\n note_validation_requests_array_base_slot: Field,\n event_validation_requests_array_base_slot: Field,\n max_note_packed_len: Field,\n max_event_serialized_len: Field,\n scope: AztecAddress,\n) {}\n\npub(crate) unconstrained fn get_logs_by_tag(\n contract_address: AztecAddress,\n log_retrieval_requests_array_base_slot: Field,\n log_retrieval_responses_array_base_slot: Field,\n scope: AztecAddress,\n) {\n get_logs_by_tag_oracle(\n contract_address,\n log_retrieval_requests_array_base_slot,\n log_retrieval_responses_array_base_slot,\n scope,\n );\n}\n\n#[oracle(aztec_utl_getLogsByTag)]\nunconstrained fn get_logs_by_tag_oracle(\n contract_address: AztecAddress,\n log_retrieval_requests_array_base_slot: Field,\n log_retrieval_responses_array_base_slot: Field,\n scope: AztecAddress,\n) {}\n\npub(crate) unconstrained fn get_message_contexts_by_tx_hash(\n contract_address: AztecAddress,\n message_context_requests_array_base_slot: Field,\n message_context_responses_array_base_slot: Field,\n scope: AztecAddress,\n) {\n get_message_contexts_by_tx_hash_oracle(\n contract_address,\n message_context_requests_array_base_slot,\n message_context_responses_array_base_slot,\n scope,\n );\n}\n\n#[oracle(aztec_utl_getMessageContextsByTxHash)]\nunconstrained fn get_message_contexts_by_tx_hash_oracle(\n contract_address: AztecAddress,\n message_context_requests_array_base_slot: Field,\n message_context_responses_array_base_slot: Field,\n scope: AztecAddress,\n) {}\n" }, "207": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/notes.nr", "source": "use crate::note::{HintedNote, note_interface::NoteType};\n\nuse crate::protocol::{address::AztecAddress, traits::Packable};\n\n/// Notifies the simulator that a note has been created, so that it can be returned in future read requests in the same\n/// transaction. This note should only be added to the non-volatile database if found in an actual block.\npub fn notify_created_note(\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n note_type_id: Field,\n packed_note: [Field; N],\n note_hash: Field,\n counter: u32,\n) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe to\n // call.\n unsafe {\n notify_created_note_oracle_wrapper(\n owner,\n storage_slot,\n randomness,\n note_type_id,\n packed_note,\n note_hash,\n counter,\n )\n };\n}\n\n/// Notifies the simulator that a note has been nullified, so that it is no longer returned in future read requests in\n/// the same transaction. This note should only be removed to the non-volatile database if its nullifier is found in an\n/// actual block.\npub fn notify_nullified_note(nullifier: Field, note_hash: Field, counter: u32) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe to\n // call.\n unsafe { notify_nullified_note_oracle_wrapper(nullifier, note_hash, counter) };\n}\n\nunconstrained fn notify_created_note_oracle_wrapper(\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n note_type_id: Field,\n packed_note: [Field; N],\n note_hash: Field,\n counter: u32,\n) {\n notify_created_note_oracle(\n owner,\n storage_slot,\n randomness,\n note_type_id,\n packed_note,\n note_hash,\n counter,\n );\n}\n\n#[oracle(aztec_prv_notifyCreatedNote)]\nunconstrained fn notify_created_note_oracle(\n _owner: AztecAddress,\n _storage_slot: Field,\n _randomness: Field,\n _note_type_id: Field,\n _packed_note: [Field; N],\n _note_hash: Field,\n _counter: u32,\n) {}\n\nunconstrained fn notify_nullified_note_oracle_wrapper(nullifier: Field, note_hash: Field, counter: u32) {\n notify_nullified_note_oracle(nullifier, note_hash, counter);\n}\n\n#[oracle(aztec_prv_notifyNullifiedNote)]\nunconstrained fn notify_nullified_note_oracle(_nullifier: Field, _note_hash: Field, _counter: u32) {}\n\n#[oracle(aztec_utl_getNotes)]\nunconstrained fn get_notes_oracle(\n _owner: Option,\n _storage_slot: Field,\n _num_selects: u8,\n _select_by_indexes: [u8; M],\n _select_by_offsets: [u8; M],\n _select_by_lengths: [u8; M],\n _select_values: [Field; M],\n _select_comparators: [u8; M],\n _sort_by_indexes: [u8; M],\n _sort_by_offsets: [u8; M],\n _sort_by_lengths: [u8; M],\n _sort_order: [u8; M],\n _limit: u32,\n _offset: u32,\n _status: u8,\n // This is always set to MAX_NOTES. We need to pass it to TS in order to correctly construct the BoundedVec\n _max_notes: u32,\n // This is always set to as Packable>::N. We need to pass it to TS in order to be able to\n // correctly construct the BoundedVec there.\n _packed_hinted_note_length: u32,\n) -> BoundedVec<[Field; as Packable>::N], MaxNotes>\nwhere\n // TODO(https://github.com/noir-lang/noir/issues/9399): `Note: Packable` should work here.\n HintedNote: Packable,\n{}\n\npub unconstrained fn get_notes(\n owner: Option,\n storage_slot: Field,\n num_selects: u8,\n select_by_indexes: [u8; M],\n select_by_offsets: [u8; M],\n select_by_lengths: [u8; M],\n select_values: [Field; M],\n select_comparators: [u8; M],\n sort_by_indexes: [u8; M],\n sort_by_offsets: [u8; M],\n sort_by_lengths: [u8; M],\n sort_order: [u8; M],\n limit: u32,\n offset: u32,\n status: u8,\n) -> [Option>; MaxNotes]\nwhere\n Note: NoteType + Packable,\n{\n let packed_hinted_notes: BoundedVec<[Field; as Packable>::N], MaxNotes> = get_notes_oracle::(\n owner,\n storage_slot,\n num_selects,\n select_by_indexes,\n select_by_offsets,\n select_by_lengths,\n select_values,\n select_comparators,\n sort_by_indexes,\n sort_by_offsets,\n sort_by_lengths,\n sort_order,\n limit,\n offset,\n status,\n MaxNotes,\n as Packable>::N,\n );\n\n let mut notes = BoundedVec::<_, MaxNotes>::new();\n for i in 0..packed_hinted_notes.len() {\n let hinted_note = HintedNote::unpack(packed_hinted_notes.get(i));\n notes.push(hinted_note);\n }\n\n // At last we convert the bounded vector to an array of options. We do this because that is what the filter\n // function needs to have on the output and we've decided to have the same type on the input and output of the\n // filter and preprocessor functions.\n //\n // We have decided to have the same type on the input and output of the filter and preprocessor functions because\n // it allows us to chain multiple filters and preprocessors together.\n //\n // So why do we want the array of options on the output of the filter function?\n //\n // Filter returns an array of options rather than a BoundedVec for performance reasons. Using an array of options\n // allows setting values at known indices in the output array which is much more efficient than pushing to a\n // BoundedVec where the write position depends on previous iterations. The array can then be efficiently converted\n // to a BoundedVec using utils/array/collapse.nr::collapse function from Aztec.nr. This avoids expensive dynamic\n // memory access patterns that would be required when building up a BoundedVec incrementally. For preprocessor\n // functions we could use BoundedVec return value as there the optimization does not matter since it is applied in\n // an unconstrained context. We, however, use the same return value type to be able to use the same function as\n // both a preprocessor and a filter.\n let mut notes_array = [Option::none(); MaxNotes];\n for i in 0..notes.len() {\n notes_array[i] = Option::some(notes.get_unchecked(i));\n }\n\n notes_array\n}\n\n// TODO: Oracles below are generic private log oracles and are not specific to notes. Move them somewhere else.\n\n/// Returns the next app tag for a given sender and recipient pair.\n///\n/// This also notifies the simulator that a tag has been used in a note, and to therefore increment the associated\n/// index so that future notes get a different tag and can be discovered by the recipient. This change should only be\n/// persisted in a non-volatile database if the tagged log is found in an actual block - otherwise e.g. a reverting\n/// transaction can cause the sender to accidentally skip indices and later produce notes that are not found by the\n/// recipient.\npub unconstrained fn get_next_app_tag_as_sender(sender: AztecAddress, recipient: AztecAddress) -> Field {\n get_next_app_tag_as_sender_oracle(sender, recipient)\n}\n\n#[oracle(aztec_prv_getNextAppTagAsSender)]\nunconstrained fn get_next_app_tag_as_sender_oracle(_sender: AztecAddress, _recipient: AztecAddress) -> Field {}\n\n/// Gets the sender for tags.\n///\n/// This unconstrained value is used as the sender when computing an unconstrained shared secret for a tag in order to\n/// emit a log. Constrained tagging should not use this as there is no guarantee that the recipient knows about the\n/// sender, and hence about the shared secret.\n///\n/// The value persists through nested calls, meaning all calls down the stack will use the same 'senderForTags' value\n/// (unless it is replaced).\npub unconstrained fn get_sender_for_tags() -> Option {\n get_sender_for_tags_oracle()\n}\n\n#[oracle(aztec_prv_getSenderForTags)]\nunconstrained fn get_sender_for_tags_oracle() -> Option {}\n\n/// Sets the sender for tags.\n///\n/// This unconstrained value is used as the sender when computing an unconstrained shared secret for a tag in order to\n/// emit a log. Constrained tagging should not use this as there is no guarantee that the recipient knows about the\n/// sender, and hence about the shared secret.\n///\n/// Account contracts typically set this value before calling other contracts. The value persists through nested calls,\n/// meaning all calls down the stack will use the same 'senderForTags' value (unless it is replaced by another call to\n/// this setter).\npub unconstrained fn set_sender_for_tags(sender_for_tags: AztecAddress) {\n set_sender_for_tags_oracle(sender_for_tags);\n}\n\n#[oracle(aztec_prv_setSenderForTags)]\nunconstrained fn set_sender_for_tags_oracle(_sender_for_tags: AztecAddress) {}\n" }, "208": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/nullifiers.nr", "source": "//! Nullifier creation, existence checks, etc.\n\nuse crate::protocol::address::aztec_address::AztecAddress;\n\n/// Notifies the simulator that a nullifier has been created, so that its correct status (pending or settled) can be\n/// determined when reading nullifiers in subsequent private function calls. The first non-revertible nullifier emitted\n/// is also used to compute note nonces.\npub fn notify_created_nullifier(inner_nullifier: Field) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe to\n // call.\n unsafe { notify_created_nullifier_oracle(inner_nullifier) };\n}\n\n#[oracle(aztec_prv_notifyCreatedNullifier)]\nunconstrained fn notify_created_nullifier_oracle(_inner_nullifier: Field) {}\n\n/// Returns `true` if the nullifier has been emitted in the same transaction, i.e. if [`notify_created_nullifier`] has\n/// been\n/// called for this inner nullifier from the contract with the specified address.\n///\n/// Note that despite sharing pending transaction information with the app, this is not a privacy leak: anyone in the\n/// network can always determine in which transaction a inner nullifier was emitted by a given contract by simply\n/// inspecting transaction effects. What _would_ constitute a leak would be to share the list of inner pending\n/// nullifiers, as that would reveal their preimages.\npub unconstrained fn is_nullifier_pending(inner_nullifier: Field, contract_address: AztecAddress) -> bool {\n is_nullifier_pending_oracle(inner_nullifier, contract_address)\n}\n\n#[oracle(aztec_prv_isNullifierPending)]\nunconstrained fn is_nullifier_pending_oracle(_inner_nullifier: Field, _contract_address: AztecAddress) -> bool {}\n\n/// Returns `true` if the nullifier exists. Note that a `true` value can be constrained by proving existence of the\n/// nullifier, but a `false` value should not be relied upon since other transactions may emit this nullifier before\n/// the current transaction is included in a block. While this might seem of little use at first, certain design\n/// patterns benefit from this abstraction (see e.g. `PrivateMutable`).\n// TODO(F-498): review naming consistency\npub unconstrained fn check_nullifier_exists(inner_nullifier: Field) -> bool {\n does_nullifier_exist_oracle(inner_nullifier)\n}\n\n#[oracle(aztec_utl_doesNullifierExist)]\nunconstrained fn does_nullifier_exist_oracle(_inner_nullifier: Field) -> bool {}\n" }, "211": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/random.nr", "source": "/// Returns an unconstrained random value. Note that it is not possible to constrain this value to prove that it is\n/// truly random: we assume that the oracle is cooperating and returning random values. In some applications this\n/// behavior might not be acceptable and other techniques might be more suitable, such as producing pseudo-random\n/// values by hashing values outside of user control (like block hashes) or secrets.\npub unconstrained fn random() -> Field {\n rand_oracle()\n}\n\n#[oracle(aztec_utl_getRandomField)]\nunconstrained fn rand_oracle() -> Field {}\n" }, "212": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/shared_secret.nr", "source": "use crate::protocol::address::aztec_address::AztecAddress;\nuse crate::protocol::point::Point;\n\n#[oracle(aztec_utl_getSharedSecret)]\nunconstrained fn get_shared_secret_oracle(\n address: AztecAddress,\n ephPk: Point,\n contract_address: AztecAddress,\n) -> Field {}\n\n/// Returns an app-siloed shared secret between `address` and someone who knows the secret key behind an ephemeral\n/// public key `ephPk`.\n///\n/// The returned value is a Field `s_app`, computed as:\n///\n/// ```text\n/// S = address_secret * ephPk (raw ECDH point)\n/// s_app = h(DOM_SEP, S.x, S.y, contract) (app-siloed scalar)\n/// ```\n///\n/// where `contract` is the address of the calling contract. The oracle host validates this matches its execution\n/// context.\n///\n/// Without app-siloing, a malicious contract could call this oracle with public information (address, ephPk) and\n/// obtain the same raw secret as the legitimate contract, enabling cross-contract decryption. By including the\n/// contract address in the hash, each contract receives a different `s_app`, preventing this attack.\n///\n/// Callers derive indexed subkeys from `s_app` via\n/// [`derive_shared_secret_subkey`](crate::keys::ecdh_shared_secret::derive_shared_secret_subkey).\npub unconstrained fn get_shared_secret(address: AztecAddress, ephPk: Point, contract_address: AztecAddress) -> Field {\n get_shared_secret_oracle(address, ephPk, contract_address)\n}\n" }, "213": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/storage.nr", "source": "use crate::protocol::{abis::block_header::BlockHeader, address::AztecAddress, traits::{Hash, Packable, ToField}};\n\n#[oracle(aztec_utl_getFromPublicStorage)]\nunconstrained fn get_from_public_storage_oracle(\n block_hash: Field,\n address: Field,\n storage_slot: Field,\n length: u32,\n) -> [Field; N] {}\n\n// TODO(F-498): review naming consistency\npub unconstrained fn raw_storage_read(\n block_hash_to_read_from: Field,\n address: AztecAddress,\n storage_slot: Field,\n) -> [Field; N] {\n get_from_public_storage_oracle(block_hash_to_read_from, address.to_field(), storage_slot, N)\n}\n\n// TODO(F-498): review naming consistency\npub unconstrained fn storage_read(header_to_read_from: BlockHeader, address: AztecAddress, storage_slot: Field) -> T\nwhere\n T: Packable,\n{\n let block_hash_to_read_from = header_to_read_from.hash();\n T::unpack(\n raw_storage_read(block_hash_to_read_from, address, storage_slot),\n )\n}\n" }, "214": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/tx_phase.nr", "source": "/// Notifies PXE of the side effect counter at which the revertible phase begins.\n///\n/// PXE uses it to classify notes and nullifiers as revertible or non-revertible in its note cache. This information is\n/// then fed to kernels as hints.\npub(crate) fn notify_revertible_phase_start(counter: u32) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe to\n // call.\n unsafe { notify_revertible_phase_start_oracle_wrapper(counter) };\n}\n\n/// Returns whether a side effect counter falls in the revertible phase of the transaction.\npub(crate) unconstrained fn is_execution_in_revertible_phase(current_counter: u32) -> bool {\n is_execution_in_revertible_phase_oracle(current_counter)\n}\n\nunconstrained fn notify_revertible_phase_start_oracle_wrapper(counter: u32) {\n notify_revertible_phase_start_oracle(counter);\n}\n\n#[oracle(aztec_prv_notifyRevertiblePhaseStart)]\nunconstrained fn notify_revertible_phase_start_oracle(_counter: u32) {}\n\n#[oracle(aztec_prv_isExecutionInRevertiblePhase)]\nunconstrained fn is_execution_in_revertible_phase_oracle(current_counter: u32) -> bool {}\n" }, "215": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/oracle/version.nr", "source": "/// The ORACLE_VERSION constant is used to check that the oracle interface is in sync between PXE and Aztec.nr. We need\n/// to version the oracle interface to ensure that developers get a reasonable error message if they use incompatible\n/// versions of Aztec.nr and PXE. The TypeScript counterpart is in `oracle_version.ts`.\n///\n/// @dev Whenever a contract function or Noir test is run, the `aztec_utl_assertCompatibleOracleVersion` oracle is\n/// called and if the oracle version is incompatible an error is thrown.\npub global ORACLE_VERSION: Field = 22;\n\n/// Asserts that the version of the oracle is compatible with the version expected by the contract.\npub fn assert_compatible_oracle_version() {\n // Safety: This oracle call returns nothing: we only call it to check Aztec.nr and Oracle interface versions are\n // compatible. It is therefore always safe to call.\n unsafe {\n assert_compatible_oracle_version_wrapper();\n }\n}\n\nunconstrained fn assert_compatible_oracle_version_wrapper() {\n assert_compatible_oracle_version_oracle(ORACLE_VERSION);\n}\n\n#[oracle(aztec_utl_assertCompatibleOracleVersion)]\nunconstrained fn assert_compatible_oracle_version_oracle(version: Field) {}\n\nmod test {\n use super::{assert_compatible_oracle_version_oracle, ORACLE_VERSION};\n\n #[test]\n unconstrained fn compatible_oracle_version() {\n assert_compatible_oracle_version_oracle(ORACLE_VERSION);\n }\n\n #[test(should_fail_with = \"Incompatible aztec cli version:\")]\n unconstrained fn incompatible_oracle_version() {\n let arbitrary_incorrect_version = 318183437;\n assert_compatible_oracle_version_oracle(arbitrary_incorrect_version);\n }\n}\n" }, "230": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/state_vars/public_immutable.nr", "source": "use crate::{\n context::{PrivateContext, PublicContext, UtilityContext},\n nullifier::utils::compute_nullifier_existence_request,\n oracle::nullifiers::check_nullifier_exists,\n state_vars::StateVariable,\n utils::WithHash,\n};\nuse crate::protocol::{\n constants::DOM_SEP__INITIALIZATION_NULLIFIER, hash::poseidon2_hash_with_separator, traits::Packable,\n};\n\nmod test;\n\n/// Immutable public values.\n///\n/// This is one of the most basic public state variables. It is similar to an `immutable` or `constant` Solidity state\n/// variable.\n///\n/// It represents a public value of type `T` that can be initialized **just once** during the lifetime of the contract,\n/// allowing this single value to be read.\n///\n/// Unlike Solidity's `immutable` or `constant`, a `PublicImmutable`'s initialization does not need to occur during\n/// contract initialization - it can happen at any point in time (but only once). This also makes it possible to have a\n/// [`Map`](crate::state_vars::Map) of `PublicImmutable`s.\n///\n/// ## Access Patterns\n///\n/// A value stored in a `PublicImmutable` can be read and initialized from public contract functions.\n///\n/// Unlike [`PublicMutable`](crate::state_vars::PublicMutable) it is **also** possible to read a `PublicImmutable` from\n/// a private contract function, though it is not possible to initialize one. A common pattern is to have these\n/// functions [enqueue a public self calls](crate::contract_self::ContractSelfPrivate::enqueue_self) in which the\n/// initialization operation is performed.\n///\n/// For a mutable (with restrictions) variant which also can be read from private functions see\n/// [`DelayedPublicMutable`](crate::state_vars::DelayedPublicMutable).\n///\n/// ## Privacy\n///\n/// `PublicImmutable` provides zero privacy in terms of the value stored and any public accesses: the entire network\n/// can see these and the data involved.\n///\n/// Reading a `PublicImmutable` from a private contract function however is completely private, and leaks zero\n/// information about the fact that the value was read.\n///\n/// ## Use Cases\n///\n/// This is suitable for any kind of fixed global configuration that needs to be accessible by private contract\n/// functions, such as token decimals, related contracts in a multi-contract configuration, etc.\n///\n/// It is also useful for fixed per-user configuration by combining it with a [`Map`](crate::state_vars::Map), e.g. a\n/// registry of their account types.\n///\n/// `PublicImmutable`'s main limitation is the immutability, which in many cases leads to\n/// [`DelayedPublicMutable`](crate::state_vars::DelayedPublicMutable) being used instead. But in those cases where\n/// fixed values are not a problem, this is a fine choice for storage.\n///\n/// ## Examples\n///\n/// Declaring a `PublicImmutable` in the contract's [`storage`](crate::macros::storage::storage) struct requires\n/// specifying the type `T` that is stored in the variable:\n///\n/// ```noir\n/// #[storage]\n/// struct Storage {\n/// decimals: PublicImmutable,\n///\n/// account_types: Map, C>,\n/// }\n/// ```\n///\n/// ## Requirements\n///\n/// The type `T` stored in the `PublicImmutable` must implement the `Eq` and\n/// [`Packable`](crate::protocol::traits::Packable) traits.\n///\n/// ## Implementation Details\n///\n/// Values are packed and stored directly in the public storage tree, along with the hash of the packed representation.\n/// A `PublicImmutable` therefore takes up as many storage slots as the packing length of the stored type `T`, plus\n/// one. This hash allows for efficient private reads, such that only a single public storage value is read. For more\n/// details, see [`WithHash`](crate::utils::WithHash).\n///\n/// An initialization nullifier prevents re-initialization of the `PublicImmutable`. This allows reading an initialized\n/// `PublicImmutable` from a private contract function, since the value is guaranteed to not change.\n///\n/// Private contract functions however **cannot** determine that a `PublicImmutable` has not been initialized, as they\n/// do not have access to the current network state, only the _past_ state at the anchor block. They _can_ perform\n/// historical non-inclusion proofs of the initialization nullifier at past times, but they have no way to guarantee\n/// that it has not been emitted since then.\npub struct PublicImmutable {\n context: Context,\n storage_slot: Field,\n}\n\n// `PublicImmutable` stores both the packed value (using M fields) and its hash (1 field), requiring M + 1 total\n// fields.\nimpl StateVariable for PublicImmutable\nwhere\n T: Packable,\n{\n fn new(context: Context, storage_slot: Field) -> Self {\n assert(storage_slot != 0, \"Storage slot 0 not allowed. Storage slots must start from 1.\");\n PublicImmutable { context, storage_slot }\n }\n\n fn get_storage_slot(self) -> Field {\n self.storage_slot\n }\n}\n\nimpl PublicImmutable {\n /// Returns the inner nullifier emitted during initialization.\n pub fn compute_initialization_inner_nullifier(self) -> Field {\n poseidon2_hash_with_separator([self.storage_slot], DOM_SEP__INITIALIZATION_NULLIFIER)\n }\n}\n\nimpl PublicImmutable {\n /// Stores a permanent value.\n ///\n /// This function can only be called once on a given `PublicImmutable`: subsequent calls will fail with a duplicate\n /// nullifier.\n ///\n /// ## Examples\n ///\n /// Contract initialization:\n /// ```noir\n /// #[external(\"public\")]\n /// #[initializer]\n /// fn initialize(decimals: u8) {\n /// self.storage.decimals.initialize(decimals);\n /// }\n /// ```\n ///\n /// Non-initializer initialization:\n /// ```noir\n /// // Can only be called once per account\n /// #[external(\"public\")]\n /// fn set_account_type(account_type: AccountType) {\n /// self.storage.account_types.at(self.msg_sender()).initialize(account_type);\n /// }\n /// ```\n ///\n /// ## Cost\n ///\n /// The `SSTORE` AVM opcode is invoked a number of times equal to `T`'s packed length plus one, and the\n /// `EMITNULLIFIER` AVM opcode is invoked once.\n pub fn initialize(self, value: T)\n where\n T: Packable + Eq,\n {\n // We emit an initialization nullifier to indicate that the struct is initialized. This also prevents the value\n // from being initialized again as each nullifier can be emitted only once.\n let nullifier = self.compute_initialization_inner_nullifier();\n self.context.push_nullifier(nullifier);\n\n self.context.storage_write(self.storage_slot, WithHash::new(value));\n }\n\n /// Reads the permanent value.\n ///\n /// This function reverts the transaction if the `PublicImmutable` is not initialized.\n ///\n /// ## Examples\n ///\n /// A public getter that returns the permanent value:\n /// ```noir\n /// #[external(\"public\")]\n /// fn get_decimals() -> u8 {\n /// self.storage.decimals.read()\n /// }\n /// ```\n ///\n /// ## Cost\n ///\n /// The `SLOAD` AVM opcode is invoked a number of times equal to `T`'s packed length, and the `NULLIFIEREXISTS` AVM\n /// opcode is invoked once.\n pub fn read(self) -> T\n where\n T: Packable + Eq,\n {\n assert(self.is_initialized(), \"Trying to read from uninitialized PublicImmutable\");\n WithHash::public_storage_read(self.context, self.storage_slot)\n }\n\n /// Reads the permanent value, skipping the initialization check.\n ///\n /// This is cheaper than [`PublicImmutable::read`], but it should only be used if the `PublicImmutable` is known to\n /// be initialized.\n ///\n /// If the `PublicImmutable` is not initialized, this returns the default empty public storage value, which is all\n /// zeroes - equivalent to `let t = T::unpack(std::mem::zeroed());`.\n ///\n /// ## Examples\n ///\n /// A public getter that returns the permanent value:\n /// ```noir\n /// #[external(\"public\")]\n /// fn get_decimals() -> u8 {\n /// // This call is safe because `decimals` is initialized in the contract's initializer function\n /// self.storage.decimals.read_unsafe()\n /// }\n /// ```\n ///\n /// ## Cost\n ///\n /// The `SLOAD` AVM opcode is invoked a number of times equal to `T`'s packed length.\n pub fn read_unsafe(self) -> T\n where\n T: Packable + Eq,\n {\n WithHash::public_storage_read(self.context, self.storage_slot)\n }\n\n /// Returns `true` if the `PublicImmutable` has been initialized.\n ///\n /// ## Examples:\n ///\n /// Conditional initialization:\n /// ```noir\n /// #[external(\"public\")]\n /// fn set_account_type_if_not_set(account_type: AccountType) {\n /// if !self.storage.account_types.at(self.msg_sender()).is_initialized() {\n /// self.storage.account_types.at(self.msg_sender()).initialize(account_type);\n /// }\n /// }\n /// ```\n ///\n /// ## Cost\n ///\n /// The `NULLIFIEREXISTS` AVM opcode is invoked once.\n pub fn is_initialized(self) -> bool {\n let nullifier = self.compute_initialization_inner_nullifier();\n\n // Safety: it is safe to assume that the state variable has been initialized if the initialization nullifier\n // exists because the nullifier was emitted in a public function, at the same time the state variable's public\n // storage was written to. This would only be unsafe if the nullifier was emitted in a private function which\n // then enqueued the public storage write, which is not the case.\n self.context.nullifier_exists_unsafe(nullifier, self.context.this_address())\n }\n}\n\nimpl PublicImmutable {\n /// Reads the permanent value.\n ///\n /// This function throws if the `PublicImmutable` is not initialized.\n ///\n /// ## Examples\n ///\n /// ```noir\n /// #[external(\"utility\")]\n /// fn get_decimals() -> u8 {\n /// self.storage.decimals.read()\n /// }\n /// ```\n pub unconstrained fn read(self) -> T\n where\n T: Packable + Eq,\n {\n assert(self.is_initialized(), \"Trying to read from uninitialized PublicImmutable\");\n WithHash::utility_public_storage_read(self.context, self.storage_slot)\n }\n\n /// Returns `true` if the `PublicImmutable` has been initialized.\n pub unconstrained fn is_initialized(self) -> bool {\n let nullifier = self.compute_initialization_inner_nullifier();\n check_nullifier_exists(nullifier)\n }\n}\n\nimpl PublicImmutable {\n /// Reads the permanent value.\n ///\n /// This function throws if the `PublicImmutable` is not initialized.\n ///\n /// ## Examples\n ///\n /// ```noir\n /// #[external(\"private\")]\n /// fn get_decimals() -> u8 {\n /// self.storage.decimals.read()\n /// }\n /// ```\n ///\n /// ## Cost\n ///\n /// A nullifier existence request is pushed to the context, which will be verified by the kernel circuit.\n /// Additionally, a historical public storage read at the anchor block (which is on the order of 4k gates) is\n /// performed for a single storage slot, **regardless of `T`'s packed length**. This is because\n /// [`PublicImmutable::initialize`] stores not just the value but also its hash: this function obtains the preimage\n /// from an oracle and proves that it matches the hash from public storage.\n ///\n /// Because of this reason it is convenient to group together all of a contract's public immutable values that are\n /// read privately in a single type `T`:\n ///\n /// ```noir\n /// // Bad: reading both `decimals` and `symbol` will require two historical public storage reads\n /// #[storage]\n /// struct Storage {\n /// decimals: PublicImmutable,\n /// symbol: PublicImmutable,\n /// }\n ///\n /// // Good: both `decimals` and `symbol` are retrieved in a single historical public storage read\n /// #[derive(Packable)]\n /// struct Config {\n /// decimals: u8,\n /// symbol: FieldCompressedString,\n /// }\n ///\n /// #[storage]\n /// struct Storage {\n /// config: PublicImmutable,\n /// }\n /// ```\n pub fn read(self) -> T\n where\n T: Packable + Eq,\n {\n let nullifier = self.compute_initialization_inner_nullifier();\n\n // Safety: We use this check to be able to test this function works properly on synthetic envs\n // like TXE. We assert the returned value only to provide a clear error message. The actual\n // constrained check that the nullifier exists is done below with `assert_nullifier_exists`\n // We should improve our synthetic envs because this check forces an unnecesary roundtrip\n let nullifier_exists = unsafe { check_nullifier_exists(nullifier) };\n assert(nullifier_exists, \"Trying to read from uninitialized PublicImmutable\");\n\n let nullifier_existence_request = compute_nullifier_existence_request(nullifier, self.context.this_address());\n self.context.assert_nullifier_exists(nullifier_existence_request);\n WithHash::historical_public_storage_read(\n self.context.get_anchor_block_header(),\n self.context.this_address(),\n self.storage_slot,\n )\n }\n}\n" }, "261": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/array/append.nr", "source": "/// Appends the elements of the second `BoundedVec` to the end of the first one. The resulting `BoundedVec` can have\n/// any arbitrary maximum length, but it must be large enough to fit all of the elements of both the first and second\n/// vectors.\npub fn append(\n a: BoundedVec,\n b: BoundedVec,\n) -> BoundedVec {\n let mut dst = BoundedVec::new();\n\n dst.extend_from_bounded_vec(a);\n dst.extend_from_bounded_vec(b);\n\n dst\n}\n\nmod test {\n use super::append;\n\n #[test]\n unconstrained fn append_empty_vecs() {\n let a: BoundedVec<_, 3> = BoundedVec::new();\n let b: BoundedVec<_, 14> = BoundedVec::new();\n\n let result: BoundedVec = append(a, b);\n\n assert_eq(result.len(), 0);\n assert_eq(result.storage(), std::mem::zeroed());\n }\n\n #[test]\n unconstrained fn append_non_empty_vecs() {\n let a: BoundedVec<_, 3> = BoundedVec::from_array([1, 2, 3]);\n let b: BoundedVec<_, 14> = BoundedVec::from_array([4, 5, 6]);\n\n let result: BoundedVec = append(a, b);\n\n assert_eq(result.len(), 6);\n assert_eq(result.storage(), [1, 2, 3, 4, 5, 6, std::mem::zeroed(), std::mem::zeroed()]);\n }\n\n #[test(should_fail_with = \"out of bounds\")]\n unconstrained fn append_non_empty_vecs_insufficient_max_len() {\n let a: BoundedVec<_, 3> = BoundedVec::from_array([1, 2, 3]);\n let b: BoundedVec<_, 14> = BoundedVec::from_array([4, 5, 6]);\n\n let _: BoundedVec = append(a, b);\n }\n}\n" }, "264": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/array/subarray.nr", "source": "/// Returns `DstLen` elements from a source array, starting at `offset`. `DstLen` must not be larger than the number of\n/// elements past `offset`.\n///\n/// Examples:\n/// ```\n/// let foo: [Field; 2] = subarray([1, 2, 3, 4, 5], 2);\n/// assert_eq(foo, [3, 4]);\n///\n/// let bar: [Field; 5] = subarray([1, 2, 3, 4, 5], 2); // fails - we can't return 5 elements since only 3 remain\n/// ```\npub fn subarray(src: [T; SrcLen], offset: u32) -> [T; DstLen] {\n assert(offset + DstLen <= SrcLen, \"DstLen too large for offset\");\n\n let mut dst: [T; DstLen] = std::mem::zeroed();\n for i in 0..DstLen {\n dst[i] = src[i + offset];\n }\n\n dst\n}\n\nmod test {\n use super::subarray;\n\n #[test]\n unconstrained fn subarray_into_empty() {\n // In all of these cases we're setting DstLen to be 0, so we always get back an empty array.\n assert_eq(subarray::([], 0), []);\n assert_eq(subarray([1, 2, 3, 4, 5], 0), []);\n assert_eq(subarray([1, 2, 3, 4, 5], 2), []);\n }\n\n #[test]\n unconstrained fn subarray_complete() {\n assert_eq(subarray::([], 0), []);\n assert_eq(subarray([1, 2, 3, 4, 5], 0), [1, 2, 3, 4, 5]);\n }\n\n #[test]\n unconstrained fn subarray_different_end_sizes() {\n // We implicitly select how many values to read in the size of the return array\n assert_eq(subarray([1, 2, 3, 4, 5], 1), [2, 3, 4, 5]);\n assert_eq(subarray([1, 2, 3, 4, 5], 1), [2, 3, 4]);\n assert_eq(subarray([1, 2, 3, 4, 5], 1), [2, 3]);\n assert_eq(subarray([1, 2, 3, 4, 5], 1), [2]);\n }\n\n #[test(should_fail_with = \"DstLen too large for offset\")]\n unconstrained fn subarray_offset_too_large() {\n // With an offset of 1 we can only request up to 4 elements\n let _: [_; 5] = subarray([1, 2, 3, 4, 5], 1);\n }\n\n #[test(should_fail)]\n unconstrained fn subarray_bad_return_value() {\n assert_eq(subarray([1, 2, 3, 4, 5], 1), [3, 3, 4, 5]);\n }\n}\n" }, "265": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/array/subbvec.nr", "source": "use crate::utils::array;\n\n/// Returns `DstMaxLen` elements from a source BoundedVec, starting at `offset`. `offset` must not be larger than the\n/// original length, and `DstLen` must not be larger than the total number of elements past `offset` (including the\n/// zeroed elements past `len()`).\n///\n/// Only elements at the beginning of the vector can be removed: it is not possible to also remove elements at the end\n/// of the vector by passing a value for `DstLen` that is smaller than `len() - offset`.\n///\n/// Examples:\n/// ```\n/// let foo = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n/// assert_eq(subbvec(foo, 2), BoundedVec::<_, 8>::from_array([3, 4, 5]));\n///\n/// let bar: BoundedVec<_, 1> = subbvec(foo, 2); // fails - we can't return just 1 element since 3 remain\n/// let baz: BoundedVec<_, 10> = subbvec(foo, 3); // fails - we can't return 10 elements since only 7 remain\n/// ```\npub fn subbvec(\n bvec: BoundedVec,\n offset: u32,\n) -> BoundedVec {\n // from_parts_unchecked does not verify that the elements past len are zeroed, but that is not an issue in our case\n // because we're constructing the new storage array as a subarray of the original one (which should have zeroed\n // storage past len), guaranteeing correctness. This is because `subarray` does not allow extending arrays past\n // their original length.\n BoundedVec::from_parts_unchecked(array::subarray(bvec.storage(), offset), bvec.len() - offset)\n}\n\nmod test {\n use super::subbvec;\n\n #[test]\n unconstrained fn subbvec_empty() {\n let bvec = BoundedVec::::from_array([]);\n assert_eq(subbvec(bvec, 0), bvec);\n }\n\n #[test]\n unconstrained fn subbvec_complete() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n assert_eq(subbvec(bvec, 0), bvec);\n\n let smaller_capacity = BoundedVec::<_, 5>::from_array([1, 2, 3, 4, 5]);\n assert_eq(subbvec(bvec, 0), smaller_capacity);\n }\n\n #[test]\n unconstrained fn subbvec_partial() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n\n assert_eq(subbvec(bvec, 2), BoundedVec::<_, 8>::from_array([3, 4, 5]));\n assert_eq(subbvec(bvec, 2), BoundedVec::<_, 3>::from_array([3, 4, 5]));\n }\n\n #[test]\n unconstrained fn subbvec_into_empty() {\n let bvec: BoundedVec<_, 10> = BoundedVec::from_array([1, 2, 3, 4, 5]);\n assert_eq(subbvec(bvec, 5), BoundedVec::<_, 5>::from_array([]));\n }\n\n #[test(should_fail)]\n unconstrained fn subbvec_offset_past_len() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n let _: BoundedVec<_, 1> = subbvec(bvec, 6);\n }\n\n #[test(should_fail)]\n unconstrained fn subbvec_insufficient_dst_len() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n\n // We're not providing enough space to hold all of the items inside the original BoundedVec. subbvec can cause\n // for the capacity to reduce, but not the length (other than by len - offset).\n let _: BoundedVec<_, 1> = subbvec(bvec, 2);\n }\n\n #[test(should_fail_with = \"DstLen too large for offset\")]\n unconstrained fn subbvec_dst_len_causes_enlarge() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n\n // subbvec does not support capacity increases\n let _: BoundedVec<_, 11> = subbvec(bvec, 0);\n }\n\n #[test(should_fail_with = \"DstLen too large for offset\")]\n unconstrained fn subbvec_dst_len_too_large_for_offset() {\n let bvec = BoundedVec::<_, 10>::from_array([1, 2, 3, 4, 5]);\n\n // This effectively requests a capacity increase, since there'd be just one element plus the 5 empty slots,\n // which is less than 7.\n let _: BoundedVec<_, 7> = subbvec(bvec, 4);\n }\n}\n" }, "268": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/conversion/bytes_to_fields.nr", "source": "use std::static_assert;\n\n// These functions are used to facilitate the conversion of log ciphertext between byte and field representations.\n//\n// `bytes_to_fields` uses fixed-size arrays since encryption contexts have compile-time size information.\n// `bytes_from_fields` uses BoundedVec for flexibility in unconstrained contexts where sizes are dynamic.\n//\n// Together they provide bidirectional conversion between bytes and fields when processing encrypted logs.\n\n/// Converts the input bytes into an array of fields. A Field is ~254 bits meaning that each field can store 31 whole\n/// bytes. Use `bytes_from_fields` to obtain the original bytes array.\n///\n/// The input bytes are chunked into chunks of 31 bytes. Each 31-byte chunk is viewed as big-endian, and is converted\n/// into a Field. For example, [1, 10, 3, ..., 0] (31 bytes) is encoded as [1 * 256^30 + 10 * 256^29 + 3 * 256^28 + ...\n/// + 0] Note: N must be a multiple of 31 bytes\npub fn bytes_to_fields(bytes: [u8; N]) -> [Field; N / 31] {\n // Assert that N is a multiple of 31\n static_assert(N % 31 == 0, \"N must be a multiple of 31\");\n\n let mut fields = [0; N / 31];\n\n // Since N is a multiple of 31, we can simply process all chunks fully\n for i in 0..N / 31 {\n let mut field = 0;\n for j in 0..31 {\n // Shift the existing value left by 8 bits and add the new byte\n field = field * 256 + bytes[i * 31 + j] as Field;\n }\n fields[i] = field;\n }\n\n fields\n}\n\n/// Converts an input BoundedVec of fields into a BoundedVec of bytes in big-endian order. Arbitrary Field arrays are\n/// not allowed: this is assumed to be an array obtained via `bytes_to_fields`, i.e. one that actually represents\n/// bytes. To convert a Field array into bytes, use `fields_to_bytes`.\n///\n/// Each input field must contain at most 31 bytes (this is constrained to be so). Each field is converted into 31\n/// big-endian bytes, and the resulting 31-byte chunks are concatenated back together in the order of the original\n/// fields.\npub fn bytes_from_fields(fields: BoundedVec) -> BoundedVec {\n let mut bytes = BoundedVec::new();\n\n for i in 0..fields.len() {\n let field = fields.get(i);\n\n // We expect that the field contains at most 31 bytes of information.\n field.assert_max_bit_size::<248>();\n\n // Now we can safely convert the field to 31 bytes.\n let field_as_bytes: [u8; 31] = field.to_be_bytes();\n\n for j in 0..31 {\n bytes.push(field_as_bytes[j]);\n }\n }\n\n bytes\n}\n\nmod tests {\n use crate::utils::array::subarray;\n use super::{bytes_from_fields, bytes_to_fields};\n\n #[test]\n unconstrained fn random_bytes_to_fields_and_back(input: [u8; 93]) {\n let fields = bytes_to_fields(input);\n\n // At this point in production, the log flies through the system and we get a BoundedVec on the other end. So\n // we need to convert the field array to a BoundedVec to be able to feed it to the `bytes_from_fields`\n // function.\n let fields_as_bounded_vec = BoundedVec::<_, 6>::from_array(fields);\n\n let bytes_back = bytes_from_fields(fields_as_bounded_vec);\n\n // Compare the original input with the round-tripped result\n assert_eq(bytes_back.len(), input.len());\n assert_eq(subarray(bytes_back.storage(), 0), input);\n }\n\n #[test(should_fail_with = \"N must be a multiple of 31\")]\n unconstrained fn bytes_to_fields_input_length_not_multiple_of_31() {\n // Try to convert 32 bytes (not a multiple of 31) to fields\n let _fields = bytes_to_fields([0; 32]);\n }\n\n}\n" }, "269": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/conversion/fields_to_bytes.nr", "source": "// These functions are used to facilitate the conversion of log plaintext represented as fields into bytes and back.\n//\n// `fields_to_bytes` uses fixed-size arrays since encryption contexts have compile-time size information.\n// `fields_from_bytes` uses BoundedVec for flexibility in unconstrained contexts where sizes are dynamic.\n//\n// Together they provide bidirectional conversion between fields and bytes.\n\n/// Converts an input array of fields into a single array of bytes. Use `fields_from_bytes` to obtain the original\n/// field array. Each field is converted to a 32-byte big-endian array.\n///\n/// For example, if you have a field array [123, 456], it will be converted to a 64-byte array:\n/// [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,123, // First field (32 bytes)\n/// 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,200] // Second field (32 bytes)\n///\n/// Since a field is ~254 bits, you'll end up with a subtle 2-bit \"gap\" at the big end, every 32 bytes. Be careful that\n/// such a gap doesn't leak information! This could happen if you for example expected the output to be\n/// indistinguishable from random bytes.\npub fn fields_to_bytes(fields: [Field; N]) -> [u8; 32 * N] {\n let mut bytes = [0; 32 * N];\n\n for i in 0..N {\n let field_as_bytes: [u8; 32] = fields[i].to_be_bytes();\n\n for j in 0..32 {\n bytes[i * 32 + j] = field_as_bytes[j];\n }\n }\n\n bytes\n}\n\n/// Converts an input BoundedVec of bytes into a BoundedVec of fields. Arbitrary byte arrays are not allowed: this is\n/// assumed to be an array obtained via `fields_to_bytes`, i.e. one that actually represents fields. To convert a byte\n/// array into Fields, use `bytes_to_fields`.\n///\n/// The input bytes are chunked into chunks of 32 bytes. Each 32-byte chunk is viewed as big-endian, and is converted\n/// into a Field. For example, [1, 10, 3, ..., 0] (32 bytes) is encoded as [1 * 256^31 + 10 * 256^30 + 3 * 256^29 + ...\n/// + 0] Note 1: N must be a multiple of 32 bytes Note 2: The max value check code was taken from\n/// std::field::to_be_bytes function.\npub fn fields_from_bytes(bytes: BoundedVec) -> BoundedVec {\n // Assert that input length is a multiple of 32\n assert(bytes.len() % 32 == 0, \"Input length must be a multiple of 32\");\n\n try_fields_from_bytes(bytes).expect(f\"Value does not fit in field\")\n}\n\n/// Converts a single 32-byte big-endian chunk (starting at `offset`) into a Field, returning\n/// `Option::none()` if the chunk's value is >= the field modulus.\nfn try_field_from_be_bytes(bytes: BoundedVec, offset: u32) -> Option {\n let p = std::field::modulus_be_bytes();\n let mut field = 0;\n // Field arithmetic silently wraps values >= the modulus, so we compare each 32-byte chunk against\n // the modulus byte-by-byte (big-endian, most significant first). The first byte that differs\n // determines the result: if our byte is smaller we're valid, if larger we've overflowed.\n // cmp tracks the result: 0 = equal so far, 1 = less than modulus, 2 = exceeds modulus.\n let mut cmp: u8 = 0;\n for j in 0..32 {\n let byte = bytes.get(offset + j);\n field = field * 256 + byte as Field;\n\n if cmp == 0 {\n if byte < p[j] {\n cmp = 1;\n } else if byte > p[j] {\n cmp = 2;\n }\n }\n }\n if cmp == 1 {\n Option::some(field)\n } else {\n Option::none()\n }\n}\n\n/// Non-panicking version of `fields_from_bytes`. Returns `Option::none()` if the input\n/// length is not a multiple of 32 or if any 32-byte chunk exceeds the field modulus.\npub(crate) fn try_fields_from_bytes(bytes: BoundedVec) -> Option> {\n if bytes.len() % 32 == 0 {\n let mut fields = BoundedVec::new();\n let num_chunks = bytes.len() / 32;\n for i in 0..num_chunks {\n let field = try_field_from_be_bytes(bytes, i * 32);\n if field.is_some() {\n fields.push(field.unwrap());\n }\n }\n if fields.len() as u32 == num_chunks {\n Option::some(fields)\n } else {\n Option::none()\n }\n } else {\n Option::none()\n }\n}\n\nmod tests {\n use crate::utils::array::subarray;\n use super::{fields_from_bytes, fields_to_bytes, try_fields_from_bytes};\n\n #[test]\n unconstrained fn random_fields_to_bytes_and_back(input: [Field; 3]) {\n // Convert to bytes\n let bytes = fields_to_bytes(input);\n\n // At this point in production, the log flies through the system and we get a BoundedVec on the other end. So\n // we need to convert the field array to a BoundedVec to be able to feed it to the `fields_from_bytes`\n // function. 113 is an arbitrary max length that is larger than the input length of 96.\n let bytes_as_bounded_vec = BoundedVec::<_, 113>::from_array(bytes);\n\n // Convert back to fields\n let fields_back = fields_from_bytes(bytes_as_bounded_vec);\n\n // Compare the original input with the round-tripped result\n assert_eq(fields_back.len(), input.len());\n assert_eq(subarray(fields_back.storage(), 0), input);\n }\n\n #[test(should_fail_with = \"Input length must be a multiple of 32\")]\n unconstrained fn to_fields_assert() {\n // 143 is an arbitrary max length that is larger than 33\n let input = BoundedVec::<_, 143>::from_array([\n 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n 30, 31, 32, 33,\n ]);\n\n // This should fail since 33 is not a multiple of 32\n let _fields = fields_from_bytes(input);\n }\n\n #[test]\n unconstrained fn fields_from_bytes_max_value() {\n let max_field_as_bytes: [u8; 32] = (-1).to_be_bytes();\n let input = BoundedVec::<_, 32>::from_array(max_field_as_bytes);\n\n let fields = fields_from_bytes(input);\n\n // The result should be a largest value storable in a field (-1 since we are modulo-ing)\n assert_eq(fields.get(0), -1);\n }\n\n // In this test we verify that overflow check works by taking the max allowed value, bumping a random byte and then\n // feeding it to `fields_from_bytes` as input.\n #[test(should_fail_with = \"Value does not fit in field\")]\n unconstrained fn fields_from_bytes_overflow(random_value: u8) {\n let index_of_byte_to_bump = random_value % 32;\n\n // Obtain the byte representation of the maximum field value\n let max_field_value_as_bytes: [u8; 32] = (-1).to_be_bytes();\n\n let byte_to_bump = max_field_value_as_bytes[index_of_byte_to_bump as u32];\n\n // Skip test execution if the selected byte is already at maximum value (255). This is acceptable since we are\n // using fuzz testing to generate many test cases.\n if byte_to_bump != 255 {\n let mut input = BoundedVec::<_, 32>::from_array(max_field_value_as_bytes);\n\n // Increment the selected byte to exceed the field's maximum value\n input.set(index_of_byte_to_bump as u32, byte_to_bump + 1);\n\n // Attempt the conversion, which should fail due to the value exceeding the field's capacity\n let _fields = fields_from_bytes(input);\n }\n }\n\n #[test]\n unconstrained fn try_fields_from_bytes_returns_none_on_unaligned_length() {\n let input = BoundedVec::<_, 64>::from_parts([0 as u8; 64], 33);\n assert(try_fields_from_bytes(input).is_none());\n }\n\n #[test]\n unconstrained fn try_fields_from_bytes_returns_none_on_field_modulus() {\n // The field modulus itself is not a valid field value (it wraps to 0).\n let p: [u8; 32] = std::field::modulus_be_bytes().as_array();\n let input = BoundedVec::::from_array(p);\n assert(try_fields_from_bytes(input).is_none());\n }\n\n #[test]\n unconstrained fn try_fields_from_bytes_round_trips(input: [Field; 3]) {\n let bytes = BoundedVec::<_, 113>::from_array(fields_to_bytes(input));\n let fields = try_fields_from_bytes(bytes).unwrap();\n\n assert_eq(fields.len(), input.len());\n assert_eq(subarray(fields.storage(), 0), input);\n }\n}\n" }, "272": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/point.nr", "source": "use crate::protocol::{point::Point, utils::field::sqrt};\n\n// I am storing the modulus minus 1 divided by 2 here because full modulus would throw \"String literal too large\" error\n// Full modulus is 21888242871839275222246405745257275088548364400416034343698204186575808495617\nglobal BN254_FR_MODULUS_DIV_2: Field = 10944121435919637611123202872628637544274182200208017171849102093287904247808;\n\n/// Returns: true if p.y <= MOD_DIV_2, else false.\npub fn get_sign_of_point(p: Point) -> bool {\n // We store only a \"sign\" of the y coordinate because the rest can be derived from the x coordinate. To get the\n // sign we check if the y coordinate is less or equal than the field's modulus minus 1 divided by 2. Ideally we'd\n // do `y <= MOD_DIV_2`, but there's no `lte` function, so instead we do `!(y > MOD_DIV_2)`, which is equivalent,\n // and then rewrite that as `!(MOD_DIV_2 < y)`, since we also have no `gt` function.\n !BN254_FR_MODULUS_DIV_2.lt(p.y)\n}\n\n/// Returns a `Point` in the Grumpkin curve given its x coordinate.\n///\n/// Because not all values in the field are valid x coordinates of points in the curve (i.e. there is no corresponding\n/// y value in the field that satisfies the curve equation), it may not be possible to reconstruct a `Point`.\n/// `Option::none()` is returned in such cases.\npub fn point_from_x_coord(x: Field) -> Option {\n // y ^ 2 = x ^ 3 - 17\n let rhs = x * x * x - 17;\n sqrt(rhs).map(|y| Point { x, y, is_infinite: false })\n}\n\n/// Returns a `Point` in the Grumpkin curve given its x coordinate and sign for the y coordinate.\n///\n/// Because not all values in the field are valid x coordinates of points in the curve (i.e. there is no corresponding\n/// y value in the field that satisfies the curve equation), it may not be possible to reconstruct a `Point`.\n/// `Option::none()` is returned in such cases.\n///\n/// @param x - The x coordinate of the point @param sign - The \"sign\" of the y coordinate - determines whether y <=\n/// (Fr.MODULUS - 1) / 2\npub fn point_from_x_coord_and_sign(x: Field, sign: bool) -> Option {\n // y ^ 2 = x ^ 3 - 17\n let rhs = x * x * x - 17;\n\n sqrt(rhs).map(|y| {\n // If there is a square root, we need to ensure it has the correct \"sign\"\n let y_is_positive = !BN254_FR_MODULUS_DIV_2.lt(y);\n let final_y = if y_is_positive == sign { y } else { -y };\n Point { x, y: final_y, is_infinite: false }\n })\n}\n\nmod test {\n use crate::protocol::point::Point;\n use crate::utils::point::{\n BN254_FR_MODULUS_DIV_2, get_sign_of_point, point_from_x_coord, point_from_x_coord_and_sign,\n };\n\n #[test]\n unconstrained fn test_point_from_x_coord_and_sign() {\n // Test positive y coordinate\n let x = 0x1af41f5de96446dc3776a1eb2d98bb956b7acd9979a67854bec6fa7c2973bd73;\n let sign = true;\n let p = point_from_x_coord_and_sign(x, sign).unwrap();\n\n assert_eq(p.x, x);\n assert_eq(p.y, 0x07fc22c7f2c7057571f137fe46ea9c95114282bc95d37d71ec4bfb88de457d4a);\n assert_eq(p.is_infinite, false);\n\n // Test negative y coordinate\n let x2 = 0x247371652e55dd74c9af8dbe9fb44931ba29a9229994384bd7077796c14ee2b5;\n let sign2 = false;\n let p2 = point_from_x_coord_and_sign(x2, sign2).unwrap();\n\n assert_eq(p2.x, x2);\n assert_eq(p2.y, 0x26441aec112e1ae4cee374f42556932001507ad46e255ffb27369c7e3766e5c0);\n assert_eq(p2.is_infinite, false);\n }\n\n #[test]\n unconstrained fn test_point_from_x_coord_valid() {\n // x = 8 is a known quadratic residue - should give a valid point\n let result = point_from_x_coord(Field::from(8));\n assert(result.is_some());\n\n let point = result.unwrap();\n assert_eq(point.x, Field::from(8));\n // Check curve equation y^2 = x^3 - 17\n assert_eq(point.y * point.y, point.x * point.x * point.x - 17);\n }\n\n #[test]\n unconstrained fn test_point_from_x_coord_invalid() {\n // x = 3 is a non-residue for this curve - should give None\n let x = Field::from(3);\n let maybe_point = point_from_x_coord(x);\n assert(maybe_point.is_none());\n }\n\n #[test]\n unconstrained fn test_both_roots_satisfy_curve() {\n // Derive a point from x = 8 (known to be valid from test_point_from_x_coord_valid)\n let x: Field = 8;\n let point = point_from_x_coord(x).unwrap();\n\n // Check y satisfies curve equation\n assert_eq(point.y * point.y, x * x * x - 17);\n\n // Check -y also satisfies curve equation\n let neg_y = 0 - point.y;\n assert_eq(neg_y * neg_y, x * x * x - 17);\n\n // Verify they are different (unless y = 0)\n assert(point.y != neg_y);\n }\n\n #[test]\n unconstrained fn test_point_from_x_coord_and_sign_invalid() {\n // x = 3 has no valid point on the curve (from test_point_from_x_coord_invalid)\n let x = Field::from(3);\n let result_positive = point_from_x_coord_and_sign(x, true);\n let result_negative = point_from_x_coord_and_sign(x, false);\n\n assert(result_positive.is_none());\n assert(result_negative.is_none());\n }\n\n #[test]\n unconstrained fn test_get_sign_of_point() {\n // Derive a point from x = 8, then test both possible y values\n let point = point_from_x_coord(8).unwrap();\n let neg_point = Point { x: point.x, y: 0 - point.y, is_infinite: false };\n\n // One should be \"positive\" (y <= MOD_DIV_2) and one \"negative\"\n let sign1 = get_sign_of_point(point);\n let sign2 = get_sign_of_point(neg_point);\n assert(sign1 != sign2);\n\n // y = 0 should return true (0 <= MOD_DIV_2)\n let zero_y_point = Point { x: 0, y: 0, is_infinite: false };\n assert(get_sign_of_point(zero_y_point) == true);\n\n // y = MOD_DIV_2 should return true (exactly at boundary)\n let boundary_point = Point { x: 0, y: BN254_FR_MODULUS_DIV_2, is_infinite: false };\n assert(get_sign_of_point(boundary_point) == true);\n\n // y = MOD_DIV_2 + 1 should return false (just over boundary)\n let over_boundary_point = Point { x: 0, y: BN254_FR_MODULUS_DIV_2 + 1, is_infinite: false };\n assert(get_sign_of_point(over_boundary_point) == false);\n }\n\n #[test]\n unconstrained fn test_point_from_x_coord_zero() {\n // x = 0: y^2 = 0^3 - 17 = -17, which is not a quadratic residue in BN254 scalar field\n let result = point_from_x_coord(0);\n assert(result.is_none());\n }\n\n #[test]\n unconstrained fn test_bn254_fr_modulus_div_2() {\n // Verify that BN254_FR_MODULUS_DIV_2 == (p - 1) / 2 This means: 2 * BN254_FR_MODULUS_DIV_2 + 1 == p == 0 (in\n // the field)\n assert_eq(2 * BN254_FR_MODULUS_DIV_2 + 1, 0);\n }\n\n}\n" }, "273": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/remove_constraints.nr", "source": "/// Calls a function and returns its return value, but removes any constraints associated with calling the function,\n/// behaving as if the function was unconstrained.\npub unconstrained fn remove_constraints(f: fn[Env]() -> T) -> T {\n f()\n}\n\n/// Calls a function and returns its return value, removing any constraints associated with calling the function if\n/// `condition` is true, behaving as if the function was unconstrained.\n///\n/// Requires `condition` to be a compile time constant.\npub fn remove_constraints_if(condition: bool, f: fn[Env]() -> T) -> T {\n // If `condition` is not a compile-time constant, then the compiler won't optimize away the branch not taken in the\n // if statement below, and we may end up with constraints for `f` regardless of the runtime value of `condition`.\n assert_constant(condition);\n\n if condition {\n // Safety: the purpose of this function is to execute `f` with no constraints when `condition` is true.\n unsafe {\n remove_constraints(f)\n }\n } else {\n f()\n }\n}\n\nmod test {\n use super::remove_constraints;\n\n fn return_unit() -> () {\n ()\n }\n\n fn return_field() -> Field {\n 5\n }\n\n #[test]\n fn returns_unit() {\n let expected = return_unit();\n // Safety: this is a test\n let actual = unsafe { remove_constraints(|| return_unit()) };\n assert_eq(actual, expected);\n }\n\n #[test]\n fn returns_original_value() {\n let expected = return_field();\n // Safety: this is a test\n let actual = unsafe { remove_constraints(|| return_field()) };\n assert_eq(actual, expected);\n }\n\n #[test]\n unconstrained fn returns_unit_unconstrained() {\n let expected = return_unit();\n let actual = remove_constraints(|| return_unit());\n assert_eq(actual, expected);\n }\n\n #[test]\n unconstrained fn returns_original_value_unconstrained() {\n let expected = return_field();\n let actual = remove_constraints(|| return_field());\n assert_eq(actual, expected);\n }\n}\n" }, "274": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/aztec/src/utils/with_hash.nr", "source": "use crate::{context::{PublicContext, UtilityContext}, history::storage::public_storage_historical_read, oracle};\nuse crate::protocol::{abis::block_header::BlockHeader, address::AztecAddress, hash::poseidon2_hash, traits::Packable};\n\n/// A struct that allows for efficient reading of value `T` from public storage in private.\n///\n/// The efficient reads are achieved by verifying large values through a single hash check and then proving inclusion\n/// only of the hash in public storage. This reduces the number of required tree inclusion proofs from `M` to 1.\n///\n/// # Type Parameters\n/// - `T`: The underlying type being wrapped, must implement `Packable`\n/// - `M`: The number of field elements required to pack values of type `T`\npub struct WithHash {\n value: T,\n packed: [Field; M],\n hash: Field,\n}\n\nimpl WithHash\nwhere\n T: Packable + Eq,\n{\n pub fn new(value: T) -> Self {\n let packed = value.pack();\n Self { value, packed, hash: poseidon2_hash(packed) }\n }\n\n pub fn get_value(self) -> T {\n self.value\n }\n\n pub fn get_hash(self) -> Field {\n self.hash\n }\n\n /// Reads the value stored in this [`WithHash`] from public storage.\n ///\n /// Note that unlike a full public storage read, this will _not_ read (not return) the stored hash of the value,\n /// resulting in reduced gas costs.\n ///\n /// This is the public counterpart to [`WithHash::utility_public_storage_read`] and\n /// [`WithHash::historical_public_storage_read`].\n pub fn public_storage_read(context: PublicContext, storage_slot: Field) -> T {\n context.storage_read(storage_slot)\n }\n\n pub unconstrained fn utility_public_storage_read(context: UtilityContext, storage_slot: Field) -> T {\n context.storage_read(storage_slot)\n }\n\n pub fn historical_public_storage_read(\n header_to_read_from: BlockHeader,\n address: AztecAddress,\n storage_slot: Field,\n ) -> T {\n // We could simply produce historical inclusion proofs for each field in `packed`, but that would require one\n // full sibling path per storage slot (since due to kernel siloing the storage is not contiguous). Instead, we\n // get an oracle to provide us the values, and instead we prove inclusion of their hash, which is both a much\n // smaller proof (a single slot), and also independent of the size of T (except in that we need to pack and\n // hash T).\n let hint = WithHash::new(\n // Safety: We verify that a hash of the hint/packed data matches the stored hash.\n unsafe { oracle::storage::storage_read(header_to_read_from, address, storage_slot) },\n );\n\n // The actual `value` (of type T, of packed length M fields) is stored in contiguous fields from the\n // `storage_slot`. The _hash_ of the `value` is stored at the end, at slot: `storage_slot + M`.\n let hash = public_storage_historical_read(header_to_read_from, storage_slot + M as Field, address);\n\n if hash != 0 {\n assert_eq(hash, hint.get_hash(), \"Hint values do not match hash\");\n } else {\n // The hash slot can only hold a zero if it is uninitialized. Therefore, the hints must then be zero (i.e.\n // the default value for public storage) as well.\n assert_eq(hint.get_value(), T::unpack(std::mem::zeroed()), \"Non-zero hint for zero hash\");\n };\n\n hint.get_value()\n }\n}\n\n// Note: I don't derive Packable on `WithHash` because `derive_serialize` function does not support setting \"N = M\" as\n// I do here 3 lines below. This could be worked around by placing the \"where\" clause directly on the `WithHash`\n// struct, but Jake mentioned that the syntax is not expected to be supported at least until Noir 1.0. Relevant\n// discussion on Slack:\n// https://aztecprotocol.slack.com/archives/C04QF64EDNV/p1752593876160699?thread_ts=1752589887.955379&cid=C04QF64EDNV\nimpl Packable for WithHash\nwhere\n T: Packable,\n{\n let N: u32 = M + 1;\n\n fn pack(self) -> [Field; Self::N] {\n let mut result: [Field; Self::N] = std::mem::zeroed();\n for i in 0..M {\n result[i] = self.packed[i];\n }\n result[M] = self.hash;\n\n result\n }\n\n fn unpack(packed: [Field; Self::N]) -> Self {\n let mut value_packed = [0; M];\n for i in 0..M {\n value_packed[i] = packed[i];\n }\n let hash = packed[M];\n\n Self { value: T::unpack(value_packed), packed: value_packed, hash }\n }\n}\n\nmod test {\n use crate::protocol::{hash::poseidon2_hash, traits::{Hash, Packable, ToField}};\n use crate::{test::{helpers::test_environment::TestEnvironment, mocks::MockStruct}, utils::with_hash::WithHash};\n\n use std::test::OracleMock;\n\n global STORAGE_SLOT: Field = 47;\n\n #[test]\n unconstrained fn create_and_recover() {\n let value = MockStruct { a: 5, b: 3 };\n let value_with_hash = WithHash::new(value);\n let recovered = WithHash::unpack(value_with_hash.pack());\n\n assert_eq(recovered.value, value);\n assert_eq(recovered.packed, value.pack());\n assert_eq(recovered.hash, poseidon2_hash(value.pack()));\n }\n\n #[test]\n unconstrained fn read_uninitialized_value() {\n let env = TestEnvironment::new();\n\n env.private_context(|context| {\n let result = WithHash::::historical_public_storage_read(\n context.anchor_block_header,\n context.this_address(),\n STORAGE_SLOT,\n );\n\n assert_eq(result, std::mem::zeroed());\n });\n }\n\n #[test]\n unconstrained fn read_initialized_value() {\n let env = TestEnvironment::new();\n\n let value = MockStruct { a: 5, b: 3 };\n let value_with_hash = WithHash::new(value);\n\n env.public_context(|context| { context.storage_write(STORAGE_SLOT, value_with_hash); });\n\n env.private_context(|context| {\n let result = WithHash::::historical_public_storage_read(\n context.anchor_block_header,\n context.this_address(),\n STORAGE_SLOT,\n );\n assert_eq(result, value);\n });\n }\n\n #[test(should_fail_with = \"Non-zero hint for zero hash\")]\n unconstrained fn bad_hint_uninitialized_value() {\n let env = TestEnvironment::new();\n\n env.private_context(|context| {\n let block_header = context.anchor_block_header;\n let address = context.this_address();\n\n // Mock the oracle to return a non-zero hint/packed value\n let value_packed = MockStruct { a: 1, b: 1 }.pack();\n let _ = OracleMock::mock(\"aztec_utl_getFromPublicStorage\")\n .with_params((block_header.hash(), address.to_field(), STORAGE_SLOT, value_packed.len()))\n .returns(value_packed)\n .times(1);\n\n // This should fail because the hint value is non-zero and the hash is zero (default value of storage)\n let _ = WithHash::::historical_public_storage_read(block_header, address, STORAGE_SLOT);\n });\n }\n\n #[test(should_fail_with = \"Hint values do not match hash\")]\n unconstrained fn bad_hint_initialized_value() {\n let env = TestEnvironment::new();\n\n env.public_context(|context| {\n // Write the value and hash separately so that the hash is wrong\n let value = MockStruct { a: 5, b: 3 };\n context.storage_write(STORAGE_SLOT, value);\n\n let incorrect_hash = 13;\n let hash_storage_slot = STORAGE_SLOT + (value.pack().len() as Field);\n context.storage_write(hash_storage_slot, [incorrect_hash]);\n });\n\n env.private_context(|context| {\n let _ = WithHash::::historical_public_storage_read(\n context.anchor_block_header,\n context.this_address(),\n STORAGE_SLOT,\n );\n });\n }\n}\n" }, "282": { "path": "/home/runner/nargo/github.com/noir-lang/poseidon/v0.2.3/src/poseidon2.nr", "source": "use std::default::Default;\nuse std::hash::Hasher;\n\ncomptime global RATE: u32 = 3;\n\npub struct Poseidon2 {\n cache: [Field; 3],\n state: [Field; 4],\n cache_size: u32,\n squeeze_mode: bool, // 0 => absorb, 1 => squeeze\n}\n\nimpl Poseidon2 {\n #[no_predicates]\n pub fn hash(input: [Field; N], message_size: u32) -> Field {\n Poseidon2::hash_internal(input, message_size)\n }\n\n pub(crate) fn new(iv: Field) -> Poseidon2 {\n let mut result =\n Poseidon2 { cache: [0; 3], state: [0; 4], cache_size: 0, squeeze_mode: false };\n result.state[RATE] = iv;\n result\n }\n\n fn perform_duplex(&mut self) {\n // add the cache into sponge state\n self.state[0] += self.cache[0];\n self.state[1] += self.cache[1];\n self.state[2] += self.cache[2];\n self.state = crate::poseidon2_permutation(self.state, 4);\n }\n\n fn absorb(&mut self, input: Field) {\n assert(!self.squeeze_mode);\n if self.cache_size == RATE {\n // If we're absorbing, and the cache is full, apply the sponge permutation to compress the cache\n self.perform_duplex();\n self.cache[0] = input;\n self.cache_size = 1;\n } else {\n // If we're absorbing, and the cache is not full, add the input into the cache\n self.cache[self.cache_size] = input;\n self.cache_size += 1;\n }\n }\n\n fn squeeze(&mut self) -> Field {\n assert(!self.squeeze_mode);\n // If we're in absorb mode, apply sponge permutation to compress the cache.\n self.perform_duplex();\n self.squeeze_mode = true;\n\n // Pop one item off the top of the permutation and return it.\n self.state[0]\n }\n\n fn hash_internal(input: [Field; N], in_len: u32) -> Field {\n let two_pow_64 = 18446744073709551616;\n let iv: Field = (in_len as Field) * two_pow_64;\n let mut state = [0; 4];\n state[RATE] = iv;\n\n if std::runtime::is_unconstrained() {\n for i in 0..(in_len / RATE) {\n state[0] += input[i * RATE];\n state[1] += input[i * RATE + 1];\n state[2] += input[i * RATE + 2];\n state = crate::poseidon2_permutation(state, 4);\n }\n\n // handle remaining elements after last full RATE-sized chunk\n let num_extra_fields = in_len % RATE;\n if num_extra_fields != 0 {\n let remainder_start = in_len - num_extra_fields;\n state[0] += input[remainder_start];\n if num_extra_fields > 1 {\n state[1] += input[remainder_start + 1]\n }\n }\n } else {\n let mut states: [[Field; 4]; N / RATE + 1] = [[0; 4]; N / RATE + 1];\n states[0] = state;\n\n // process all full RATE-sized chunks, storing state after each permutation\n for chunk_idx in 0..(N / RATE) {\n for i in 0..RATE {\n state[i] += input[chunk_idx * RATE + i];\n }\n state = crate::poseidon2_permutation(state, 4);\n states[chunk_idx + 1] = state;\n }\n\n // get state at the last full block before in_len\n let first_partially_filled_chunk = in_len / RATE;\n state = states[first_partially_filled_chunk];\n\n // handle remaining elements after last full RATE-sized chunk\n let remainder_start = (in_len / RATE) * RATE;\n for j in 0..RATE {\n let idx = remainder_start + j;\n if idx < in_len {\n state[j] += input[idx];\n }\n }\n }\n\n // always run final permutation unless we just completed a full chunk\n // still need to permute once if in_len is 0\n if (in_len == 0) | (in_len % RATE != 0) {\n state = crate::poseidon2_permutation(state, 4)\n };\n\n state[0]\n }\n}\n\npub struct Poseidon2Hasher {\n _state: [Field],\n}\n\nimpl Hasher for Poseidon2Hasher {\n fn finish(self) -> Field {\n let iv: Field = (self._state.len() as Field) * 18446744073709551616; // iv = (self._state.len() << 64)\n let mut sponge = Poseidon2::new(iv);\n for i in 0..self._state.len() {\n sponge.absorb(self._state[i]);\n }\n sponge.squeeze()\n }\n\n fn write(&mut self, input: Field) {\n self._state = self._state.push_back(input);\n }\n}\n\nimpl Default for Poseidon2Hasher {\n fn default() -> Self {\n Poseidon2Hasher { _state: &[] }\n }\n}\n" }, "293": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/abis/block_header.nr", "source": "use crate::{\n abis::{\n append_only_tree_snapshot::AppendOnlyTreeSnapshot, global_variables::GlobalVariables,\n state_reference::StateReference,\n },\n constants::{BLOCK_HEADER_LENGTH, DOM_SEP__BLOCK_HEADER_HASH, GENESIS_BLOCK_HEADER_HASH},\n hash::poseidon2_hash_with_separator,\n traits::{Deserialize, Empty, Hash, Serialize},\n};\nuse std::meta::derive;\n\n// docs:start:block-header\n#[derive(Deserialize, Eq, Serialize)]\npub struct BlockHeader {\n pub last_archive: AppendOnlyTreeSnapshot,\n pub state: StateReference,\n\n // The hash of the sponge blob for this block, which commits to the tx effects added in this block.\n // Note: it may also include tx effects from previous blocks within the same checkpoint.\n // When proving tx effects from this block only, we must refer to the `sponge_blob_hash` in the previous block\n // header to show that the effect was added after the previous block.\n // The previous block header can be validated using a membership proof of the last leaf in `last_archive`.\n pub sponge_blob_hash: Field,\n\n pub global_variables: GlobalVariables,\n pub total_fees: Field,\n pub total_mana_used: Field,\n}\n// docs:end:block-header\n\nimpl BlockHeader {\n pub fn chain_id(self) -> Field {\n self.global_variables.chain_id\n }\n\n pub fn version(self) -> Field {\n self.global_variables.version\n }\n\n pub fn block_number(self) -> u32 {\n self.global_variables.block_number\n }\n\n pub fn timestamp(self) -> u64 {\n self.global_variables.timestamp\n }\n}\n\nimpl Empty for BlockHeader {\n fn empty() -> Self {\n Self {\n last_archive: AppendOnlyTreeSnapshot::empty(),\n state: StateReference::empty(),\n sponge_blob_hash: 0,\n global_variables: GlobalVariables::empty(),\n total_fees: 0,\n total_mana_used: 0,\n }\n }\n}\n\nimpl Hash for BlockHeader {\n fn hash(self) -> Field {\n poseidon2_hash_with_separator(self.serialize(), DOM_SEP__BLOCK_HEADER_HASH)\n }\n}\n\n#[test]\nfn serialization_of_empty() {\n let header = BlockHeader::empty();\n // We use the BLOCK_HEADER_LENGTH constant to ensure that there is a match\n // between the derived trait implementation and the constant.\n let serialized: [Field; BLOCK_HEADER_LENGTH] = header.serialize();\n let deserialized = BlockHeader::deserialize(serialized);\n assert(header.eq(deserialized));\n}\n\n#[test]\nfn hash_of_genesis_block_header() {\n let mut header = BlockHeader::empty();\n // The following values are taken from world_state.test.cpp > WorldStateTest.GetInitialTreeInfoForAllTrees.\n header.state.l1_to_l2_message_tree.root =\n 0x0d582c10ff8115413aa5b70564fdd2f3cefe1f33a1e43a47bc495081e91e73e5;\n header.state.partial.note_hash_tree.root =\n 0x2ac5dda169f6bb3b9ca09bbac34e14c94d1654597db740153a1288d859a8a30a;\n header.state.partial.nullifier_tree.root =\n 0x1ec3788cd1c32e54d889d67fe29e481114f9d4afe9b44b229aa29d8ad528dd31;\n header.state.partial.nullifier_tree.next_available_leaf_index = 128;\n header.state.partial.public_data_tree.root =\n 0x23c08a6b1297210c5e24c76b9a936250a1ce2721576c26ea797c7ec35f9e46a9;\n header.state.partial.public_data_tree.next_available_leaf_index = 128;\n\n let hash = header.hash();\n assert_eq(hash, GENESIS_BLOCK_HEADER_HASH);\n}\n\n#[test]\nfn hash_of_empty_block_header_match_typescript() {\n let header = BlockHeader::empty();\n let hash = header.hash();\n\n // Value from block_header.test.ts \"computes empty hash\" test\n let test_data_empty_hash = 0x0bdc537052dea0f80db9698585dff9f32063b86b6d4934ac17c30c81e8e416d3;\n assert_eq(hash, test_data_empty_hash);\n}\n" }, "302": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/abis/function_selector.nr", "source": "use crate::traits::{Deserialize, Empty, FromField, Serialize, ToField};\nuse std::meta::derive;\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct FunctionSelector {\n // 1st 4-bytes (big-endian leftmost) of abi-encoding of an event.\n pub inner: u32,\n}\n\nimpl FromField for FunctionSelector {\n fn from_field(field: Field) -> Self {\n Self { inner: field as u32 }\n }\n}\n\nimpl ToField for FunctionSelector {\n fn to_field(self) -> Field {\n self.inner as Field\n }\n}\n\nimpl Empty for FunctionSelector {\n fn empty() -> Self {\n Self { inner: 0 as u32 }\n }\n}\n\nimpl FunctionSelector {\n pub fn from_u32(value: u32) -> Self {\n Self { inner: value }\n }\n\n pub fn from_signature(signature: str) -> Self {\n let bytes = signature.as_bytes();\n let hash = crate::hash::poseidon2_hash_bytes(bytes);\n\n // `hash` is automatically truncated to fit within 32 bits.\n FunctionSelector::from_field(hash)\n }\n\n pub fn zero() -> Self {\n Self { inner: 0 }\n }\n}\n\n#[test]\nfn test_is_valid_selector() {\n let selector = FunctionSelector::from_signature(\"IS_VALID()\");\n assert_eq(selector.to_field(), 0x73cdda47);\n}\n\n#[test]\nfn test_long_selector() {\n let selector =\n FunctionSelector::from_signature(\"foo_and_bar_and_baz_and_foo_bar_baz_and_bar_foo\");\n assert_eq(selector.to_field(), 0x7590a997);\n}\n" }, "340": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/address/aztec_address.nr", "source": "use crate::{\n address::{\n partial_address::PartialAddress, salted_initialization_hash::SaltedInitializationHash,\n },\n constants::{AZTEC_ADDRESS_LENGTH, DOM_SEP__CONTRACT_ADDRESS_V1, MAX_FIELD_VALUE},\n contract_class_id::ContractClassId,\n hash::poseidon2_hash_with_separator,\n public_keys::{IvpkM, NpkM, OvpkM, PublicKeys, ToPoint, TpkM},\n traits::{Deserialize, Empty, FromField, Packable, Serialize, ToField},\n utils::field::sqrt,\n};\n\n// We do below because `use crate::point::Point;` does not work\nuse std::embedded_curve_ops::EmbeddedCurvePoint as Point;\n\nuse crate::public_keys::AddressPoint;\nuse std::{\n embedded_curve_ops::{EmbeddedCurveScalar, fixed_base_scalar_mul as derive_public_key},\n ops::Add,\n};\nuse std::meta::derive;\n\n// Aztec address\n#[derive(Deserialize, Eq, Packable, Serialize)]\npub struct AztecAddress {\n pub inner: Field,\n}\n\nimpl Empty for AztecAddress {\n fn empty() -> Self {\n Self { inner: 0 }\n }\n}\n\nimpl ToField for AztecAddress {\n fn to_field(self) -> Field {\n self.inner\n }\n}\n\nimpl FromField for AztecAddress {\n fn from_field(value: Field) -> AztecAddress {\n AztecAddress { inner: value }\n }\n}\n\nimpl AztecAddress {\n pub fn zero() -> Self {\n Self { inner: 0 }\n }\n\n /// Returns `true` if the address is valid.\n ///\n /// An invalid address is one that can be proven to not be correctly derived, meaning it contains no contract code,\n /// public keys, etc., and can therefore not receive messages nor execute calls.\n pub fn is_valid(self) -> bool {\n self.get_y().is_some()\n }\n\n /// Returns an address's [`AddressPoint`].\n ///\n /// This can be used to create shared secrets with the owner of the address. If the address is invalid (see\n /// [`AztecAddress::is_valid`]) then this returns `Option::none()`, and no shared secrets can be created.\n pub fn to_address_point(self) -> Option {\n self.get_y().map(|y| {\n // If we get a negative y coordinate (y > (r - 1) / 2), we swap it to the\n // positive one (where y <= (r - 1) / 2) by negating it.\n let final_y = if Self::is_positive(y) { y } else { -y };\n\n AddressPoint { inner: Point { x: self.inner, y: final_y, is_infinite: false } }\n })\n }\n\n /// Determines whether a y-coordinate is in the lower (positive) or upper (negative) \"half\" of the field.\n /// I.e.\n /// y <= (r - 1)/2 => positive.\n /// y > (r - 1)/2 => negative.\n /// An AddressPoint always uses the \"positive\" y.\n fn is_positive(y: Field) -> bool {\n // Note: The field modulus r is MAX_FIELD_VALUE + 1.\n let MID = MAX_FIELD_VALUE / 2; // (r - 1) / 2\n let MID_PLUS_1 = MID + 1; // (r - 1)/2 + 1\n // Note: y <= m implies y < m + 1.\n y.lt(MID_PLUS_1)\n }\n\n /// Returns one of the two possible y-coordinates.\n ///\n /// Not all `AztecAddresses` are valid, in which case there is no corresponding y-coordinate. This returns\n /// `Option::none()` for invalid addresses.\n ///\n /// An `AztecAddress` is defined by an x-coordinate, for which two y-coordinates exist as solutions to the curve\n /// equation. This function returns either of them. Note that an [`AddressPoint`] must **always** have a positive\n /// y-coordinate - if trying to obtain the underlying point use [`AztecAddress::to_address_point`] instead.\n fn get_y(self) -> Option {\n // We compute the address point by taking our address as x, and then solving for y in the\n // equation which defines the grumpkin curve:\n // y^2 = x^3 - 17; x = address\n let x = self.inner;\n let y_squared = x * x * x - 17;\n\n sqrt(y_squared)\n }\n\n pub fn compute(public_keys: PublicKeys, partial_address: PartialAddress) -> AztecAddress {\n //\n // address = address_point.x\n // |\n // address_point = pre_address * G + Ivpk_m (always choose \"positive\" y-coord)\n // | ^\n // | |.....................\n // pre_address .\n // / \\ .\n // / \\ .\n // partial_address public_keys_hash .\n // / \\ / / \\ \\ .\n // / \\ Npk_m Ivpk_m Ovpk_m Tpk_m .\n // contract_class_id \\ |...................\n // / | \\ \\\n // artifact_hash | public_bytecode_commitment salted_initialization_hash\n // | / / \\\n // private_function_tree_root deployer_address salt initialization_hash\n // / \\ / \\\n // ... ... constructor_fn_selector constructor_args_hash\n // / \\\n // / \\ / \\\n // leaf leaf leaf leaf\n // ^\n // |\n // |---h(function_selector, vk_hash)\n // ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n // Each of these represents a private function of the contract.\n\n let public_keys_hash = public_keys.hash();\n\n let pre_address = poseidon2_hash_with_separator(\n [public_keys_hash.to_field(), partial_address.to_field()],\n DOM_SEP__CONTRACT_ADDRESS_V1,\n );\n\n // Note: `.add()` will fail within the blackbox fn if either of the points are not on the curve. (See tests below).\n let address_point = derive_public_key(EmbeddedCurveScalar::from_field(pre_address)).add(\n public_keys.ivpk_m.to_point(),\n );\n\n // Note that our address is only the x-coordinate of the full address_point. This is okay because when people want to encrypt something and send it to us\n // they can recover our full point using the x-coordinate (our address itself). To do this, they recompute the y-coordinate according to the equation y^2 = x^3 - 17.\n // When they do this, they may get a positive y-coordinate (a value that is less than or equal to MAX_FIELD_VALUE / 2) or\n // a negative y-coordinate (a value that is more than MAX_FIELD_VALUE), and we cannot dictate which one they get and hence the recovered point may sometimes be different than the one\n // our secret can decrypt. Regardless though, they should and will always encrypt using point with the positive y-coordinate by convention.\n // This ensures that everyone encrypts to the same point given an arbitrary x-coordinate (address). This is allowed because even though our original point may not have a positive y-coordinate,\n // with our original secret, we will be able to derive the secret to the point with the flipped (and now positive) y-coordinate that everyone encrypts to.\n AztecAddress::from_field(address_point.x)\n }\n\n pub fn compute_from_class_id(\n contract_class_id: ContractClassId,\n salted_initialization_hash: SaltedInitializationHash,\n public_keys: PublicKeys,\n ) -> Self {\n let partial_address = PartialAddress::compute_from_salted_initialization_hash(\n contract_class_id,\n salted_initialization_hash,\n );\n\n AztecAddress::compute(public_keys, partial_address)\n }\n\n pub fn is_zero(self) -> bool {\n self.inner == 0\n }\n\n pub fn assert_is_zero(self) {\n assert(self.to_field() == 0);\n }\n}\n\n#[test]\nfn check_max_field_value() {\n // Check that it is indeed r-1.\n assert_eq(MAX_FIELD_VALUE + 1, 0);\n}\n\n#[test]\nfn check_is_positive() {\n assert(AztecAddress::is_positive(0));\n assert(AztecAddress::is_positive(1));\n assert(!AztecAddress::is_positive(-1));\n assert(AztecAddress::is_positive(MAX_FIELD_VALUE / 2));\n assert(!AztecAddress::is_positive((MAX_FIELD_VALUE / 2) + 1));\n}\n\n// Gives us confidence that we don't need to manually check that the input public keys need to be on the curve for `add`,\n// because the blackbox function does this check for us.\n#[test(should_fail_with = \"is not on curve\")]\nfn check_embedded_curve_point_add() {\n // Choose a point not on the curve:\n let p1 = Point { x: 1, y: 1, is_infinite: false };\n let p2 = Point::generator();\n let _ = p1 + p2;\n}\n\n// Gives us confidence that we don't need to manually check that the input public keys need to be on the curve for `add`,\n// because the blackbox function does this check for us.\n#[test(should_fail_with = \"is not on curve\")]\nfn check_embedded_curve_point_add_2() {\n // Choose a point not on the curve in the 2nd position.\n let p1 = Point::generator();\n let p2 = Point { x: 1, y: 1, is_infinite: false };\n let _ = p1 + p2;\n}\n\n#[test]\nfn compute_address_from_partial_and_pub_keys() {\n let public_keys = PublicKeys {\n npk_m: NpkM {\n inner: Point {\n x: 0x22f7fcddfa3ce3e8f0cc8e82d7b94cdd740afa3e77f8e4a63ea78a239432dcab,\n y: 0x0471657de2b6216ade6c506d28fbc22ba8b8ed95c871ad9f3e3984e90d9723a7,\n is_infinite: false,\n },\n },\n ivpk_m: IvpkM {\n inner: Point {\n x: 0x111223493147f6785514b1c195bb37a2589f22a6596d30bb2bb145fdc9ca8f1e,\n y: 0x273bbffd678edce8fe30e0deafc4f66d58357c06fd4a820285294b9746c3be95,\n is_infinite: false,\n },\n },\n ovpk_m: OvpkM {\n inner: Point {\n x: 0x09115c96e962322ffed6522f57194627136b8d03ac7469109707f5e44190c484,\n y: 0x0c49773308a13d740a7f0d4f0e6163b02c5a408b6f965856b6a491002d073d5b,\n is_infinite: false,\n },\n },\n tpk_m: TpkM {\n inner: Point {\n x: 0x00d3d81beb009873eb7116327cf47c612d5758ef083d4fda78e9b63980b2a762,\n y: 0x2f567d22d2b02fe1f4ad42db9d58a36afd1983e7e2909d1cab61cafedad6193a,\n is_infinite: false,\n },\n },\n };\n\n let partial_address = PartialAddress::from_field(\n 0x0a7c585381b10f4666044266a02405bf6e01fa564c8517d4ad5823493abd31de,\n );\n\n let address = AztecAddress::compute(public_keys, partial_address);\n\n // The following value was generated by `derivation.test.ts`.\n // --> Run the test with AZTEC_GENERATE_TEST_DATA=1 flag to update test data.\n let expected_computed_address_from_partial_and_pubkeys =\n 0x2f66081d4bb077fbe8e8abe96a3516a713a3d7e34360b4e985da0da95092b37d;\n assert(address.to_field() == expected_computed_address_from_partial_and_pubkeys);\n}\n\n#[test]\nfn compute_preaddress_from_partial_and_pub_keys() {\n let pre_address = poseidon2_hash_with_separator([1, 2], DOM_SEP__CONTRACT_ADDRESS_V1);\n let expected_computed_preaddress_from_partial_and_pubkey =\n 0x286c7755f2924b1e53b00bcaf1adaffe7287bd74bba7a02f4ab867e3892d28da;\n assert(pre_address == expected_computed_preaddress_from_partial_and_pubkey);\n}\n\n#[test]\nfn from_field_to_field() {\n let address = AztecAddress { inner: 37 };\n assert_eq(FromField::from_field(address.to_field()), address);\n}\n\n#[test]\nfn serde() {\n let address = AztecAddress { inner: 37 };\n // We use the AZTEC_ADDRESS_LENGTH constant to ensure that there is a match between the derived trait\n // implementation and the constant.\n let serialized: [Field; AZTEC_ADDRESS_LENGTH] = address.serialize();\n let deserialized = AztecAddress::deserialize(serialized);\n assert_eq(address, deserialized);\n}\n\n#[test]\nfn to_address_point_valid() {\n // x = 8 where x^3 - 17 = 512 - 17 = 495, which is a residue in this field\n let address = AztecAddress { inner: 8 };\n\n assert(address.get_y().is_some()); // We don't bother checking the result of get_y as it is only used internally\n assert(address.is_valid());\n\n let maybe_point = address.to_address_point();\n assert(maybe_point.is_some());\n\n let point = maybe_point.unwrap().inner;\n // check that x is preserved\n assert_eq(point.x, Field::from(8));\n\n // check that the curve equation holds: y^2 == x^3 - 17\n assert_eq(point.y * point.y, point.x * point.x * point.x - 17);\n}\n\n#[test]\nunconstrained fn to_address_point_invalid() {\n // x = 3 where x^3 - 17 = 27 - 17 = 10, which is a non-residue in this field\n let address = AztecAddress { inner: 3 };\n\n assert(address.get_y().is_none());\n assert(!address.is_valid());\n\n assert(address.to_address_point().is_none());\n}\n" }, "343": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/address/partial_address.nr", "source": "use crate::{\n address::{aztec_address::AztecAddress, salted_initialization_hash::SaltedInitializationHash},\n constants::DOM_SEP__PARTIAL_ADDRESS,\n contract_class_id::ContractClassId,\n hash::poseidon2_hash_with_separator,\n traits::{Deserialize, Empty, Serialize, ToField},\n};\nuse std::meta::derive;\n\n// Partial address\n#[derive(Deserialize, Eq, Serialize)]\npub struct PartialAddress {\n pub inner: Field,\n}\n\nimpl ToField for PartialAddress {\n fn to_field(self) -> Field {\n self.inner\n }\n}\n\nimpl Empty for PartialAddress {\n fn empty() -> Self {\n Self { inner: 0 }\n }\n}\n\nimpl PartialAddress {\n pub fn from_field(field: Field) -> Self {\n Self { inner: field }\n }\n\n pub fn compute(\n contract_class_id: ContractClassId,\n salt: Field,\n initialization_hash: Field,\n deployer: AztecAddress,\n ) -> Self {\n PartialAddress::compute_from_salted_initialization_hash(\n contract_class_id,\n SaltedInitializationHash::compute(salt, initialization_hash, deployer),\n )\n }\n\n pub fn compute_from_salted_initialization_hash(\n contract_class_id: ContractClassId,\n salted_initialization_hash: SaltedInitializationHash,\n ) -> Self {\n PartialAddress::from_field(poseidon2_hash_with_separator(\n [contract_class_id.to_field(), salted_initialization_hash.to_field()],\n DOM_SEP__PARTIAL_ADDRESS,\n ))\n }\n\n pub fn to_field(self) -> Field {\n self.inner\n }\n\n pub fn is_zero(self) -> bool {\n self.to_field() == 0\n }\n\n pub fn assert_is_zero(self) {\n assert(self.to_field() == 0);\n }\n}\n\nmod test {\n use crate::{address::partial_address::PartialAddress, traits::{Deserialize, Serialize}};\n\n #[test]\n fn serialization_of_partial_address() {\n let item = PartialAddress::from_field(1);\n let serialized: [Field; 1] = item.serialize();\n let deserialized = PartialAddress::deserialize(serialized);\n assert_eq(item, deserialized);\n }\n}\n" }, "345": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/address/salted_initialization_hash.nr", "source": "use crate::{\n address::aztec_address::AztecAddress, constants::DOM_SEP__PARTIAL_ADDRESS,\n hash::poseidon2_hash_with_separator, traits::ToField,\n};\n\n// Salted initialization hash. Used in the computation of a partial address.\n#[derive(Eq)]\npub struct SaltedInitializationHash {\n pub inner: Field,\n}\n\nimpl ToField for SaltedInitializationHash {\n fn to_field(self) -> Field {\n self.inner\n }\n}\n\nimpl SaltedInitializationHash {\n pub fn from_field(field: Field) -> Self {\n Self { inner: field }\n }\n\n pub fn compute(salt: Field, initialization_hash: Field, deployer: AztecAddress) -> Self {\n SaltedInitializationHash::from_field(poseidon2_hash_with_separator(\n [salt, initialization_hash, deployer.to_field()],\n DOM_SEP__PARTIAL_ADDRESS,\n ))\n }\n\n pub fn assert_is_zero(self) {\n assert(self.to_field() == 0);\n }\n}\n" }, "355": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/contract_instance.nr", "source": "use crate::{\n address::{aztec_address::AztecAddress, partial_address::PartialAddress},\n contract_class_id::ContractClassId,\n public_keys::PublicKeys,\n traits::{Deserialize, Hash, Serialize, ToField},\n};\nuse std::meta::derive;\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct ContractInstance {\n pub salt: Field,\n pub deployer: AztecAddress,\n pub contract_class_id: ContractClassId,\n pub initialization_hash: Field,\n pub public_keys: PublicKeys,\n}\n\nimpl Hash for ContractInstance {\n fn hash(self) -> Field {\n self.to_address().to_field()\n }\n}\n\nimpl ContractInstance {\n pub fn to_address(self) -> AztecAddress {\n AztecAddress::compute(\n self.public_keys,\n PartialAddress::compute(\n self.contract_class_id,\n self.salt,\n self.initialization_hash,\n self.deployer,\n ),\n )\n }\n}\n\nmod test {\n use crate::{\n address::AztecAddress,\n constants::CONTRACT_INSTANCE_LENGTH,\n contract_class_id::ContractClassId,\n contract_instance::ContractInstance,\n public_keys::PublicKeys,\n traits::{Deserialize, FromField, Serialize},\n };\n\n #[test]\n fn serde() {\n let instance = ContractInstance {\n salt: 6,\n deployer: AztecAddress::from_field(12),\n contract_class_id: ContractClassId::from_field(13),\n initialization_hash: 156,\n public_keys: PublicKeys::default(),\n };\n\n // We use the CONTRACT_INSTANCE_LENGTH constant to ensure that there is a match between the derived trait\n // implementation and the constant.\n let serialized: [Field; CONTRACT_INSTANCE_LENGTH] = instance.serialize();\n\n let deserialized = ContractInstance::deserialize(serialized);\n\n assert(instance.eq(deserialized));\n }\n\n}\n" }, "359": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/data/public_data_tree_leaf_preimage.nr", "source": "use crate::{\n data::public_data_tree_leaf::PublicDataTreeLeaf,\n merkle_tree::{IndexedTreeLeafPreimage, LeafPreimage},\n traits::{Deserialize, Empty, Hash, Serialize},\n};\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct PublicDataTreeLeafPreimage {\n pub slot: Field,\n pub value: Field,\n pub next_slot: Field,\n pub next_index: Field,\n}\n\n/// An empty public data tree leaf preimage never actually exists in the tree.\n///\n/// Unlike the nullifier tree, where nullifiers are inserted in fixed-size batches (padded with empty preimages when\n/// a transaction doesn't fill a full batch), the AVM inserts public data leaves one by one, so no padding is needed.\n///\n/// The tree is initialized with non-empty leaf preimages (see table below). To insert an empty leaf preimage\n/// (slot = 0), we would need to find a valid low leaf satisfying: low_leaf.slot < 0 < low_leaf.next_slot.\n/// Since all slots in the tree are non-negative, no such low leaf exists, making insertion impossible.\n///\n/// In fact, slot 0 already exists in the initial tree state. The preimage { slot: 0, value: 0, next_slot: 1,\n/// next_index: 1 } can be used to prove membership and update the value at slot 0. However, it's practically\n/// impossible for a contract to produce a storage slot where `compute_public_data_leaf_slot` returns 0.\n///\n/// Note: Tree leaves beyond next_available_leaf_index contain zero hashes (the default empty leaf value in Merkle\n/// trees). These are NOT hashes of empty leaf preimages - the hash of an empty public data leaf preimage is\n/// 0x20be000d93b852c82d4e02c94e9e61fa3a0e79949aac3af4010620651d416134\n///\n/// Initial state of the Public Data Tree (initialized in content_addressed_indexed_tree.hpp with N = 128):\n///\n/// Index | slot value next_slot next_index\n/// ----- | ---- ----- --------- ---------\n/// 0 | 0 0 1 1\n/// 1 | 1 0 2 2\n/// 2 | 2 0 3 3\n/// ... | ... ... ... ...\n/// N-2 | N-2 0 N-1 N-1\n/// N-1 | N-1 0 0 0 (points to infinity)\n///\nimpl Empty for PublicDataTreeLeafPreimage {\n fn empty() -> Self {\n Self { slot: 0, value: 0, next_slot: 0, next_index: 0 }\n }\n}\n\nimpl Hash for PublicDataTreeLeafPreimage {\n fn hash(self) -> Field {\n crate::hash::poseidon2_hash(self.serialize())\n }\n}\n\nimpl LeafPreimage for PublicDataTreeLeafPreimage {\n fn get_key(self) -> Field {\n self.slot\n }\n\n fn as_leaf(self) -> Field {\n self.hash()\n }\n}\n\nimpl IndexedTreeLeafPreimage for PublicDataTreeLeafPreimage {\n fn get_next_key(self) -> Field {\n self.next_slot\n }\n\n fn points_to_infinity(self) -> bool {\n (self.next_slot == 0) & (self.next_index == 0)\n }\n\n fn update_pointers(self, next_slot: Field, next_index: Field) -> Self {\n Self { slot: self.slot, value: self.value, next_slot, next_index }\n }\n\n fn update_value(self, write: PublicDataTreeLeaf) -> Self {\n Self {\n slot: self.slot,\n value: write.value,\n next_slot: self.next_slot,\n next_index: self.next_index,\n }\n }\n\n fn build_insertion_leaf(write: PublicDataTreeLeaf, low_leaf: Self) -> Self {\n Self {\n slot: write.slot,\n value: write.value,\n next_slot: low_leaf.next_slot,\n next_index: low_leaf.next_index,\n }\n }\n}\n\n#[test]\nfn public_data_tree_leaf_preimage_hash_matches_typescript() {\n let leaf = PublicDataTreeLeafPreimage { slot: 123, value: 45, next_slot: 67, next_index: 890 };\n let hash = leaf.hash();\n\n // The following value was generated by `yarn-project/stdlib/src/trees/public_data_leaf.test.ts`\n let hash_from_ts = 0x2efdfcfc865cbb7543183fae69374ee5106dde9741545afd2fbf12868b550614;\n\n assert_eq(hash, hash_from_ts);\n}\n" }, "360": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/data/storage_read.nr", "source": "use crate::{\n constants::PUBLIC_DATA_TREE_HEIGHT,\n data::public_data_tree_leaf_preimage::PublicDataTreeLeafPreimage,\n merkle_tree::{conditionally_check_membership, MembershipWitness},\n};\n\n/// Read the value from public storage at the given `leaf_slot`.\n///\n/// Parameters:\n/// - `public_data_tree_root`: The root of the public data tree.\n/// - `public_data_leaf_slot`: The slot of the leaf in the public data tree.\n/// - `witness`: A hint to prove that the `leaf_preimage` exists in the tree.\n///\n/// The slot could either exist (has been initialized) in the tree or not:\n/// - If it's in the tree, `leaf_preimage` should be the preimage for the slot.\n/// - If it's not in the tree, `leaf_preimage` should be the preimage for the low leaf of the slot.\n///\n/// Returns:\n/// - `Field`: The value read from the public data tree.\npub fn public_data_storage_read(\n public_data_tree_root: Field,\n public_data_leaf_slot: Field,\n witness: MembershipWitness,\n leaf_preimage: PublicDataTreeLeafPreimage,\n) -> Field {\n // Use the given `leaf_preimage` hint to determine whether we are reading a slot that's been written to the tree or\n // not.\n let is_slot_in_tree = leaf_preimage.slot == public_data_leaf_slot;\n\n let (_, is_expected_leaf, is_leaf_in_tree) = conditionally_check_membership(\n public_data_leaf_slot,\n is_slot_in_tree,\n leaf_preimage,\n witness,\n public_data_tree_root,\n );\n\n // In the case of the state existing in the tree, `is_leaf_in_tree = true`.\n // In the case of the state not existing in the tree, the above membership check was done against the _low_ leaf,\n // so rather confusingly, we still expect `is_leaf_in_tree = true` (because it's a different kind of leaf in this case).\n assert(is_leaf_in_tree, \"Proving public value inclusion failed\");\n assert(\n is_expected_leaf,\n \"The provided public data tree leaf preimage is not the correct leaf for the requested slot\",\n );\n\n // Note: An empty leaf preimage would pass validation as a low leaf. However, it is not a valid leaf and is never\n // inserted into the tree, since we do not perform batch insertions with padded empty leaves in the public data\n // tree, and the tree's genesis state contains only non-empty leaves.\n // (See the comment in `PublicDataTreeLeafPreimage` for more details.)\n // Therefore, even if an empty leaf preimage is provided, it will fail the membership check because\n // `is_leaf_in_tree` will be false.\n\n if is_slot_in_tree {\n leaf_preimage.value\n } else {\n 0\n }\n}\n\nmod tests {\n use crate::{\n constants::PUBLIC_DATA_TREE_HEIGHT,\n data::public_data_tree_leaf_preimage::PublicDataTreeLeafPreimage,\n merkle_tree::{MembershipWitness, test_utils::SingleSubtreeMerkleTree},\n traits::{Empty, Hash},\n };\n use super::public_data_storage_read;\n\n struct TestBuilder {\n public_data_tree_root: Field,\n public_data_leaf_slot: Field,\n witness: MembershipWitness,\n leaf_preimage: PublicDataTreeLeafPreimage,\n public_data_tree: SingleSubtreeMerkleTree<4, 2, PUBLIC_DATA_TREE_HEIGHT>,\n }\n\n impl TestBuilder {\n pub fn new() -> Self {\n // Create a tree with 4 prefilled leaves.\n // slot 0 -> slot 11 -> slot 22 -> slot 33 -> infinity\n let prefilled_leaves = [\n PublicDataTreeLeafPreimage { slot: 0, value: 0, next_slot: 11, next_index: 3 },\n PublicDataTreeLeafPreimage { slot: 22, value: 123, next_slot: 33, next_index: 2 },\n PublicDataTreeLeafPreimage { slot: 33, value: 789, next_slot: 0, next_index: 0 },\n PublicDataTreeLeafPreimage { slot: 11, value: 456, next_slot: 22, next_index: 1 },\n ];\n let public_data_tree = SingleSubtreeMerkleTree::<4, 2, PUBLIC_DATA_TREE_HEIGHT>::new(\n prefilled_leaves.map(|l| l.hash()),\n );\n\n let public_data_tree_root = public_data_tree.get_root();\n\n // Default to reading from slot 22 (exists in tree at index 1).\n let leaf_index = 1;\n let public_data_leaf_slot = 22;\n let leaf_preimage = prefilled_leaves[1];\n let witness = MembershipWitness {\n leaf_index,\n sibling_path: public_data_tree.get_sibling_path(leaf_index),\n };\n\n Self {\n public_data_tree_root,\n public_data_leaf_slot,\n witness,\n leaf_preimage,\n public_data_tree,\n }\n }\n\n pub fn read_value(self) -> Field {\n public_data_storage_read(\n self.public_data_tree_root,\n self.public_data_leaf_slot,\n self.witness,\n self.leaf_preimage,\n )\n }\n }\n\n #[test]\n fn read_existing_slot() {\n let builder = TestBuilder::new();\n\n let value = builder.read_value();\n\n // Slot 22 has value 123.\n assert_eq(value, 123);\n }\n\n #[test]\n fn read_non_existing_slot_using_low_leaf() {\n let mut builder = TestBuilder::new();\n\n // Read from slot 30, which doesn't exist in the tree.\n // Use the leaf at slot 22 as the low leaf (22 < 30 < 33).\n builder.public_data_leaf_slot = 30;\n // The leaf preimage at index 1 (slot 22) is a valid low leaf for slot 30.\n // The witness is already pointing to index 1.\n\n let value = builder.read_value();\n\n // Non-existing slot should return 0.\n assert_eq(value, 0);\n }\n\n #[test]\n fn read_non_existing_slot_greater_than_max() {\n let mut builder = TestBuilder::new();\n\n // Read from slot 600, which is greater than the max slot in the tree (33).\n // Use the leaf at slot 33 as the low leaf (points to infinity).\n builder.public_data_leaf_slot = 600;\n let leaf_index = 2;\n builder.witness = MembershipWitness {\n leaf_index,\n sibling_path: builder.public_data_tree.get_sibling_path(leaf_index),\n };\n builder.leaf_preimage =\n PublicDataTreeLeafPreimage { slot: 33, value: 789, next_slot: 0, next_index: 0 };\n\n let value = builder.read_value();\n\n // Non-existing slot should return 0.\n assert_eq(value, 0);\n }\n\n #[test(should_fail_with = \"Proving public value inclusion failed\")]\n fn wrong_membership_witness_index() {\n let mut builder = TestBuilder::new();\n\n // Use the wrong leaf index for the witness.\n builder.witness.leaf_index += 1;\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"Proving public value inclusion failed\")]\n fn wrong_tree_root() {\n let mut builder = TestBuilder::new();\n\n // Use a different tree root.\n builder.public_data_tree_root = 12345;\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"The provided public data tree leaf preimage is not the correct leaf for the requested slot\")]\n fn wrong_leaf_preimage_for_existing_slot() {\n let mut builder = TestBuilder::new();\n\n // Slot 22 exists, but provide a different preimage (for slot 11).\n let leaf_index = 3;\n builder.witness = MembershipWitness {\n leaf_index,\n sibling_path: builder.public_data_tree.get_sibling_path(leaf_index),\n };\n builder.leaf_preimage =\n PublicDataTreeLeafPreimage { slot: 11, value: 456, next_slot: 22, next_index: 1 };\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"The provided public data tree leaf preimage is not the correct leaf for the requested slot\")]\n fn invalid_low_leaf_slot_too_high() {\n let mut builder = TestBuilder::new();\n\n // Try to read slot 15. Provide low leaf at slot 22, which is > 15.\n builder.public_data_leaf_slot = 15;\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"The provided public data tree leaf preimage is not the correct leaf for the requested slot\")]\n fn invalid_low_leaf_next_slot_too_low() {\n let mut builder = TestBuilder::new();\n\n // Try to read slot 25. Use low leaf at slot 11, whose next_slot = 22, which is < 25.\n builder.public_data_leaf_slot = 25;\n let leaf_index = 3;\n builder.witness = MembershipWitness {\n leaf_index,\n sibling_path: builder.public_data_tree.get_sibling_path(leaf_index),\n };\n builder.leaf_preimage =\n PublicDataTreeLeafPreimage { slot: 11, value: 456, next_slot: 22, next_index: 1 };\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"Proving public value inclusion failed\")]\n fn bypass_existing_slot_using_empty_leaf() {\n let mut builder = TestBuilder::new();\n\n // Slot 22 exists, but try to bypass it using an empty leaf at an unused index.\n let leaf_index = builder.public_data_tree.get_next_available_index();\n builder.witness = MembershipWitness {\n leaf_index,\n sibling_path: builder.public_data_tree.get_sibling_path(leaf_index),\n };\n builder.leaf_preimage = PublicDataTreeLeafPreimage::empty();\n\n let _ = builder.read_value();\n }\n\n #[test(should_fail_with = \"Proving public value inclusion failed\")]\n fn leaf_preimage_mismatch_value() {\n let mut builder = TestBuilder::new();\n\n // Provide a preimage with the correct slot but wrong value.\n // The slot matches, so the function treats this as \"slot exists in tree\".\n // But the preimage hash won't match the actual leaf in the tree, so membership fails.\n builder.leaf_preimage =\n PublicDataTreeLeafPreimage { slot: 22, value: 9999, next_slot: 33, next_index: 2 };\n\n let _ = builder.read_value();\n }\n}\n" }, "371": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/hash.nr", "source": "mod poseidon2_chunks;\n\nuse crate::{\n abis::{\n contract_class_function_leaf_preimage::ContractClassFunctionLeafPreimage,\n function_selector::FunctionSelector, nullifier::Nullifier, private_log::PrivateLog,\n transaction::tx_request::TxRequest,\n },\n address::{AztecAddress, EthAddress},\n constants::{\n CONTRACT_CLASS_LOG_SIZE_IN_FIELDS, DOM_SEP__NOTE_HASH_NONCE,\n DOM_SEP__PRIVATE_LOG_FIRST_FIELD, DOM_SEP__SILOED_NOTE_HASH, DOM_SEP__SILOED_NULLIFIER,\n DOM_SEP__UNIQUE_NOTE_HASH, FUNCTION_TREE_HEIGHT, NULL_MSG_SENDER_CONTRACT_ADDRESS,\n TWO_POW_64,\n },\n merkle_tree::root_from_sibling_path,\n messaging::l2_to_l1_message::L2ToL1Message,\n poseidon2::Poseidon2Sponge,\n side_effect::{Counted, Scoped},\n traits::{FromField, Hash, ToField},\n utils::field::{field_from_bytes, field_from_bytes_32_trunc},\n};\n\npub use poseidon2_chunks::poseidon2_absorb_in_chunks_existing_sponge;\nuse poseidon2_chunks::poseidon2_absorb_in_chunks;\nuse std::embedded_curve_ops::EmbeddedCurveScalar;\n\n// TODO: refactor these into their own files: sha256, poseidon2, some protocol-specific hash computations, some merkle computations.\n\npub fn sha256_to_field(bytes_to_hash: [u8; N]) -> Field {\n let sha256_hashed = sha256::digest(bytes_to_hash);\n let hash_in_a_field = field_from_bytes_32_trunc(sha256_hashed);\n\n hash_in_a_field\n}\n\npub fn private_functions_root_from_siblings(\n selector: FunctionSelector,\n vk_hash: Field,\n function_leaf_index: Field,\n function_leaf_sibling_path: [Field; FUNCTION_TREE_HEIGHT],\n) -> Field {\n let function_leaf_preimage = ContractClassFunctionLeafPreimage { selector, vk_hash };\n let function_leaf = function_leaf_preimage.hash();\n root_from_sibling_path(\n function_leaf,\n function_leaf_index,\n function_leaf_sibling_path,\n )\n}\n\n/// Siloing in the context of Aztec refers to the process of hashing a note hash with a contract address (this way\n/// the note hash is scoped to a specific contract). This is used to prevent intermingling of notes between contracts.\npub fn compute_siloed_note_hash(contract_address: AztecAddress, note_hash: Field) -> Field {\n poseidon2_hash_with_separator(\n [contract_address.to_field(), note_hash],\n DOM_SEP__SILOED_NOTE_HASH,\n )\n}\n\n/// Computes unique, siloed note hashes from siloed note hashes.\n///\n/// The protocol injects uniqueness into every note_hash, so that every single note_hash in the\n/// tree is unique. This prevents faerie gold attacks, where a malicious sender could create\n/// two identical note_hashes for a recipient (meaning only one would be nullifiable in future).\n///\n/// Most privacy protocols will inject the note's leaf_index (its position in the Note Hashes Tree)\n/// into the note, but this requires the creator of a note to wait until their tx is included in\n/// a block to know the note's final note hash (the unique, siloed note hash), because inserting\n/// leaves into trees is the job of a block producer.\n///\n/// We took a different approach so that the creator of a note will know each note's unique, siloed\n/// note hash before broadcasting their tx to the network.\n/// (There was also a historical requirement relating to \"chained transactions\" -- a feature that\n/// Aztec Connect had to enable notes to be spent from distinct txs earlier in the same block,\n/// and hence before an archive block root had been established for that block -- but that feature\n/// was abandoned for the Aztec Network for having too many bad tradeoffs).\n///\n/// (\n/// Edit: it is no longer true that all final note_hashes will be known by the creator of a tx\n/// before they send it to the network. If a tx makes public function calls, then _revertible_\n/// note_hashes that are created in private will not be made unique in private by the Reset circuit,\n/// but will instead be made unique by the AVM, because the `note_index_in_tx` will not be known\n/// until the AVM has executed the public functions of the tx. (See an explanation in\n/// reset_output_composer.nr for why).\n/// For some such txs, the `note_index_in_tx` might still be predictable through simulation, but\n/// for txs whose public functions create a varying number of non-revertible notes (determined at\n/// runtime), the `note_index_in_tx` will not be deterministically derivable before submitting the\n/// tx to the network.\n/// )\n///\n/// We use the `first_nullifier` of a tx as a seed of uniqueness. We have a guarantee that there will\n/// always be at least one nullifier per tx, because the init circuit will create one if one isn't\n/// created naturally by any functions of the tx. (Search \"protocol_nullifier\").\n/// We combine the `first_nullifier` with the note's index (its position within this tx's new\n/// note_hashes array) (`note_index_in_tx`) to get a truly unique value to inject into a note, which\n/// we call a `note_nonce`.\npub fn compute_unique_note_hash(note_nonce: Field, siloed_note_hash: Field) -> Field {\n let inputs = [note_nonce, siloed_note_hash];\n poseidon2_hash_with_separator(inputs, DOM_SEP__UNIQUE_NOTE_HASH)\n}\n\npub fn compute_note_hash_nonce(first_nullifier_in_tx: Field, note_index_in_tx: u32) -> Field {\n // Hashing the first nullifier with note index in tx is guaranteed to be unique (because all nullifiers are also\n // unique).\n poseidon2_hash_with_separator(\n [first_nullifier_in_tx, note_index_in_tx as Field],\n DOM_SEP__NOTE_HASH_NONCE,\n )\n}\n\npub fn compute_note_nonce_and_unique_note_hash(\n siloed_note_hash: Field,\n first_nullifier: Field,\n note_index_in_tx: u32,\n) -> Field {\n let note_nonce = compute_note_hash_nonce(first_nullifier, note_index_in_tx);\n compute_unique_note_hash(note_nonce, siloed_note_hash)\n}\n\npub fn compute_siloed_nullifier(contract_address: AztecAddress, nullifier: Field) -> Field {\n poseidon2_hash_with_separator(\n [contract_address.to_field(), nullifier],\n DOM_SEP__SILOED_NULLIFIER,\n )\n}\n\npub fn create_protocol_nullifier(tx_request: TxRequest) -> Scoped> {\n // The protocol nullifier is ascribed a special side-effect counter of 1. No other side-effect\n // can have counter 1 (see `validate_as_first_call` for that assertion).\n Nullifier { value: tx_request.hash(), note_hash: 0 }.count(1).scope(\n NULL_MSG_SENDER_CONTRACT_ADDRESS,\n )\n}\n\npub fn compute_log_tag(raw_tag: Field, dom_sep: u32) -> Field {\n poseidon2_hash_with_separator([raw_tag], dom_sep)\n}\n\npub fn compute_siloed_private_log_first_field(\n contract_address: AztecAddress,\n field: Field,\n) -> Field {\n poseidon2_hash_with_separator(\n [contract_address.to_field(), field],\n DOM_SEP__PRIVATE_LOG_FIRST_FIELD,\n )\n}\n\npub fn compute_siloed_private_log(contract_address: AztecAddress, log: PrivateLog) -> PrivateLog {\n let mut fields = log.fields;\n fields[0] = compute_siloed_private_log_first_field(contract_address, fields[0]);\n PrivateLog::new(fields, log.length)\n}\n\npub fn compute_contract_class_log_hash(log: [Field; CONTRACT_CLASS_LOG_SIZE_IN_FIELDS]) -> Field {\n poseidon2_hash(log)\n}\n\npub fn compute_app_siloed_secret_key(\n master_secret_key: EmbeddedCurveScalar,\n app_address: AztecAddress,\n key_type_domain_separator: Field,\n) -> Field {\n poseidon2_hash_with_separator(\n [master_secret_key.hi, master_secret_key.lo, app_address.to_field()],\n key_type_domain_separator,\n )\n}\n\npub fn compute_l2_to_l1_message_hash(\n message: Scoped,\n rollup_version_id: Field,\n chain_id: Field,\n) -> Field {\n let contract_address_bytes: [u8; 32] = message.contract_address.to_field().to_be_bytes();\n let recipient_bytes: [u8; 20] = message.inner.recipient.to_be_bytes();\n let content_bytes: [u8; 32] = message.inner.content.to_be_bytes();\n let rollup_version_id_bytes: [u8; 32] = rollup_version_id.to_be_bytes();\n let chain_id_bytes: [u8; 32] = chain_id.to_be_bytes();\n\n let mut bytes: [u8; 148] = std::mem::zeroed();\n for i in 0..32 {\n bytes[i] = contract_address_bytes[i];\n bytes[i + 32] = rollup_version_id_bytes[i];\n // 64 - 84 are for recipient.\n bytes[i + 84] = chain_id_bytes[i];\n bytes[i + 116] = content_bytes[i];\n }\n\n for i in 0..20 {\n bytes[64 + i] = recipient_bytes[i];\n }\n\n sha256_to_field(bytes)\n}\n\n// TODO: consider a variant that enables domain separation with a u32 (we seem to have standardised u32s for domain separators)\n/// Computes sha256 hash of 2 input fields.\n///\n/// @returns A truncated field (i.e., the first byte is always 0).\npub fn accumulate_sha256(v0: Field, v1: Field) -> Field {\n // Concatenate two fields into 32 x 2 = 64 bytes\n let v0_as_bytes: [u8; 32] = v0.to_be_bytes();\n let v1_as_bytes: [u8; 32] = v1.to_be_bytes();\n let hash_input_flattened = v0_as_bytes.concat(v1_as_bytes);\n\n sha256_to_field(hash_input_flattened)\n}\n\npub fn poseidon2_hash(inputs: [Field; N]) -> Field {\n poseidon::poseidon2::Poseidon2::hash(inputs, N)\n}\n\n#[no_predicates]\npub fn poseidon2_hash_with_separator(inputs: [Field; N], separator: T) -> Field\nwhere\n T: ToField,\n{\n let inputs_with_separator = [separator.to_field()].concat(inputs);\n poseidon2_hash(inputs_with_separator)\n}\n\n/// Computes a Poseidon2 hash over a dynamic-length subarray of the given input.\n/// Only the first `in_len` fields of `input` are absorbed; any remaining fields are ignored.\n/// The caller is responsible for ensuring that the input is padded with zeros if required.\n#[no_predicates]\npub fn poseidon2_hash_subarray(input: [Field; N], in_len: u32) -> Field {\n let mut sponge = poseidon2_absorb_in_chunks(input, in_len);\n sponge.squeeze()\n}\n\n// This function is unconstrained because it is intended to be used in unconstrained context only as\n// in constrained contexts it would be too inefficient.\npub unconstrained fn poseidon2_hash_with_separator_bounded_vec(\n inputs: BoundedVec,\n separator: T,\n) -> Field\nwhere\n T: ToField,\n{\n let in_len = inputs.len() + 1;\n let iv: Field = (in_len as Field) * TWO_POW_64;\n let mut sponge = Poseidon2Sponge::new(iv);\n sponge.absorb(separator.to_field());\n\n for i in 0..inputs.len() {\n sponge.absorb(inputs.get(i));\n }\n\n sponge.squeeze()\n}\n\n#[no_predicates]\npub fn poseidon2_hash_bytes(inputs: [u8; N]) -> Field {\n let mut fields = [0; (N + 30) / 31];\n let mut field_index = 0;\n let mut current_field = [0; 31];\n for i in 0..inputs.len() {\n let index = i % 31;\n current_field[index] = inputs[i];\n if index == 30 {\n fields[field_index] = field_from_bytes(current_field, false);\n current_field = [0; 31];\n field_index += 1;\n }\n }\n if field_index != fields.len() {\n fields[field_index] = field_from_bytes(current_field, false);\n }\n poseidon2_hash(fields)\n}\n\n#[test]\nfn subarray_hash_matches_fixed() {\n let mut values_to_hash = [3; 17];\n let mut padded = values_to_hash.concat([0; 11]);\n let subarray_hash = poseidon2_hash_subarray(padded, values_to_hash.len());\n\n // Hash the entire values_to_hash.\n let fixed_len_hash = poseidon::poseidon2::Poseidon2::hash(values_to_hash, values_to_hash.len());\n\n assert_eq(subarray_hash, fixed_len_hash);\n}\n\n#[test]\nfn subarray_hash_matches_variable() {\n let mut values_to_hash = [3; 17];\n let mut padded = values_to_hash.concat([0; 11]);\n let subarray_hash = poseidon2_hash_subarray(padded, values_to_hash.len());\n\n // Hash up to values_to_hash.len() fields of the padded array.\n let variable_len_hash = poseidon::poseidon2::Poseidon2::hash(padded, values_to_hash.len());\n\n assert_eq(subarray_hash, variable_len_hash);\n}\n\n#[test]\nfn smoke_sha256_to_field() {\n let full_buffer = [\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,\n 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,\n 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,\n 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,\n 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,\n 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n ];\n let result = sha256_to_field(full_buffer);\n\n assert(result == 0x448ebbc9e1a31220a2f3830c18eef61b9bd070e5084b7fa2a359fe729184c7);\n\n // to show correctness of the current ver (truncate one byte) vs old ver (mod full bytes):\n let result_bytes = sha256::digest(full_buffer);\n let truncated_field = crate::utils::field::field_from_bytes_32_trunc(result_bytes);\n assert(truncated_field == result);\n let mod_res = result + (result_bytes[31] as Field);\n assert(mod_res == 0x448ebbc9e1a31220a2f3830c18eef61b9bd070e5084b7fa2a359fe729184e0);\n}\n\n#[test]\nfn unique_siloed_note_hash_matches_typescript() {\n let inner_note_hash = 1;\n let contract_address = AztecAddress::from_field(2);\n let first_nullifier = 3;\n let note_index_in_tx = 4;\n\n let siloed_note_hash = compute_siloed_note_hash(contract_address, inner_note_hash);\n let siloed_note_hash_from_ts =\n 0x1986a4bea3eddb1fff917d629a13e10f63f514f401bdd61838c6b475db949169;\n assert_eq(siloed_note_hash, siloed_note_hash_from_ts);\n\n let nonce: Field = compute_note_hash_nonce(first_nullifier, note_index_in_tx);\n let note_hash_nonce_from_ts =\n 0x28e7799791bf066a57bb51fdd0fbcaf3f0926414314c7db515ea343f44f5d58b;\n assert_eq(nonce, note_hash_nonce_from_ts);\n\n let unique_siloed_note_hash_from_nonce = compute_unique_note_hash(nonce, siloed_note_hash);\n let unique_siloed_note_hash = compute_note_nonce_and_unique_note_hash(\n siloed_note_hash,\n first_nullifier,\n note_index_in_tx,\n );\n assert_eq(unique_siloed_note_hash_from_nonce, unique_siloed_note_hash);\n\n let unique_siloed_note_hash_from_ts =\n 0x29949aef207b715303b24639737c17fbfeb375c1d965ecfa85c7e4f0febb7d16;\n assert_eq(unique_siloed_note_hash, unique_siloed_note_hash_from_ts);\n}\n\n#[test]\nfn siloed_nullifier_matches_typescript() {\n let contract_address = AztecAddress::from_field(123);\n let nullifier = 456;\n\n let res = compute_siloed_nullifier(contract_address, nullifier);\n\n let siloed_nullifier_from_ts =\n 0x169b50336c1f29afdb8a03d955a81e485f5ac7d5f0b8065673d1e407e5877813;\n\n assert_eq(res, siloed_nullifier_from_ts);\n}\n\n#[test]\nfn siloed_private_log_first_field_matches_typescript() {\n let contract_address = AztecAddress::from_field(123);\n let field = 456;\n let res = compute_siloed_private_log_first_field(contract_address, field);\n\n let siloed_private_log_first_field_from_ts =\n 0x29480984f7b9257fded523d50addbcfc8d1d33adcf2db73ef3390a8fd5cdffaa;\n\n assert_eq(res, siloed_private_log_first_field_from_ts);\n}\n\n#[test]\nfn empty_l2_to_l1_message_hash_matches_typescript() {\n // All zeroes\n let res = compute_l2_to_l1_message_hash(\n L2ToL1Message { recipient: EthAddress::zero(), content: 0 }.scope(AztecAddress::from_field(\n 0,\n )),\n 0,\n 0,\n );\n\n let empty_l2_to_l1_msg_hash_from_ts =\n 0x003b18c58c739716e76429634a61375c45b3b5cd470c22ab6d3e14cee23dd992;\n\n assert_eq(res, empty_l2_to_l1_msg_hash_from_ts);\n}\n\n#[test]\nfn l2_to_l1_message_hash_matches_typescript() {\n let message = L2ToL1Message { recipient: EthAddress::from_field(1), content: 2 }.scope(\n AztecAddress::from_field(3),\n );\n let version = 4;\n let chainId = 5;\n\n let hash = compute_l2_to_l1_message_hash(message, version, chainId);\n\n // The following value was generated by `yarn-project/stdlib/src/hash/hash.test.ts`\n let l2_to_l1_message_hash_from_ts =\n 0x0081edf209e087ad31b3fd24263698723d57190bd1d6e9fe056fc0c0a68ee661;\n\n assert_eq(hash, l2_to_l1_message_hash_from_ts);\n}\n\n#[test]\nunconstrained fn poseidon2_hash_with_separator_bounded_vec_matches_non_bounded_vec_version() {\n let inputs = BoundedVec::::from_array([1, 2, 3]);\n let separator = 42;\n\n // Hash using bounded vec version\n let bounded_result = poseidon2_hash_with_separator_bounded_vec(inputs, separator);\n\n // Hash using regular version\n let regular_result = poseidon2_hash_with_separator([1, 2, 3], separator);\n\n // Results should match\n assert_eq(bounded_result, regular_result);\n}\n" }, "373": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/logging.nr", "source": "// Log levels matching the JS logger:\n\n// global SILENT_LOG_LEVEL: u8 = 0;\nglobal FATAL_LOG_LEVEL: u8 = 1;\nglobal ERROR_LOG_LEVEL: u8 = 2;\nglobal WARN_LOG_LEVEL: u8 = 3;\nglobal INFO_LOG_LEVEL: u8 = 4;\nglobal VERBOSE_LOG_LEVEL: u8 = 5;\nglobal DEBUG_LOG_LEVEL: u8 = 6;\nglobal TRACE_LOG_LEVEL: u8 = 7;\n\n// --- Per-level log functions (no format args) ---\n\npub fn fatal_log(msg: str) {\n fatal_log_format(msg, []);\n}\n\npub fn error_log(msg: str) {\n error_log_format(msg, []);\n}\n\npub fn warn_log(msg: str) {\n warn_log_format(msg, []);\n}\n\npub fn info_log(msg: str) {\n info_log_format(msg, []);\n}\n\npub fn verbose_log(msg: str) {\n verbose_log_format(msg, []);\n}\n\npub fn debug_log(msg: str) {\n debug_log_format(msg, []);\n}\n\npub fn trace_log(msg: str) {\n trace_log_format(msg, []);\n}\n\n// --- Per-level log functions (with format args) ---\n\npub fn fatal_log_format(msg: str, args: [Field; N]) {\n log_format(FATAL_LOG_LEVEL, msg, args);\n}\n\npub fn error_log_format(msg: str, args: [Field; N]) {\n log_format(ERROR_LOG_LEVEL, msg, args);\n}\n\npub fn warn_log_format(msg: str, args: [Field; N]) {\n log_format(WARN_LOG_LEVEL, msg, args);\n}\n\npub fn info_log_format(msg: str, args: [Field; N]) {\n log_format(INFO_LOG_LEVEL, msg, args);\n}\n\npub fn verbose_log_format(msg: str, args: [Field; N]) {\n log_format(VERBOSE_LOG_LEVEL, msg, args);\n}\n\npub fn debug_log_format(msg: str, args: [Field; N]) {\n log_format(DEBUG_LOG_LEVEL, msg, args);\n}\n\npub fn trace_log_format(msg: str, args: [Field; N]) {\n log_format(TRACE_LOG_LEVEL, msg, args);\n}\n\nfn log_format(log_level: u8, msg: str, args: [Field; N]) {\n // Safety: This oracle call returns nothing: we only call it for its side effects. It is therefore always safe\n // to call.\n unsafe { log_oracle_wrapper(log_level, msg, args) };\n}\n\nunconstrained fn log_oracle_wrapper(\n log_level: u8,\n msg: str,\n args: [Field; N],\n) {\n log_oracle(log_level, msg, N, args);\n}\n\n// While the length parameter might seem unnecessary given that we have N, we keep it around because at the AVM\n// bytecode level we want to support non-comptime-known lengths for such opcodes, even if Noir code will not generally\n// take that route. The AVM transpiler maps this oracle to the DEBUGLOG opcode, which reads the fields size from memory.\n#[oracle(aztec_utl_log)]\nunconstrained fn log_oracle(\n log_level: u8,\n msg: str,\n length: u32,\n args: [Field; N],\n) {}\n" }, "375": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/merkle_tree/indexed_tree/low_leaf.nr", "source": "use crate::merkle_tree::leaf_preimage::IndexedTreeLeafPreimage;\n\n/// Check if the key is in range of the low leaf.\n///\n/// Returns:\n/// - is_valid: `true` if the key is in range of the low leaf, `false` otherwise.\n/// - is_greater_than_low: `true` if the key is greater than the low leaf's key, `false` otherwise.\n/// - is_less_than_next: `true` if the key is less than the low leaf's next key, `false` otherwise.\npub fn is_valid_low_leaf(\n key: Field,\n low_leaf_preimage: LeafPreimage,\n) -> (bool, bool, bool)\nwhere\n LeafPreimage: IndexedTreeLeafPreimage,\n{\n let low_key = low_leaf_preimage.get_key();\n let next_key = low_leaf_preimage.get_next_key();\n\n let is_greater_than_low = low_key.lt(key);\n let is_less_than_next = key.lt(next_key) | low_leaf_preimage.points_to_infinity();\n let is_valid = is_greater_than_low & is_less_than_next;\n\n (is_valid, is_greater_than_low, is_less_than_next)\n}\n\nmod tests {\n use crate::merkle_tree::test_utils::TestLeafPreimage;\n use super::is_valid_low_leaf;\n\n #[test]\n fn valid_low_leaf() {\n let key = 12;\n let leaf = TestLeafPreimage { value: 11, next_value: 13 };\n assert_eq(is_valid_low_leaf(key, leaf), (true, true, true));\n }\n\n #[test]\n fn empty_low_leaf() {\n // An all-zero low leaf should be valid. It could be used as the first dummy leaf in a tree.\n // It's not possible to prove against an empty leaf at an uninitialized index.\n // The membership check will fail because the leaf value hash(0, 0) is not 0.\n let key = 12;\n let leaf = TestLeafPreimage { value: 0, next_value: 0 };\n assert_eq(is_valid_low_leaf(key, leaf), (true, true, true));\n }\n\n #[test]\n fn invalid_low_leaf_larger_than_key() {\n let key = 12;\n let leaf = TestLeafPreimage { value: 13, next_value: 15 };\n assert_eq(is_valid_low_leaf(key, leaf), (false, false, true));\n }\n\n #[test]\n fn invalid_low_leaf_equal_to_key() {\n let key = 12;\n let leaf = TestLeafPreimage { value: 12, next_value: 15 };\n assert_eq(is_valid_low_leaf(key, leaf), (false, false, true));\n }\n\n #[test]\n fn invalid_low_leaf_next_key_smaller_than_key() {\n let key = 12;\n let leaf = TestLeafPreimage { value: 9, next_value: 11 };\n assert_eq(is_valid_low_leaf(key, leaf), (false, true, false));\n }\n\n #[test]\n fn invalid_low_leaf_next_key_equal_to_key() {\n // Key exactly equals next_key - should be invalid (key must be strictly less than next)\n let key = 15;\n let leaf = TestLeafPreimage { value: 10, next_value: 15 };\n assert_eq(is_valid_low_leaf(key, leaf), (false, true, false));\n }\n\n #[test]\n fn valid_low_leaf_key_one_more_than_low() {\n // Minimum valid key: exactly one more than low_key\n let key = 11;\n let leaf = TestLeafPreimage { value: 10, next_value: 20 };\n assert_eq(is_valid_low_leaf(key, leaf), (true, true, true));\n }\n\n #[test]\n fn valid_low_leaf_key_one_less_than_next() {\n // Maximum valid key: exactly one less than next_key\n let key = 19;\n let leaf = TestLeafPreimage { value: 10, next_value: 20 };\n assert_eq(is_valid_low_leaf(key, leaf), (true, true, true));\n }\n\n #[test]\n fn invalid_key_zero_cannot_be_in_range() {\n // Key 0 can never be \"greater than\" any non-negative low_key\n // This is important for indexed tree security - 0 is the sentinel\n let key = 0;\n let leaf = TestLeafPreimage { value: 0, next_value: 100 };\n // is_greater_than_low = 0.lt(0) = false\n // Even though 0 < 100, the key is not greater than low_key\n assert_eq(is_valid_low_leaf(key, leaf), (false, false, true));\n }\n\n #[test]\n fn valid_max_leaf_points_to_infinity() {\n // A max leaf (points to infinity) is valid for any key greater than its key\n // next_value: 0 means points_to_infinity() returns true\n let key = 100;\n let leaf = TestLeafPreimage { value: 50, next_value: 0 };\n assert_eq(is_valid_low_leaf(key, leaf), (true, true, true));\n }\n\n #[test]\n fn invalid_max_leaf_key_less_than_low() {\n // Even if leaf points to infinity, key must still be greater than low_key\n let key = 30;\n let leaf = TestLeafPreimage { value: 50, next_value: 0 };\n // is_greater_than_low = 50.lt(30) = false\n // is_less_than_next = 30.lt(0) | true = true (points to infinity)\n // is_valid = false & true = false\n assert_eq(is_valid_low_leaf(key, leaf), (false, false, true));\n }\n\n #[test]\n fn invalid_max_leaf_key_equal_to_low() {\n // Key equals low_key on a max leaf - should be invalid\n let key = 50;\n let leaf = TestLeafPreimage { value: 50, next_value: 0 };\n assert_eq(is_valid_low_leaf(key, leaf), (false, false, true));\n }\n}\n" }, "378": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/merkle_tree/membership.nr", "source": "use crate::{\n merkle_tree::{\n indexed_tree::low_leaf::is_valid_low_leaf,\n leaf_preimage::IndexedTreeLeafPreimage,\n root::{root_from_sibling_path, root_from_sibling_path_with_hasher},\n },\n traits::{Deserialize, Empty, Serialize},\n};\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct MembershipWitness {\n pub leaf_index: Field,\n pub sibling_path: [Field; N],\n}\n\nimpl Empty for MembershipWitness {\n fn empty() -> Self {\n MembershipWitness { leaf_index: 0, sibling_path: [0; N] }\n }\n}\n\n/// Check whether a leaf exists in the tree with the given `root`.\n/// `membership_witness` is a hint to prove that the `leaf` exists in the tree with the given `root`.\n///\n/// Returns:\n/// - bool: `true` if the leaf exists in the tree, `false` otherwise.\npub fn check_membership(\n leaf: Field,\n membership_witness: MembershipWitness,\n root: Field,\n) -> bool {\n let calculated_root = root_from_sibling_path(\n leaf,\n membership_witness.leaf_index,\n membership_witness.sibling_path,\n );\n calculated_root == root\n}\n\n/// Check whether a leaf exists in the tree with the given `root`.\n/// Same as `check_membership` but uses a custom hasher.\n/// `membership_witness` is a hint to prove that the `leaf` exists in the tree with the given `root`.\n///\n/// Returns:\n/// - bool: `true` if the leaf exists in the tree, `false` otherwise.\npub fn check_membership_with_hasher(\n leaf: Field,\n membership_witness: MembershipWitness,\n root: Field,\n hasher: fn(Field, Field) -> Field,\n) -> bool {\n let calculated_root = root_from_sibling_path_with_hasher(\n leaf,\n membership_witness.leaf_index,\n membership_witness.sibling_path,\n hasher,\n );\n calculated_root == root\n}\n\n/// Check a key's non-existence in the indexed tree with the given `tree_root`.\n/// `low_leaf_preimage` is the preimage for the low leaf of the key. The key should be larger than the low leaf's key,\n/// and smaller than the low leaf's next key.\n/// `low_leaf_membership_witness` is a hint to prove that the low leaf exists in the tree with the given `tree_root`.\n///\n/// Returns:\n/// - non_existence: `true` if the key does not exist in the tree, `false` otherwise.\n/// - is_valid_low_leaf: `true` if the key is in range of the low leaf, `false` otherwise.\n/// - low_leaf_exists: `true` if the low leaf exists in the tree, `false` otherwise.\npub fn check_non_membership(\n key: Field,\n low_leaf_preimage: LEAF_PREIMAGE,\n low_leaf_membership_witness: MembershipWitness,\n tree_root: Field,\n) -> (bool, bool, bool)\nwhere\n LEAF_PREIMAGE: IndexedTreeLeafPreimage,\n{\n let (is_valid_low_leaf_to_key, _, _) = is_valid_low_leaf(key, low_leaf_preimage);\n\n let low_leaf_exists = check_membership(\n low_leaf_preimage.as_leaf(),\n low_leaf_membership_witness,\n tree_root,\n );\n\n let non_existence = is_valid_low_leaf_to_key & low_leaf_exists;\n\n (non_existence, is_valid_low_leaf_to_key, low_leaf_exists)\n}\n\n/// Check either membership or non-membership depending on the value of `exists`:\n/// - If `exists` == true, `key` is in the tree. `leaf_preimage` is for the key itself.\n/// - If `exists` == false, `key` is not in the tree. `leaf_preimage` is for the low leaf of the key.\n/// `membership_witness` is the hint to prove that the `leaf_preimage` exists in the tree with the given `tree_root`.\n///\n/// Returns:\n/// - is_valid: `true` if the membership (`exists == true`) or non-membership (`exists == false`) check succeeds,\n/// `false` otherwise.\n/// - is_expected_leaf: `true` if the leaf preimage is the expected matching leaf (`exists == true`) or low leaf\n/// (`exists == false`) for the key, `false` otherwise.\n/// - is_leaf_in_tree: `true` if the leaf preimage exists in the tree, `false` otherwise.\npub fn conditionally_check_membership(\n key: Field,\n exists: bool,\n leaf_preimage: LEAF_PREIMAGE,\n membership_witness: MembershipWitness,\n tree_root: Field,\n) -> (bool, bool, bool)\nwhere\n LEAF_PREIMAGE: IndexedTreeLeafPreimage,\n{\n let is_matching_leaf = key == leaf_preimage.get_key();\n let (is_low_leaf, _, _) = is_valid_low_leaf(key, leaf_preimage);\n let is_expected_leaf = if exists {\n is_matching_leaf\n } else {\n is_low_leaf\n };\n\n let is_leaf_in_tree = check_membership(leaf_preimage.as_leaf(), membership_witness, tree_root);\n\n let is_valid = is_expected_leaf & is_leaf_in_tree;\n\n (is_valid, is_expected_leaf, is_leaf_in_tree)\n}\n\nmod tests {\n use crate::merkle_tree::{\n leaf_preimage::LeafPreimage,\n test_utils::{SingleSubtreeMerkleTree, TestLeafPreimage},\n };\n use super::{\n check_membership, check_non_membership, conditionally_check_membership, MembershipWitness,\n };\n\n global leaf_preimages: [TestLeafPreimage; 4] = [\n TestLeafPreimage { value: 20, next_value: 30 },\n TestLeafPreimage { value: 40, next_value: 0 },\n TestLeafPreimage { value: 10, next_value: 20 },\n TestLeafPreimage { value: 30, next_value: 40 },\n ];\n\n fn build_tree() -> SingleSubtreeMerkleTree<4, 2, 3> {\n SingleSubtreeMerkleTree::new(leaf_preimages.map(|leaf_preimage: TestLeafPreimage| {\n leaf_preimage.as_leaf()\n }))\n }\n\n fn check_membership_at_index(leaf_index: Field, leaf: Field) -> bool {\n let tree = build_tree();\n let tree_root = tree.get_root();\n\n check_membership(\n leaf,\n MembershipWitness { leaf_index, sibling_path: tree.get_sibling_path(leaf_index) },\n tree_root,\n )\n }\n\n fn check_non_membership_at_index(low_leaf_index: Field, key: Field) -> (bool, bool, bool) {\n let tree = build_tree();\n let tree_root = tree.get_root();\n let leaf_preimage = if low_leaf_index.lt(leaf_preimages.len() as Field) {\n leaf_preimages[low_leaf_index as u32]\n } else {\n TestLeafPreimage { value: 0, next_value: 0 }\n };\n\n check_non_membership(\n key,\n leaf_preimage,\n MembershipWitness {\n leaf_index: low_leaf_index,\n sibling_path: tree.get_sibling_path(low_leaf_index),\n },\n tree_root,\n )\n }\n\n fn conditionally_check_membership_at_index(\n exists: bool,\n low_leaf_index: Field,\n key: Field,\n ) -> (bool, bool, bool) {\n let tree = build_tree();\n let tree_root = tree.get_root();\n let leaf_preimage = if low_leaf_index.lt(leaf_preimages.len() as Field) {\n leaf_preimages[low_leaf_index as u32]\n } else {\n TestLeafPreimage { value: 0, next_value: 0 }\n };\n\n conditionally_check_membership(\n key,\n exists,\n leaf_preimage,\n MembershipWitness {\n leaf_index: low_leaf_index,\n sibling_path: tree.get_sibling_path(low_leaf_index),\n },\n tree_root,\n )\n }\n\n #[test]\n fn test_check_membership() {\n assert_eq(check_membership_at_index(0, leaf_preimages[0].as_leaf()), true);\n assert_eq(check_membership_at_index(2, leaf_preimages[2].as_leaf()), true);\n }\n\n #[test]\n fn test_check_membership_false_wrong_leaf() {\n assert_eq(check_membership_at_index(0, leaf_preimages[1].as_leaf()), false);\n assert_eq(check_membership_at_index(2, leaf_preimages[0].as_leaf()), false);\n }\n\n #[test]\n fn test_check_membership_false_wrong_root() {\n let tree = build_tree();\n let tree_root = 56;\n\n let res = check_membership(\n leaf_preimages[0].as_leaf(),\n MembershipWitness { leaf_index: 0, sibling_path: tree.get_sibling_path(0) },\n tree_root,\n );\n assert_eq(res, false);\n }\n\n #[test]\n fn test_check_non_membership() {\n assert_eq(check_non_membership_at_index(0, 25), (true, true, true));\n }\n\n #[test]\n fn test_check_non_membership_key_greater_than_max_leaf_in_tree() {\n assert_eq(check_non_membership_at_index(1, 45), (true, true, true));\n }\n\n #[test]\n fn test_check_non_membership_failed_low_leaf_larger_than_key() {\n assert_eq(check_non_membership_at_index(3, 25), (false, false, true));\n }\n\n #[test]\n fn test_check_non_membership_failed_next_key_smaller_than_key() {\n assert_eq(check_non_membership_at_index(2, 25), (false, false, true));\n }\n\n #[test]\n fn test_check_non_membership_low_leaf_not_exist() {\n let tree = build_tree();\n let tree_root = tree.get_root();\n\n let fake_leaf = TestLeafPreimage { value: 50, next_value: 60 };\n assert_eq(\n check_non_membership(\n 55,\n fake_leaf,\n MembershipWitness { leaf_index: 1, sibling_path: tree.get_sibling_path(1) },\n tree_root,\n ),\n (false, true, false),\n );\n }\n\n #[test]\n fn test_conditionally_check_membership_exists() {\n assert_eq(\n conditionally_check_membership_at_index(true, 1, leaf_preimages[1].get_key()),\n (true, true, true),\n );\n }\n\n #[test]\n fn test_conditionally_check_membership_not_exists() {\n assert_eq(\n conditionally_check_membership_at_index(false, 1, leaf_preimages[1].get_key() + 1),\n (true, true, true),\n );\n }\n\n #[test]\n fn test_conditionally_check_membership_exists_value_mismatch() {\n assert_eq(\n conditionally_check_membership_at_index(true, 1, leaf_preimages[1].get_key() + 1),\n (false, false, true),\n );\n }\n\n #[test]\n fn test_conditionally_check_membership_failed_not_exists_wrong_low_leaf() {\n assert_eq(conditionally_check_membership_at_index(false, 3, 25), (false, false, true));\n }\n\n #[test]\n fn test_conditionally_check_membership_failed_not_exists_wrong_next_key() {\n assert_eq(conditionally_check_membership_at_index(false, 2, 25), (false, false, true));\n }\n\n #[test]\n fn test_conditionally_check_membership_failed_exists_invalid_leaf() {\n let tree = build_tree();\n let tree_root = tree.get_root();\n let fake_leaf = TestLeafPreimage { value: 50, next_value: 60 };\n let exists = true;\n assert_eq(\n conditionally_check_membership(\n 50,\n exists,\n fake_leaf,\n MembershipWitness { leaf_index: 1, sibling_path: tree.get_sibling_path(1) },\n tree_root,\n ),\n (false, true, false),\n );\n }\n\n #[test]\n fn test_conditionally_check_membership_failed_not_exists_invalid_leaf() {\n let tree = build_tree();\n let tree_root = tree.get_root();\n let fake_leaf = TestLeafPreimage { value: 50, next_value: 60 };\n let exists = false;\n assert_eq(\n conditionally_check_membership(\n 55,\n exists,\n fake_leaf,\n MembershipWitness { leaf_index: 1, sibling_path: tree.get_sibling_path(1) },\n tree_root,\n ),\n (false, true, false),\n );\n }\n}\n" }, "379": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/merkle_tree/merkle_tree.nr", "source": "use crate::{\n hash::{accumulate_sha256, poseidon2_hash},\n traits::Empty,\n utils::math::is_power_of_2_u32,\n};\n\npub fn merkle_hash(left: Field, right: Field) -> Field {\n poseidon2_hash([left, right])\n}\n\npub fn sha_merkle_hash(left: Field, right: Field) -> Field {\n accumulate_sha256(left, right)\n}\n\n#[derive(Eq)]\npub struct MerkleTree {\n pub leaves: [Field; N],\n pub nodes: [Field; N - 1],\n}\n\nimpl Empty for MerkleTree {\n fn empty() -> Self {\n MerkleTree { leaves: [0; N], nodes: [0; N - 1] }\n }\n}\n\nimpl MerkleTree {\n pub fn new(leaves: [Field; N]) -> Self {\n let nodes = compute_merkle_tree_nodes(leaves, merkle_hash);\n MerkleTree { leaves, nodes }\n }\n\n pub fn new_sha(leaves: [Field; N]) -> Self {\n let nodes = compute_merkle_tree_nodes(leaves, sha_merkle_hash);\n MerkleTree { leaves, nodes }\n }\n\n pub fn get_root(self) -> Field {\n self.nodes[N - 2]\n }\n\n pub fn get_sibling_path(self, leaf_index: u32) -> [Field; K] {\n assert_eq(2.pow_32(K as Field), N as Field, \"Invalid path length\");\n\n let mut path = [0; K];\n let mut current_index = leaf_index;\n let mut subtree_width = N;\n\n let mut current_sibling_index = sibling_index(current_index);\n\n path[0] = self.leaves[current_sibling_index];\n\n let mut subtree_offset: u32 = 0;\n\n for i in 1..K {\n current_index = current_index / 2;\n subtree_width = subtree_width / 2;\n\n current_sibling_index = sibling_index(current_index);\n\n path[i] = self.nodes[subtree_offset + current_sibling_index];\n\n subtree_offset += subtree_width;\n }\n\n path\n }\n}\n\npub fn sibling_index(index: u32) -> u32 {\n if index % 2 == 0 {\n index + 1\n } else {\n index - 1\n }\n}\n\npub fn compute_merkle_tree_nodes(\n leaves: [Field; N],\n hasher: fn(Field, Field) -> Field,\n) -> [Field; N - 1] {\n // Note: `N` must be a power of 2.\n std::static_assert(is_power_of_2_u32(N), \"N must be a power of 2\");\n std::static_assert(N != 1, \"2 must divide N\");\n\n let mut nodes = [0; N - 1];\n\n let total_nodes = N - 1;\n let half_size = N / 2;\n\n // Hash base layer.\n for i in 0..half_size {\n nodes[i] = hasher(leaves[2 * i], leaves[2 * i + 1]);\n }\n\n // Hash the other layers.\n for i in 0..(total_nodes - half_size) {\n nodes[half_size + i] = hasher(nodes[2 * i], nodes[2 * i + 1]);\n }\n\n nodes\n}\n" }, "381": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/merkle_tree/root.nr", "source": "use crate::merkle_tree::merkle_tree::{compute_merkle_tree_nodes, merkle_hash, sha_merkle_hash};\n\n/// Calculate the Merkle tree root from the sibling path and leaf, using the default merkle hash.\npub fn root_from_sibling_path(\n leaf: Field,\n leaf_index: Field,\n sibling_path: [Field; N],\n) -> Field {\n root_from_sibling_path_with_hasher(leaf, leaf_index, sibling_path, merkle_hash)\n}\n\n/// Calculate the Merkle tree root from the sibling path and leaf, using a custom hasher.\n///\n/// The leaf is hashed with its sibling, the result is then hashed with the next sibling in the path. and so on.\n/// The last hash is the root.\npub fn root_from_sibling_path_with_hasher(\n leaf: Field,\n leaf_index: Field,\n sibling_path: [Field; N],\n hasher: fn(Field, Field) -> Field,\n) -> Field {\n let mut node = leaf;\n let indices: [u1; N] = leaf_index.to_le_bits();\n\n for i in 0..N {\n let (hash_left, hash_right) = if indices[i] == 1 {\n (sibling_path[i], node)\n } else {\n (node, sibling_path[i])\n };\n node = hasher(hash_left, hash_right);\n }\n node\n}\n\npub fn compute_tree_root(leaves: [Field; N]) -> Field {\n compute_tree_root_with_hasher(leaves, merkle_hash)\n}\n\npub fn compute_sha_tree_root(leaves: [Field; N]) -> Field {\n compute_tree_root_with_hasher(leaves, sha_merkle_hash)\n}\n\nfn compute_tree_root_with_hasher(\n leaves: [Field; N],\n hasher: fn(Field, Field) -> Field,\n) -> Field {\n compute_merkle_tree_nodes(leaves, hasher)[N - 2]\n}\n\npub fn compute_empty_tree_root() -> Field {\n compute_empty_tree_root_with_hasher::(merkle_hash)\n}\n\npub fn compute_empty_sha_tree_root() -> Field {\n compute_empty_tree_root_with_hasher::(sha_merkle_hash)\n}\n\npub fn compute_empty_tree_root_with_hasher(\n hasher: fn(Field, Field) -> Field,\n) -> Field {\n let mut hashes = [0; TreeHeight + 1];\n for i in 1..TreeHeight + 1 {\n hashes[i] = hasher(hashes[i - 1], hashes[i - 1]);\n }\n hashes[TreeHeight]\n}\n\n#[test]\nfn test_merkle_roots_match_typescript() {\n // The following hardcoded values are generated from yarn-project/foundation/src/trees/balanced_merkle_tree_root.test.ts\n\n let root = compute_tree_root([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);\n let expected_tree_root_from_ts =\n 0x1528946361c480e8dc1e9ae3f8c31c997625fa1ddeddc7db5ad0dce3ac58fc4c;\n assert_eq(root, expected_tree_root_from_ts);\n\n let empty_root = compute_tree_root([0; 16]);\n let expected_empty_root_from_ts =\n 0x2373ea368857ec7af97e7b470d705848e2bf93ed7bef142a490f2119bcf82d8e;\n assert_eq(empty_root, expected_empty_root_from_ts);\n\n let sha_root = compute_sha_tree_root([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);\n let expected_sha_root_from_ts =\n 0x00b007869b8a5e2a9b3b580a318e702cea04b2f5438f2e26743f545e4d1ecbdb;\n assert_eq(sha_root, expected_sha_root_from_ts);\n}\n\n#[test]\nfn test_empty_tree_root() {\n assert_eq(compute_empty_tree_root::<0>(), 0);\n\n assert_eq(\n compute_empty_tree_root::<1>(),\n 0x0b63a53787021a4a962a452c2921b3663aff1ffd8d5510540f8e659e782956f1,\n );\n\n assert_eq(\n compute_empty_tree_root::<2>(),\n 0x0e34ac2c09f45a503d2908bcb12f1cbae5fa4065759c88d501c097506a8b2290,\n );\n\n assert_eq(\n compute_empty_tree_root::<6>(),\n 0x01c28fe1059ae0237b72334700697bdf465e03df03986fe05200cadeda66bd76,\n );\n\n assert_eq(\n compute_empty_tree_root::<10>(),\n 0x2a775ea761d20435b31fa2c33ff07663e24542ffb9e7b293dfce3042eb104686,\n );\n}\n" }, "389": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/meta/mod.nr", "source": "pub use serde::serialization::{derive_deserialize, derive_serialize};\n\npub mod utils;\n\n/// Generates the generic parameter declarations for a struct's trait implementation.\n///\n/// This function takes a struct type definition and generates the generic parameter declarations\n/// that go after the `impl` keyword. For example, given a struct with generics `N: u32` and `T`,\n/// it generates ``.\n///\n/// # Parameters\n/// - `s`: The struct type definition to generate generic declarations for\n///\n/// # Returns\n/// A quoted code block containing the generic parameter declarations, or an empty quote if the struct\n/// has no generic parameters\n///\n/// # Example\n/// For a struct defined as:\n/// ```\n/// struct Container {\n/// items: [T; N],\n/// count: u32\n/// }\n/// ```\n///\n/// This function generates:\n/// ```\n/// \n/// ```\ncomptime fn get_generics_declarations(s: TypeDefinition) -> Quoted {\n let generics = s.generics();\n\n if generics.len() > 0 {\n let generics_declarations_items = generics\n .map(|(name, maybe_integer_typ)| {\n // The second item in the generics tuple is an Option of an integer type that is Some only if\n // the generic is numeric.\n if maybe_integer_typ.is_some() {\n // The generic is numeric, so we return a quote defined as e.g. \"let N: u32\"\n let integer_type = maybe_integer_typ.unwrap();\n quote {let $name: $integer_type}\n } else {\n // The generic is not numeric, so we return a quote containing the name of the generic (e.g. \"T\")\n quote {$name}\n }\n })\n .join(quote {,});\n quote {<$generics_declarations_items>}\n } else {\n // The struct doesn't have any generics defined, so we just return an empty quote.\n quote {}\n }\n}\n\n/// Generates the `where` clause for a trait implementation that constrains non-numeric generic type parameters.\n///\n/// This function takes a struct type definition and a trait name, and generates a `where` clause that\n/// requires all non-numeric generic type parameters to implement the specified trait.\n///\n/// # Parameters\n/// - `s`: The struct type definition to generate the where clause for\n/// - `trait_name`: The name of the trait that non-numeric generic parameters must implement\n///\n/// # Returns\n/// A quoted code block containing the where clause, or an empty quote if the struct has no non-numeric\n/// generic parameters\n///\n/// # Example\n/// For a struct defined as:\n/// ```\n/// struct Container {\n/// items: [T; N],\n/// count: u32\n/// }\n/// ```\n///\n/// And trait name \"Serialize\", this function generates:\n/// ```\n/// where T: Serialize\n/// ```\ncomptime fn get_where_trait_clause(s: TypeDefinition, trait_name: Quoted) -> Quoted {\n let generics = s.generics();\n\n // The second item in the generics tuple is an Option of an integer type that is Some only if the generic is\n // numeric.\n let non_numeric_generics =\n generics.filter(|(_, maybe_integer_typ)| maybe_integer_typ.is_none());\n\n if non_numeric_generics.len() > 0 {\n let non_numeric_generics_declarations =\n non_numeric_generics.map(|(name, _)| quote {$name: $trait_name}).join(quote {,});\n quote {where $non_numeric_generics_declarations}\n } else {\n // There are no non-numeric generics, so we return an empty quote.\n quote {}\n }\n}\n\n/// Generates a [`Packable`](crate::traits::Packable) trait implementation for a given struct `s`.\n///\n/// # Arguments\n/// * `s` - The struct type definition to generate the implementation for\n///\n/// # Returns\n/// A `Quoted` block containing the generated trait implementation\n///\n/// # Requirements\n/// Each struct member type must implement the `Packable` trait (it gets used in the generated code).\n///\n/// # Example\n/// For a struct like:\n/// ```\n/// struct MyStruct {\n/// x: AztecAddress,\n/// y: Field,\n/// }\n/// ```\n///\n/// This generates:\n/// ```\n/// impl Packable for MyStruct {\n/// let N: u32 = 2;\n///\n/// fn pack(self) -> [Field; 2] {\n/// let mut result: [Field; 2] = [0_Field; 2];\n/// let mut offset: u32 = 0_u32;\n/// let packed_member: [Field; 1] = self.x.pack();\n/// let packed_member_len: u32 = ::N;\n/// for i in 0_u32..packed_member_len {\n/// {\n/// result[i + offset] = packed_member[i];\n/// }\n/// }\n/// offset = offset + packed_member_len;\n/// let packed_member: [Field; 1] = self.y.pack();\n/// let packed_member_len: u32 = ::N;\n/// for i in 0_u32..packed_member_len {\n/// {\n/// result[i + offset] = packed_member[i];\n/// }\n/// }\n/// offset = offset + packed_member_len;\n/// result\n/// }\n///\n/// fn unpack(packed: [Field; 2]) -> Self {\n/// let mut offset: u32 = 0_u32;\n/// let mut member_fields: [Field; 1] = [0_Field; 1];\n/// for i in 0_u32..::N {\n/// member_fields[i] = packed[i + offset];\n/// }\n/// let x: AztecAddress = ::unpack(member_fields);\n/// offset = offset + ::N;\n/// let mut member_fields: [Field; 1] = [0_Field; 1];\n/// for i in 0_u32..::N {\n/// member_fields[i] = packed[i + offset];\n/// }\n/// let y: Field = ::unpack(member_fields);\n/// offset = offset + ::N;\n/// Self { x: x, y: y }\n/// }\n/// }\n/// ```\npub comptime fn derive_packable(s: TypeDefinition) -> Quoted {\n let typ = s.as_type();\n let nested_struct = typ.as_data_type().unwrap();\n let params = nested_struct.0.fields(nested_struct.1);\n\n // Generates the generic parameter declarations (to be placed after the `impl` keyword) and the `where` clause\n // for the `Packable` trait.\n let generics_declarations = get_generics_declarations(s);\n let where_packable_clause = get_where_trait_clause(s, quote {Packable});\n\n // The following will give us:\n // ::N + ::N + ...\n // (or 0 if the struct has no members)\n let right_hand_side_of_definition_of_n = if params.len() > 0 {\n params\n .map(|(_, param_type, _): (Quoted, Type, Quoted)| {\n quote {\n <$param_type as $crate::traits::Packable>::N\n }\n })\n .join(quote {+})\n } else {\n quote {0}\n };\n\n // For structs containing a single member, we can enhance performance by directly returning the packed member,\n // bypassing the need for loop-based array construction. While this optimization yields significant benefits in\n // Brillig where the loops are expected to not be optimized, it is not relevant in ACIR where the loops are\n // expected to be optimized away.\n let pack_function_body = if params.len() > 1 {\n // For multiple struct members, generate packing code that:\n // 1. Packs each member\n // 2. Copies the packed fields into the result array at the correct offset\n // 3. Updates the offset for the next member\n let packing_of_struct_members = params\n .map(|(param_name, param_type, _): (Quoted, Type, Quoted)| {\n quote {\n let packed_member = $crate::traits::Packable::pack(self.$param_name);\n let packed_member_len = <$param_type as $crate::traits::Packable>::N;\n for i in 0..packed_member_len {\n result[i + offset] = packed_member[i];\n }\n offset += packed_member_len;\n }\n })\n .join(quote {});\n\n quote {\n let mut result = [0; Self::N];\n let mut offset = 0;\n\n $packing_of_struct_members\n\n result\n }\n } else if params.len() == 1 {\n let param_name = params[0].0;\n quote {\n $crate::traits::Packable::pack(self.$param_name)\n }\n } else {\n quote {\n [0; Self::N]\n }\n };\n\n // For structs containing a single member, we can enhance performance by directly unpacking the input array,\n // bypassing the need for loop-based array construction. While this optimization yields significant benefits in\n // Brillig where the loops are expected to not be optimized, it is not relevant in ACIR where the loops are\n // expected to be optimized away.\n let unpack_function_body = if params.len() > 1 {\n // For multiple struct members, generate unpacking code that:\n // 1. Unpacks each member\n // 2. Copies packed fields into member array at correct offset\n // 3. Updates offset for next member\n let unpacking_of_struct_members = params\n .map(|(param_name, param_type, _): (Quoted, Type, Quoted)| {\n quote {\n let mut member_fields = [0; <$param_type as $crate::traits::Packable>::N];\n for i in 0..<$param_type as $crate::traits::Packable>::N {\n member_fields[i] = packed[i + offset];\n }\n let $param_name = <$param_type as $crate::traits::Packable>::unpack(member_fields);\n offset += <$param_type as $crate::traits::Packable>::N;\n }\n })\n .join(quote {});\n\n // We join the struct member names with a comma to be used in the `Self { ... }` syntax\n let struct_members = params\n .map(|(param_name, _, _): (Quoted, Type, Quoted)| quote { $param_name })\n .join(quote {,});\n\n quote {\n let mut offset = 0;\n $unpacking_of_struct_members\n Self { $struct_members }\n }\n } else if params.len() == 1 {\n let param_name = params[0].0;\n quote {\n Self { $param_name: $crate::traits::Packable::unpack(packed) }\n }\n } else {\n quote {\n Self {}\n }\n };\n\n quote {\n impl$generics_declarations $crate::traits::Packable for $typ\n $where_packable_clause\n {\n let N: u32 = $right_hand_side_of_definition_of_n;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n $pack_function_body\n }\n\n #[inline_always]\n fn unpack(packed: [Field; Self::N]) -> Self {\n $unpack_function_body\n }\n }\n }\n}\n\nmod test {\n use crate::traits::{Deserialize, Packable, Serialize};\n\n #[derive(Deserialize, Eq, Packable, Serialize)]\n pub struct Empty {}\n\n #[derive(Deserialize, Eq, Packable, Serialize)]\n pub struct Smol {\n a: Field,\n b: Field,\n }\n\n #[derive(Deserialize, Eq, Serialize)]\n pub struct HasArray {\n a: [Field; 2],\n b: bool,\n }\n\n #[derive(Deserialize, Eq, Serialize)]\n pub struct Fancier {\n a: Smol,\n b: [Field; 2],\n c: [u8; 3],\n d: str<16>,\n }\n\n #[derive(Deserialize, Eq, Packable, Serialize)]\n pub struct HasArrayWithGenerics {\n pub fields: [T; N],\n pub length: u32,\n }\n\n #[test]\n fn packable_on_empty() {\n let original = Empty {};\n let packed = original.pack();\n assert_eq(packed, [], \"Packed does not match empty array\");\n let unpacked = Empty::unpack(packed);\n assert_eq(unpacked, original, \"Unpacked does not match original\");\n }\n\n #[test]\n fn packable_on_smol() {\n let smol = Smol { a: 1, b: 2 };\n let serialized = smol.serialize();\n assert(serialized == [1, 2], serialized);\n\n // None of the struct members implements the `Packable` trait so the packed and serialized data should be the same\n let packed = smol.pack();\n assert_eq(packed, serialized, \"Packed does not match serialized\");\n }\n\n #[test]\n fn packable_on_contains_array_with_generics() {\n let struct_with_array_of_generics = HasArrayWithGenerics { fields: [1, 2, 3], length: 3 };\n let packed = struct_with_array_of_generics.pack();\n assert(packed == [1, 2, 3, 3], packed);\n\n let unpacked = HasArrayWithGenerics::unpack(packed);\n assert(unpacked == struct_with_array_of_generics);\n }\n\n}\n" }, "391": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/point.nr", "source": "pub use std::embedded_curve_ops::EmbeddedCurvePoint as Point;\nuse crate::{hash::poseidon2_hash, traits::{Deserialize, Empty, Hash, Packable, Serialize}};\n\npub global POINT_LENGTH: u32 = 3;\n\nimpl Hash for Point {\n fn hash(self) -> Field {\n poseidon2_hash(self.serialize())\n }\n}\n\nimpl Empty for Point {\n /// Note: Does not return a valid point on curve - instead represents an empty/\"unpopulated\" point struct (e.g.\n /// empty/unpopulated value in an array of points).\n fn empty() -> Self {\n Point { x: 0, y: 0, is_infinite: false }\n }\n}\n\npub fn validate_on_curve(p: Point) {\n // y^2 == x^3 - 17\n let x = p.x;\n let y = p.y;\n if p.is_infinite {\n // Assert the canonical representation of infinity\n assert_eq(x, 0, \"Point at infinity should have canonical representation (0 0)\");\n assert_eq(y, 0, \"Point at infinity should have canonical representation (0 0)\");\n } else {\n assert_eq(y * y, x * x * x - 17, \"Point not on curve\");\n }\n}\n\n// TODO(#11356): use compact representation here.\nimpl Packable for Point {\n let N: u32 = POINT_LENGTH;\n\n fn pack(self) -> [Field; Self::N] {\n self.serialize()\n }\n\n fn unpack(packed: [Field; Self::N]) -> Self {\n Self::deserialize(packed)\n }\n}\n\nmod tests {\n use super::validate_on_curve;\n use std::embedded_curve_ops::EmbeddedCurvePoint as Point;\n\n #[test]\n unconstrained fn test_validate_on_curve_generator() {\n // The generator point should be on the curve\n let generator = Point::generator();\n validate_on_curve(generator);\n }\n\n #[test]\n unconstrained fn test_validate_on_curve_infinity() {\n // Canonical infinite point (x=0, y=0) should pass\n let infinity = Point { x: 0, y: 0, is_infinite: true };\n validate_on_curve(infinity);\n }\n\n #[test(should_fail_with = \"Point not on curve\")]\n unconstrained fn test_validate_on_curve_invalid_point() {\n // A point not on the curve should fail\n let invalid = Point { x: 1, y: 1, is_infinite: false };\n validate_on_curve(invalid);\n }\n\n #[test(should_fail_with = \"Point at infinity should have canonical representation (0 0)\")]\n unconstrained fn test_validate_on_curve_infinity_non_canonical_x() {\n // Infinite point with non-zero x should fail\n let invalid_infinity = Point { x: 1, y: 0, is_infinite: true };\n validate_on_curve(invalid_infinity);\n }\n\n #[test(should_fail_with = \"Point at infinity should have canonical representation (0 0)\")]\n unconstrained fn test_validate_on_curve_infinity_non_canonical_y() {\n // Infinite point with non-zero y should fail\n let invalid_infinity = Point { x: 0, y: 1, is_infinite: true };\n validate_on_curve(invalid_infinity);\n }\n}\n" }, "392": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/poseidon2.nr", "source": "use crate::constants::TWO_POW_64;\nuse crate::traits::{Deserialize, Serialize};\nuse std::meta::derive;\n// NB: This is a clone of noir/noir-repo/noir_stdlib/src/hash/poseidon2.nr\n// It exists as we sometimes need to perform custom absorption, but the stdlib version\n// has a private absorb() method (it's also designed to just be a hasher)\n// Can be removed when standalone noir poseidon lib exists: See noir#6679\n\ncomptime global RATE: u32 = 3;\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct Poseidon2Sponge {\n pub cache: [Field; 3],\n pub state: [Field; 4],\n pub cache_size: u32,\n pub squeeze_mode: bool, // 0 => absorb, 1 => squeeze\n}\n\nimpl Poseidon2Sponge {\n #[no_predicates]\n pub fn hash(input: [Field; N], message_size: u32) -> Field {\n Poseidon2Sponge::hash_internal(input, message_size, message_size != N)\n }\n\n pub(crate) fn new(iv: Field) -> Poseidon2Sponge {\n let mut result =\n Poseidon2Sponge { cache: [0; 3], state: [0; 4], cache_size: 0, squeeze_mode: false };\n result.state[RATE] = iv;\n result\n }\n\n fn perform_duplex(&mut self) {\n // add the cache into sponge state\n for i in 0..RATE {\n // We effectively zero-pad the cache by only adding to the state\n // cache that is less than the specified `cache_size`\n if i < self.cache_size {\n self.state[i] += self.cache[i];\n }\n }\n self.state = std::hash::poseidon2_permutation(self.state, 4);\n }\n\n pub fn absorb(&mut self, input: Field) {\n assert(!self.squeeze_mode);\n if self.cache_size == RATE {\n // If we're absorbing, and the cache is full, apply the sponge permutation to compress the cache\n self.perform_duplex();\n self.cache[0] = input;\n self.cache_size = 1;\n } else {\n // If we're absorbing, and the cache is not full, add the input into the cache\n self.cache[self.cache_size] = input;\n self.cache_size += 1;\n }\n }\n\n pub fn squeeze(&mut self) -> Field {\n assert(!self.squeeze_mode);\n // If we're in absorb mode, apply sponge permutation to compress the cache.\n self.perform_duplex();\n self.squeeze_mode = true;\n\n // Pop one item off the top of the permutation and return it.\n self.state[0]\n }\n\n fn hash_internal(\n input: [Field; N],\n in_len: u32,\n is_variable_length: bool,\n ) -> Field {\n let iv: Field = (in_len as Field) * TWO_POW_64;\n let mut sponge = Poseidon2Sponge::new(iv);\n for i in 0..input.len() {\n if i < in_len {\n sponge.absorb(input[i]);\n }\n }\n\n sponge.squeeze()\n }\n}\n" }, "399": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/public_keys.nr", "source": "use crate::{\n address::public_keys_hash::PublicKeysHash,\n constants::{\n DEFAULT_IVPK_M_X, DEFAULT_IVPK_M_Y, DEFAULT_NPK_M_X, DEFAULT_NPK_M_Y, DEFAULT_OVPK_M_X,\n DEFAULT_OVPK_M_Y, DEFAULT_TPK_M_X, DEFAULT_TPK_M_Y, DOM_SEP__PUBLIC_KEYS_HASH,\n },\n hash::poseidon2_hash_with_separator,\n point::validate_on_curve,\n traits::{Deserialize, Hash, Serialize},\n};\n\nuse std::{default::Default, meta::derive};\nuse std::embedded_curve_ops::EmbeddedCurvePoint as Point;\n\npub trait ToPoint {\n fn to_point(self) -> Point;\n}\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct NpkM {\n pub inner: Point,\n}\n\nimpl ToPoint for NpkM {\n fn to_point(self) -> Point {\n self.inner\n }\n}\n\n// Note: If we store npk_m_hash directly we can remove this trait implementation. See #8091\nimpl Hash for NpkM {\n fn hash(self) -> Field {\n self.inner.hash()\n }\n}\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct IvpkM {\n pub inner: Point,\n}\n\nimpl ToPoint for IvpkM {\n fn to_point(self) -> Point {\n self.inner\n }\n}\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct OvpkM {\n pub inner: Point,\n}\n\nimpl Hash for OvpkM {\n fn hash(self) -> Field {\n self.inner.hash()\n }\n}\n\nimpl ToPoint for OvpkM {\n fn to_point(self) -> Point {\n self.inner\n }\n}\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct TpkM {\n pub inner: Point,\n}\n\nimpl ToPoint for TpkM {\n fn to_point(self) -> Point {\n self.inner\n }\n}\n\n#[derive(Deserialize, Eq, Serialize)]\npub struct PublicKeys {\n pub npk_m: NpkM,\n pub ivpk_m: IvpkM,\n pub ovpk_m: OvpkM,\n pub tpk_m: TpkM,\n}\n\nimpl Default for PublicKeys {\n fn default() -> Self {\n PublicKeys {\n npk_m: NpkM {\n inner: Point { x: DEFAULT_NPK_M_X, y: DEFAULT_NPK_M_Y, is_infinite: false },\n },\n ivpk_m: IvpkM {\n inner: Point { x: DEFAULT_IVPK_M_X, y: DEFAULT_IVPK_M_Y, is_infinite: false },\n },\n ovpk_m: OvpkM {\n inner: Point { x: DEFAULT_OVPK_M_X, y: DEFAULT_OVPK_M_Y, is_infinite: false },\n },\n tpk_m: TpkM {\n inner: Point { x: DEFAULT_TPK_M_X, y: DEFAULT_TPK_M_Y, is_infinite: false },\n },\n }\n }\n}\n\nimpl PublicKeys {\n pub fn hash(self) -> PublicKeysHash {\n PublicKeysHash::from_field(poseidon2_hash_with_separator(\n self.serialize(),\n DOM_SEP__PUBLIC_KEYS_HASH as Field,\n ))\n }\n\n pub fn validate_on_curve(self) {\n validate_on_curve(self.npk_m.inner);\n validate_on_curve(self.ivpk_m.inner);\n validate_on_curve(self.ovpk_m.inner);\n validate_on_curve(self.tpk_m.inner);\n }\n}\n\npub struct AddressPoint {\n pub inner: Point,\n}\n\nimpl ToPoint for AddressPoint {\n fn to_point(self) -> Point {\n self.inner\n }\n}\n\nmod test {\n use crate::{\n point::POINT_LENGTH,\n public_keys::{IvpkM, NpkM, OvpkM, PublicKeys, TpkM},\n traits::{Deserialize, Serialize},\n };\n use std::embedded_curve_ops::EmbeddedCurvePoint as Point;\n\n #[test]\n unconstrained fn compute_public_keys_hash() {\n let keys = PublicKeys {\n npk_m: NpkM { inner: Point { x: 1, y: 2, is_infinite: false } },\n ivpk_m: IvpkM { inner: Point { x: 3, y: 4, is_infinite: false } },\n ovpk_m: OvpkM { inner: Point { x: 5, y: 6, is_infinite: false } },\n tpk_m: TpkM { inner: Point { x: 7, y: 8, is_infinite: false } },\n };\n\n let actual = keys.hash();\n\n // The following value was generated by `public_keys.test.ts`.\n let expected_public_keys_hash =\n 0x056998309f6c119e4d753e404f94fef859dddfa530a9379634ceb0854b29bf7a;\n\n assert(actual.to_field() == expected_public_keys_hash);\n }\n\n #[test]\n unconstrained fn compute_default_hash() {\n let keys = PublicKeys::default();\n\n let actual = keys.hash();\n\n // The following value was generated by `public_keys.test.ts`.\n let test_data_default_hash =\n 0x023547e676dba19784188825b901a0e70d8ad978300d21d6185a54281b734da0;\n\n assert(actual.to_field() == test_data_default_hash);\n }\n\n #[test]\n unconstrained fn serde() {\n let keys = PublicKeys {\n npk_m: NpkM { inner: Point { x: 1, y: 2, is_infinite: false } },\n ivpk_m: IvpkM { inner: Point { x: 3, y: 4, is_infinite: false } },\n ovpk_m: OvpkM { inner: Point { x: 5, y: 6, is_infinite: false } },\n tpk_m: TpkM { inner: Point { x: 7, y: 8, is_infinite: false } },\n };\n\n // We use the PUBLIC_KEYS_LENGTH constant to ensure that there is a match between the derived trait\n let serialized: [Field; POINT_LENGTH * 4] = keys.serialize();\n let deserialized = PublicKeys::deserialize(serialized);\n\n assert_eq(keys, deserialized);\n }\n}\n" }, "411": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/traits/to_field.nr", "source": "use crate::utils::field::field_from_bytes;\n\npub trait ToField {\n fn to_field(self) -> Field;\n}\n\nimpl ToField for Field {\n #[inline_always]\n fn to_field(self) -> Field {\n self\n }\n}\n\nimpl ToField for bool {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u1 {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u8 {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u16 {\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u32 {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u64 {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for u128 {\n #[inline_always]\n fn to_field(self) -> Field {\n self as Field\n }\n}\nimpl ToField for str {\n #[inline_always]\n fn to_field(self) -> Field {\n assert(N < 32, \"String doesn't fit in a field, consider using Serialize instead\");\n field_from_bytes(self.as_bytes(), true)\n }\n}\n" }, "412": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/type_packing.nr", "source": "use crate::traits::Packable;\n\nglobal BOOL_PACKED_LEN: u32 = 1;\nglobal U8_PACKED_LEN: u32 = 1;\nglobal U16_PACKED_LEN: u32 = 1;\nglobal U32_PACKED_LEN: u32 = 1;\nglobal U64_PACKED_LEN: u32 = 1;\nglobal U128_PACKED_LEN: u32 = 1;\nglobal FIELD_PACKED_LEN: u32 = 1;\nglobal I8_PACKED_LEN: u32 = 1;\nglobal I16_PACKED_LEN: u32 = 1;\nglobal I32_PACKED_LEN: u32 = 1;\nglobal I64_PACKED_LEN: u32 = 1;\n\nimpl Packable for bool {\n let N: u32 = BOOL_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> bool {\n (fields[0] as u1) != 0\n }\n}\n\nimpl Packable for u8 {\n let N: u32 = U8_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u8\n }\n}\n\nimpl Packable for u16 {\n let N: u32 = U16_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u16\n }\n}\n\nimpl Packable for u32 {\n let N: u32 = U32_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u32\n }\n}\n\nimpl Packable for u64 {\n let N: u32 = U64_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u64\n }\n}\n\nimpl Packable for u128 {\n let N: u32 = U128_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u128\n }\n}\n\nimpl Packable for Field {\n let N: u32 = FIELD_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0]\n }\n}\n\nimpl Packable for i8 {\n let N: u32 = I8_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as u8 as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u8 as i8\n }\n}\n\nimpl Packable for i16 {\n let N: u32 = I16_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as u16 as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u16 as i16\n }\n}\n\nimpl Packable for i32 {\n let N: u32 = I32_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as u32 as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u32 as i32\n }\n}\n\nimpl Packable for i64 {\n let N: u32 = I64_PACKED_LEN;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n [self as u64 as Field]\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n fields[0] as u64 as i64\n }\n}\n\nimpl Packable for [T; M]\nwhere\n T: Packable,\n{\n let N: u32 = M * ::N;\n\n #[inline_always]\n fn pack(self) -> [Field; Self::N] {\n let mut result: [Field; Self::N] = std::mem::zeroed();\n for i in 0..M {\n let serialized = self[i].pack();\n for j in 0..::N {\n result[i * ::N + j] = serialized[j];\n }\n }\n result\n }\n\n #[inline_always]\n fn unpack(fields: [Field; Self::N]) -> Self {\n let mut reader = crate::utils::reader::Reader::new(fields);\n let mut result: [T; M] = std::mem::zeroed();\n reader.read_struct_array::::N, M>(Packable::unpack, result)\n }\n}\n\n#[test]\nfn test_u16_packing() {\n let a: u16 = 10;\n assert_eq(a, u16::unpack(a.pack()));\n}\n\n#[test]\nfn test_i8_packing() {\n let a: i8 = -10;\n assert_eq(a, i8::unpack(a.pack()));\n}\n\n#[test]\nfn test_i16_packing() {\n let a: i16 = -10;\n assert_eq(a, i16::unpack(a.pack()));\n}\n\n#[test]\nfn test_i32_packing() {\n let a: i32 = -10;\n assert_eq(a, i32::unpack(a.pack()));\n}\n\n#[test]\nfn test_i64_packing() {\n let a: i64 = -10;\n assert_eq(a, i64::unpack(a.pack()));\n}\n" }, "419": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/types/src/utils/field.nr", "source": "pub fn field_from_bytes(bytes: [u8; N], big_endian: bool) -> Field {\n assert(bytes.len() < 32, \"field_from_bytes: N must be less than 32\");\n let mut as_field = 0;\n let mut offset = 1;\n for i in 0..N {\n let mut index = i;\n if big_endian {\n index = N - i - 1;\n }\n as_field += (bytes[index] as Field) * offset;\n offset *= 256;\n }\n\n as_field\n}\n\n// Convert a 32 byte array to a field element by truncating the final byte\npub fn field_from_bytes_32_trunc(bytes32: [u8; 32]) -> Field {\n // Convert it to a field element\n let mut v = 1;\n let mut high = 0 as Field;\n let mut low = 0 as Field;\n\n for i in 0..15 {\n // covers bytes 16..30 (31 is truncated and ignored)\n low = low + (bytes32[15 + 15 - i] as Field) * v;\n v = v * 256;\n // covers bytes 0..14\n high = high + (bytes32[14 - i] as Field) * v;\n }\n // covers byte 15\n low = low + (bytes32[15] as Field) * v;\n\n low + high * v\n}\n\n// TODO to radix returns u8, so we cannot use bigger radixes. It'd be ideal to use a radix of the maximum range-constrained integer noir supports\npub fn full_field_less_than(lhs: Field, rhs: Field) -> bool {\n lhs.lt(rhs)\n}\n\npub fn full_field_greater_than(lhs: Field, rhs: Field) -> bool {\n rhs.lt(lhs)\n}\n\npub fn min(f1: Field, f2: Field) -> Field {\n if f1.lt(f2) {\n f1\n } else {\n f2\n }\n}\n\n// TODO: write doc-comments and tests for these magic constants.\n\nglobal KNOWN_NON_RESIDUE: Field = 5; // This is a non-residue in Noir's native Field.\nglobal C1: u32 = 28;\nglobal C3: Field = 40770029410420498293352137776570907027550720424234931066070132305055;\nglobal C5: Field = 19103219067921713944291392827692070036145651957329286315305642004821462161904;\n\n// @dev: only use this for _huge_ exponents y, when writing a constrained function.\n// If you're only exponentiating by a small value, first consider writing-out the multiplications by hand.\n// Only after you've measured the gates of that approach, consider using the native Field::pow_32 function.\n// Only if your exponent is larger than 32 bits, resort to using this function.\npub fn pow(x: Field, y: Field) -> Field {\n let mut r = 1 as Field;\n let b: [u1; 254] = y.to_le_bits();\n\n for i in 0..254 {\n r *= r;\n r *= (b[254 - 1 - i] as Field) * x + (1 - b[254 - 1 - i] as Field);\n }\n\n r\n}\n\n/// Returns Option::some(sqrt) if there is a square root, and Option::none() if there isn't.\npub fn sqrt(x: Field) -> Option {\n // Safety: if the hint returns the square root of x, then we simply square it\n // check the result equals x. If x is not square, we return a value that\n // enables us to prove that fact (see the `else` clause below).\n let (is_sq, maybe_sqrt) = unsafe { __sqrt(x) };\n\n if is_sq {\n let sqrt = maybe_sqrt;\n validate_sqrt_hint(x, sqrt);\n Option::some(sqrt)\n } else {\n let not_sqrt_hint = maybe_sqrt;\n validate_not_sqrt_hint(x, not_sqrt_hint);\n Option::none()\n }\n}\n\n// Boolean indicating whether Field element is a square, i.e. whether there exists a y in Field s.t. x = y*y.\nunconstrained fn is_square(x: Field) -> bool {\n let v = pow(x, -1 / 2);\n v * (v - 1) == 0\n}\n\n// Tonelli-Shanks algorithm for computing the square root of a Field element.\n// Requires C1 = max{c: 2^c divides (p-1)}, where p is the order of Field\n// as well as C3 = (C2 - 1)/2, where C2 = (p-1)/(2^c1),\n// and C5 = ZETA^C2, where ZETA is a non-square element of Field.\n// These are pre-computed above as globals.\nunconstrained fn tonelli_shanks_sqrt(x: Field) -> Field {\n let mut z = pow(x, C3);\n let mut t = z * z * x;\n z *= x;\n let mut b = t;\n let mut c = C5;\n\n for i in 0..(C1 - 1) {\n for _j in 1..(C1 - i - 1) {\n b *= b;\n }\n\n z *= if b == 1 { 1 } else { c };\n\n c *= c;\n\n t *= if b == 1 { 1 } else { c };\n\n b = t;\n }\n\n z\n}\n\n// NB: this doesn't return an option, because in the case of there _not_ being a square root, we still want to return a field element that allows us to then assert in the _constrained_ sqrt function that there is no sqrt.\nunconstrained fn __sqrt(x: Field) -> (bool, Field) {\n let is_sq = is_square(x);\n if is_sq {\n let sqrt = tonelli_shanks_sqrt(x);\n (true, sqrt)\n } else {\n // Demonstrate that x is not a square (a.k.a. a \"quadratic non-residue\").\n // Facts:\n // The Legendre symbol (\"LS\") of x, is x^((p-1)/2) (mod p).\n // - If x is a square, LS(x) = 1\n // - If x is not a square, LS(x) = -1\n // - If x = 0, LS(x) = 0.\n //\n // Hence:\n // sq * sq = sq // 1 * 1 = 1\n // non-sq * non-sq = sq // -1 * -1 = 1\n // sq * non-sq = non-sq // -1 * 1 = -1\n //\n // See: https://en.wikipedia.org/wiki/Legendre_symbol\n let demo_x_not_square = x * KNOWN_NON_RESIDUE;\n let not_sqrt = tonelli_shanks_sqrt(demo_x_not_square);\n (false, not_sqrt)\n }\n}\n\nfn validate_sqrt_hint(x: Field, hint: Field) {\n assert(hint * hint == x, f\"The claimed_sqrt {hint} is not the sqrt of x {x}\");\n}\n\nfn validate_not_sqrt_hint(x: Field, hint: Field) {\n // We need this assertion, because x = 0 would pass the other assertions in this\n // function, and we don't want people to be able to prove that 0 is not square!\n assert(x != 0, \"0 has a square root; you cannot claim it is not square\");\n // Demonstrate that x is not a square (a.k.a. a \"quadratic non-residue\").\n //\n // Facts:\n // The Legendre symbol (\"LS\") of x, is x^((p-1)/2) (mod p).\n // - If x is a square, LS(x) = 1\n // - If x is not a square, LS(x) = -1\n // - If x = 0, LS(x) = 0.\n //\n // Hence:\n // 1. sq * sq = sq // 1 * 1 = 1\n // 2. non-sq * non-sq = sq // -1 * -1 = 1\n // 3. sq * non-sq = non-sq // -1 * 1 = -1\n //\n // See: https://en.wikipedia.org/wiki/Legendre_symbol\n //\n // We want to demonstrate that this below multiplication falls under bullet-point (2):\n let demo_x_not_square = x * KNOWN_NON_RESIDUE;\n // I.e. we want to demonstrate that `demo_x_not_square` has Legendre symbol 1\n // (i.e. that it is a square), so we prove that it is square below.\n // Why do we want to prove that it has LS 1?\n // Well, since it was computed with a known-non-residue, its squareness implies we're\n // in case 2 (something multiplied by a known-non-residue yielding a result which\n // has a LS of 1), which implies that x must be a non-square. The unconstrained\n // function gave us the sqrt of demo_x_not_square, so all we need to do is\n // assert its squareness:\n assert(\n hint * hint == demo_x_not_square,\n f\"The hint {hint} does not demonstrate that {x} is not a square\",\n );\n}\n\n#[test]\nunconstrained fn bytes_field_test() {\n // Tests correctness of field_from_bytes_32_trunc against existing methods\n // Bytes representing 0x543e0a6642ffeb8039296861765a53407bba62bd1c97ca43374de950bbe0a7\n let inputs = [\n 84, 62, 10, 102, 66, 255, 235, 128, 57, 41, 104, 97, 118, 90, 83, 64, 123, 186, 98, 189, 28,\n 151, 202, 67, 55, 77, 233, 80, 187, 224, 167,\n ];\n let field = field_from_bytes(inputs, true);\n let return_bytes: [u8; 31] = field.to_be_bytes();\n assert_eq(inputs, return_bytes);\n // 32 bytes - we remove the final byte, and check it matches the field\n let inputs2 = [\n 84, 62, 10, 102, 66, 255, 235, 128, 57, 41, 104, 97, 118, 90, 83, 64, 123, 186, 98, 189, 28,\n 151, 202, 67, 55, 77, 233, 80, 187, 224, 167, 158,\n ];\n let field2 = field_from_bytes_32_trunc(inputs2);\n let return_bytes2: [u8; 31] = field2.to_be_bytes();\n\n assert_eq(return_bytes2, return_bytes);\n assert_eq(field2, field);\n}\n\n#[test]\nunconstrained fn max_field_test() {\n // Tests the hardcoded value in constants.nr vs underlying modulus\n // NB: We can't use 0-1 in constants.nr as it will be transpiled incorrectly to ts and sol constants files\n let max_value = crate::constants::MAX_FIELD_VALUE;\n assert_eq(max_value, 0 - 1);\n // modulus == 0 is tested elsewhere, so below is more of a sanity check\n let max_bytes: [u8; 32] = max_value.to_be_bytes();\n let mod_bytes = std::field::modulus_be_bytes();\n for i in 0..31 {\n assert_eq(max_bytes[i], mod_bytes[i]);\n }\n assert_eq(max_bytes[31], mod_bytes[31] - 1);\n}\n\n#[test]\nunconstrained fn sqrt_valid_test() {\n let x = 16; // examples: 16, 9, 25, 81\n let result = sqrt(x);\n assert(result.is_some());\n assert_eq(result.unwrap() * result.unwrap(), x);\n}\n\n#[test]\nunconstrained fn sqrt_invalid_test() {\n let x = KNOWN_NON_RESIDUE; // has no square root in the field\n let result = sqrt(x);\n assert(result.is_none());\n}\n\n#[test]\nunconstrained fn sqrt_zero_test() {\n let result = sqrt(0);\n assert(result.is_some());\n assert_eq(result.unwrap(), 0);\n}\n\n#[test]\nunconstrained fn sqrt_one_test() {\n let result = sqrt(1);\n assert(result.is_some());\n assert_eq(result.unwrap() * result.unwrap(), 1);\n}\n\n#[test]\nunconstrained fn field_from_bytes_empty_test() {\n let empty: [u8; 0] = [];\n let result = field_from_bytes(empty, true);\n assert_eq(result, 0);\n\n let result_le = field_from_bytes(empty, false);\n assert_eq(result_le, 0);\n}\n\n#[test]\nunconstrained fn field_from_bytes_little_endian_test() {\n // Test little-endian conversion: [0x01, 0x02] should be 0x0201 = 513\n let bytes = [0x01, 0x02];\n let result_le = field_from_bytes(bytes, false);\n assert_eq(result_le, 0x0201);\n\n // Compare with big-endian: [0x01, 0x02] should be 0x0102 = 258\n let result_be = field_from_bytes(bytes, true);\n assert_eq(result_be, 0x0102);\n}\n\n#[test]\nunconstrained fn pow_test() {\n assert_eq(pow(2, 0), 1);\n assert_eq(pow(2, 1), 2);\n assert_eq(pow(2, 10), 1024);\n assert_eq(pow(3, 5), 243);\n assert_eq(pow(0, 5), 0);\n assert_eq(pow(1, 100), 1);\n}\n\n#[test]\nunconstrained fn min_test() {\n assert_eq(min(5, 10), 5);\n assert_eq(min(10, 5), 5);\n assert_eq(min(7, 7), 7);\n assert_eq(min(0, 1), 0);\n}\n\n#[test]\nunconstrained fn full_field_comparison_test() {\n assert(full_field_less_than(5, 10));\n assert(!full_field_less_than(10, 5));\n assert(!full_field_less_than(5, 5));\n\n assert(full_field_greater_than(10, 5));\n assert(!full_field_greater_than(5, 10));\n assert(!full_field_greater_than(5, 5));\n}\n\n#[test]\nunconstrained fn sqrt_has_two_roots_test() {\n // Every square has two roots: r and -r (i.e., p - r)\n // sqrt(16) can return 4 or -4\n let x = 16;\n let result = sqrt(x).unwrap();\n assert(result * result == x);\n // The other root is -result\n let other_root = 0 - result;\n assert(other_root * other_root == x);\n // Verify they are different (unless x = 0)\n assert(result != other_root);\n\n // Same for 9: roots are 3 and -3\n let y = 9;\n let result_y = sqrt(y).unwrap();\n assert(result_y * result_y == y);\n let other_root_y = 0 - result_y;\n assert(other_root_y * other_root_y == y);\n assert(result_y != other_root_y);\n}\n\n#[test]\nunconstrained fn sqrt_negative_one_test() {\n let x = 0 - 1;\n let result = sqrt(x);\n assert(result.unwrap() == 0x30644e72e131a029048b6e193fd841045cea24f6fd736bec231204708f703636);\n}\n\n#[test]\nunconstrained fn validate_sqrt_hint_valid_test() {\n // 4 is a valid sqrt of 16\n validate_sqrt_hint(16, 4);\n // -4 is also a valid sqrt of 16\n validate_sqrt_hint(16, 0 - 4);\n // 0 is a valid sqrt of 0\n validate_sqrt_hint(0, 0);\n // 1 is a valid sqrt of 1\n validate_sqrt_hint(1, 1);\n // -1 is also a valid sqrt of 1\n validate_sqrt_hint(1, 0 - 1);\n}\n\n#[test(should_fail_with = \"is not the sqrt of x\")]\nunconstrained fn validate_sqrt_hint_invalid_test() {\n // 5 is not a valid sqrt of 16\n validate_sqrt_hint(16, 5);\n}\n\n#[test]\nunconstrained fn validate_not_sqrt_hint_valid_test() {\n // 5 (KNOWN_NON_RESIDUE) is not a square.\n let x = KNOWN_NON_RESIDUE;\n let hint = tonelli_shanks_sqrt(x * KNOWN_NON_RESIDUE);\n validate_not_sqrt_hint(x, hint);\n}\n\n#[test(should_fail_with = \"0 has a square root\")]\nunconstrained fn validate_not_sqrt_hint_zero_test() {\n // 0 has a square root, so we cannot claim it is not square\n validate_not_sqrt_hint(0, 0);\n}\n\n#[test(should_fail_with = \"does not demonstrate that\")]\nunconstrained fn validate_not_sqrt_hint_wrong_hint_test() {\n // Provide a wrong hint for a non-square\n let x = KNOWN_NON_RESIDUE;\n validate_not_sqrt_hint(x, 123);\n}\n" }, "425": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/serde/src/reader.nr", "source": "pub struct Reader {\n data: [Field; N],\n offset: u32,\n}\n\nimpl Reader {\n pub fn new(data: [Field; N]) -> Self {\n Self { data, offset: 0 }\n }\n\n pub fn read(&mut self) -> Field {\n let result = self.data[self.offset];\n self.offset += 1;\n result\n }\n\n pub fn read_u32(&mut self) -> u32 {\n self.read() as u32\n }\n\n pub fn read_u64(&mut self) -> u64 {\n self.read() as u64\n }\n\n pub fn read_bool(&mut self) -> bool {\n self.read() != 0\n }\n\n pub fn read_array(&mut self) -> [Field; K] {\n let mut result = [0; K];\n for i in 0..K {\n result[i] = self.data[self.offset + i];\n }\n self.offset += K;\n result\n }\n\n pub fn read_struct(&mut self, deserialise: fn([Field; K]) -> T) -> T {\n let result = deserialise(self.read_array());\n result\n }\n\n pub fn read_struct_array(\n &mut self,\n deserialise: fn([Field; K]) -> T,\n mut result: [T; C],\n ) -> [T; C] {\n for i in 0..C {\n result[i] = self.read_struct(deserialise);\n }\n result\n }\n\n pub fn peek_offset(&mut self, offset: u32) -> Field {\n self.data[self.offset + offset]\n }\n\n pub fn advance_offset(&mut self, offset: u32) {\n self.offset += offset;\n }\n\n pub fn finish(self) {\n assert_eq(self.offset, self.data.len(), \"Reader did not read all data\");\n }\n}\n" }, "426": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/serde/src/serialization.nr", "source": "use crate::{reader::Reader, writer::Writer};\n\n// docs:start:serialize\n/// Trait for serializing Noir types into arrays of Fields.\n///\n/// An implementation of the Serialize trait has to follow Noir's intrinsic serialization (each member of a struct\n/// converted directly into one or more Fields without any packing or compression). This trait (and Deserialize) are\n/// typically used to communicate between Noir and TypeScript (via oracles and function arguments).\n///\n/// # On Following Noir's Intrinsic Serialization\n/// When calling a Noir function from TypeScript (TS), first the function arguments are serialized into an array\n/// of fields. This array is then included in the initial witness. Noir's intrinsic serialization is then used\n/// to deserialize the arguments from the witness. When the same Noir function is called from Noir this Serialize trait\n/// is used instead of the serialization in TS. For this reason we need to have a match between TS serialization,\n/// Noir's intrinsic serialization and the implementation of this trait. If there is a mismatch, the function calls\n/// fail with an arguments hash mismatch error message.\n///\n/// # Associated Constants\n/// * `N` - The length of the output Field array, known at compile time\n///\n/// # Example\n/// ```\n/// impl Serialize for str {\n/// let N: u32 = N;\n///\n/// fn serialize(self) -> [Field; Self::N] {\n/// let mut writer: Writer = Writer::new();\n/// self.stream_serialize(&mut writer);\n/// writer.finish()\n/// }\n///\n/// fn stream_serialize(self, writer: &mut Writer) {\n/// let bytes = self.as_bytes();\n/// for i in 0..bytes.len() {\n/// writer.write(bytes[i] as Field);\n/// }\n/// }\n/// }\n/// ```\n#[derive_via(derive_serialize)]\npub trait Serialize {\n let N: u32;\n\n fn serialize(self) -> [Field; Self::N];\n\n fn stream_serialize(self, writer: &mut Writer);\n}\n\n/// Generates a `Serialize` trait implementation for a struct type.\n///\n/// # Parameters\n/// - `s`: The struct type definition to generate the implementation for\n///\n/// # Returns\n/// A quoted code block containing the trait implementation\n///\n/// # Example\n/// For a struct defined as:\n/// ```\n/// struct Log {\n/// fields: [Field; N],\n/// length: u32\n/// }\n/// ```\n///\n/// This function generates code equivalent to:\n/// ```\n/// impl Serialize for Log {\n/// let N: u32 = <[Field; N] as Serialize>::N + ::N;\n///\n/// fn serialize(self) -> [Field; Self::N] {\n/// let mut writer: Writer = Writer::new();\n/// self.stream_serialize(&mut writer);\n/// writer.finish()\n/// }\n///\n/// #[inline_always]\n/// fn stream_serialize(self, writer: &mut Writer) {\n/// Serialize::stream_serialize(self.fields, writer);\n/// Serialize::stream_serialize(self.length, writer);\n/// }\n/// }\n/// ```\npub comptime fn derive_serialize(s: TypeDefinition) -> Quoted {\n let typ = s.as_type();\n let nested_struct = typ.as_data_type().unwrap();\n\n // We care only about the name and type so we drop the last item of the tuple\n let params = nested_struct.0.fields(nested_struct.1).map(|(name, typ, _)| (name, typ));\n\n // Generates the generic parameter declarations (to be placed after the `impl` keyword) and the `where` clause\n // for the `Serialize` trait.\n let generics_declarations = get_generics_declarations(s);\n let where_serialize_clause = get_where_trait_clause(s, quote {Serialize});\n\n let params_len_quote = get_params_len_quote(params);\n\n let function_body = params\n .map(|(name, _typ): (Quoted, Type)| {\n quote {\n $crate::serialization::Serialize::stream_serialize(self.$name, writer);\n }\n })\n .join(quote {});\n\n quote {\n impl$generics_declarations $crate::serialization::Serialize for $typ\n $where_serialize_clause\n {\n let N: u32 = $params_len_quote;\n\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: $crate::writer::Writer = $crate::writer::Writer::new();\n $crate::serialization::Serialize::stream_serialize(self, &mut writer);\n writer.finish()\n }\n\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut $crate::writer::Writer) {\n $function_body\n }\n }\n }\n}\n\n// docs:start:deserialize\n/// Trait for deserializing Noir types from arrays of Fields.\n///\n/// An implementation of the Deserialize trait has to follow Noir's intrinsic serialization (each member of a struct\n/// converted directly into one or more Fields without any packing or compression). This trait is typically used when\n/// deserializing return values from function calls in Noir. Since the same function could be called from TypeScript\n/// (TS), in which case the TS deserialization would get used, we need to have a match between the 2.\n///\n/// # Associated Constants\n/// * `N` - The length of the input Field array, known at compile time\n///\n/// # Example\n/// ```\n/// impl Deserialize for str {\n/// let N: u32 = M;\n///\n/// fn deserialize(fields: [Field; Self::N]) -> Self {\n/// let mut reader = Reader::new(fields);\n/// let result = Self::stream_deserialize(&mut reader);\n/// reader.finish();\n/// result\n/// }\n///\n/// fn stream_deserialize(reader: &mut Reader) -> Self {\n/// let mut bytes = [0 as u8; M];\n/// for i in 0..M {\n/// bytes[i] = reader.read() as u8;\n/// }\n/// str::::from(bytes)\n/// }\n/// }\n/// ```\n#[derive_via(derive_deserialize)]\npub trait Deserialize {\n let N: u32;\n\n fn deserialize(fields: [Field; Self::N]) -> Self;\n\n fn stream_deserialize(reader: &mut Reader) -> Self;\n}\n\n/// Generates a `Deserialize` trait implementation for a given struct `s`.\n///\n/// # Arguments\n/// * `s` - The struct type definition to generate the implementation for\n///\n/// # Returns\n/// A `Quoted` block containing the generated trait implementation\n///\n/// # Requirements\n/// Each struct member type must implement the `Deserialize` trait (it gets used in the generated code).\n///\n/// # Example\n/// For a struct like:\n/// ```\n/// struct MyStruct {\n/// x: AztecAddress,\n/// y: Field,\n/// }\n/// ```\n///\n/// This generates:\n/// ```\n/// impl Deserialize for MyStruct {\n/// let N: u32 = ::N + ::N;\n///\n/// fn deserialize(fields: [Field; Self::N]) -> Self {\n/// let mut reader = Reader::new(fields);\n/// let result = Self::stream_deserialize(&mut reader);\n/// reader.finish();\n/// result\n/// }\n///\n/// #[inline_always]\n/// fn stream_deserialize(reader: &mut Reader) -> Self {\n/// let x = ::stream_deserialize(reader);\n/// let y = ::stream_deserialize(reader);\n/// Self { x, y }\n/// }\n/// }\n/// ```\npub comptime fn derive_deserialize(s: TypeDefinition) -> Quoted {\n let typ = s.as_type();\n let nested_struct = typ.as_data_type().unwrap();\n let params = nested_struct.0.fields(nested_struct.1);\n\n // Generates the generic parameter declarations (to be placed after the `impl` keyword) and the `where` clause\n // for the `Deserialize` trait.\n let generics_declarations = get_generics_declarations(s);\n let where_deserialize_clause = get_where_trait_clause(s, quote {Deserialize});\n\n // The following will give us:\n // ::N + ::N + ...\n // (or 0 if the struct has no members)\n let right_hand_side_of_definition_of_n = if params.len() > 0 {\n params\n .map(|(_, param_type, _): (Quoted, Type, Quoted)| {\n quote {\n <$param_type as $crate::serialization::Deserialize>::N\n }\n })\n .join(quote {+})\n } else {\n quote {0}\n };\n\n // For structs containing a single member, we can enhance performance by directly deserializing the input array,\n // bypassing the need for loop-based array construction. While this optimization yields significant benefits in\n // Brillig where the loops are expected to not be optimized, it is not relevant in ACIR where the loops are\n // expected to be optimized away.\n let function_body = if params.len() > 1 {\n // This generates deserialization code for each struct member and concatenates them together.\n let deserialization_of_struct_members = params\n .map(|(param_name, param_type, _): (Quoted, Type, Quoted)| {\n quote {\n let $param_name = <$param_type as Deserialize>::stream_deserialize(reader);\n }\n })\n .join(quote {});\n\n // We join the struct member names with a comma to be used in the `Self { ... }` syntax\n // This will give us e.g. `a, b, c` for a struct with three fields named `a`, `b`, and `c`.\n let struct_members = params\n .map(|(param_name, _, _): (Quoted, Type, Quoted)| quote { $param_name })\n .join(quote {,});\n\n quote {\n $deserialization_of_struct_members\n\n Self { $struct_members }\n }\n } else if params.len() == 1 {\n let param_name = params[0].0;\n quote {\n Self { $param_name: $crate::serialization::Deserialize::stream_deserialize(reader) }\n }\n } else {\n quote {\n Self {}\n }\n };\n\n quote {\n impl$generics_declarations $crate::serialization::Deserialize for $typ\n $where_deserialize_clause\n {\n let N: u32 = $right_hand_side_of_definition_of_n;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = $crate::reader::Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut $crate::reader::Reader) -> Self {\n $function_body\n }\n }\n }\n}\n\n/// Generates a quoted expression that computes the total serialized length of function parameters.\n///\n/// # Parameters\n/// * `params` - An array of tuples where each tuple contains a quoted parameter name and its Type. The type needs\n/// to implement the Serialize trait.\n///\n/// # Returns\n/// A quoted expression that evaluates to:\n/// * `0` if there are no parameters\n/// * `(::N + ::N + ...)` for one or more parameters\ncomptime fn get_params_len_quote(params: [(Quoted, Type)]) -> Quoted {\n if params.len() == 0 {\n quote { 0 }\n } else {\n let params_quote_without_parentheses = params\n .map(|(_, param_type): (Quoted, Type)| {\n quote {\n <$param_type as $crate::serialization::Serialize>::N\n }\n })\n .join(quote {+});\n quote { ($params_quote_without_parentheses) }\n }\n}\n\ncomptime fn get_generics_declarations(s: TypeDefinition) -> Quoted {\n let generics = s.generics();\n\n if generics.len() > 0 {\n let generics_declarations_items = generics\n .map(|(name, maybe_integer_typ)| {\n // The second item in the generics tuple is an Option of an integer type that is Some only if\n // the generic is numeric.\n if maybe_integer_typ.is_some() {\n // The generic is numeric, so we return a quote defined as e.g. \"let N: u32\"\n let integer_type = maybe_integer_typ.unwrap();\n quote {let $name: $integer_type}\n } else {\n // The generic is not numeric, so we return a quote containing the name of the generic (e.g. \"T\")\n quote {$name}\n }\n })\n .join(quote {,});\n quote {<$generics_declarations_items>}\n } else {\n // The struct doesn't have any generics defined, so we just return an empty quote.\n quote {}\n }\n}\n\ncomptime fn get_where_trait_clause(s: TypeDefinition, trait_name: Quoted) -> Quoted {\n let generics = s.generics();\n\n // The second item in the generics tuple is an Option of an integer type that is Some only if the generic is\n // numeric.\n let non_numeric_generics =\n generics.filter(|(_, maybe_integer_typ)| maybe_integer_typ.is_none());\n\n if non_numeric_generics.len() > 0 {\n let non_numeric_generics_declarations =\n non_numeric_generics.map(|(name, _)| quote {$name: $trait_name}).join(quote {,});\n quote {where $non_numeric_generics_declarations}\n } else {\n // There are no non-numeric generics, so we return an empty quote.\n quote {}\n }\n}\n" }, "428": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/serde/src/type_impls.nr", "source": "use crate::{reader::Reader, serialization::{Deserialize, Serialize}, writer::Writer};\nuse std::embedded_curve_ops::EmbeddedCurvePoint;\nuse std::embedded_curve_ops::EmbeddedCurveScalar;\n\nglobal U1_SERIALIZED_LEN: u32 = 1;\nglobal BOOL_SERIALIZED_LEN: u32 = 1;\nglobal U8_SERIALIZED_LEN: u32 = 1;\nglobal U16_SERIALIZED_LEN: u32 = 1;\nglobal U32_SERIALIZED_LEN: u32 = 1;\nglobal U64_SERIALIZED_LEN: u32 = 1;\nglobal U128_SERIALIZED_LEN: u32 = 1;\nglobal FIELD_SERIALIZED_LEN: u32 = 1;\nglobal I8_SERIALIZED_LEN: u32 = 1;\nglobal I16_SERIALIZED_LEN: u32 = 1;\nglobal I32_SERIALIZED_LEN: u32 = 1;\nglobal I64_SERIALIZED_LEN: u32 = 1;\n\nimpl Serialize for bool {\n let N: u32 = BOOL_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for bool {\n let N: u32 = BOOL_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> bool {\n reader.read() != 0\n }\n}\n\nimpl Serialize for u1 {\n let N: u32 = U1_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u1 {\n let N: u32 = U1_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u1\n }\n}\n\nimpl Serialize for u8 {\n let N: u32 = U8_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u8 {\n let N: u32 = U8_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u8\n }\n}\n\nimpl Serialize for u16 {\n let N: u32 = U16_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u16 {\n let N: u32 = U16_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u16\n }\n}\n\nimpl Serialize for u32 {\n let N: u32 = U32_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u32 {\n let N: u32 = U32_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u32\n }\n}\n\nimpl Serialize for u64 {\n let N: u32 = U64_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u64 {\n let N: u32 = U64_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u64\n }\n}\n\nimpl Serialize for u128 {\n let N: u32 = U128_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as Field);\n }\n}\n\nimpl Deserialize for u128 {\n let N: u32 = U128_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u128\n }\n}\n\nimpl Serialize for Field {\n let N: u32 = FIELD_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self);\n }\n}\n\nimpl Deserialize for Field {\n let N: u32 = FIELD_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read()\n }\n}\n\nimpl Serialize for i8 {\n let N: u32 = I8_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as u8 as Field);\n }\n}\n\nimpl Deserialize for i8 {\n let N: u32 = I8_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u8 as i8\n }\n}\n\nimpl Serialize for i16 {\n let N: u32 = I16_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as u16 as Field);\n }\n}\n\nimpl Deserialize for i16 {\n let N: u32 = I16_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u16 as i16\n }\n}\n\nimpl Serialize for i32 {\n let N: u32 = I32_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as u32 as Field);\n }\n}\n\nimpl Deserialize for i32 {\n let N: u32 = I32_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u32 as i32\n }\n}\n\nimpl Serialize for i64 {\n let N: u32 = I64_SERIALIZED_LEN;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self as u64 as Field);\n }\n}\n\nimpl Deserialize for i64 {\n let N: u32 = I64_SERIALIZED_LEN;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n reader.read() as u64 as i64\n }\n}\n\nimpl Serialize for [T; M]\nwhere\n T: Serialize,\n{\n let N: u32 = ::N * M;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n for i in 0..M {\n self[i].stream_serialize(writer);\n }\n }\n}\n\nimpl Deserialize for [T; M]\nwhere\n T: Deserialize,\n{\n let N: u32 = ::N * M;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n let mut result: [T; M] = std::mem::zeroed();\n for i in 0..M {\n result[i] = T::stream_deserialize(reader);\n }\n result\n }\n}\n\nimpl Serialize for Option\nwhere\n T: Serialize,\n{\n let N: u32 = ::N + 1;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write_bool(self.is_some());\n if self.is_some() {\n self.unwrap_unchecked().stream_serialize(writer);\n } else {\n writer.advance_offset(::N);\n }\n }\n}\n\nimpl Deserialize for Option\nwhere\n T: Deserialize,\n{\n let N: u32 = ::N + 1;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n if reader.read_bool() {\n Option::some(::stream_deserialize(reader))\n } else {\n reader.advance_offset(::N);\n Option::none()\n }\n }\n}\n\nglobal SCALAR_SIZE: u32 = 2;\n\nimpl Serialize for EmbeddedCurveScalar {\n\n let N: u32 = SCALAR_SIZE;\n\n fn serialize(self) -> [Field; SCALAR_SIZE] {\n [self.lo, self.hi]\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self.lo);\n writer.write(self.hi);\n }\n}\n\nimpl Deserialize for EmbeddedCurveScalar {\n let N: u32 = SCALAR_SIZE;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n Self { lo: fields[0], hi: fields[1] }\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n Self { lo: reader.read(), hi: reader.read() }\n }\n}\n\nglobal POINT_SIZE: u32 = 3;\n\nimpl Serialize for EmbeddedCurvePoint {\n let N: u32 = POINT_SIZE;\n\n fn serialize(self) -> [Field; Self::N] {\n [self.x, self.y, self.is_infinite as Field]\n }\n\n fn stream_serialize(self, writer: &mut Writer) {\n writer.write(self.x);\n writer.write(self.y);\n writer.write(self.is_infinite as Field);\n }\n}\n\nimpl Deserialize for EmbeddedCurvePoint {\n let N: u32 = POINT_SIZE;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n Self { x: fields[0], y: fields[1], is_infinite: fields[2] != 0 }\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n Self { x: reader.read(), y: reader.read(), is_infinite: reader.read_bool() }\n }\n}\n\nimpl Deserialize for str {\n let N: u32 = M;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n let u8_arr = <[u8; Self::N] as Deserialize>::stream_deserialize(reader);\n str::::from(u8_arr)\n }\n}\n\nimpl Serialize for str {\n let N: u32 = M;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n self.as_bytes().stream_serialize(writer);\n }\n}\n\n// Note: Not deriving this because it's not supported to call derive_serialize on a \"remote\" struct (and it will never\n// be supported).\nimpl Deserialize for BoundedVec\nwhere\n T: Deserialize,\n{\n let N: u32 = ::N * M + 1;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n let mut new_bounded_vec: BoundedVec = BoundedVec::new();\n let payload_len = Self::N - 1;\n\n // Length is stored in the last field as we need to match intrinsic Noir serialization and the `len` struct\n // field is after `storage` struct field (see `bounded_vec.nr` in noir-stdlib)\n let len = reader.peek_offset(payload_len) as u32;\n\n for i in 0..M {\n if i < len {\n new_bounded_vec.push(::stream_deserialize(reader));\n }\n }\n\n // +1 for the length of the BoundedVec\n reader.advance_offset((M - len) * ::N + 1);\n\n new_bounded_vec\n }\n}\n\n// This may cause issues if used as program input, because noir disallows empty arrays for program input.\n// I think this is okay because I don't foresee a unit type being used as input. But leaving this comment as a hint\n// if someone does run into this in the future.\nimpl Deserialize for () {\n let N: u32 = 0;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(_reader: &mut Reader) -> Self {\n ()\n }\n}\n\n// Note: Not deriving this because it's not supported to call derive_serialize on a \"remote\" struct (and it will never\n// be supported).\nimpl Serialize for BoundedVec\nwhere\n T: Serialize,\n{\n let N: u32 = ::N * M + 1; // +1 for the length of the BoundedVec\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: Writer = Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n self.storage().stream_serialize(writer);\n // Length is stored in the last field as we need to match intrinsic Noir serialization and the `len` struct\n // field is after `storage` struct field (see `bounded_vec.nr` in noir-stdlib)\n writer.write_u32(self.len() as u32);\n }\n}\n\n// Create a slice of the given length with each element made from `f(i)` where `i` is the current index\ncomptime fn make_slice(length: u32, f: fn[Env](u32) -> T) -> [T] {\n let mut slice = @[];\n for i in 0..length {\n slice = slice.push_back(f(i));\n }\n slice\n}\n\n// Implements Serialize and Deserialize for an arbitrary tuple type\ncomptime fn impl_serialize_for_tuple(_m: Module, length: u32) -> Quoted {\n // `T0`, `T1`, `T2`\n let type_names = make_slice(length, |i| f\"T{i}\".quoted_contents());\n\n // `result0`, `result1`, `result2`\n let result_names = make_slice(length, |i| f\"result{i}\".quoted_contents());\n\n // `T0, T1, T2`\n let field_generics = type_names.join(quote [,]);\n\n // `::N + ::N + ::N`\n let full_size_serialize = type_names\n .map(|type_name| quote {\n <$type_name as Serialize>::N\n })\n .join(quote [+]);\n\n // `::N + ::N + ::N`\n let full_size_deserialize = type_names\n .map(|type_name| quote {\n <$type_name as Deserialize>::N\n })\n .join(quote [+]);\n\n // `T0: Serialize, T1: Serialize, T2: Serialize,`\n let serialize_constraints = type_names\n .map(|field_name| quote {\n $field_name: Serialize,\n })\n .join(quote []);\n\n // `T0: Deserialize, T1: Deserialize, T2: Deserialize,`\n let deserialize_constraints = type_names\n .map(|field_name| quote {\n $field_name: Deserialize,\n })\n .join(quote []);\n\n // Statements to serialize each field\n let serialized_fields = type_names\n .mapi(|i, _type_name| quote {\n $crate::serialization::Serialize::stream_serialize(self.$i, writer);\n })\n .join(quote []);\n\n // Statements to deserialize each field\n let deserialized_fields = type_names\n .mapi(|i, type_name| {\n let result_name = result_names[i];\n quote {\n let $result_name = <$type_name as $crate::serialization::Deserialize>::stream_deserialize(reader);\n }\n })\n .join(quote []);\n let deserialize_results = result_names.join(quote [,]);\n\n quote {\n impl<$field_generics> Serialize for ($field_generics) where $serialize_constraints {\n let N: u32 = $full_size_serialize;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: $crate::writer::Writer = $crate::writer::Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut $crate::writer::Writer) {\n\n $serialized_fields\n }\n }\n\n impl<$field_generics> Deserialize for ($field_generics) where $deserialize_constraints {\n let N: u32 = $full_size_deserialize;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = $crate::reader::Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n \n #[inline_always]\n fn stream_deserialize(reader: &mut $crate::reader::Reader) -> Self {\n $deserialized_fields\n ($deserialize_results)\n }\n }\n }\n}\n\n// Keeping these manual impls. They are more efficient since they do not\n// require copying sub-arrays from any serialized arrays.\nimpl Serialize for (T1,)\nwhere\n T1: Serialize,\n{\n let N: u32 = ::N;\n\n fn serialize(self) -> [Field; Self::N] {\n let mut writer: crate::writer::Writer = crate::writer::Writer::new();\n self.stream_serialize(&mut writer);\n writer.finish()\n }\n\n #[inline_always]\n fn stream_serialize(self, writer: &mut Writer) {\n self.0.stream_serialize(writer);\n }\n}\n\nimpl Deserialize for (T1,)\nwhere\n T1: Deserialize,\n{\n let N: u32 = ::N;\n\n fn deserialize(fields: [Field; Self::N]) -> Self {\n let mut reader = crate::reader::Reader::new(fields);\n let result = Self::stream_deserialize(&mut reader);\n reader.finish();\n result\n }\n\n #[inline_always]\n fn stream_deserialize(reader: &mut Reader) -> Self {\n (::stream_deserialize(reader),)\n }\n}\n\n#[impl_serialize_for_tuple(2)]\n#[impl_serialize_for_tuple(3)]\n#[impl_serialize_for_tuple(4)]\n#[impl_serialize_for_tuple(5)]\n#[impl_serialize_for_tuple(6)]\nmod impls {\n use crate::serialization::{Deserialize, Serialize};\n}\n\n#[test]\nunconstrained fn bounded_vec_serialization() {\n // Test empty BoundedVec\n let empty_vec: BoundedVec = BoundedVec::from_array([]);\n let serialized = empty_vec.serialize();\n let deserialized = BoundedVec::::deserialize(serialized);\n assert_eq(empty_vec, deserialized);\n assert_eq(deserialized.len(), 0);\n\n // Test partially filled BoundedVec\n let partial_vec: BoundedVec<[u32; 2], 3> = BoundedVec::from_array([[1, 2]]);\n let serialized = partial_vec.serialize();\n let deserialized = BoundedVec::<[u32; 2], 3>::deserialize(serialized);\n assert_eq(partial_vec, deserialized);\n assert_eq(deserialized.len(), 1);\n assert_eq(deserialized.get(0), [1, 2]);\n\n // Test full BoundedVec\n let full_vec: BoundedVec<[u32; 2], 3> = BoundedVec::from_array([[1, 2], [3, 4], [5, 6]]);\n let serialized = full_vec.serialize();\n let deserialized = BoundedVec::<[u32; 2], 3>::deserialize(serialized);\n assert_eq(full_vec, deserialized);\n assert_eq(deserialized.len(), 3);\n assert_eq(deserialized.get(0), [1, 2]);\n assert_eq(deserialized.get(1), [3, 4]);\n assert_eq(deserialized.get(2), [5, 6]);\n}\n" }, "429": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/noir-protocol-circuits/crates/serde/src/writer.nr", "source": "pub struct Writer {\n data: [Field; N],\n offset: u32,\n}\n\nimpl Writer {\n pub fn new() -> Self {\n Self { data: [0; N], offset: 0 }\n }\n\n pub fn write(&mut self, value: Field) {\n self.data[self.offset] = value;\n self.offset += 1;\n }\n\n pub fn write_u32(&mut self, value: u32) {\n self.write(value as Field);\n }\n\n pub fn write_u64(&mut self, value: u64) {\n self.write(value as Field);\n }\n\n pub fn write_bool(&mut self, value: bool) {\n self.write(value as Field);\n }\n\n pub fn write_array(&mut self, value: [Field; K]) {\n for i in 0..K {\n self.data[i + self.offset] = value[i];\n }\n self.offset += K;\n }\n\n pub fn write_struct(&mut self, value: T, serialize: fn(T) -> [Field; K]) {\n self.write_array(serialize(value));\n }\n\n pub fn write_struct_array(\n &mut self,\n value: [T; C],\n serialize: fn(T) -> [Field; K],\n ) {\n for i in 0..C {\n self.write_struct(value[i], serialize);\n }\n }\n\n pub fn advance_offset(&mut self, offset: u32) {\n self.offset += offset;\n }\n\n pub fn finish(self) -> [Field; N] {\n assert_eq(self.offset, self.data.len(), \"Writer did not write all data\");\n self.data\n }\n}\n" }, "442": { "path": "/home/runner/nargo/github.com/AztecProtocol/aztec-packages/v4.2.0-aztecnr-rc.2/noir-projects/aztec-nr/uint-note/src/uint_note.nr", "source": "use aztec::{\n context::{PrivateContext, PublicContext},\n history::nullifier::assert_nullifier_existed_by,\n keys::getters::{get_nhk_app, get_public_keys, try_get_public_keys},\n macros::notes::custom_note,\n messages::{\n logs::partial_note::encode_partial_note_private_message,\n message_delivery::{do_private_message_delivery, MessageDelivery},\n },\n note::{note_interface::{NoteHash, NoteType}, utils::compute_note_nullifier},\n oracle::random::random,\n protocol::{\n address::AztecAddress,\n constants::{\n DOM_SEP__NOTE_COMPLETION_LOG_TAG, DOM_SEP__NOTE_HASH, DOM_SEP__PARTIAL_NOTE_VALIDITY_COMMITMENT,\n PRIVATE_LOG_CIPHERTEXT_LEN,\n },\n hash::{compute_log_tag, compute_siloed_nullifier, poseidon2_hash_with_separator},\n traits::{Deserialize, FromField, Hash, Packable, Serialize, ToField},\n },\n};\n\n// UintNote supports partial notes, i.e. the ability to create an incomplete note in private, hiding certain values\n// (the owner and randomness), and then completing the note in public with the ones missing (the storage slot and\n// amount). Partial notes are being actively developed and are not currently fully supported via macros, and so we\n// rely on the #[custom_note] macro to implement it manually, resulting in some boilerplate. This is expected to be\n// unnecessary once macro support is expanded.\n\n/// A private note representing a numeric value associated to an account (e.g. a token balance).\n// docs:start:uint_note_def\n#[derive(Deserialize, Eq, Serialize, Packable)]\n#[custom_note]\npub struct UintNote {\n /// The number stored in the note.\n pub value: u128,\n}\n// docs:end:uint_note_def\n\nimpl NoteHash for UintNote {\n // docs:start:compute_note_hash\n fn compute_note_hash(self, owner: AztecAddress, storage_slot: Field, randomness: Field) -> Field {\n // Partial notes can be implemented by having the note hash be either the result of multiscalar multiplication\n // (MSM), or two rounds of poseidon. MSM results in more constraints and is only required when multiple\n // variants of partial notes are supported. Because UintNote has just one variant (where the value is public),\n // we use poseidon instead.\n\n // We must compute the same note hash as would be produced by a partial note created and completed with the\n // same values, so that notes all behave the same way regardless of how they were created. To achieve this, we\n // perform both steps of the partial note computation.\n\n // First we create the partial note from a commitment to the private content.\n let partial_note = PartialUintNote { commitment: compute_partial_commitment(owner, randomness) };\n\n // Then compute the completion note hash. In a real partial note this step would be performed in public.\n partial_note.compute_complete_note_hash(storage_slot, self.value)\n }\n // docs:end:compute_note_hash\n\n // The nullifiers are nothing special - this is just the canonical implementation that would be injected by the\n // #[note] macro.\n\n fn compute_nullifier(\n self,\n context: &mut PrivateContext,\n owner: AztecAddress,\n note_hash_for_nullification: Field,\n ) -> Field {\n let owner_npk_m = get_public_keys(owner).npk_m;\n let owner_npk_m_hash = owner_npk_m.hash();\n let secret = context.request_nhk_app(owner_npk_m_hash);\n compute_note_nullifier(note_hash_for_nullification, [secret])\n }\n\n unconstrained fn compute_nullifier_unconstrained(\n self,\n owner: AztecAddress,\n note_hash_for_nullification: Field,\n ) -> Option {\n try_get_public_keys(owner).map(|public_keys| {\n let owner_npk_m = public_keys.npk_m;\n let owner_npk_m_hash = owner_npk_m.hash();\n let secret = get_nhk_app(owner_npk_m_hash);\n compute_note_nullifier(note_hash_for_nullification, [secret])\n })\n }\n}\n\nimpl UintNote {\n /// Creates a partial note that will hide the owner but not the value or storage slot, since the note will be\n /// later completed in public. This is a powerful technique for scenarios in which the value cannot be known in\n /// private (e.g. because it depends on some public state, such as a DEX).\n ///\n /// This function inserts a partial note validity commitment into the nullifier tree to be later on able to verify\n /// that the partial note and completer are legitimate. See function docs of `compute_validity_commitment` for more\n /// details.\n ///\n /// Each partial note should only be used once, since otherwise multiple notes would be linked together and known\n /// to belong to the same owner.\n ///\n /// As part of the partial note creation process, a log will be sent to `recipient` so that they can discover the\n /// note. `recipient` will typically be the same as `owner`.\n pub fn partial(\n owner: AztecAddress,\n context: &mut PrivateContext,\n recipient: AztecAddress,\n completer: AztecAddress,\n ) -> PartialUintNote {\n // Safety: We use the randomness to preserve the privacy of the note recipient by preventing brute-forcing, so\n // a malicious sender could use non-random values to make the note less private. But they already know the full\n // note pre-image anyway, and so the recipient already trusts them to not disclose this information. We can\n // therefore assume that the sender will cooperate in the random value generation.\n let randomness = unsafe { random() };\n\n // We create a commitment to the private data, which we then use to construct the log we send to the recipient.\n let commitment = compute_partial_commitment(owner, randomness);\n\n // Our partial note log encoding scheme includes a field with the tag of the public completion log, and we use\n // the commitment as the tag. This is good for multiple reasons:\n // - the commitment is uniquely tied to this partial note\n // - the commitment is already public information, so we're not revealing anything else\n // - we don't need to create any additional information, private or public, for the tag\n // - other contracts cannot impersonate us and emit logs with the same tag due to public log siloing\n let private_log_content = UintPartialNotePrivateLogContent {};\n\n do_private_message_delivery(\n context,\n || encode_partial_note_private_message(private_log_content, owner, randomness, commitment),\n Option::none(),\n recipient,\n MessageDelivery.ONCHAIN_UNCONSTRAINED,\n );\n\n let partial_note = PartialUintNote { commitment };\n\n // Now we compute the validity commitment and push it to the nullifier tree. It can be safely pushed to the\n // nullifier tree since it uses its own separator, making collisions with actual note nullifiers practically\n // impossible.\n let validity_commitment = partial_note.compute_validity_commitment(completer);\n context.push_nullifier(validity_commitment);\n\n partial_note\n }\n}\n\n/// Computes a commitment to the private content of a partial UintNote, i.e. the fields that will remain private. All\n/// other note fields will be made public.\n// docs:start:compute_partial_commitment\nfn compute_partial_commitment(owner: AztecAddress, randomness: Field) -> Field {\n poseidon2_hash_with_separator([owner.to_field(), randomness], DOM_SEP__NOTE_HASH)\n}\n// docs:end:compute_partial_commitment\n\n#[derive(Packable)]\n// This note does not have any non-metadata (i.e. storage slot, owner, randomness) private content, as the only field\n// (value) will be public in the partial note.\nstruct UintPartialNotePrivateLogContent {}\n\nimpl NoteType for UintPartialNotePrivateLogContent {\n fn get_id() -> Field {\n UintNote::get_id()\n }\n}\n\n/// A partial instance of a UintNote. This value represents a private commitment to the owner and randomness, but the\n/// storage slot and value fields have not yet been set. A partial note can be completed in public with the `complete`\n/// function (revealing the storage slot and value to the public), resulting in a UintNote that can be used like any\n/// other one (except of course that its value is known).\n// docs:start:partial_uint_note_def\n#[derive(Packable, Serialize, Deserialize, Eq)]\npub struct PartialUintNote {\n commitment: Field,\n}\n// docs:end:partial_uint_note_def\n\nglobal NOTE_COMPLETION_PAYLOAD_LENGTH: u32 = 2;\n\nimpl PartialUintNote {\n /// Completes the partial note, creating a new note that can be used like any other UintNote.\n pub fn complete(self, context: PublicContext, completer: AztecAddress, storage_slot: Field, value: u128) {\n // A note with a value of zero is valid, but we cannot currently complete a partial note with such a value\n // because this will result in the completion log having its last field set to 0. Public logs currently do not\n // track their length, and so trailing zeros are simply trimmed. This results in the completion log missing its\n // last field (the value), and note discovery failing. TODO(#11636): remove this\n assert(value != 0, \"Cannot complete a PartialUintNote with a value of 0\");\n\n // We verify that the partial note we're completing is valid (i.e. completer is correct, it uses the correct\n // state variable's storage slot, and it is internally consistent).\n let validity_commitment = self.compute_validity_commitment(completer);\n // Safety: we're using the existence of the nullifier as proof of the contract having validated the partial\n // note's preimage, which is safe.\n assert(\n context.nullifier_exists_unsafe(validity_commitment, context.this_address()),\n \"Invalid partial note or completer\",\n );\n\n // We need to do two things:\n // - emit a public log containing the public fields (the storage slot and value). The contract will later find\n // it by searching for the domain-separated commitment as the tag.\n // - insert the completion note hash (i.e. the hash of the note) into the note hash tree. This is typically\n // only done in private to hide the preimage of the hash that is inserted, but completed partial notes are\n // inserted in public as the public values are provided and the note hash computed.\n let log_tag = compute_log_tag(self.commitment, DOM_SEP__NOTE_COMPLETION_LOG_TAG);\n context.emit_public_log_unsafe(log_tag, [storage_slot, value.to_field()]);\n context.push_note_hash(self.compute_complete_note_hash(storage_slot, value));\n }\n\n /// Completes the partial note, creating a new note that can be used like any other UintNote. Same as `complete`\n /// function but works from private context.\n pub fn complete_from_private(\n self,\n context: &mut PrivateContext,\n completer: AztecAddress,\n storage_slot: Field,\n value: u128,\n ) {\n // We verify that the partial note we're completing is valid (i.e. completer is correct, it uses the correct\n // state variable's storage slot, and it is internally consistent).\n let validity_commitment = self.compute_validity_commitment(completer);\n // `assert_nullifier_existed_by` function expects the nullifier to be siloed (hashed with the address of the\n // contract that emitted the nullifier) as it checks the value directly against the nullifier tree and all the\n // nullifiers in the tree are siloed by the protocol.\n let siloed_validity_commitment = compute_siloed_nullifier(context.this_address(), validity_commitment);\n assert_nullifier_existed_by(\n context.get_anchor_block_header(),\n siloed_validity_commitment,\n );\n\n // We need to do two things:\n // - emit an unencrypted log containing the public fields (the storage slot and value) via the private log\n // channel. The contract will later find it by searching for the domain-separated commitment as the tag.\n // - insert the completion note hash (i.e. the hash of the note) into the note hash tree. This is typically\n // only done in private to hide the preimage of the hash that is inserted, but completed partial notes are\n // inserted in public as the public values are provided and the note hash computed.\n let log_tag = compute_log_tag(self.commitment, DOM_SEP__NOTE_COMPLETION_LOG_TAG);\n let padded_payload = self.compute_note_completion_payload_padded_for_private_log(storage_slot, value);\n context.emit_private_log_unsafe(log_tag, padded_payload, NOTE_COMPLETION_PAYLOAD_LENGTH);\n context.push_note_hash(self.compute_complete_note_hash(storage_slot, value));\n }\n\n /// Computes a validity commitment for this partial note. The commitment cryptographically binds the note's private\n /// data with the designated completer address. When the note is later completed in public execution, we can load\n /// this commitment from the nullifier tree and verify that both the partial note (e.g. that the storage slot\n /// corresponds to the correct owner, and that we're using the correct state variable) and completer are\n /// legitimate.\n pub fn compute_validity_commitment(self, completer: AztecAddress) -> Field {\n poseidon2_hash_with_separator(\n [self.commitment, completer.to_field()],\n DOM_SEP__PARTIAL_NOTE_VALIDITY_COMMITMENT,\n )\n }\n\n fn compute_note_completion_payload_padded_for_private_log(\n _self: Self,\n storage_slot: Field,\n value: u128,\n ) -> [Field; PRIVATE_LOG_CIPHERTEXT_LEN] {\n let payload = [storage_slot, value.to_field()];\n payload.concat([0; PRIVATE_LOG_CIPHERTEXT_LEN - NOTE_COMPLETION_PAYLOAD_LENGTH])\n }\n\n // docs:start:compute_complete_note_hash\n fn compute_complete_note_hash(self, storage_slot: Field, value: u128) -> Field {\n // Here we finalize the note hash by including the (public) storage slot and value into the partial note\n // commitment. Note that we use the same separator as we used for the first round of poseidon - this is not\n // an issue.\n poseidon2_hash_with_separator(\n [self.commitment, storage_slot, value.to_field()],\n DOM_SEP__NOTE_HASH,\n )\n }\n // docs:end:compute_complete_note_hash\n}\n\nimpl ToField for PartialUintNote {\n fn to_field(self) -> Field {\n self.commitment\n }\n}\n\nimpl FromField for PartialUintNote {\n fn from_field(field: Field) -> Self {\n Self { commitment: field }\n }\n}\n\nmod test {\n use super::{compute_partial_commitment, PartialUintNote, UintNote};\n use aztec::{note::note_interface::NoteHash, protocol::{address::AztecAddress, traits::FromField}};\n\n global value: u128 = 17;\n global randomness: Field = 42;\n global owner: AztecAddress = AztecAddress::from_field(50);\n global storage_slot: Field = 13;\n\n #[test]\n fn note_hash_matches_completed_partial_note_hash() {\n // Tests that a UintNote has the same note hash as a PartialUintNote created and then completed with the same\n // private values. This requires for the same hash function to be used in both flows, with the fields in the\n // same order.\n let note = UintNote { value };\n let note_hash = note.compute_note_hash(owner, storage_slot, randomness);\n\n let partial_note = PartialUintNote { commitment: compute_partial_commitment(owner, randomness) };\n let completed_partial_note_hash = partial_note.compute_complete_note_hash(storage_slot, value);\n\n assert_eq(note_hash, completed_partial_note_hash);\n }\n}\n" }, "499": { "path": "/home/runner/work/aztec-standards/aztec-standards/src/nft_contract/src/types/nft_note.nr", "source": "use aztec::{\n context::{PrivateContext, PublicContext},\n keys::getters::{get_nhk_app, get_public_keys, try_get_public_keys},\n macros::notes::custom_note,\n messages::{\n logs::partial_note::encode_partial_note_private_message,\n message_delivery::{do_private_message_delivery, MessageDelivery},\n },\n note::{note_interface::{NoteHash, NoteType}, utils::compute_note_nullifier},\n oracle::random::random,\n protocol::{\n address::AztecAddress,\n constants::{\n DOM_SEP__NOTE_COMPLETION_LOG_TAG, DOM_SEP__NOTE_HASH,\n DOM_SEP__PARTIAL_NOTE_VALIDITY_COMMITMENT,\n },\n hash::{compute_log_tag, poseidon2_hash_with_separator},\n traits::{Deserialize, Hash, Packable, Serialize, ToField},\n },\n};\n\n// NFTNote supports partial notes, i.e. the ability to create an incomplete note in private, hiding certain values (the\n// owner, storage slot and randomness), and then completing the note in public with the ones missing (the token id).\n// Partial notes are being actively developed and are not currently fully supported via macros, and so we rely on the\n// #[custom_note] macro to implement it manually, resulting in some boilerplate. This is expected to be unnecessary once\n// macro support is expanded.\n\n/// A private note representing a token id associated to an account.\n#[derive(Eq, Serialize, Packable)]\n#[custom_note]\npub struct NFTNote {\n /// The ID of the token represented by this note.\n pub token_id: Field,\n}\n\nimpl NoteHash for NFTNote {\n fn compute_note_hash(\n self,\n owner: AztecAddress,\n storage_slot: Field,\n randomness: Field,\n ) -> Field {\n // Partial notes can be implemented by having the note hash be either the result of multiscalar multiplication\n // (MSM), or two rounds of poseidon. MSM results in more constraints and is only required when multiple variants\n // of partial notes are supported. Because NFTNote has just one variant (where the token id is public), we use\n // poseidon instead.\n\n // We must compute the same note hash as would be produced by a partial note created and completed with the same\n // values, so that notes all behave the same way regardless of how they were created. To achieve this, we\n // perform both steps of the partial note computation.\n\n // First we create the partial note from a commitment to the private content (including storage slot).\n let partial_note =\n PartialNFTNote { commitment: compute_partial_commitment(owner, randomness) };\n\n // Then compute the completion note hash. In a real partial note this step would be performed in public.\n partial_note.compute_complete_note_hash(storage_slot, self.token_id)\n }\n\n // The nullifiers are nothing special - this is just the canonical implementation that would be injected by the\n // #[note] macro.\n\n fn compute_nullifier(\n self,\n context: &mut PrivateContext,\n owner: AztecAddress,\n note_hash_for_nullification: Field,\n ) -> Field {\n let owner_npk_m = get_public_keys(owner).npk_m;\n let owner_npk_m_hash = owner_npk_m.hash();\n let secret = context.request_nhk_app(owner_npk_m_hash);\n compute_note_nullifier(note_hash_for_nullification, [secret])\n }\n\n unconstrained fn compute_nullifier_unconstrained(\n self,\n owner: AztecAddress,\n note_hash_for_nullification: Field,\n ) -> Option {\n try_get_public_keys(owner).map(|public_keys| {\n let owner_npk_m = public_keys.npk_m;\n let owner_npk_m_hash = owner_npk_m.hash();\n let secret = get_nhk_app(owner_npk_m_hash);\n compute_note_nullifier(note_hash_for_nullification, [secret])\n })\n }\n}\n\nimpl NFTNote {\n /// Creates a partial note that will hide the owner and storage slot but not the token id, since the note will be\n /// later completed in public. This is a powerful technique for scenarios in which the token id cannot be known in\n /// private (e.g. because it depends on some public state, such as a DEX).\n ///\n /// This function inserts a partial note validity commitment into the nullifier tree to be later on able to verify\n /// that the partial note and completer are legitimate. See function docs of `compute_validity_commitment` for more\n /// details.\n ///\n /// Each partial note should only be used once, since otherwise multiple notes would be linked together and known to\n /// belong to the same owner.\n ///\n /// As part of the partial note creation process, a log will be sent to `recipient` from `sender` so that they can\n /// discover the note. `recipient` will typically be the same as `owner`.\n pub fn partial(\n owner: AztecAddress,\n context: &mut PrivateContext,\n recipient: AztecAddress,\n completer: AztecAddress,\n ) -> PartialNFTNote {\n // Safety: We use the randomness to preserve the privacy of the note recipient by preventing brute-forcing,\n // so a malicious sender could use non-random values to make the note less private. But they already know\n // the full note pre-image anyway, and so the recipient already trusts them to not disclose this\n // information. We can therefore assume that the sender will cooperate in the random value generation.\n let randomness = unsafe { random() };\n\n // We create a commitment to the private data, which we then use to construct the log we send to the recipient.\n let commitment = compute_partial_commitment(owner, randomness);\n\n // Our partial note log encoding scheme includes a field with the tag of the public completion log, and we use\n // the commitment as the tag. This is good for multiple reasons:\n // - the commitment is uniquely tied to this partial note\n // - the commitment is already public information, so we're not revealing anything else\n // - we don't need to create any additional information, private or public, for the tag\n // - other contracts cannot impersonate us and emit logs with the same tag due to public log siloing\n let private_log_content = NFTPartialNotePrivateLogContent {};\n\n do_private_message_delivery(\n context,\n || {\n encode_partial_note_private_message(\n private_log_content,\n owner,\n randomness,\n commitment,\n )\n },\n Option::none(),\n recipient,\n MessageDelivery.ONCHAIN_UNCONSTRAINED,\n );\n\n let partial_note = PartialNFTNote { commitment };\n\n // Now we compute the validity commitment and push it to the nullifier tree. It can be safely pushed to\n // the nullifier tree since it uses its own separator, making collisions with actual note nullifiers\n // practically impossible.\n let validity_commitment = partial_note.compute_validity_commitment(completer);\n context.push_nullifier(validity_commitment);\n\n partial_note\n }\n}\n\n/// Computes a commitment to the private content of a partial NFTNote, i.e. the fields that will remain private. All\n/// other note fields will be made public.\nfn compute_partial_commitment(owner: AztecAddress, randomness: Field) -> Field {\n poseidon2_hash_with_separator([owner.to_field(), randomness], DOM_SEP__NOTE_HASH)\n}\n\n#[derive(Packable)]\n// This note does not have any non-metadata (i.e. storage slot, owner, randomness) private content, as the only field\n// (token_id) will be public in the partial note.\nstruct NFTPartialNotePrivateLogContent {}\n\nimpl NoteType for NFTPartialNotePrivateLogContent {\n fn get_id() -> Field {\n NFTNote::get_id()\n }\n}\n\n/// A partial instance of a NFTNote. This value represents a private commitment to the owner, randomness and storage\n/// slot, but the token id field has not yet been set. A partial note can be completed in public with the `complete`\n/// function (revealing the token id to the public), resulting in a NFTNote that can be used like any other one (except\n/// of course that its token id is known).\n#[derive(Packable, Serialize, Deserialize)]\npub struct PartialNFTNote {\n pub commitment: Field,\n}\n\nimpl PartialNFTNote {\n /// Completes the partial note, creating a new note that can be used like any other NFTNote.\n pub fn complete(\n self,\n context: PublicContext,\n completer: AztecAddress,\n storage_slot: Field,\n token_id: Field,\n ) {\n // A note with a value of zero is valid, but we cannot currently complete a partial note with such a value\n // because this will result in the completion log having its last field set to 0. Public logs currently do not\n // track their length, and so trailing zeros are simply trimmed. This results in the completion log missing its\n // last field (the value), and note discovery failing.\n // TODO(#11636): remove this\n assert(token_id != 0, \"Cannot complete a PartialNFTNote with a value of 0\");\n\n // We verify that the partial note we're completing is valid (i.e. completer is correct, it uses the correct\n // state variable's storage slot, and it is internally consistent).\n let validity_commitment = self.compute_validity_commitment(completer);\n // Safety: we're using the existence of the nullifier as proof of the contract having validated the partial\n // note's preimage, which is safe.\n assert(\n context.nullifier_exists_unsafe(validity_commitment, context.this_address()),\n \"Invalid partial note or completer\",\n );\n\n // We need to do two things:\n // - emit a public log containing the public fields (the storage slot and token id). The contract will later\n // find it by searching for the domain-separated commitment as the tag.\n // - insert the completion note hash (i.e. the hash of the note) into the note hash tree. This is typically\n // only done in private to hide the preimage of the hash that is inserted, but completed partial notes are\n // inserted in public as the public values are provided and the note hash computed.\n let log_tag = compute_log_tag(self.commitment, DOM_SEP__NOTE_COMPLETION_LOG_TAG);\n context.emit_public_log_unsafe(log_tag, [storage_slot, token_id]);\n context.push_note_hash(self.compute_complete_note_hash(storage_slot, token_id));\n }\n\n /// Computes a validity commitment for this partial note. The commitment cryptographically binds the note's private\n /// data with the designated completer address. When the note is later completed in public execution, we can load\n /// this commitment from the nullifier tree and verify that both the partial note (e.g. that the storage slot\n /// corresponds to the correct owner, and that we're using the correct state variable) and completer are\n /// legitimate.\n pub fn compute_validity_commitment(self, completer: AztecAddress) -> Field {\n poseidon2_hash_with_separator(\n [self.commitment, completer.to_field()],\n DOM_SEP__PARTIAL_NOTE_VALIDITY_COMMITMENT,\n )\n }\n\n fn compute_complete_note_hash(self, storage_slot: Field, token_id: Field) -> Field {\n // Here we finalize the note hash by including the (public) storage slot and token id into the partial note\n // commitment. Note that we use the same separator as we used for the first round of poseidon - this is not\n // an issue.\n poseidon2_hash_with_separator(\n [self.commitment, storage_slot, token_id],\n DOM_SEP__NOTE_HASH,\n )\n }\n}\n" }, "501": { "path": "/home/runner/work/aztec-standards/aztec-standards/src/escrow_contract/src/key_derivation.nr", "source": "use bignum::bignum::BigNum;\nuse bignum::U512;\nuse sha512::sha512;\n\nuse aztec::protocol::constants::{DOM_SEP__IVSK_M, DOM_SEP__NHK_M, DOM_SEP__OVSK_M, DOM_SEP__TSK_M};\nuse aztec::protocol::public_keys::{IvpkM, NpkM, OvpkM, PublicKeys, TpkM};\nuse std::embedded_curve_ops::{EmbeddedCurvePoint, EmbeddedCurveScalar, fixed_base_scalar_mul};\n\npub use crate::types::master_secret_keys::MasterSecretKeys;\n\n// ============ Constants ============\n\n/// 2^120 as a Field constant for U512 limb reconstruction\nglobal TWO_POW_120: Field = 0x1000000000000000000000000000000;\n/// 2^112 as a Field constant for hi limb reconstruction\nglobal TWO_POW_112: Field = 0x10000000000000000000000000000;\n\n/// Fq modulus (BN254 base field / Grumpkin scalar field) as U512 limbs (little-endian, 120-bit each)\n/// Fq.MODULUS = 0x30644e72e131a029b85045b68181585d97816a916871ca8d3c208c16d87cfd47\nglobal FQ_MODULUS_U512: U512 = U512::from_limbs([\n 0x816a916871ca8d3c208c16d87cfd47,\n 0x4e72e131a029b85045b68181585d97,\n 0x3064,\n 0,\n 0,\n]);\n\n// ============ Internal helpers ============\n\n/// Reduces a 64-byte SHA512 hash modulo the Fq modulus, returning an EmbeddedCurveScalar.\n/// This performs: result = hash_value % Fq.MODULUS, then splits the remainder into\n/// lo (lower 128 bits) and hi (upper bits) for EmbeddedCurveScalar construction.\nfn reduce_to_scalar(hash: [u8; 64]) -> EmbeddedCurveScalar {\n // Pad hash to 65 bytes for U512 (which has 513 MOD_BITS)\n let mut hash_padded: [u8; 65] = [0; 65];\n for i in 0..64 {\n hash_padded[i + 1] = hash[i];\n }\n\n let hash_value: U512 = U512::from_be_bytes(hash_padded);\n let (_, remainder) = hash_value.udiv_mod(FQ_MODULUS_U512);\n\n // U512 limbs are 120-bit each (little-endian).\n // EmbeddedCurveScalar splits at 128 bits: lo = lower 128 bits, hi = upper bits.\n //\n // limb[0] = bits [0..120) -- all 120 bits go to lo\n // limb[1] = bits [120..240) -- lower 8 bits go to lo, upper 112 bits go to hi\n // limb[2] = bits [240..360) -- all bits go to hi\n //\n // lo = limb[0] + lower_8_bits(limb[1]) * 2^120\n // hi = upper_112_bits(limb[1]) + limb[2] * 2^112\n let limb0 = remainder.get_limb(0);\n let limb1 = remainder.get_limb(1);\n let limb2 = remainder.get_limb(2);\n\n // Split limb[1] at the 8-bit boundary using u128 integer arithmetic\n let limb1_u128 = limb1 as u128;\n let limb1_lo = (limb1_u128 % 256) as Field; // lower 8 bits\n let limb1_hi = (limb1_u128 / 256) as Field; // upper 112 bits\n\n let lo = (limb0 as Field) + limb1_lo * TWO_POW_120;\n let hi = limb1_hi + (limb2 as Field) * TWO_POW_112;\n\n EmbeddedCurveScalar { lo, hi }\n}\n\n/// Sets the generator index bytes in the last 4 bytes of the buffer\nfn set_generator_index(buffer: &mut [u8; 36], generator_index: u32) {\n buffer[32] = ((generator_index >> 24) & 0xff) as u8;\n buffer[33] = ((generator_index >> 16) & 0xff) as u8;\n buffer[34] = ((generator_index >> 8) & 0xff) as u8;\n buffer[35] = (generator_index & 0xff) as u8;\n}\n\n/// Computes SHA512 of the buffer and reduces the result to an EmbeddedCurveScalar\nfn hash_and_reduce(buffer: [u8; 36]) -> EmbeddedCurveScalar {\n let hash = sha512::digest(buffer);\n reduce_to_scalar(hash)\n}\n\n/// Derives a master secret key from a secret key and generator index\n/// using SHA512 followed by modular reduction to Fq\nfn sha512_to_grumpkin_scalar(secret_key: Field, generator_index: u32) -> EmbeddedCurveScalar {\n let mut buffer: [u8; 36] = [0; 36];\n let sk_bytes: [u8; 32] = secret_key.to_be_bytes();\n for i in 0..32 {\n buffer[i] = sk_bytes[i];\n }\n set_generator_index(&mut buffer, generator_index);\n hash_and_reduce(buffer)\n}\n\n// ============ Public API ============\n\n/// Derive master nullifier hiding key (nhk_m) from secret key\npub fn derive_master_nullifier_hiding_key(secret_key: Field) -> EmbeddedCurveScalar {\n sha512_to_grumpkin_scalar(secret_key, DOM_SEP__NHK_M)\n}\n\n/// Derive master incoming viewing secret key (ivsk_m) from secret key\npub fn derive_master_incoming_viewing_secret_key(secret_key: Field) -> EmbeddedCurveScalar {\n sha512_to_grumpkin_scalar(secret_key, DOM_SEP__IVSK_M)\n}\n\n/// Derive master outgoing viewing secret key (ovsk_m) from secret key\npub fn derive_master_outgoing_viewing_secret_key(secret_key: Field) -> EmbeddedCurveScalar {\n sha512_to_grumpkin_scalar(secret_key, DOM_SEP__OVSK_M)\n}\n\n/// Derive master tagging secret key (tsk_m) from secret key\npub fn derive_master_tagging_secret_key(secret_key: Field) -> EmbeddedCurveScalar {\n sha512_to_grumpkin_scalar(secret_key, DOM_SEP__TSK_M)\n}\n\n/// Derive all four master secret keys at once from a secret key\n/// Optimized to serialize the secret key only once and reuse the buffer\npub fn derive_keys(secret_key: Field) -> MasterSecretKeys {\n let sk_bytes: [u8; 32] = secret_key.to_be_bytes();\n let mut buffer: [u8; 36] = [0; 36];\n for i in 0..32 {\n buffer[i] = sk_bytes[i];\n }\n\n set_generator_index(&mut buffer, DOM_SEP__NHK_M);\n let nhk_m = hash_and_reduce(buffer);\n\n set_generator_index(&mut buffer, DOM_SEP__IVSK_M);\n let ivsk_m = hash_and_reduce(buffer);\n\n set_generator_index(&mut buffer, DOM_SEP__OVSK_M);\n let ovsk_m = hash_and_reduce(buffer);\n\n set_generator_index(&mut buffer, DOM_SEP__TSK_M);\n let tsk_m = hash_and_reduce(buffer);\n\n MasterSecretKeys { nhk_m, ivsk_m, ovsk_m, tsk_m }\n}\n\n/// @notice Derives public keys from master secret keys.\n/// @param master_secret_keys The master secret keys\n/// @return PublicKeys containing the derived public keys.\npub fn master_secret_keys_to_public_keys(master_secret_keys: MasterSecretKeys) -> PublicKeys {\n let npk_m: EmbeddedCurvePoint = fixed_base_scalar_mul(master_secret_keys.nhk_m);\n let ivpk_m: EmbeddedCurvePoint = fixed_base_scalar_mul(master_secret_keys.ivsk_m);\n let ovpk_m: EmbeddedCurvePoint = fixed_base_scalar_mul(master_secret_keys.ovsk_m);\n let tpk_m: EmbeddedCurvePoint = fixed_base_scalar_mul(master_secret_keys.tsk_m);\n\n PublicKeys {\n npk_m: NpkM { inner: npk_m },\n ivpk_m: IvpkM { inner: ivpk_m },\n ovpk_m: OvpkM { inner: ovpk_m },\n tpk_m: TpkM { inner: tpk_m },\n }\n}\n\n/// @notice Derives public keys from a secret key (full pipeline: sk -> msks -> pks).\n/// @param secret_key The secret key\n/// @return PublicKeys containing the derived public keys.\npub fn secret_key_to_public_keys(secret_key: Field) -> PublicKeys {\n let msks = derive_keys(secret_key);\n master_secret_keys_to_public_keys(msks)\n}\n" }, "502": { "path": "/home/runner/work/aztec-standards/aztec-standards/src/escrow_contract/src/library/logic.nr", "source": "// aztec library\nuse aztec::{\n context::PrivateContext,\n event::event_emission::emit_event_in_private,\n messages::message_delivery::MessageDelivery,\n protocol::{\n address::AztecAddress,\n contract_class_id::ContractClassId,\n contract_instance::ContractInstance,\n public_keys::PublicKeys,\n traits::{FromField, ToField},\n },\n};\n// escrow contract\nuse crate::Escrow;\n// key derivation\nuse crate::key_derivation::secret_key_to_public_keys;\n// escrow details event\npub use crate::types::escrow_details_event::EscrowDetailsLogContent;\n\n/** ==========================================================\n * =================== LOGIC LIBRARIES =======================\n * ======================================================== */\n\n/// @notice Returns the escrow address that corresponds to the given secret key and class ID.\n/// @param context The private context\n/// @param escrow_class_id The contract class id of the escrow contract\n/// @param secret_key The secret key used to derive master secret keys and public keys\n/// @return The escrow address\n#[contract_library_method]\npub fn _get_escrow(\n context: &mut PrivateContext,\n escrow_class_id: Field,\n secret_key: Field,\n) -> AztecAddress {\n // Compute the public keys from the secret key (sk -> msks -> pks)\n let computed_public_keys: PublicKeys = secret_key_to_public_keys(secret_key);\n\n let escrow_instance = ContractInstance {\n salt: context.this_address().to_field(),\n deployer: AztecAddress::from_field(0),\n contract_class_id: ContractClassId::from_field(escrow_class_id),\n initialization_hash: 0,\n public_keys: computed_public_keys,\n };\n\n escrow_instance.to_address()\n}\n\n/// @notice Shares the escrow details needed to find and use the escrow contract\n/// @dev Emits a private log with the escrow details\n/// @param context The private context\n/// @param account The address of the account that will use the escrow\n/// @param escrow The address of the escrow\n/// @param secret_key The secret key used to derive master secret keys and public keys\n#[contract_library_method]\npub fn _share_escrow(\n context: &mut PrivateContext,\n account: AztecAddress,\n escrow: AztecAddress,\n secret_key: Field,\n) {\n let event_struct = EscrowDetailsLogContent { escrow, secret_key };\n\n emit_event_in_private(context, event_struct).deliver_to(\n account,\n MessageDelivery.ONCHAIN_CONSTRAINED,\n );\n}\n\n/// @notice Withdraws an amount of tokens from the provided escrow.\n/// @param context The private context\n/// @param escrow The address of the escrow\n/// @param account The address of the account that will receive the tokens\n/// @param token The address of the token\n/// @param amount The amount of tokens to withdraw from the escrow\n#[contract_library_method]\npub fn _withdraw(\n context: &mut PrivateContext,\n escrow: AztecAddress,\n account: AztecAddress,\n token: AztecAddress,\n amount: u128,\n) {\n Escrow::at(escrow).withdraw(token, amount, account).call(context);\n}\n\n/// @notice Withdraws an NFT from the provided escrow.\n/// @param context The private context\n/// @param escrow The address of the escrow\n/// @param account The address of the account that will receive the NFT\n/// @param nft The address of the NFT contract\n/// @param token_id The id of the token to withdraw from the escrow\n#[contract_library_method]\npub fn _withdraw_nft(\n context: &mut PrivateContext,\n escrow: AztecAddress,\n account: AztecAddress,\n nft: AztecAddress,\n token_id: Field,\n) {\n Escrow::at(escrow).withdraw_nft(nft, token_id, account).call(context);\n}\n" }, "505": { "path": "/home/runner/work/aztec-standards/aztec-standards/src/escrow_contract/src/test/test_logic_contract/src/main.nr", "source": "mod test;\n\nuse aztec::macros::aztec;\n\n#[aztec]\ncontract TestLogic {\n // aztec library\n use aztec::{\n macros::{events::event, functions::{external, initializer}, storage::storage},\n protocol::{address::AztecAddress, public_keys::PublicKeys},\n state_vars::PublicImmutable,\n };\n use escrow_contract::key_derivation;\n use escrow_contract::library::logic::{_get_escrow, _share_escrow, _withdraw, _withdraw_nft};\n\n /// @notice Data privately emitted to an account that will use the escrow\n /// @dev We declare the event struct here to expose it in the abi as macro event cannot be declared in libraries, only in contracts\n /// @dev See to library/logic.nr _share_escrow function for the event emission implementation\n /// @dev #[allow(dead_code)] added because is used because EscrowDetailsLogContent is defined for logging but not directly instantiated.\n /// @param escrow The address of the escrow\n /// @param secret_key The secret key used to derive master secret keys and public keys\n #[event]\n #[allow(dead_code)]\n struct EscrowDetailsLogContent {\n escrow: AztecAddress,\n secret_key: Field,\n }\n\n // @param escrow_class_id The contract class id of the escrow contract\n #[storage]\n struct Storage {\n escrow_class_id: PublicImmutable,\n }\n\n /// @dev Initialize the contract\n /// @param escrow_class_id The contract class id of the escrow contract\n #[external(\"public\")]\n #[initializer]\n fn constructor(escrow_class_id: Field) {\n self.storage.escrow_class_id.initialize(escrow_class_id);\n }\n\n /// @notice Returns the escrow address that corresponds to the given secret key and class ID.\n /// @param secret_key The secret key used to derive master secret keys and public keys\n /// @return The escrow address\n #[external(\"private\")]\n fn get_escrow(secret_key: Field) -> AztecAddress {\n _get_escrow(\n self.context,\n self.storage.escrow_class_id.read(),\n secret_key,\n )\n }\n\n /// @notice Shares the escrow details needed to find and use the escrow contract\n /// @dev Emits a private log with the escrow details\n /// @param account The address of the account that will receive the escrow details\n /// @param escrow The address of the escrow\n /// @param secret_key The secret key used to derive master secret keys and public keys\n #[external(\"private\")]\n fn share_escrow(account: AztecAddress, escrow: AztecAddress, secret_key: Field) {\n _share_escrow(self.context, account, escrow, secret_key);\n }\n\n /// @notice Withdraws an amount of tokens from the provided escrow.\n /// @param escrow The address of the escrow\n /// @param account The address of the account that will receive the tokens\n /// @param token The address of the token\n /// @param amount The amount of tokens to withdraw from the escrow\n #[external(\"private\")]\n fn withdraw(escrow: AztecAddress, account: AztecAddress, token: AztecAddress, amount: u128) {\n _withdraw(self.context, escrow, account, token, amount);\n }\n\n /// @notice Withdraws an NFT from the provided escrow.\n /// @param escrow The address of the escrow\n /// @param account The address of the account that will receive the NFT\n /// @param nft The address of the NFT contract\n /// @param token_id The id of the token to withdraw from the escrow\n #[external(\"private\")]\n fn withdraw_nft(\n escrow: AztecAddress,\n account: AztecAddress,\n nft: AztecAddress,\n token_id: Field,\n ) {\n _withdraw_nft(self.context, escrow, account, nft, token_id);\n }\n\n /// @notice Derives public keys from a secret key.\n /// @param secret_key The secret key used to derive master secret keys and public keys\n /// @return PublicKeys containing the derived public keys.\n #[external(\"private\")]\n fn secret_key_to_public_keys(secret_key: Field) -> PublicKeys {\n key_derivation::secret_key_to_public_keys(secret_key)\n }\n}\n" }, "522": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/bignum.nr", "source": "use crate::fns::constrained_ops::limbs_to_field;\nuse crate::params::BigNumParams;\nuse std::ops::{Add, Div, Mul, Neg, Sub};\n\n/// The core trait for arbitrary-precision modular arithmetic.\n///\n/// `BigNum` represents integers modulo a compile-time known modulus. Values are stored as\n/// arrays of 120-bit limbs in little-endian format.\n///\n/// # Important Notes\n///\n/// - **Single-limb BigNum is not supported**: This library assumes at least two 120-bit limbs\n/// (modulus >= 2^120). Operations on single-limb BigNums may produce incorrect results.\n///\n/// - **Constrained vs Unconstrained**: Methods prefixed with `__` are unconstrained and do not\n/// generate circuit constraints. Use them for witness computation, then constrain results with\n/// `evaluate_quadratic_expression` for optimal performance.\n///\n/// - **Performance**: The overloaded operators (`+`, `-`, `*`, `/`) are constrained and\n/// expensive. For multiple operations, prefer using unconstrained methods followed by\n/// `evaluate_quadratic_expression`.\n///\n/// - **Modular vs Integer semantics**: Most operations are modular (mod `MOD`). For integer\n/// division/remainder, use `udiv`, `umod`, `udiv_mod`.\n///\n/// # Example\n/// ```\n/// use dep::bignum::U256;\n/// use dep::bignum::BigNum;\n///\n/// fn example(a: U256, b: U256) -> U256 {\n/// // Constrained multiplication\n/// a * b\n/// }\n/// ```\npub trait BigNum: Neg + Add + Sub + Mul + Div + Eq {\n /// Number of 120-bit limbs used to represent this BigNum.\n let N: u32;\n /// Number of bits in the modulus.\n let MOD_BITS: u32;\n\n /// Returns the parameter set for this BigNum type.\n ///\n /// The parameters include the modulus, precomputed reduction constants, and whether\n /// the modulus is prime (has multiplicative inverses).\n fn params() -> BigNumParams;\n\n /// Returns the number of bits in the modulus.\n ///\n /// Note: Takes a dummy `Self` parameter for trait method resolution.\n fn modulus_bits(_: Self) -> u32;\n\n /// Returns the number of limbs used to represent this BigNum.\n ///\n /// Note: Takes a dummy `Self` parameter for trait method resolution.\n fn num_limbs(_: Self) -> u32;\n\n /// Returns the modulus as a BigNum value.\n ///\n /// # Note\n /// The modulus itself is considered equivalent to zero in modular arithmetic.\n /// `is_zero(modulus())` returns `true`.\n fn modulus() -> Self;\n\n /// Creates a new BigNum with value 0.\n ///\n /// Equivalent to `zero()`.\n fn new() -> Self;\n\n /// Returns the additive identity (0).\n fn zero() -> Self;\n\n /// Returns the multiplicative identity (1).\n fn one() -> Self;\n\n /// Constructs a BigNum from raw limbs.\n ///\n /// # Safety\n /// **FOOTGUN**: The limbs are NOT validated. You MUST call `validate_in_range()` on the\n /// result if the limbs come from untrusted input, otherwise malicious witnesses could\n /// bypass range checks.\n ///\n /// Each limb should be at most 120 bits, except the top limb which should be at most\n /// `MOD_BITS - 120 * (N - 1)` bits.\n ///\n /// # Example\n /// ```\n /// let bn = MyBigNum::from_limbs([1, 0, 0]);\n /// bn.validate_in_range(); // Always validate untrusted input!\n /// ```\n fn from_limbs(limbs: [u128; N]) -> Self;\n\n /// Returns the raw limb representation.\n ///\n /// Limbs are in little-endian order: `limbs[0]` contains the least significant 120 bits.\n fn get_limbs(self) -> [u128; N];\n\n /// Sets a specific limb to a new value.\n ///\n /// # Safety\n /// **FOOTGUN**: The new limb value is NOT validated. You MUST call `validate_in_range()`\n /// after modification if the value comes from untrusted input.\n fn set_limb(self: &mut Self, idx: u32, value: u128);\n\n /// Returns the limb at the given index.\n fn get_limb(self: Self, idx: u32) -> u128;\n\n /// Generates a deterministic pseudorandom BigNum from a seed.\n ///\n /// Uses Poseidon2 hash to derive `MOD_BITS * 2` bits of entropy, then reduces modulo `MOD`.\n ///\n /// # Security Warning\n /// **NOT cryptographically secure for all use cases**. The output is deterministic and\n /// uniformly distributed modulo `MOD`, but the seed-to-output mapping is publicly computable.\n /// Do not use for secret key generation where the seed might be guessable.\n ///\n /// # Constrained\n /// This is a constrained function that generates range check constraints.\n fn derive_from_seed(seed: [u8; SeedBytes]) -> Self;\n\n /// Unconstrained version of `derive_from_seed`.\n ///\n /// # Safety\n /// No constraints are generated. Use only for witness computation in unconstrained contexts.\n unconstrained fn __derive_from_seed(seed: [u8; SeedBytes]) -> Self;\n\n /// Constructs a BigNum from big-endian bytes.\n ///\n /// The byte array length must be exactly `(MOD_BITS + 7) / 8`.\n ///\n /// # Panics\n /// Panics if the value represented by the bytes is >= 2^MOD_BITS.\n fn from_be_bytes(x: [u8; (MOD_BITS + 7) / 8]) -> Self;\n\n /// Converts to big-endian byte representation.\n fn to_be_bytes(self) -> [u8; (MOD_BITS + 7) / 8];\n\n /// Constructs a BigNum from little-endian bytes.\n ///\n /// The byte array length must be exactly `(MOD_BITS + 7) / 8`.\n ///\n /// # Panics\n /// Panics if the value represented by the bytes is >= 2^MOD_BITS.\n fn from_le_bytes(x: [u8; (MOD_BITS + 7) / 8]) -> Self;\n\n /// Converts to little-endian byte representation.\n fn to_le_bytes(self) -> [u8; (MOD_BITS + 7) / 8];\n\n /// Unconstrained equality check.\n ///\n /// Returns `true` if `self == other` as integers (not modular equality).\n ///\n /// # Safety\n /// No constraints are generated. Use `==` operator for constrained equality.\n unconstrained fn __eq(self: Self, other: Self) -> bool;\n\n /// Unconstrained zero check.\n ///\n /// Returns `true` if all limbs are zero.\n ///\n /// # Note\n /// This checks for integer zero only, not modular zero.\n /// `modulus().__is_zero()` returns `false` even though `modulus == 0 (mod MOD)`.\n ///\n /// # Safety\n /// No constraints are generated. Use `is_zero()` for constrained check.\n unconstrained fn __is_zero(self: Self) -> bool;\n\n /// Unconstrained negation: computes `-self (mod MOD)`.\n ///\n /// # Note\n /// When `self` is zero, this returns the modulus (which is equivalent to zero mod MOD).\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __neg(self) -> Self;\n\n /// Unconstrained addition: computes `self + other (mod MOD)`.\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __add(self, other: Self) -> Self;\n\n /// Unconstrained subtraction: computes `self - other (mod MOD)`.\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __sub(self, other: Self) -> Self;\n\n /// Unconstrained multiplication: computes `self * other (mod MOD)`.\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __mul(self, other: Self) -> Self;\n\n /// Unconstrained squaring: computes `self * self (mod MOD)`.\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __sqr(self) -> Self;\n\n /// Unconstrained modular division: computes `self * other^{-1} (mod MOD)`.\n ///\n /// # Requirements\n /// - `MOD` must be prime (`params.has_multiplicative_inverse == true`)\n /// - `other` must not be zero\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Requires modular exponentiation during witness computation.\n /// Consider using `batch_invert` for multiple divisions.\n ///\n /// # Safety\n /// No constraints are generated. Constrain the result using `evaluate_quadratic_expression`.\n unconstrained fn __div(self, other: Self) -> Self;\n\n /// Unconstrained integer division with remainder.\n ///\n /// Returns `(quotient, remainder)` where `self == quotient * divisor + remainder`\n /// and `0 <= remainder < divisor`.\n ///\n /// # Note\n /// This is INTEGER division, not modular. Use when `MOD` is not prime or when you\n /// need the actual quotient/remainder, not the modular inverse.\n ///\n /// # Safety\n /// No constraints are generated. Use `udiv_mod()` for the constrained version.\n unconstrained fn __udiv_mod(self, divisor: Self) -> (Self, Self);\n\n /// Unconstrained modular inverse: computes `self^{-1} (mod MOD)`.\n ///\n /// # Requirements\n /// - `MOD` must be prime (`params.has_multiplicative_inverse == true`)\n /// - `self` must not be zero\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Requires modular exponentiation. For multiple inverses, use `batch_invert`.\n ///\n /// # Safety\n /// No constraints are generated.\n unconstrained fn __invmod(self) -> Self;\n\n /// Unconstrained modular exponentiation: computes `self^exponent (mod MOD)`.\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Complexity is O(MOD_BITS) multiplications.\n ///\n /// # Safety\n /// No constraints are generated.\n unconstrained fn __pow(self, exponent: Self) -> Self;\n\n /// **Deprecated**: use `__sqrt` instead.\n unconstrained fn __tonelli_shanks_sqrt(self) -> std::option::Option;\n\n /// Unconstrained modular square root.\n ///\n /// Returns `Some(root)` where `root * root == self (mod MOD)`, or `None` if no square root exists.\n ///\n /// # Requirements\n /// - `MOD` must be prime (`params.has_multiplicative_inverse == true`)\n ///\n /// # Note\n /// When a square root exists, there are always two: `root` and `-root (mod MOD)`.\n /// This function returns one of them (implementation-defined which one).\n ///\n /// # Safety\n /// No constraints are generated.\n unconstrained fn __sqrt(self) -> std::option::Option;\n\n /// Asserts that `self != other (mod MOD)`.\n ///\n /// # Performance Note\n /// This is cheaper than `assert(self != other)` because it avoids creating a boolean result.\n ///\n /// # Soundness\n /// Sound for `MOD < circuit_field_modulus`. For very large moduli approaching the circuit\n /// field size, there's a negligible (~3/p) probability of false positives.\n fn assert_is_not_equal(self: Self, other: Self);\n\n /// Returns `true` if `self == 0 (mod MOD)`.\n ///\n /// # Note\n /// This treats both the zero limb vector AND the modulus itself as zero, since\n /// `modulus == 0 (mod MOD)`.\n ///\n /// # Performance Note\n /// Cheaper than `self == BigNum::zero()`.\n fn is_zero(self) -> bool;\n\n /// Returns `true` if all limbs are zero (integer zero, not modular).\n ///\n /// # Note\n /// Unlike `is_zero()`, this returns `false` for the modulus value even though\n /// `modulus == 0 (mod MOD)`.\n fn is_zero_integer(self) -> bool;\n\n /// Asserts that `self != 0 (mod MOD)`.\n ///\n /// # Performance Note\n /// Cheaper than `assert(!self.is_zero())` or `assert(self != BigNum::zero())`.\n fn assert_is_not_zero(self);\n\n /// Asserts that at least one limb is non-zero (integer assertion).\n ///\n /// # Note\n /// Unlike `assert_is_not_zero()`, this will pass for the modulus value.\n ///\n /// # Performance Note\n /// Cheaper than `assert(!self.is_zero_integer())`.\n fn assert_is_not_zero_integer(self);\n\n /// Validates that each limb is properly range-constrained.\n ///\n /// Ensures:\n /// - Limbs 0 to N-2 are < 2^120\n /// - Limb N-1 is < 2^(MOD_BITS - 120*(N-1))\n ///\n /// # When to Use\n /// Call this after `from_limbs()` or `set_limb()` when the input comes from untrusted sources.\n ///\n /// # Performance Note\n /// Range constraints are deduplicated per value, so calling this multiple times on the same\n /// BigNum does not add duplicate constraints.\n ///\n /// # Note\n /// This does NOT guarantee `self < MOD`. Use `validate_in_field()` for that.\n fn validate_in_range(self);\n\n /// Validates that `self <= MOD`.\n ///\n /// # Note\n /// - This allows `self == MOD` (which equals 0 in modular arithmetic), consistent with\n /// the library's treatment of the modulus as a valid representation of zero.\n /// - This is a STRONGER check than `validate_in_range()`.\n /// - Unlike `validate_in_range()`, this check is NOT deduplicated. Repeated calls will\n /// add redundant constraints.\n fn validate_in_field(self);\n\n /// Constrained squaring: computes `self * self (mod MOD)`.\n ///\n /// # Performance Note\n /// For multiple operations, prefer using `__sqr()` followed by `evaluate_quadratic_expression`.\n fn sqr(self) -> Self;\n\n /// Constrained integer division with remainder.\n ///\n /// Returns `(quotient, remainder)` where `self == quotient * divisor + remainder`\n /// and `0 <= remainder < divisor`.\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Requires long binary division.\n ///\n /// # Panics\n /// Will fail constraint if `divisor == 0`.\n ///\n /// # Note\n /// This is INTEGER division. For modular division on prime fields, use the `/` operator.\n fn udiv_mod(self, divisor: Self) -> (Self, Self);\n\n /// Constrained integer division: returns `floor(self / divisor)`.\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Internally calls `udiv_mod()`.\n ///\n /// # Note\n /// This is INTEGER division, not modular. The remainder is discarded.\n fn udiv(self, divisor: Self) -> Self;\n\n /// Constrained integer modulo: returns `self % divisor`.\n ///\n /// # Performance Warning\n /// **EXPENSIVE**: Internally calls `udiv_mod()`.\n ///\n /// # Note\n /// This is INTEGER modulo. The result is the remainder after integer division.\n fn umod(self, divisor: Self) -> Self;\n}\n\n// We need macros that implement the BigNum, Default, From, Neg, Add, Sub, Mul, Div, Eq, Ord traits for each bignum type\npub comptime fn derive_bignum(\n strukt: TypeDefinition,\n N: u32,\n MOD_BITS: u32,\n params: Quoted,\n) -> Quoted {\n let typ = strukt.as_type();\n quote {\n\n // implement BigNum for BigNum \n impl $crate::BigNum for $typ {\n let N: u32 = $N; \n let MOD_BITS: u32 = $MOD_BITS;\n \n fn modulus_bits(_: Self) -> u32 {\n $MOD_BITS\n }\n \n fn num_limbs(_: Self) -> u32 {\n $N\n }\n\n fn modulus() -> Self {\n Self { limbs: Self::params().modulus }\n }\n\n fn params() -> $crate::params::BigNumParams<$N, $MOD_BITS> {\n $params\n }\n\n fn new() -> Self {\n Self {limbs: [0; $N]}\n }\n\n /// Note: You have to properly constrain the limbs prior calling this method\n fn from_limbs(limbs: [u128; $N]) -> Self {\n Self { limbs }\n }\n\n fn get_limbs(self: Self) -> [u128; $N] {\n self.limbs\n }\n\n /// Note: You have to properly constrain the limb prior calling this method\n fn set_limb(self: &mut Self, idx: u32, value: u128) {\n self.limbs[idx] = value;\n }\n\n fn get_limb(self: Self, idx: u32) -> u128 {\n self.limbs[idx]\n }\n\n fn zero() -> Self {\n Self { limbs: [0; $N] }\n }\n\n fn one() -> Self {\n let mut limbs = [0; $N];\n limbs[0] = 1;\n Self { limbs }\n }\n\n fn derive_from_seed(seed: [u8; SeedBytes]) -> Self {\n let params = Self::params();\n $typ::from_limbs($crate::internal::derive_from_seed::<$N, $MOD_BITS, SeedBytes>(params, seed))\n }\n\n unconstrained fn __derive_from_seed(seed: [u8; SeedBytes]) -> Self {\n let params = Self::params();\n Self { limbs: $crate::internal::__derive_from_seed::<$N, $MOD_BITS, SeedBytes>(params, seed) }\n }\n\n fn from_be_bytes(x: [u8; ($MOD_BITS + 7) / 8]) -> Self {\n Self { limbs: $crate::internal::from_be_bytes::<$N, $MOD_BITS>(x) }\n }\n\n fn to_be_bytes(self) -> [u8; ($MOD_BITS + 7) / 8] {\n $crate::internal::to_be_bytes::<$N, $MOD_BITS>(self.limbs)\n }\n\n fn from_le_bytes(x: [u8; ($MOD_BITS + 7) / 8]) -> Self {\n Self { limbs: $crate::internal::from_le_bytes::<$N, $MOD_BITS>(x) }\n }\n\n fn to_le_bytes(self) -> [u8; ($MOD_BITS + 7) / 8] {\n $crate::internal::to_le_bytes::<$N, $MOD_BITS>(self.limbs)\n }\n\n unconstrained fn __eq(self: Self, other: Self) -> bool {\n $crate::internal::__eq(self.get_limbs(), other.get_limbs())\n }\n\n unconstrained fn __is_zero(self: Self) -> bool {\n $crate::internal::__is_zero(self.get_limbs())\n }\n\n unconstrained fn __neg(self: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__neg(params.modulus, self.get_limbs())}\n }\n\n unconstrained fn __add(self: Self, other: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__add(params.modulus, self.get_limbs(), other.get_limbs())}\n }\n\n unconstrained fn __sub(self: Self, other: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__sub(params.modulus, self.get_limbs(), other.get_limbs())}\n }\n\n unconstrained fn __mul(self: Self, other: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__mul(params, self.get_limbs(), other.get_limbs())}\n }\n\n unconstrained fn __sqr(self: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__sqr(params, self.get_limbs()) }\n }\n\n unconstrained fn __div(self: Self, divisor: Self) -> Self {\n let params = Self::params();\n if $params.has_multiplicative_inverse {\n Self { limbs: $crate::internal::__div(params, self.get_limbs(), divisor.get_limbs()) }\n } else {\n Self { limbs: $crate::internal::__udiv_mod(self.get_limbs(), divisor.get_limbs()).0 }\n }\n }\n\n unconstrained fn __udiv_mod(self: Self, divisor: Self) -> (Self, Self) {\n let (q, r) = $crate::internal::__udiv_mod(self.get_limbs(), divisor.get_limbs());\n (Self{limbs: q}, Self{limbs: r})\n }\n\n unconstrained fn __invmod(self: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__invmod(params, self.get_limbs())}\n }\n\n unconstrained fn __pow(self: Self, exponent: Self) -> Self {\n let params = Self::params();\n Self {limbs: $crate::internal::__pow(params, self.get_limbs(), exponent.get_limbs())}\n }\n\n #[deprecated(\"use __sqrt\")]\n unconstrained fn __tonelli_shanks_sqrt(self: Self) -> std::option::Option {\n let params = Self::params();\n let maybe_limbs: Option<[u128; $N]> = $crate::internal::__sqrt(params, self.get_limbs());\n maybe_limbs.map(|limbs| Self {limbs: limbs})\n }\n\n unconstrained fn __sqrt(self: Self) -> std::option::Option {\n let params = Self::params();\n let maybe_limbs: Option<[u128; $N]> = $crate::internal::__sqrt(params, self.get_limbs());\n maybe_limbs.map(|limbs| Self {limbs: limbs })\n }\n\n fn assert_is_not_equal(self: Self, other: Self) {\n let params = Self::params();\n $crate::internal::assert_is_not_equal(\n params,\n self.get_limbs(),\n other.get_limbs(),\n );\n }\n\n fn validate_in_field(self: Self) {\n let params = Self::params();\n $crate::internal::validate_in_field::<$N, $MOD_BITS>(params, self.get_limbs());\n }\n\n fn validate_in_range(self: Self) {\n $crate::internal::validate_in_range::(self.get_limbs());\n }\n\n fn sqr(self: Self) -> Self {\n let params = Self::params();\n Self { limbs: $crate::internal::sqr::<$N, $MOD_BITS>(params, self.get_limbs()) }\n }\n\n fn udiv_mod(self: Self, divisor: Self) -> (Self, Self) {\n let (q, r) = $crate::internal::udiv_mod::<$N, $MOD_BITS>(self.get_limbs(), divisor.get_limbs());\n (Self {limbs: q}, Self {limbs: r})\n }\n\n fn udiv(self: Self, divisor: Self) -> Self {\n Self {limbs: $crate::internal::udiv::<$N, $MOD_BITS>(self.get_limbs(), divisor.get_limbs())}\n }\n\n fn umod(self: Self, divisor: Self) -> Self {\n Self {limbs: $crate::internal::umod::<$N, $MOD_BITS>(self.get_limbs(), divisor.get_limbs())}\n }\n\n fn is_zero(self: Self) -> bool {\n let params = Self::params();\n $crate::internal::is_zero::<$N, $MOD_BITS>(params, self.get_limbs())\n }\n\n fn is_zero_integer(self: Self) -> bool {\n $crate::internal::is_zero_integer::(self.get_limbs())\n }\n\n fn assert_is_not_zero(self: Self) {\n let params = Self::params();\n $crate::internal::assert_is_not_zero::<$N, $MOD_BITS>(params, self.get_limbs());\n }\n\n fn assert_is_not_zero_integer(self: Self) {\n $crate::internal::assert_is_not_zero_integer(self.get_limbs());\n }\n }\n\n // implement Default for BigNum\n impl Default for $typ {\n fn default() -> Self {\n $typ::from_limbs([0; $N])\n }\n }\n\n impl std::convert::From for $typ {\n fn from(input: Field) -> Self {\n $typ { limbs: $crate::internal::from_field::<$N, $MOD_BITS>($params, input) }\n }\n }\n\n impl std::ops::Neg for $typ {\n fn neg(self) -> Self {\n $typ { limbs: $crate::internal::neg::<$N, $MOD_BITS>($params, self.limbs) }\n }\n }\n\n impl std::ops::Add for $typ {\n fn add(self, other: Self) -> Self {\n $typ { limbs: $crate::internal::add::<$N, $MOD_BITS>($params, self.limbs, other.limbs) }\n }\n }\n\n impl std::ops::Sub for $typ {\n fn sub(self, other: Self) -> Self {\n $typ { limbs: $crate::internal::sub::<$N, $MOD_BITS>($params, self.limbs, other.limbs) }\n }\n }\n\n impl std::ops::Mul for $typ {\n fn mul(self, other: Self) -> Self {\n $typ { limbs: $crate::internal::mul::<$N, $MOD_BITS>($params, self.limbs, other.limbs) }\n }\n }\n\n impl std::ops::Div for $typ {\n fn div(self, other: Self) -> Self {\n if $params.has_multiplicative_inverse {\n $typ { limbs: $crate::internal::div::<$N, $MOD_BITS>($params, self.limbs, other.limbs) }\n } else {\n $typ { limbs: $crate::internal::udiv::<$N, $MOD_BITS>(self.limbs, other.limbs) }\n }\n }\n }\n\n impl std::cmp::Eq for $typ {\n fn eq(self, other: Self) -> bool {\n $crate::internal::eq::<$N, $MOD_BITS>($params, self.limbs, other.limbs)\n }\n }\n\n impl std::cmp::Ord for $typ {\n fn cmp(self, other: Self) -> std::cmp::Ordering {\n $crate::internal::cmp::<$N, $MOD_BITS>(self.limbs, other.limbs)\n }\n }\n\n }\n}\n\n/// Conditionally selects between two BigNum values.\n///\n/// Returns `lhs` if `predicate` is `true`, otherwise returns `rhs`.\n///\n/// # Example\n/// ```\n/// let result = conditional_select(a, b, condition);\n/// // Equivalent to: if condition { a } else { b }\n/// ```\n///\n/// # Note\n/// This is useful for constant-time selection where you want to avoid branching\n/// on secret values in constrained code.\npub fn conditional_select(lhs: T, rhs: T, predicate: bool) -> T {\n if predicate {\n lhs\n } else {\n rhs\n }\n}\n\n/// Computes the quotient and remainder of a quadratic expression (unconstrained).\n///\n/// Evaluates `sum_i(product(lhs_terms[i]) * product(rhs_terms[i])) + sum(linear_terms) = q * MOD + r`\n/// and returns `(q, r)`.\n///\n/// # Parameters\n/// - `lhs_terms`: Left-hand side terms for each product. Shape: `[NUM_PRODUCTS][LHS_N]`\n/// - `lhs_flags`: If `true`, the corresponding lhs_term is negated\n/// - `rhs_terms`: Right-hand side terms for each product. Shape: `[NUM_PRODUCTS][RHS_N]`\n/// - `rhs_flags`: If `true`, the corresponding rhs_term is negated\n/// - `linear_terms`: Terms to add to the sum. Shape: `[ADD_N]`\n/// - `linear_flags`: If `true`, the corresponding linear_term is negated\n///\n/// # Safety\n/// **UNCONSTRAINED**: This function generates no constraints. Use `evaluate_quadratic_expression`\n/// to constrain the result, or use the returned values as witnesses that you constrain separately.\n///\n/// # Example\n/// Computing `a * b + c`:\n/// ```\n/// // Safety: unconstrained witness computation\n/// let (q, r) = unsafe {\n/// compute_quadratic_expression(\n/// [[a]], // lhs_terms\n/// [[false]], // lhs_flags (not negated)\n/// [[b]], // rhs_terms\n/// [[false]], // rhs_flags\n/// [c], // linear_terms\n/// [false], // linear_flags\n/// )\n/// };\n/// // r now contains (a * b + c) mod MOD\n/// ```\npub unconstrained fn compute_quadratic_expression(\n lhs_terms: [[T; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[T; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [T; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> (T, T) {\n let params = T::params();\n let (q_limbs, r_limbs) = crate::fns::expressions::__compute_quadratic_expression(\n params,\n crate::utils::map::map(\n lhs_terms,\n |bns| crate::utils::map::map(bns, |bn: T| bn.get_limbs()),\n ),\n lhs_flags,\n crate::utils::map::map(\n rhs_terms,\n |bns| crate::utils::map::map(bns, |bn: T| bn.get_limbs()),\n ),\n rhs_flags,\n crate::utils::map::map(linear_terms, |bn: T| bn.get_limbs()),\n linear_flags,\n );\n (T::from_limbs(q_limbs), T::from_limbs(r_limbs))\n}\n\n/// Constrains a quadratic expression to equal zero modulo `MOD`.\n///\n/// This is the **recommended** way to constrain BigNum arithmetic for optimal performance.\n/// Instead of using multiple constrained operations (`+`, `-`, `*`), compute results using\n/// unconstrained functions (`__add`, `__mul`, etc.) and then constrain them with a single\n/// call to this function.\n///\n/// # Expression Format\n/// Constrains: `sum_i(product(lhs_terms[i]) * product(rhs_terms[i])) + sum(linear_terms) == 0 (mod MOD)`\n///\n/// # Parameters\n/// - `lhs_terms`: Left-hand side terms for each product. Shape: `[NUM_PRODUCTS][LHS_N]`\n/// - `lhs_flags`: If `true`, the corresponding lhs_term is negated\n/// - `rhs_terms`: Right-hand side terms for each product. Shape: `[NUM_PRODUCTS][RHS_N]`\n/// - `rhs_flags`: If `true`, the corresponding rhs_term is negated\n/// - `linear_terms`: Terms to add to the sum. Shape: `[ADD_N]`\n/// - `linear_flags`: If `true`, the corresponding linear_term is negated\n///\n/// # Example: Constraining `a * b + c - result == 0`\n/// ```\n/// // First compute result unconstrainedly\n/// // Safety: witness computation, constrained below\n/// let result = unsafe { a.__mul(b).__add(c) };\n///\n/// // Then constrain: a * b + c - result == 0\n/// evaluate_quadratic_expression(\n/// [[a]], // lhs: [a]\n/// [[false]], // not negated\n/// [[b]], // rhs: [b]\n/// [[false]], // not negated\n/// [c, result], // linear: c + (-result)\n/// [false, true], // c positive, result negated\n/// );\n/// ```\n///\n/// # Example: Constraining `(a + b) * c - d == 0`\n/// ```\n/// // Safety: witness computation\n/// let d = unsafe { (a.__add(b)).__mul(c) };\n///\n/// // Constrain: (a + b) * c - d == 0\n/// evaluate_quadratic_expression(\n/// [[a, b]], // lhs: (a + b)\n/// [[false, false]],\n/// [[c]], // rhs: c\n/// [[false]],\n/// [d], // linear: -d\n/// [true], // d is negated\n/// );\n/// ```\n///\n/// # Performance Note\n/// This function generates far fewer constraints than equivalent sequences of `+`, `-`, `*`\n/// operations. For complex expressions, the savings can be substantial.\n///\n/// # Panics\n/// Fails if the expression does not equal zero modulo `MOD`.\npub fn evaluate_quadratic_expression(\n lhs_terms: [[T; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[T; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [T; ADD_N],\n linear_flags: [bool; ADD_N],\n) {\n let params = T::params();\n crate::fns::expressions::evaluate_quadratic_expression(\n params,\n crate::utils::map::map(\n lhs_terms,\n |bns| crate::utils::map::map(bns, |bn: T| bn.get_limbs()),\n ),\n lhs_flags,\n crate::utils::map::map(\n rhs_terms,\n |bns| crate::utils::map::map(bns, |bn: T| bn.get_limbs()),\n ),\n rhs_flags,\n crate::utils::map::map(linear_terms, |bn: T| bn.get_limbs()),\n linear_flags,\n )\n}\n\n/// Computes the modular inverses of multiple BigNum values efficiently (unconstrained).\n///\n/// Uses Montgomery's batch inversion trick to compute all inverses with only a single\n/// modular inversion, making this much more efficient than calling `__invmod` repeatedly.\n///\n/// # Requirements\n/// - `MOD` must be prime (`params.has_multiplicative_inverse == true`)\n/// - Zero elements are allowed and will map to zero in the output\n///\n/// # Performance\n/// Computes M inverses using:\n/// - 3(M-1) multiplications\n/// - 1 inversion\n///\n/// This is much cheaper than M separate inversions for M > 1.\n///\n/// # Safety\n/// **UNCONSTRAINED**: No constraints are generated. Use the results as witnesses and\n/// constrain them appropriately (e.g., verify `x[i] * result[i] == 1` using\n/// `evaluate_quadratic_expression`).\n///\n/// # Example\n/// ```\n/// // Safety: unconstrained batch inversion\n/// let inverses = unsafe { batch_invert([a, b, c]) };\n/// // inverses[0] == a^{-1}, inverses[1] == b^{-1}, inverses[2] == c^{-1}\n/// ```\npub unconstrained fn batch_invert(x: [T; M]) -> [T; M] {\n let params = T::params();\n assert(params.has_multiplicative_inverse);\n crate::fns::unconstrained_ops::batch_invert(params, x.map(|bn: T| bn.get_limbs())).map(|limbs| {\n T::from_limbs(limbs)\n })\n}\n\n/// Computes the modular inverses of a slice of BigNum values efficiently (unconstrained).\n///\n/// Same as `batch_invert` but accepts a dynamically-sized slice instead of a fixed-size array.\n///\n/// # Requirements\n/// - `MOD` must be prime (`params.has_multiplicative_inverse == true`)\n/// - Zero elements are allowed and will map to zero in the output\n///\n/// # Safety\n/// **UNCONSTRAINED**: No constraints are generated.\n///\n/// See `batch_invert` for more details.\npub unconstrained fn batch_invert_slice(x: [T]) -> [T] {\n let params = T::params();\n assert(params.has_multiplicative_inverse);\n crate::fns::unconstrained_ops::batch_invert_slice(params, x.map(|bn: T| bn.get_limbs()))\n .map(|limbs| T::from_limbs(limbs))\n}\n\n/// Converts a BigNum to a native circuit Field element.\n///\n/// # Requirements\n/// - The BigNum value must be less than the circuit field modulus (Grumpkin: ~2^254)\n/// - For N > 2, the value is validated to be less than the Grumpkin modulus\n///\n/// # Panics\n/// - Fails range validation if the value is >= circuit field modulus\n/// - Fails if limbs are not properly range-constrained\n///\n/// # Note\n/// This is a constrained operation that validates the BigNum is representable as a Field.\n/// For BigNums with `MOD_BITS > 253`, ensure your value is actually less than the circuit\n/// field modulus before calling this.\npub fn to_field(bn: T) -> Field {\n let params = T::params();\n limbs_to_field(params, bn.get_limbs())\n}\n" }, "554": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/fns/constrained_ops.nr", "source": "use crate::constants::{GRUMPKIN_MODULUS, TWO_POW_120, TWO_POW_240};\n\nuse crate::fns::{\n expressions::{evaluate_quadratic_expression, validate_udiv_mod_expression},\n unconstrained_helpers::{\n __from_field, compute_add_flags, compute_borrow_flags, compute_gte_flags, compute_sub_flags,\n },\n unconstrained_ops::{__add, __div, __mul, __neg, __sqr, __sub, __udiv_mod},\n};\n\nuse crate::params::BigNumParams;\n\nuse std::cmp::Ordering;\n\n/// Lift the limbs of a `BigNum` value onto the circuit `Field`\n///\n/// Descent the `BigNum` value back into the `Field` and\n/// - check that it's a proper `BigNum` value\n/// - validate the limbs sum up to a `Field` value\npub(crate) fn limbs_to_field(\n _params: BigNumParams,\n limbs: [u128; N],\n) -> Field {\n validate_in_range::(limbs);\n if N > 2 {\n // validate that the `BigNum` is less than the Grumpkin modulus\n let mut grumpkin_modulus: [u128; N] = [0; N];\n grumpkin_modulus[0] = GRUMPKIN_MODULUS[0];\n grumpkin_modulus[1] = GRUMPKIN_MODULUS[1];\n grumpkin_modulus[2] = GRUMPKIN_MODULUS[2];\n validate_gt::(grumpkin_modulus, limbs);\n }\n\n if N < 2 {\n limbs[0] as Field\n } else if N == 2 {\n (limbs[0] as Field) + (limbs[1] as Field) * (TWO_POW_120 as Field)\n } else {\n (limbs[0] as Field)\n + (limbs[1] as Field) * (TWO_POW_120 as Field)\n + (limbs[2] as Field) * TWO_POW_240\n }\n}\n\n/// Construct a `BigNum` value from a native `Field`\n///\n/// Decomposes the `Field` value into 120-bit limbs\n/// then we have three cases:\n/// - MOD_BITS < 253 (grumpkin_mod_bits - 1): it is enough to call for `validate_in_field`, which is basically `val <= MOD`\n/// - MOD_BITS > 253: we need to verify that the obtained `BigNum` `val < GRUMPKIN_MODULUS`\n/// - MOD_BITS = 253: verify that `val < min(MOD, GRUMPKIN_MODULUS)`\n/// Next we verify that all the limbs are properly ranged\n/// and that the accumulated limbs are equal to the input `Field` value\npub fn from_field(\n _params: BigNumParams,\n val: Field,\n) -> [u128; N] {\n // Safety: we check that the resulting limbs represent the intended field element\n // we check the bit length, the limbs being max 120 bits, and the value in total is less than the field modulus\n let result: [u128; N] = unsafe { __from_field::(val) };\n\n if !std::runtime::is_unconstrained() {\n // validate the limbs are in range and the value in total is less than 2^254\n if MOD_BITS < 253 {\n // this means that the field modulus is smaller than grumpkin modulus so we have to check if the element fields in the field size\n validate_in_field(_params, result);\n } else {\n let mut grumpkin_modulus: [u128; N] = [0; N];\n grumpkin_modulus[0] = GRUMPKIN_MODULUS[0];\n grumpkin_modulus[1] = GRUMPKIN_MODULUS[1];\n grumpkin_modulus[2] = GRUMPKIN_MODULUS[2];\n\n if MOD_BITS > 253 {\n // this means that the field modulus is larger than grumpkin modulus so we have to check if the element fields in the field size are less than the grumpkin modulus.\n // also for correct params N is always larger than 3 here\n validate_gt::(grumpkin_modulus, result);\n } else {\n // this is the tricky part, when MOD_BITS = 253, we have to compare the limbs of the modulus to the grumpkin modulus limbs\n // any `BigNum` with 253 bits will have 3 limbs\n\n // if MOD is less than grumpkin modulus, this will be true\n let mut mod_lt_grumpkin: bool = false;\n for i in 0..3 {\n if !mod_lt_grumpkin & (_params.modulus[2 - i] < grumpkin_modulus[2 - i]) {\n mod_lt_grumpkin = true;\n }\n }\n let min_modulus: [u128; N] = if mod_lt_grumpkin {\n _params.modulus\n } else {\n grumpkin_modulus\n };\n validate_gt::(min_modulus, result);\n }\n }\n validate_in_range::(result);\n\n // validate the limbs sum up to the field value\n let field_val: Field = if N < 2 {\n result[0] as Field\n } else if N == 2 {\n (result[0] as Field) + (result[1] as Field) * (TWO_POW_120 as Field)\n } else {\n (result[0] as Field)\n + (result[1] as Field) * (TWO_POW_120 as Field)\n + (result[2] as Field) * TWO_POW_240\n };\n assert_eq(field_val, val);\n }\n\n result\n}\n\n/// Given an input seed, generate a pseudorandom `BigNum` value\n///\n/// This function *should* produce a uniformly randomly distributed value modulo `MOD`\n///\n/// First we take the seed and pack it's 31-byte chunks into `Field`s\n/// We use a hash function that can be modelled as a random oracle\n/// We hash the packed seed using Poseidon2 to produce `MOD_BITS * 2` bits of entropy\n///\n/// From these bits we construct 4(in case N = 2) or 3(N > 2) `BigNum` values:\n/// - We fill first `N - 1` limbs and leave the top limb empty\n///\n/// Then we accumulate the resulting BigNum values using:\n/// B = 2^{120 * (N - 1)}\n/// res = x3 + B * x2 + B^2 * x1 + B^3 * x0\n///\n/// ## Note\n/// This function will always produce an `x3` `BigNum`\n/// If `MOD = 2^{120 * (N - 1)}`\n/// It will use only `MOD_BITS - 1` bits of entropy\npub fn derive_from_seed(\n params: BigNumParams,\n seed: [u8; SeedBytes],\n) -> [u128; N] {\n // Pack seed bytes into Fields.\n // For the seed of length M, we construct a rolling_hash_field of size ceil(M / 31).\n // i.e. 31 bytes per Field\n // NOTE: the Fields produced are 248 bits in size\n let mut rolling_hash_fields: [Field; (SeedBytes + 30) / 31] = [0; (SeedBytes + 30) / 31];\n let mut seed_ptr: u32 = 0;\n for i in 0..(SeedBytes + 30) / 31 {\n let mut packed: Field = 0;\n for _ in 0..31 {\n if (seed_ptr < SeedBytes) {\n packed *= 256;\n packed += seed[seed_ptr] as Field;\n seed_ptr += 1;\n }\n }\n rolling_hash_fields[i] = packed;\n }\n\n let compressed: Field =\n poseidon::poseidon2::Poseidon2::hash(rolling_hash_fields, (SeedBytes + 30) / 31);\n let mut rolling_hash: [Field; 2] = [compressed, 0];\n\n // 120 bit limb has 15 bytes in it\n // we buffer (N * 15) * 2 bytes for 2N limbs\n let mut hash_buffer: [u8; N * 15 * 2] = [0; N * 15 * 2];\n\n // We produce 32 bytes (254 bits) per hash iteration\n // We take only 30 bytes, so we need ceil(N * 2 * 15 / 30) hashes to fill them up\n for i in 0..N {\n let hash: Field = poseidon::poseidon2::Poseidon2::hash(rolling_hash, 2);\n let hash: [u8; 32] = hash.to_le_bytes();\n for j in 0..30 {\n hash_buffer[i * 30 + j] = hash[j];\n }\n rolling_hash[1] += 1;\n }\n\n let num_bits: u32 = MOD_BITS * 2;\n let num_bytes: u32 = (num_bits + 7) / 8;\n\n // Truncate the final byte that will be used in `BigNum` creation\n let bits_in_last_byte: u8 = (num_bits as u8) % 8;\n if bits_in_last_byte != 0 {\n let last_byte_mask: u8 = ((1 as u8) << bits_in_last_byte) - 1;\n hash_buffer[num_bytes - 1] &= last_byte_mask;\n }\n\n let num_bigfield_chunks: u32 = if N == 2 { 4 } else { 3 };\n let mut byte_ptr: u32 = 0;\n\n // We want to convert our hash_buffer into bigfield chunks, with each `BigNum` having at most N - 1 limbs filled\n // We sample only N - 1 limbs mostly because we do not wont to deal with accidental overflows in the top limb\n // In any case the security is preserved\n //\n // For all integer values N > 1, the number of chunks is either 4(for N = 2) or 3(for N > 2) (ceil(2*N / (N - 1)))\n //\n // To determine the exact number of chunks, we need the `!=` or `>` operator which is not available when defining array sizes\n // so we overestimate at 4\n // e.g. if N = 20, then we have 40 limbs we want to reduce, but each bigfield chunk is 19 limbs, so we need 3\n // if N = 2, we have 4 limbs we want to reduce but each bigfield chunk is only 1 limb, so we need 4\n let mut bigfield_chunks: [[u128; N]; 4] = [[0; N]; 4];\n\n for k in 0..num_bigfield_chunks {\n let mut bigfield_limbs: [u128; N] = [0; N];\n\n // Before the current limb, we filled out (N - 1) limbs, k times\n // 15 bytes per limb\n // resulting in total of 15 * k * (N - 1)\n let mut num_filled_bytes: u32 = 15 * k * (N - 1);\n if num_bytes > num_filled_bytes {\n // Static assert for completeness\n assert(num_filled_bytes == byte_ptr);\n\n let mut num_remaining_bytes_to_sample: u32 = num_bytes - num_filled_bytes;\n let mut num_remaining_limbs_to_sample: u32 = (num_remaining_bytes_to_sample + 14) / 15;\n\n // Sample at most (N - 1) limbs from hash_buffer\n let mut num_limbs_to_sample: u32 = if num_remaining_limbs_to_sample > (N - 1) {\n N - 1\n } else {\n num_remaining_limbs_to_sample\n };\n\n for j in 0..num_limbs_to_sample {\n let mut limb: Field = 0;\n // Construct a 120 bit limb\n for _ in 0..15 {\n if byte_ptr < num_bytes {\n let mut byte: u8 = hash_buffer[byte_ptr];\n limb *= 256;\n limb += byte as Field;\n byte_ptr += 1;\n }\n }\n // crucial for performance\n limb.assert_max_bit_size::<120>();\n // Accumulate limbs from top to bottom\n bigfield_limbs[num_limbs_to_sample - 1 - j] = limb as u128;\n }\n // Accumulate `BigNum` values from top to bottom\n bigfield_chunks[num_bigfield_chunks - 1 - k] = bigfield_limbs;\n }\n }\n\n // B = 2^{120 * (N - 1)}, we know it is \\leq `MOD`, since the top limb\n // have to be at least one\n let mut bigfield_rhs_limbs: [u128; N] = [0; N];\n bigfield_rhs_limbs[N - 1] = 1;\n\n let mut result: [u128; N] = bigfield_chunks[0];\n\n for i in 1..num_bigfield_chunks {\n result = mul(params, result, bigfield_rhs_limbs);\n result = add(params, result, bigfield_chunks[i]);\n }\n\n result\n}\n\n// ------------------------------ COMPARISON FUNCTIONS ------------------------------\n\n/// Validate lhs != rhs\n///\n/// We compare `A` and `B` via their encodings in the circuit `Field`\n///\n/// Under our range assumptions, equality in `Field` implies equality of the underlying\n/// `BigNum` values, hence equality (mod `MOD`)\n///\n/// In this library it is possible that A or B is a little bit greater than `MOD`\n/// So A == B (mod `MOD`) implies that A == B, A == B + MOD or A == B - MOD over the integers\n/// Hence we can compute everything (mod p) and constrain that\n/// (A - B) * (A - B + MOD) * (A - B - MOD) != 0 (mod p)\n///\n/// ## Soundness\n/// This method is *sound* for checking `A != B (mod MOD)`\n///\n/// If `A == B (mod MOD)`, then `A - B` is in `{0, +-MOD}` as an integer,\n/// so one of the factors `A-B`, `A-B+MOD`, `A-B-MOD` is zero in `Field`, and the product is zero\n/// Therefore, whenever the assertion `target != 0` holds, we must have `A != B (mod MOD)`\n/// under our range assumptions\n///\n/// ## Completeness\n/// In general, this method is not *complete*: if the admissible range of `A` and `B`\n/// is large enough relative to the circuit `Field` prime `p`, an honest prover with\n/// `A != B (mod MOD)` can still hit an alias where\n/// (A - B) * (A - B + MOD) * (A - B - MOD) == 0 (mod p),\n/// i.e.\n/// - A = B (mod p), or\n/// - A = B + MOD (mod p), or\n/// - A = B - MOD (mod p).\n///\n/// For random `A, B` in such a wide range, the probability of this collision is\n/// roughly 3/p.\n///\n/// In case `MOD` < `p` this function becomes *complete*\npub fn assert_is_not_equal(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) {\n let mut l: Field = 0;\n let mut r: Field = 0;\n let mut modulus_mod_p: Field = 0;\n for i in 0..N {\n l *= TWO_POW_120 as Field;\n r *= TWO_POW_120 as Field;\n modulus_mod_p *= TWO_POW_120 as Field;\n\n l += lhs[N - i - 1] as Field;\n r += rhs[N - i - 1] as Field;\n modulus_mod_p += params.modulus[N - i - 1] as Field;\n }\n\n let diff: Field = l - r;\n let target: Field = diff * (diff + modulus_mod_p) * (diff - modulus_mod_p);\n assert(target != 0, \"assert_is_not_equal fail\");\n}\n\n/// Compute equality flag\n///\n/// A == B (mod MOD)\n/// We compute A - B and check whether it is `0` or `MOD`\n/// This is due to subtract constrains the diff value to be < 2^MOD_BITS, not < `MOD`\n///\n/// ## Soundness\n/// This function is conditionally *sound*. See `sub` for details\n///\n/// ## Completeness\n/// This function is *complete*. An honest prover will always be able to execute it.\n///\n/// ## TODO\n/// can do this more efficiently via witngen in unconstrained functions?\npub fn eq(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> bool {\n let diff: [u128; N] = sub::(params, lhs, rhs);\n is_zero::(params, diff)\n}\n\n/// Validate that `val` is not equal to zero when interpreted as an integer.\n///\n/// This enforces that at least one limb of `val` is non-zero.\n/// It does *not* check \"BigNum zero\" in the modular sense (e.g. it\n/// treats `MOD` as non-zero).\n///\n/// ## Assumptions\n/// * Each limb of `val` is range-constrained to be a 120-bit value:\n/// `0 <= val[i] < 2^120`.\n/// * For our concrete fields and limb counts we have `N * 2^120 < p`,\n/// so the sum of all limbs fits strictly inside the field modulus.\n///\n/// ## Completeness\n/// If `val` is non-zero as an integer, then at least one limb is non-zero,\n/// so the integer sum of the limbs satisfies `0 < limb_sum < p`. In this\n/// case `limb_sum != 0` in the `Field`, and the assertion passes.\n///\n/// ## Soundness\n/// If all limbs of `val` are zero, then `limb_sum` is zero as an integer\n/// and as a field element, so the assertion fails. A witness representing\n/// the zero integer can never satisfy this check.\n///\n/// ## Note\n/// This is slightly cheaper than doing `val != [0; N]`, as we avoid\n/// creating per-limb boolean equalities and chaining them with `and`s.\npub fn assert_is_not_zero_integer(val: [T; N])\nwhere\n T: Into,\n{\n assert(!is_zero_integer(val), \"assert_is_not_zero_integer fail\");\n}\n\n/// Check whether `val` is the zero `BigNum` in the integer sense.\n///\n/// This returns `true` iff all limbs of `val` are zero. It does *not*\n/// treat `MOD` as zero; for modular `BigNum` zero use `is_zero`.\n///\n/// See `assert_is_not_zero_integer` for the underlying assumptions.\n///\n/// ## Note\n/// This is slightly cheaper than testing `val == [0; N]`, as we avoid\n/// creating per-limb boolean equalities and chaining them with `and`s.\npub fn is_zero_integer(val: [T; N]) -> bool\nwhere\n T: Into,\n{\n let mut limb_sum: Field = 0;\n for i in 0..N {\n limb_sum += val[i].into();\n }\n limb_sum == 0\n}\n\n/// Validate that a `BigNum` value is not zero modulo `MOD`.\n///\n/// Convenience wrapper around `assert_is_not_equal`.\npub fn assert_is_not_zero(\n params: BigNumParams,\n val: [u128; N],\n) {\n assert_is_not_equal::(params, val, [0; N]);\n}\n\n/// Check whether a `BigNum` value is zero modulo `MOD`.\n///\n/// This treats both the all-zero limb vector and `params.modulus` as\n/// representing zero. It assumes that all valid `BigNum` values are\n/// range-constrained so that no other representatives of `0 (mod MOD)`\n/// can appear.\n///\n/// ## Note\n/// This is cheaper than calling `eq(val, [0; N])`\npub fn is_zero(\n params: BigNumParams,\n val: [u128; N],\n) -> bool {\n is_zero_integer(val) | (val == params.modulus)\n}\n\n/// Validate a `BigNum` instance is correctly range constrained to contain no more than `MOD_BITS` bits\n///\n/// Constrain the `BigNum` instance to be < 2^MOD_BITS by:\n/// - Constraining each limb(0..N-2) to be 120-bit limb\n/// - Constraining the last limb to be `MOD_BITS - 120 * (N - 1)`\n///\n/// ## Note\n/// This can be a very expensive function, when the `TOP_LIMB_BITS` is uncommon\n///\n/// For example: BLS12_377Fr, with `TOP_LIMB_BITS`=13\n/// It creates a new 13-bit range table, which consists of roughly 3k variables\n/// And ~2k circuit gates\n///\n/// Compare this to BLS12_377Fq, with `TOP_LIMB_BITS`=17\n/// It is nicely decomposed into\n/// 14-bit range check and 3-bit range check - much much cheaper, since 14-bit range checks\n/// are already pretty common for 120-bit range checks\npub fn validate_in_range(limbs: [T; N])\nwhere\n T: Into,\n{\n for i in 0..(N - 1) {\n limbs[i].into().assert_max_bit_size::<120>();\n }\n\n limbs[N - 1].into().assert_max_bit_size::();\n}\n\n/// Validate quotient produced from `evaluate_quadratic_expression` is well-formed\n///\n/// Because the inputs into `evaluate_quadratic_expression` may cause the quotient to extend beyond `Params::modulus_bits`.\n/// We allow the quotient to extend `6` bits beyond `Params::modulus_bits()`\n/// Why is this?\n/// several factors: 1. quotient * modulus , limbs cannot overflow `Field` boundary (254 bits)\n/// 2. in `evaluate_quadratic_expression`, we require that for `expression - quotient * modulus`,\n/// limbs cannot exceed `246` bits (246 magic number due to a higher number adding extra range check gates)\n/// because of factor 2 and the fact that modulus limbs are 120 bits, quotient limbs cannot be > 126 bits\npub(crate) fn validate_quotient_in_range(limbs: [u128; N]) {\n for i in 0..(N - 1) {\n (limbs[i] as Field).assert_max_bit_size::<120>();\n }\n (limbs[N - 1] as Field).assert_max_bit_size::();\n}\n\n/// Validate that `lhs - rhs` does not underflow i.e. that lhs > rhs over the integers\n///\n/// Compute `result = lhs - rhs` along with `borrow_flags`,\n/// then constrain `result` to be a valid `BigNum` value.\n///\n/// ## Completeness\n/// This function is complete and will work only if `lhs > rhs` over the integers.\n///\n/// ## Soundness\n/// This function is sound:\n/// result[0] = lhs[0] - rhs[0] + bf[0] * 2^{120} < 2^{120}\n/// result[i] = lhs[i] - rhs[i] + bf[i] * 2^{120} - bf[i - 1] < 2^{120}, i = 1..N-2\n/// result[N - 1] = lhs[N - 1] - rhs[N - 1] - bf[N - 2] < 2^{TOP_LIMB_BITS}\n/// result != 0\n///\n/// If `lhs < rhs`, then some limb of `result` would have to borrow from a higher limb,\n/// which is impossible because the top limb cannot borrow\n/// Without the extra borrow, the resulting difference will be wrapped around the `Field` modulus\n/// And won't satisfy the range constraint, since all the limbs are < 2^120\n///\n/// ## Note\n/// `assert_is_not_zero_integer(result)` is crucial. Without it, we could always provide\n/// two identical inputs `x`, `x` and set `borrow_flags = [false; N]`,\n/// which would satisfy the limb constraints.\npub(crate) fn validate_gt(lhs: [u128; N], rhs: [u128; N]) {\n // Safety: borrow_flags are constrained by `assert_sub_no_overflow`:\n // - incorrect flags cause the computed result to fail 128-bit range checks\n // - if lhs < rhs, the subtraction overflows Field and fails range checks\n let borrow_flags: [bool; N - 1] = unsafe { compute_borrow_flags(lhs, rhs) };\n\n // Compute and validate the result in constrained code\n let result: [u128; N] = assert_sub_no_overflow::(lhs, rhs, borrow_flags);\n\n // Constrain it to be strict inequality\n assert_is_not_zero_integer(result);\n}\n\n/// Compute the result of lhs - rhs given borrow flags and validate it\n///\n/// This is the constrained analog of the result computation in the unconstrained helpers.\n/// Given borrow_flags, computes result such that:\n/// result[0] = lhs[0] - rhs[0] + bf[0] * 2^{120}\n/// result[i] = lhs[i] - rhs[i] + bf[i] * 2^{120} - bf[i - 1], i = 1..N-2\n/// result[N - 1] = lhs[N - 1] - rhs[N - 1] - bf[N - 2]\n///\n/// Also validates that the result is a valid BigNum value (each limb in range).\n/// If lhs < rhs, the result will have a negative value (wrapped in Field) and fail validation.\nfn assert_sub_no_overflow(\n lhs: [u128; N],\n rhs: [u128; N],\n borrow_flags: [bool; N - 1],\n) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n\n // Compute first limb\n let limb_0: Field =\n (lhs[0] as Field) - (rhs[0] as Field) + (borrow_flags[0] as Field) * (TWO_POW_120 as Field);\n limb_0.assert_max_bit_size::<128>();\n result[0] = limb_0 as u128;\n\n // Compute middle limbs\n for i in 1..N - 1 {\n let limb_i: Field = (lhs[i] as Field) - (rhs[i] as Field)\n + (borrow_flags[i] as Field) * (TWO_POW_120 as Field)\n - (borrow_flags[i - 1] as Field);\n limb_i.assert_max_bit_size::<128>();\n result[i] = limb_i as u128;\n }\n\n // Compute last limb\n let limb_last: Field =\n (lhs[N - 1] as Field) - (rhs[N - 1] as Field) - (borrow_flags[N - 2] as Field);\n limb_last.assert_max_bit_size::<128>();\n result[N - 1] = limb_last as u128;\n\n // Validate that the result is a valid BigNum value (120-bit limbs, TOP_LIMB_BITS for last)\n validate_in_range::(result);\n\n result\n}\n\n/// Compute the result of modular subtraction given flags and validate it\n///\n/// Computes `result = lhs - rhs + (underflow ? modulus : 0)` given carry and borrow flags.\n/// The formula for each limb is:\n/// result[0] = lhs[0] - rhs[0] + addend[0] + bf[0] * 2^120 - cf[0] * 2^120\n/// result[i] = lhs[i] - rhs[i] + addend[i] + bf[i] * 2^120 - cf[i] * 2^120 - bf[i-1] + cf[i-1], i = 1..N-2\n/// result[N - 1] = lhs[N - 1] - rhs[N - 1] + addend[N - 1] - bf[N-2] + cf[N-2]\n///\n/// Also validates that the result is a valid BigNum value (each limb in range).\nfn compute_sub_result(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n carry_flags: [bool; N - 1],\n borrow_flags: [bool; N - 1],\n underflow: bool,\n) -> [u128; N] {\n let addend: [u128; N] = if underflow { modulus } else { [0; N] };\n\n let mut result: [u128; N] = [0; N];\n\n // Compute first limb\n let limb_0: Field = (lhs[0] as Field) - (rhs[0] as Field)\n + (addend[0] as Field)\n + (borrow_flags[0] as Field) * (TWO_POW_120 as Field)\n - (carry_flags[0] as Field) * (TWO_POW_120 as Field);\n limb_0.assert_max_bit_size::<128>();\n result[0] = limb_0 as u128;\n\n // Compute middle limbs\n for i in 1..N - 1 {\n let limb_i: Field = (lhs[i] as Field) - (rhs[i] as Field)\n + (addend[i] as Field)\n + (borrow_flags[i] as Field) * (TWO_POW_120 as Field)\n - (carry_flags[i] as Field) * (TWO_POW_120 as Field)\n - (borrow_flags[i - 1] as Field)\n + (carry_flags[i - 1] as Field);\n limb_i.assert_max_bit_size::<128>();\n result[i] = limb_i as u128;\n }\n\n // Compute last limb\n let limb_last: Field = (lhs[N - 1] as Field) - (rhs[N - 1] as Field) + (addend[N - 1] as Field)\n - (borrow_flags[N - 2] as Field)\n + (carry_flags[N - 2] as Field);\n limb_last.assert_max_bit_size::<128>();\n result[N - 1] = limb_last as u128;\n\n // Validate that the result is a valid BigNum value (120-bit limbs, TOP_LIMB_BITS for last)\n validate_in_range::(result);\n\n result\n}\n\n/// Compute the result of modular addition given flags and validate it\n///\n/// Computes `result = lhs + rhs - (overflow ? modulus : 0)` given carry and borrow flags.\n/// The formula for each limb is:\n/// result[0] = lhs[0] + rhs[0] - subtrahend[0] + bf[0] * 2^120 - cf[0] * 2^120\n/// result[i] = lhs[i] + rhs[i] - subtrahend[i] + bf[i] * 2^120 - cf[i] * 2^120 - bf[i-1] + cf[i-1], i = 1..N-2\n/// result[N - 1] = lhs[N - 1] + rhs[N - 1] - subtrahend[N - 1] - bf[N-2] + cf[N-2]\n///\n/// Also validates that the result is a valid BigNum value (each limb in range).\nfn compute_add_result(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n carry_flags: [bool; N - 1],\n borrow_flags: [bool; N - 1],\n overflow: bool,\n) -> [u128; N] {\n let subtrahend: [u128; N] = if overflow { modulus } else { [0; N] };\n\n let mut result: [u128; N] = [0; N];\n\n // Compute first limb\n let limb_0: Field = (lhs[0] as Field) + (rhs[0] as Field) - (subtrahend[0] as Field)\n + (borrow_flags[0] as Field) * (TWO_POW_120 as Field)\n - (carry_flags[0] as Field) * (TWO_POW_120 as Field);\n limb_0.assert_max_bit_size::<128>();\n result[0] = limb_0 as u128;\n\n // Compute middle limbs\n for i in 1..N - 1 {\n let limb_i: Field = (lhs[i] as Field) + (rhs[i] as Field)\n - (subtrahend[i] as Field)\n - (borrow_flags[i - 1] as Field)\n + (carry_flags[i - 1] as Field)\n + (borrow_flags[i] as Field) * (TWO_POW_120 as Field)\n - (carry_flags[i] as Field) * (TWO_POW_120 as Field);\n limb_i.assert_max_bit_size::<128>();\n result[i] = limb_i as u128;\n }\n\n // Compute last limb\n let limb_last: Field = (lhs[N - 1] as Field) + (rhs[N - 1] as Field)\n - (subtrahend[N - 1] as Field)\n - (borrow_flags[N - 2] as Field)\n + (carry_flags[N - 2] as Field);\n limb_last.assert_max_bit_size::<128>();\n result[N - 1] = limb_last as u128;\n\n // Validate that the result is a valid BigNum value (120-bit limbs, TOP_LIMB_BITS for last)\n validate_in_range::(result);\n\n result\n}\n\n/// Validate that `val` <= `MOD`\n///\n/// Compute `result = MOD - val` along with `borrow_flags`,\n/// then constrain `result` to be a valid `BigNum` value.\n///\n/// Basically the same as `validate_gt` but we compute the result on the fly\n/// It is just a bit more optimized as we expect each `BigNum` value to be \\leq `MOD`\n///\n/// ## Note\n/// In contrast to `validate_gt`, we allow the value to be `MOD`\n/// Since it is consistent with the rest of the library\npub fn validate_in_field(\n params: BigNumParams,\n val: [u128; N],\n) {\n let modulus: [u128; N] = params.modulus;\n\n // Safety: borrow_flags are constrained by the `validate_in_range` check on `p_minus_self`:\n // - incorrect flags cause `p_minus_self` limbs to overflow Field, failing range checks\n // - if val > modulus, the subtraction overflows and fails the range check\n let borrow_flags: [bool; (N - 1)] = unsafe { compute_borrow_flags(modulus, val) };\n\n let mut p_minus_self: [Field; N] = [0; N];\n p_minus_self[0] = (modulus[0] as Field) - (val[0] as Field)\n + (borrow_flags[0] as Field) * (TWO_POW_120 as Field);\n for i in 1..N - 1 {\n p_minus_self[i] = (modulus[i] as Field) - (val[i] as Field)\n + (borrow_flags[i] as Field) * (TWO_POW_120 as Field)\n - (borrow_flags[i - 1] as Field);\n }\n p_minus_self[N - 1] =\n (modulus[N - 1] as Field) - (val[N - 1] as Field) - (borrow_flags[N - 2] as Field);\n validate_in_range::(p_minus_self);\n}\n\n/// Compare two `BigNum` values\n///\n/// Returns `lhs > rhs`\n///\n/// ## Note\n/// This is a strict value comparison over the integers,\n/// the values do not have to be reduced modulo `MOD`.\npub fn cmp(lhs: [u128; N], rhs: [u128; N]) -> Ordering {\n // Safety: underflow and borrow_flags are constrained by `assert_sub_no_overflow`:\n // - we swap (a, b) based on underflow, then compute a - b\n // - if underflow is wrong, a < b and the subtraction overflows, failing range checks\n // - if borrow_flags are wrong, the computed limbs fail 128-bit range checks\n let (underflow, borrow_flags): (bool, [bool; N - 1]) = unsafe { compute_gte_flags(lhs, rhs) };\n\n // if underflow is true, swap lhs and rhs so we compute larger - smaller\n let (a, b): ([u128; N], [u128; N]) = if underflow { (rhs, lhs) } else { (lhs, rhs) };\n\n // Enforce correctness of `underflow` by asserting that the subtraction does not overflow.\n let _: [u128; N] = assert_sub_no_overflow::(a, b, borrow_flags);\n\n if lhs == rhs {\n Ordering::equal()\n } else if underflow {\n Ordering::less()\n } else {\n Ordering::greater()\n }\n}\n\n// ------------------------------ ARITHMETIC FUNCTIONS ------------------------------\n\n/// Negate a `BigNum` value\n///\n/// Computes `result = MOD - val` using limb-wise subtraction with borrow flags,\n/// then constrains:\n/// - all `result` limbs to be a valid `BigNum` value, and\n/// - the subtraction relation with the borrow flags\n///\n/// ## Assumptions\n/// - `val` is a valid `BigNum` in the range `0 <= val <= MOD`.\n///\n/// ## Soundness\n/// This function constrains the following relations:\n/// result[0] = MOD[0] - val[0] + bf[0] * 2^{120} < 2^{120}\n/// result[i] = MOD[i] - val[i] + bf[i] * 2^{120} - bf[i - 1] < 2^{120}, i = 1..N-2\n/// result[N - 1] = MOD[N - 1] - val[N - 1] - bf[N - 2] < 2^{TOP_LIMB_BITS}\n///\n/// If all `MOD` and `val` limbs are valid `BigNum` limbs, these constraints\n/// ensure that:\n/// - the borrow flags `bf[i]` form a valid limb-wise subtraction chain, and\n/// - no underflow can occur in the subtraction `MOD - val`.\n///\n/// ## Completeness\n/// This function is complete for inputs in the range `0 <= val <= MOD`.\n/// If a value `val > MOD` is passed in (while still `< 2^{MOD_BITS}`), the\n/// constraints above will fail, since there is no valid borrow chain making\n/// `MOD - val` a well-formed `BigNum`.\n///\n/// In practice, honest provers should not hit this case: all functions in this\n/// module are expected to return values `< MOD`.\n///\n/// ## Note\n/// This function returns `MOD` when `val` is zero.\npub fn neg(\n params: BigNumParams,\n val: [u128; N],\n) -> [u128; N] {\n if std::runtime::is_unconstrained() {\n // Safety: unconstrained runtime requires no constraints\n unsafe {\n __neg(params.modulus, val)\n }\n } else {\n // Safety: borrow_flags are constrained by `assert_sub_no_overflow`:\n // - incorrect flags cause computed limbs to fail 128-bit range checks\n // - if val > modulus, the subtraction overflows and fails range checks\n // (but val > modulus violates function preconditions)\n let borrow_flags: [bool; N - 1] = unsafe { compute_borrow_flags(params.modulus, val) };\n\n // Subtract `val` from the modulus to negate.\n assert_sub_no_overflow::(params.modulus, val, borrow_flags)\n }\n}\n\n/// Add two `BigNum` values\n///\n/// Computes `result = lhs + rhs` using limb-wise addition with carry flags,\n/// and an optional subtraction of `MOD` using borrow flags. The function then\n/// constrains:\n/// - all `result` limbs to be a valid `BigNum` value, and\n/// - the addition/subtraction relation with the carry/borrow flags and the\n/// `overflow_modulus` bit.\n///\n/// ## Assumptions\n/// - All limbs of `lhs`, `rhs` and `MOD` are valid `BigNum` limbs\n/// (120-bit for non-top limbs, `TOP_LIMB_BITS` for the top limb).\n/// - Semantically, we intend to use this only with `0 <= lhs, rhs < MOD`,\n/// even though the limb/range constraints allow values up to `< 2^{MOD_BITS}`.\n///\n/// ## Soundness (intended relation)\n/// This function constrains the following equations:\n///\n/// result[0] = lhs[0] + rhs[0]\n/// - sub[0]\n/// + bf[0] * 2^{120}\n/// - cf[0] < 2^{120}\n///\n/// result[i] = lhs[i] + rhs[i]\n/// - sub[i]\n/// + bf[i] * 2^{120} - bf[i - 1]\n/// - cf[i] * 2^{120} + cf[i - 1] < 2^{120},\n/// for i = 1..N-2\n///\n/// result[N - 1] = lhs[N - 1] + rhs[N - 1]\n/// - sub[N - 1]\n/// - bf[N - 2]\n/// + cf[N - 2] < 2^{TOP_LIMB_BITS}\n///\n/// where:\n/// - `bf[i]` are the borrow flags of the optional subtraction,\n/// - `cf[i]` are the carry flags of the addition, and\n/// - `sub` is either the zero vector or `MOD`, depending on the\n/// `overflow_modulus` flag:\n/// * `overflow_modulus = 0` => `sub = 0`\n/// * `overflow_modulus = 1` => `sub = MOD`\n///\n/// If `lhs`, `rhs` and `MOD` are valid `BigNum` limbs and the witness for\n/// `(bf, cf, overflow_modulus)` is the honest one produced by `compute_add_flags`,\n/// these constraints enforce:\n/// - a valid limb-wise carry chain for `lhs + rhs`, and\n/// - a valid limb-wise subtraction chain for either `lhs + rhs` or\n/// `lhs + rhs - MOD`, with no underflow in any limb.\n///\n/// Under these assumptions the constrained result equals:\n///\n/// result = lhs + rhs (mod MOD)\n///\n/// in the intended arithmetic.\n///\n/// ## Limitations / extra satisfying witnesses\n///\n/// The constraint system itself does **not** uniquely determine the carry/borrow\n/// flags nor the `overflow_modulus` bit:\n///\n/// - For each limb `i`, `bf[i]` and `cf[i]` only appear in the combination\n/// `bf[i] * 2^{120} - cf[i] * 2^{120}`. This means that both\n///\n/// (bf[i], cf[i]) = (0, 0) and (bf[i], cf[i]) = (1, 1)\n///\n/// give the same contribution to the equation. As a result, there are multiple\n/// valid flag assignments for the *same* `lhs`, `rhs`, `sub` and `result`.\n///\n/// - More importantly, if `lhs + rhs` is greater than `MOD` but still strictly\n/// less than `2^{MOD_BITS}`, there exist *spurious* witnesses where:\n/// * `overflow_modulus` is set inconsistently with the true arithmetic\n/// overflow, and\n/// * the `(bf, cf)` flags are adjusted accordingly,\n///\n/// such that all equations above still hold and all `result` limbs remain\n/// within range. In particular, when\n///\n/// lhs + rhs + MOD < 2^{MOD_BITS},\n///\n/// a malicious prover can \"hide\" an extra `MOD` inside the choice of\n/// `overflow_modulus`, `bf` and `cf`, so that the circuit is satisfied by a\n/// witness\n///\n/// Consequently, this function is only *conditionally* sound: we rely on the\n/// out-of-circuit implementation of `compute_add_flags` to provide the honest\n/// `(borrow_flags, carry_flags, overflow_modulus)` witness. Under that\n/// assumption, the constrained `result` matches `lhs + rhs (mod MOD)`.\n///\n/// ## Completeness\n///\n/// For inputs in the range `0 <= lhs, rhs < MOD` and honest flags from\n/// `compute_add_flags`, the constraints are complete: every valid `BigNum` sum\n/// `lhs + rhs (mod MOD)` admits a satisfying assignment.\n///\n/// Inputs with `lhs` or `rhs` in `[MOD, 2^{MOD_BITS})` are still representable\n/// as limb arrays and may admit satisfying witnesses, but then the operation\n/// no longer corresponds to a unique, well-defined addition in the field\n/// `Z / MOD Z`. Such uses are outside the intended semantics of this function.\npub fn add(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n if std::runtime::is_unconstrained() {\n // Safety: unconstrained runtime requires no constraints\n unsafe {\n __add(params.modulus, lhs, rhs)\n }\n } else {\n // Safety: flags are constrained by `compute_add_result`:\n // - incorrect flags cause computed limbs to fail 128-bit range checks\n // - wrong overflow causes result to be off by modulus, failing `validate_in_range`\n let (carry_flags, borrow_flags, overflow): ([bool; N - 1], [bool; N - 1], bool) =\n unsafe { compute_add_flags(params.modulus, lhs, rhs) };\n\n // Compute and validate the result in constrained code\n compute_add_result::(\n params.modulus,\n lhs,\n rhs,\n carry_flags,\n borrow_flags,\n overflow,\n )\n }\n}\n\n/// Subtract two `BigNum` values\n///\n/// Computes `result = lhs - rhs` using limb-wise subtraction with borrow flags,\n/// and an optional addition of `MOD` using carry flags. The function then\n/// constrains:\n/// - all `result` limbs to be a valid `BigNum` value, and\n/// - the subtraction/addition relation with the carry/borrow flags and the\n/// `underflow_modulus` bit.\n///\n/// ## Assumptions\n/// - All limbs of `lhs`, `rhs` and `MOD` are valid `BigNum` limbs\n/// (120-bit for non-top limbs, `TOP_LIMB_BITS` for the top limb).\n/// - Semantically, we intend to use this only with `0 <= lhs, rhs < MOD`,\n/// even though the limb/range constraints allow values up to `< 2^{MOD_BITS}`.\n///\n/// ## Soundness (intended relation)\n/// This function constrains the following equations:\n///\n/// result[0] = lhs[0] - rhs[0]\n/// + add[0]\n/// + bf[0] * 2^{120}\n/// - cf[0] < 2^{120}\n///\n/// result[i] = lhs[i] - rhs[i]\n/// + add[i]\n/// + bf[i] * 2^{120} - bf[i - 1]\n/// - cf[i] * 2^{120} + cf[i - 1] < 2^{120},\n/// for i = 1..N-2\n///\n/// result[N - 1] = lhs[N - 1] - rhs[N - 1]\n/// + add[N - 1]\n/// - bf[N - 2]\n/// + cf[N - 2] < 2^{TOP_LIMB_BITS}\n///\n/// where:\n/// - `bf[i]` are the borrow flags of the subtraction,\n/// - `cf[i]` are the carry flags of the optional addition, and\n/// - `add` is either the zero vector or `MOD`, depending on the\n/// `underflow_modulus` flag:\n/// * `underflow_modulus = 0` => `add = 0`\n/// * `underflow_modulus = 1` => `add = MOD`\n///\n/// If `lhs`, `rhs` and `MOD` are valid `BigNum` limbs and the witness for\n/// `(bf, cf, underflow_modulus)` is the honest one produced by `compute_sub_flags`,\n/// these constraints enforce:\n/// - a valid limb-wise borrow chain for `lhs - rhs`, and\n/// - a valid limb-wise addition chain for either `lhs - rhs` or\n/// `lhs - rhs + MOD`, with no underflow in any limb.\n///\n/// Under these assumptions the constrained result equals:\n///\n/// result = lhs - rhs (mod MOD)\n///\n/// in the intended arithmetic.\n///\n/// ## Limitations / extra satisfying witnesses\n///\n/// The constraint system itself does **not** uniquely determine the carry/borrow\n/// flags nor the `underflow_modulus` bit:\n///\n/// - For each limb `i`, `bf[i]` and `cf[i]` only appear in the combination\n/// `bf[i] * 2^{120} - cf[i] * 2^{120}`. This means that both\n///\n/// (bf[i], cf[i]) = (0, 0) and (bf[i], cf[i]) = (1, 1)\n///\n/// give the same contribution to the equation. As a result, there are multiple\n/// valid flag assignments for the *same* `lhs`, `rhs`, `add` and `result`.\n///\n/// - More importantly, when `lhs < rhs`, the true field result is\n///\n/// lhs - rhs + MOD,\n///\n/// and as long as\n///\n/// lhs - rhs + MOD < 2^{MOD_BITS},\n///\n/// a malicious prover can:\n/// * set `underflow_modulus` inconsistently with the true underflow, and\n/// * adjust the `(bf, cf)` flags accordingly,\n///\n/// such that all equations above still hold and all `result` limbs remain\n/// within range. In other words, the circuit can be satisfied by a witness\n/// that does **not** correspond to the unique intended subtraction modulo\n/// `MOD` for some inputs with `lhs < rhs`.\n///\n/// Consequently, this function is only *conditionally* sound: we rely on the\n/// out-of-circuit implementation of `compute_sub_flags` to provide the honest\n/// `(borrow_flags, carry_flags, underflow_modulus)` witness. Under that\n/// assumption, the constrained `result` matches `lhs - rhs (mod MOD)`.\n///\n/// ## Completeness\n///\n/// For inputs in the range `0 <= lhs, rhs < MOD` and honest flags from\n/// `compute_sub_flags`, the constraints are complete: every valid `BigNum`\n/// difference `lhs - rhs (mod MOD)` admits a satisfying assignment.\n///\n/// Inputs with `lhs` or `rhs` in `[MOD, 2^{MOD_BITS})` are still representable\n/// as limb arrays and may admit satisfying witnesses, but then the operation\n/// no longer corresponds to a unique, well-defined subtraction in the field\n/// `Z / MOD Z`. Such uses are outside the intended semantics of this function.\npub fn sub(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n if std::runtime::is_unconstrained() {\n // Safety: unconstrained runtime requires no constraints\n unsafe {\n __sub(params.modulus, lhs, rhs)\n }\n } else {\n // Safety: flags are constrained by `compute_sub_result`:\n // - incorrect flags cause computed limbs to fail 128-bit range checks\n // - wrong underflow causes result to be off by modulus, failing `validate_in_range`\n let (carry_flags, borrow_flags, underflow): ([bool; N - 1], [bool; N - 1], bool) =\n unsafe { compute_sub_flags(params.modulus, lhs, rhs) };\n\n // Compute and validate the result in constrained code\n compute_sub_result::(\n params.modulus,\n lhs,\n rhs,\n carry_flags,\n borrow_flags,\n underflow,\n )\n }\n}\n\n/// Compute the `BigNum` multiplication\n///\n/// Computes `result = lhs * rhs (mod MOD)` by:\n/// 1. Computing `result` out of circuit via `__mul`.\n/// 2. Constraining the quadratic relation `lhs * rhs - result = 0` with\n/// `evaluate_quadratic_expression`.\n///\n/// ## Soundness\n/// Soundness reduces to `evaluate_quadratic_expression` for the relation\n/// `lhs * rhs - result = 0`\n///\n/// ## Note\n/// When possible, prefer expressing your computation directly as a quadratic\n/// relation and calling `evaluate_quadratic_expression` instead of using `mul`\npub fn mul(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n // Safety: we constrain the multiplication result immediately after\n let result: [u128; N] = unsafe { __mul::(params, lhs, rhs) };\n if !std::runtime::is_unconstrained() {\n // lhs * rhs - result = 0\n evaluate_quadratic_expression(\n params,\n [[lhs]],\n [[false]],\n [[rhs]],\n [[false]],\n [result],\n [true],\n );\n }\n result\n}\n\n/// Compute the `BigNum` squaring\n///\n/// Computes `result = val * val (mod MOD)` by:\n/// 1. Computing `result` out of circuit via `__sqr`.\n/// 2. Constraining the quadratic relation `val * val - result = 0` with\n/// `evaluate_quadratic_expression`.\n///\n/// ## Soundness\n/// Soundness reduces to `evaluate_quadratic_expression` for the relation\n/// `val * val - result = 0`\n///\n/// ## Note\n/// When possible, prefer expressing your computation directly as a quadratic\n/// relation and calling `evaluate_quadratic_expression` instead of using `sqr`\npub fn sqr(\n params: BigNumParams,\n val: [u128; N],\n) -> [u128; N] {\n // Safety: we constrain the multiplication result immediately after\n let result: [u128; N] = unsafe { __sqr::<_, MOD_BITS>(params, val) };\n if !std::runtime::is_unconstrained() {\n // val * val - result = 0\n evaluate_quadratic_expression(\n params,\n [[val]],\n [[false]],\n [[val]],\n [[false]],\n [result],\n [true],\n );\n }\n result\n}\n\n/// Compute the `BigNum` division\n///\n/// Computes `result = lhs * rhs^{-1} (mod MOD)` by:\n/// 1. Computing `result` out of circuit via `__div`.\n/// 2. Constraining the quadratic relation `result * rhs - lhs = 0` with\n/// `evaluate_quadratic_expression`.\n/// 3. Enforcing `rhs != 0 (mod MOD)`.\n///\n/// ## Soundness\n/// Soundness reduces to `evaluate_quadratic_expression` for the relation\n/// `result * rhs - lhs = 0`, together with the non-zero check on `rhs` and the\n/// assumption that `MOD` has multiplicative inverses for all non-zero elements\n/// (`params.has_multiplicative_inverse = true`).\n///\n/// ## Note\n/// - This is **expensive** in witness time due to modular inversion.\n/// - When possible, prefer expressing your computation directly as a quadratic\n/// relation and calling `evaluate_quadratic_expression` instead of using `div`.\n/// - In the unconstrained runtime, the behavior of `__div` on zero divisors is\n/// not constrained by this function.\npub fn div(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n assert(\n params.has_multiplicative_inverse,\n \"BigNum has no multiplicative inverse. Use udiv for unsigned integer division\",\n );\n // Safety: We constrain the result of division immediately after\n let result: [u128; N] = unsafe { __div::<_, MOD_BITS>(params, lhs, rhs) };\n if !std::runtime::is_unconstrained() {\n // result * rhs - lhs = 0\n evaluate_quadratic_expression(\n params,\n [[result]],\n [[false]],\n [[rhs]],\n [[false]],\n [lhs],\n [true],\n );\n assert_is_not_zero(params, rhs);\n }\n result\n}\n\n/// Compute the `BigNum` integer division with remainder\n///\n/// Computes `quotient = floor(numerator / divisor)` and\n/// `remainder = numerator % divisor` by:\n/// 1. Computing `(quotient, remainder)` out of circuit via `__udiv_mod`.\n/// 2. Constraining the quadratic relation\n/// quotient * divisor + remainder - numerator = 0\n/// with `validate_udiv_mod_expression`.\n/// 3. Enforcing `remainder < divisor`.\n///\n/// ## Soundness\n/// Soundness reduces to `validate_udiv_mod_expression` for the relation\n/// quotient * divisor + remainder - numerator = 0,\n/// together with `remainder < divisor` check enforced via `validate_gt`.\n///\n/// Under these checks, any satisfying assignment corresponds to a valid\n/// integer division `numerator = quotient * divisor + remainder` with\n/// `0 <= remainder < divisor`\n///\n/// ## Note\n/// Enforcing `divisor != 0` is not necessary. `remainder < divisor`\n/// Already enforces this.\npub fn udiv_mod(\n numerator: [u128; N],\n divisor: [u128; N],\n) -> ([u128; N], [u128; N]) {\n // Safety: We constrain the result of __udiv_mod immediately after\n let (quotient, remainder): ([u128; N], [u128; N]) = unsafe { __udiv_mod(numerator, divisor) };\n if !std::runtime::is_unconstrained() {\n // quotient * divisor + remainder - numerator = 0\n validate_udiv_mod_expression::(numerator, divisor, quotient, remainder);\n // remainder < divisor\n validate_gt::(divisor, remainder);\n }\n (quotient, remainder)\n}\n\n/// Compute the `BigNum` integer division\n///\n/// Returns `floor(numerator / divisor)`.\n/// All constraints and soundness details are handled inside `udiv_mod`.\npub fn udiv(numerator: [u128; N], divisor: [u128; N]) -> [u128; N] {\n udiv_mod::(numerator, divisor).0\n}\n\n/// Compute the `BigNum` remainder\n///\n/// Returns `numerator % divisor`.\n/// All constraints and soundness details are handled inside `udiv_mod`.\npub fn umod(numerator: [u128; N], divisor: [u128; N]) -> [u128; N] {\n udiv_mod::(numerator, divisor).1\n}\n" }, "555": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/fns/expressions.nr", "source": "use crate::utils::split_bits;\n\nuse crate::constants::{TWO_POW_120, TWO_POW_126, TWO_POW_246};\n\nuse crate::fns::constrained_ops::{validate_in_range, validate_quotient_in_range};\nuse crate::fns::unconstrained_helpers::__barrett_reduction;\nuse crate::fns::unconstrained_ops::__is_zero;\n\nuse crate::params::BigNumParams;\n\n// ------------------------------ UNCONSTRAINED EXPRESSIONS ------------------------------\n\n/// Compute the result of a linear combination of (possibly negative) `BigNum` values (unconstrained)\n///\n/// ## Note\n/// 1. `modulus2` is structured such that all limbs will be greater than `0`, even when subtracting.\n/// To do this, when computing `p - x`, we ensure that each limb in `p` is greater than each limb in `x`.\n/// We know that, for a valid bignum element, the limbs in `x` will be < 2^{120}\n/// Therefore each of the limbs in `p` (except the most significant) will borrow 2^{120} from the more significant limb.\n/// Finally, to ensure we do not underflow in the most significant limb, we use `2p` instead of `p`\n///\n/// 2. Returns the `Field` values that are not normalized to be 120-bit\nunconstrained fn __add_linear_expression(\n params: BigNumParams,\n vals: [[u128; N]; M],\n flags: [bool; M],\n) -> ([Field; N]) {\n let mut sum: [Field; N] = [0; N];\n let modulus2: [u128; N] = params.double_modulus;\n for i in 0..M {\n if (flags[i]) {\n for j in 0..N {\n sum[j] = sum[j] + (modulus2[j] as Field) - (vals[i][j] as Field);\n }\n } else {\n for j in 0..N {\n sum[j] = sum[j] + (vals[i][j] as Field);\n }\n }\n }\n sum\n}\n\n/// Compute the limb products of a quadratic expression (unconstrained)\n///\n/// See `__compute_quadratic_expression_with_borrow_flags` for full description\n///\n/// ## Note\n/// Returns the `Field` values that are not normalized to be 120-bit\nunconstrained fn __compute_quadratic_expression_product(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> [Field; 2 * N] {\n let mut lhs: [[Field; N]; NUM_PRODUCTS] = [[0; N]; NUM_PRODUCTS];\n let mut rhs: [[Field; N]; NUM_PRODUCTS] = [[0; N]; NUM_PRODUCTS];\n\n for i in 0..NUM_PRODUCTS {\n lhs[i] = __add_linear_expression(params, lhs_terms[i], lhs_flags[i]);\n rhs[i] = __add_linear_expression(params, rhs_terms[i], rhs_flags[i]);\n }\n let add: [Field; N] = __add_linear_expression(params, linear_terms, linear_flags);\n\n let mut mulout: [Field; 2 * N] = [0; 2 * N];\n for i in 0..N {\n for j in 0..N {\n for k in 0..NUM_PRODUCTS {\n mulout[i + j] += lhs[k][i] * rhs[k][j];\n }\n }\n mulout[i] += add[i];\n }\n mulout\n}\n\n/// Compute the borrow flags for a limb-wise subtraction `lhs - rhs` (unconstrained).\n///\n/// This helper operates on `N`-limb values where each limb is interpreted as a\n/// 120-bit chunk (base `B = 2^{120}`), but where intermediate limbs may exceed\n/// `2^{120}` due to sums of products.\n///\n/// Conceptually, we want to model the integer subtraction\n///\n/// lhs - rhs\n///\n/// as a base-`B` subtraction with borrows, without allowing any intermediate\n/// values to go negative in the circuit field. We achieve this by:\n///\n/// 1. Working with widened 246-bit ranges per limb.\n/// 2. Encoding a borrow from limb `i+1` into limb `i` by:\n/// - adding `2^{246}` into limb `i`,\n/// - subtracting `2^{126}` (= 2^{246 - 120}) from limb `i+1` after\n/// scaling by `2^{-120}`.\n///\n/// The returned `borrow_flags[i]` indicate whether the canonical integer\n/// subtraction would borrow from limb `i+1` into limb `i`. These flags are\n/// later re-applied in-circuit via `apply_borrow_flags`, and the resulting\n/// limbs are checked by `validate_expression_is_zero`.\nunconstrained fn __compute_borrow_flags(\n mut lhs_limbs: [Field; N],\n rhs_limbs: [Field; N],\n) -> [bool; N - 1] {\n // compute borrow flags from mulout_p and mulout_n\n let mut borrow_flags: [bool; N - 1] = [false; N - 1];\n let borrow_shift: Field = TWO_POW_246; // 2^{246}\n let borrow_carry: Field = TWO_POW_126; // 2^{126} = 2^{246 - 120}\n let downshift_120: Field = 1 / (TWO_POW_120 as Field);\n\n // determine whether we need to borrow from more significant limbs.\n // initial limb is \"simple\" comparison operation\n borrow_flags[0] = lhs_limbs[0].lt(rhs_limbs[0]);\n\n // we have N - 1 borrow flags. The number of limbs is N\n // and there is nothing to borrow against for the final limb.\n let mut hi_bits: Field =\n (lhs_limbs[0] - rhs_limbs[0] + ((borrow_flags[0] as Field) * borrow_shift)) * downshift_120;\n\n for i in 1..(N - 1) {\n // compute the contribution from limb `i-1` that gets added into limb `i`, and add into limb `i`\n lhs_limbs[i] += hi_bits;\n\n let subtrahend: Field = rhs_limbs[i] + (borrow_flags[i - 1] as Field) * borrow_carry;\n\n // determine whether negative limb values are greater than positive limb values\n borrow_flags[i] = lhs_limbs[i].lt(subtrahend);\n let minuend: Field = lhs_limbs[i] + (borrow_flags[i] as Field) * borrow_shift;\n\n hi_bits = (minuend - subtrahend) * downshift_120;\n }\n borrow_flags\n}\n\n/// Given a degree-2 `BigNum` expression that is equal to `0 mod MOD`, compute\n/// the quotient and the borrow flags (unconstrained).\n///\n/// The expression has the form:\n///\n/// sum_{k=0}^{NUM_PRODUCTS-1} (L_k * R_k) + sum_{i=0}^{ADD_N-1} A_i = quotient * MOD\n///\n/// where each `L_k`, `R_k`, `A_i` is an `N`-limb `BigNum` assembled from the\n/// `(terms, flags)` arrays.\n///\n/// This helper:\n/// 1. Evaluates the quadratic expression into `mulout_p` as a `2N`-limb\n/// non-normalized value.\n/// 2. Normalizes `mulout_p` into 120-bit limbs and applies Barrett\n/// reduction to obtain `(quotient, remainder)`.\n/// 3. Asserts that the integer remainder is zero (debugging aid; range\n/// checks enforce this later).\n/// 4. Reconstructs `mulout_n = quotient * MOD`.\n/// 5. Computes `borrow_flags` for the limb-wise subtraction\n/// `mulout_p - mulout_n`,\n/// using the 2^{246}/2^{126} encoding described in\n/// `evaluate_quadratic_expression`.\n///\n/// The returned `quotient` and `borrow_flags` are later constrained\n/// in-circuit by `compute_quadratic_expression_with_modulus` and\n/// `evaluate_quadratic_expression`.\nunconstrained fn __compute_quadratic_expression_with_borrow_flags(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> ([u128; N], [bool; 2 * N - 2]) {\n let mulout_p: [Field; 2 * N] = __compute_quadratic_expression_product(\n params,\n lhs_terms,\n lhs_flags,\n rhs_terms,\n rhs_flags,\n linear_terms,\n linear_flags,\n );\n\n // `__normalize_limbs` will validate that we do not overflow 2N, normally we should not overflow 2N-1\n let mut relation_result: [u128; 2 * N] = split_bits::__normalize_limbs(mulout_p);\n let (quotient, remainder): ([u128; N], [u128; N]) =\n __barrett_reduction(relation_result, params.redc_param, MOD_BITS, params.modulus);\n\n // This is verified later by the range checks but left for debugging purposes\n assert(__is_zero(remainder));\n\n // We do not normalize `mulout_n` so we won't fill the `2 * N - 1`\n let mut mulout_n: [Field; 2 * N] = [0; 2 * N];\n for i in 0..N {\n for j in 0..N {\n mulout_n[i + j] += (quotient[i] as Field) * (params.modulus[j] as Field);\n }\n }\n\n let borrow_flags: [bool; 2 * N - 1] = __compute_borrow_flags(mulout_p, mulout_n);\n\n // We have to copy it because we know that borrow_flags[2 * N - 1] is always 0\n // But we also have to provide 2 * N limbs to `__barrett_reduction`.\n // And keep `__compute_borrow_flags` generic enough\n let mut borrow_flags_real: [bool; 2 * N - 2] = [false; 2 * N - 2];\n for i in 0..2 * N - 2 {\n borrow_flags_real[i] = borrow_flags[i];\n }\n\n (quotient, borrow_flags_real)\n}\n\n/// Computes the quotient/remainder of a quadratic expression (unconstrained)\n///\n/// See `__compute_quadratic_expression_with_borrow_flags` for full description\npub(crate) unconstrained fn __compute_quadratic_expression(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> ([u128; N], [u128; N]) {\n let mulout: [Field; 2 * N] = __compute_quadratic_expression_product(\n params,\n lhs_terms,\n lhs_flags,\n rhs_terms,\n rhs_flags,\n linear_terms,\n linear_flags,\n );\n // __normalize_limbs will validate that we do not overflow 2N, normally we should not overflow 2N-1\n let mut relation_result: [u128; 2 * N] = split_bits::__normalize_limbs(mulout);\n\n let (quotient, remainder): ([u128; N], [u128; N]) =\n __barrett_reduction(relation_result, params.redc_param, MOD_BITS, params.modulus);\n\n (quotient, remainder)\n}\n\n// ------------------------------ CONSTRAINED EXPRESSIONS ------------------------------\n\n/// Constrained version of `__add_linear_expression`\n///\n/// Computes all the linear parts of an expression in-circuit\n///\n/// ## Note\n/// 1. Negative terms are implemented by adding `double_modulus`\n/// `double_modulus` is chosen so that all limbs except the top one\n/// are > 2^{120}, which prevents underflows in intermediate computations.\n///\n/// 2. For the most significant limb we slightly reduce the padding (to keep the\n/// overall value equal to `2 * MOD`), so in principle there is a narrow edge\n/// case where that limb could underflow if enough negative contributions are\n/// accumulated and the top limb of `MOD` is very small. In practice, the\n/// global `BigNum` range and operand-count assumptions in\n/// `evaluate_quadratic_expression` rule out such patterns.\nfn compute_linear_expressions(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> ([[Field; N]; NUM_PRODUCTS], [[Field; N]; NUM_PRODUCTS], [Field; N]) {\n // lhs linear terms\n let mut lhs_linear: [[Field; N]; NUM_PRODUCTS] = [[0; N]; NUM_PRODUCTS];\n // rhs linear terms\n let mut rhs_linear: [[Field; N]; NUM_PRODUCTS] = [[0; N]; NUM_PRODUCTS];\n // linear terms\n let mut lin_expr: [Field; N] = [0; N];\n\n for k in 0..NUM_PRODUCTS {\n for i in 0..N {\n for j in 0..LHS_N {\n // Note: if lhs_flags[k][j] - `is_negative` is not known at comptime this is very expensive\n if (lhs_flags[k][j]) {\n lhs_linear[k][i] -= lhs_terms[k][j][i] as Field;\n lhs_linear[k][i] += params.double_modulus[i] as Field;\n } else {\n lhs_linear[k][i] += lhs_terms[k][j][i] as Field;\n }\n }\n for j in 0..RHS_N {\n // Note: if rhs_flags[k][j] - `is_negative` is not known at comptime this is very expensive\n if (rhs_flags[k][j]) {\n rhs_linear[k][i] -= rhs_terms[k][j][i] as Field;\n rhs_linear[k][i] += params.double_modulus[i] as Field;\n } else {\n rhs_linear[k][i] += rhs_terms[k][j][i] as Field;\n }\n }\n }\n }\n\n for i in 0..N {\n for j in 0..ADD_N {\n // Note: if linear_flags[j] - `is_negative` is not known at comptime this is very expensive\n if (linear_flags[j]) {\n lin_expr[i] -= linear_terms[j][i] as Field;\n lin_expr[i] += params.double_modulus[i] as Field;\n } else {\n lin_expr[i] += linear_terms[j][i] as Field;\n }\n }\n }\n\n (lhs_linear, rhs_linear, lin_expr)\n}\n\n/// Constrained version of `__compute_quadratic_expression_product`\n///\n/// Computes the following expression in-circuit:\n/// \\sum (L_i * R_i) + \\sum (A_i) - QUOTIENT * MOD\n///\n/// Because of the subtraction of `QUOTIENT * MODULUS`, the resulting limbs may\n/// underflow and represent *negative* values. To account for this, we allow the\n/// prover to choose a sequence of borrow flags and interpret the limbs with\n/// additional terms:\n///\n/// - for each limb `i` we may add `2^{246}` (via a flag at position `i`);\n/// - for each limb `i` we may subtract `2^{126 = 246 - 120}` (via the flag\n/// at position `i - 1`).\n///\n/// This corresponds to borrowing `2^{126}` from limb `i + 1` and adding\n/// `2^{246}` into limb `i`. After this adjustment, an honest prover can ensure\n/// that every limb lies in `[0, 2^{246})` and that the adjusted limbs encode\n/// the correct integer value.\n///\n/// Additionally, we constrain the quotient limbs via `validate_quotient_in_range`:\n/// - limbs `0..N-2` of `quotient` are < 2^{120};\n/// - the top limb is < 2^{TOP_LIMB_BITS + 6}.\n/// Which validates `quotient < 2^{MOD_BITS + 6}`\n///\n/// ## TODO\n/// Apply static or runtime checks in this method to validate that the effective\n/// `twiddle_factor` does not exceed 6 under the chosen parameters.\nfn compute_quadratic_expression_with_modulus(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) -> [Field; 2 * N - 1] {\n // Safety: use an unconstrained function to compute the value of the quotient and borrow_flags out-of-circuit\n let (quotient, borrow_flags): ([u128; N], [bool; 2 * N - 2]) = unsafe {\n __compute_quadratic_expression_with_borrow_flags::(\n params,\n lhs_terms,\n lhs_flags,\n rhs_terms,\n rhs_flags,\n linear_terms,\n linear_flags,\n )\n };\n\n // Constrain the `quotient < 2^{MOD_BITS + 6}`\n // By constraining limbs(0..N-2) to be < 2^120 and the the top limb < 2^{TOP_LIMB_BITS + 6}\n validate_quotient_in_range::(quotient);\n\n // Compute the linear sums that represent L_i, R_i, A\n let (lhs_linear, rhs_linear, lin_expr): ([[Field; N]; NUM_PRODUCTS], [[Field; N]; NUM_PRODUCTS], [Field; N]) = compute_linear_expressions::(\n params,\n lhs_terms,\n lhs_flags,\n rhs_terms,\n rhs_flags,\n linear_terms,\n linear_flags,\n );\n\n // We want to evaluate that L * R + A - Quotient * MOD = 0, evaluated over the integers\n // For this we need to be able to borrow values from more-significant limbs into less-significant limbs,\n // so that we can ensure that no limbs will underflow\n let mut expression_limbs: [Field; 2 * N - 1] = [0; 2 * N - 1];\n\n // Compute the product t0 * t1 + t4 - Quotient * MOD\n // TODO: this is super nasty as it requires a multiplication\n for i in 0..N {\n for j in 0..N {\n for k in 0..NUM_PRODUCTS {\n if k == 0 {\n let new_term: Field = lhs_linear[k][i] * rhs_linear[k][j]\n - (quotient[i] as Field) * (params.modulus[j] as Field);\n // width-4 optimization\n std::as_witness(new_term);\n expression_limbs[i + j] += new_term;\n } else {\n expression_limbs[i + j] += lhs_linear[k][i] * rhs_linear[k][j];\n }\n }\n // This is the fallback for pure linear expression\n if (NUM_PRODUCTS == 0) {\n expression_limbs[i + j] -= (quotient[i] as Field) * (params.modulus[j] as Field);\n }\n }\n expression_limbs[i] += lin_expr[i];\n }\n\n apply_borrow_flags(expression_limbs, borrow_flags)\n}\n\n/// Apply a precomputed borrow chain to a limb array.\n///\n/// Given:\n///\n/// - `expression_limbs`: an `N`-limb array of `Field` values representing a\n/// (possibly non-normalized) degree-2-style expression, and\n/// - `borrow_flags[i]` indicating that we \"borrow\" from limb `i+1` into limb `i`,\n///\n/// this function applies the same 2^{246}/2^{126} encoding used in\n/// `__compute_borrow_flags` to produce an adjusted limb array\n///\n/// This matches the behavior of `__compute_borrow_flags`, which conceptually:\n///\n/// 1. Adds `2^{246}` into a limb when a borrow is taken at that limb;\n/// 2. After scaling by `2^{-120}`, contributes `2^{126}` into the next limb.\n///\n/// The resulting `expression_limbs` can then be passed to\n/// `validate_expression_is_zero`, which:\n///\n/// - right-shifts by 120 bits per limb,\n/// - enforces a 126-bit bound,\n/// - and propagates carries forward, finally checking that the most\n/// significant limb is zero.\n///\n/// ## Assumptions\n///\n/// - `borrow_flags` was computed consistently with the original construction\n/// of `expression_limbs` (e.g. via `__compute_borrow_flags` on the\n/// corresponding unconstrained expression).\n/// - The caller has ensured that each adjusted limb remains < 2^{246} for\n/// honest witnesses (e.g. via `NUM_PRODUCTS < 64`).\n///\n/// ## Note\n///\n/// This function does not itself enforce any bit-size bounds; it only applies\n/// the borrow encoding. The actual range checks happen later in\n/// `validate_expression_is_zero`.\n///\n/// ## TODO\n/// define trade-offs regarding the value of borrow_shift\n/// (the larger the value, the greater the range check that is required on product_limbs)\n/// (126-bit range check is a sweet spot for the barretenberg backend as it decomposes into 9 14-bit range checks)\n/// (the barretenberg backend can evaluate these in 5.25 gates. 127 bits costs 6.5 gates)\nfn apply_borrow_flags(\n mut expression_limbs: [Field; N],\n borrow_flags: [bool; N - 1],\n) -> [Field; N] {\n let borrow_shift: Field = TWO_POW_246; // 2^{246}\n let borrow_carry: Field = TWO_POW_126; // 2^{246 - 120} = 2^{126}\n\n // Get the product_limbs into the form where each entry is a 246-bit value\n expression_limbs[0] += (borrow_flags[0] as Field) * borrow_shift;\n for i in 1..(N - 1) {\n expression_limbs[i] += (borrow_flags[i] as Field) * borrow_shift\n - (borrow_flags[i - 1] as Field) * borrow_carry;\n }\n expression_limbs[N - 1] -= (borrow_flags[N - 2] as Field) * borrow_carry;\n expression_limbs\n}\n\n/// Validate that `limbs` represent the integer value `0`\n///\n/// ## Assumptions\n/// - `limbs` is an array of `Field` values that was derived arithmetically as\n/// a degree-2 expression\n/// - each limb satisfies `limbs[i] < 2^{246}`, as ensured by\n/// `compute_quadratic_expression_with_modulus` under its parameter bounds.\n///\n/// ## Details\n/// Each element `i` in `limbs` overlaps in bit-range with element `i+1`, EXCEPT for the low 120 bits\n/// i.e. we need to do the following for each limb `i`:\n/// 1. validate the limb's low-120 bits equals zero\n/// 2. compute the limb \"carry\" by right-shifting by 2^{120}\n/// 3. propagate the \"carry\" into limb `i+1`\n/// We can efficiently do all of the above by multiplying the limb by 2^{-120} and constraining the result to be <2^{126}\n///\n/// If the low 120 bits are nonzero, there is no value in `[0, 2^{126})` that\n/// could have produced this limb after multiplying by `2^{120}`. Since\n/// multiplication by `2^{120}` is a bijection on the Field, any limb with\n/// non-zero low 120 bits must map outside the `[0, 2^{126})` range after\n/// scaling by `2^{-120}`.\n///\n/// The most significant limb has no limb to \"carry\" values into - the entire limb must equal zero\n///\n/// ## Note\n/// The constant 126 is not arbitrary. We use 120-bit limbs and allow up to 64\n/// products per limb, which contributes at most `log2(64) = 6` bits of headroom.\n/// After scaling by `2^{-120}`, honest witnesses fit in 126 bits. We could in\n/// principle go higher (up to roughly `CircuitModulusBits - 121`), but 126 is\n/// the minimal bound consistent with `NUM_PRODUCTS < 64` and is significantly\n/// cheaper than larger bounds for the barretenberg backend.\nfn validate_expression_is_zero(mut limbs: [Field; N]) {\n let hi_shift: Field = TWO_POW_120 as Field;\n let hi_downshift: Field = 1 / hi_shift;\n for i in 0..N - 1 {\n limbs[i] *= hi_downshift;\n std::as_witness(limbs[i]);\n limbs[i].assert_max_bit_size::<126>(); // N.B. is this sufficient? going beyond 126 costs us 1 gate per limb\n limbs[i + 1] += limbs[i];\n }\n assert(limbs[N - 1] == 0);\n}\n\n/// Constrain a degree-2 `BigNum` expression to be equal to 0 (mod `MOD`)\n///\n//\n/// This method is intended for relations where the remainder term of the\n/// degree-2 expression is exactly zero as an integer relation. In other words,\n/// we use it only when we expect:\n///\n/// \\sum (L_i * R_i) + \\sum (A_i) - QUOTIENT * MOD = 0\n///\n/// as integers, not just modulo the circuit field or `BigNum` field.\n///\n/// ## Details\n///\n/// The constrained expression, viewed over the integers, is:\n///\n/// \\sum_{i=0}^{NUM_PRODUCTS-1} (L_i * R_i)\n/// + \\sum_{i=0}^{ADD_N-1} (A_i)\n/// - QUOTIENT * MOD\n/// = 0\n///\n/// Each `L_i`, `R_i`, `A_i` is an `N`-limb `BigNum` assembled from the\n/// `(terms, flags)` arrays. For example, for `i = 0`:\n///\n/// L_0 = \\sum_{j=0}^{LHS_N-1} lhs[0][j] as a `BigNum`\n/// R_0 = \\sum_{j=0}^{RHS_N-1} rhs[0][j] as a `BigNum`\n///\n/// The intent is to capture a generic degree-2 expression within Noir's\n/// limitations (no efficient dynamically sized vectors).\n///\n/// The expensive parts of this algorithm are:\n/// 1. evaluating the limb products required to compute the `L_i * R_i` values;\n/// 2. applying range constraints to validate that the result encodes 0.\n///\n/// ## Note\n/// When the expression is evaluated over `N`-limb `BigNum` values, the product\n/// has up to `2N - 1` significant limbs. Each limb is a sum of at most\n/// `NUM_PRODUCTS` products of `linear` 120-bit limbs and `quadratic` 240-bit-ish limbs\n///\n/// Note that:\n/// * limb-wise multiplication is not uniform across indices. For example:\n///\n/// [x0, x1, x2] * [y0, y1, y2] =\n/// [\n/// x0*y0, // 1 term\n/// x0*y1 + x1*y0, // 2 terms\n/// x0*y2 + x1*y1 + x2*y0, // 3 terms\n/// x1*y2 + x2*y1, // 2 terms\n/// x2*y2 // 1 term\n/// ]\n/// The number of partial products per limb grows linearly from 1 to N,\n/// then decreases linearly back to 1.\n///\n/// * we also allow linear combinations inside the products, which further increases the bound\n/// on a `quadratic` limb\n///\n/// * finally, when `is_negative` flag is set, we effectively add 2 * MOD to each limb, where\n/// 2 * MOD limbs are structured in such a way that they all are > 2**120\n///\n/// We allow `NUM_PRODUCTS < 64` completeness-wise, but it certainly can overflow the 2^{240 + `twiddle_factor=6`} bound\n/// in edge cases. See completeness section for an example.\n///\n/// ## Assumptions\n///\n/// Each `BigNum` value used in this gadget is already range constrained:\n/// - limbs `0..N-2` satisfy `limb_i < 2^{120}`\n/// - limb `N-1` satisfies `limb_{N-1} < 2^{TOP_LIMB_BITS}`\n///\n/// ## Completeness\n/// If an honest prover supplies inputs that satisfy the assumptions above,\n/// it can always find a `quotient` and `borrow_flags` such that the integer\n/// relation holds and all constraints are satisfied.\n///\n/// The only possibility to break completeness is by providing too many inputs, for example:\n///\n/// (a0 + a1 + a2) * (b0 + b1 + b2) + ... (60 times), with each a_i having its limbs at maximum value of 2^{120} - 1\n/// The middle limb will contain a value that will definitely overflow the 2^{246} bound.\n///\n/// ## Soundness\n/// This function is conditionally sound: it enforces that the degree-2 relation\n/// holds modulo `MOD`, but it does not, by itself, enforce that any particular\n/// term (for example a result `z`) is a *canonical* representative in\n/// `[0, MOD)`.\n///\n/// In other words, if the surrounding relation is invariant under adding a\n/// multiple of `MOD` to one of its terms, then a dishonest prover can exploit\n/// this. For example, consider a multiplication relation:\n///\n/// x * y - z = 0 (mod MOD)\n///\n/// Internally we encode this as:\n///\n/// x * y + 2 * MOD - z - quotient * MOD = 0\n///\n/// which is equivalent to:\n///\n/// x * y - z = (quotient - 2) * MOD\n///\n/// Suppose the honest witness uses some `z` satisfying\n/// `0 <= z < MOD` and some `quotient`. If the `BigNum` encoding allows\n/// `z' = z + MOD` (i.e. `z' < 2^{MOD_BITS}` still holds), then a dishonest\n/// prover can instead provide:\n///\n/// z' = z + MOD\n/// quotient' = quotient - 1\n///\n/// and still satisfy:\n///\n/// x * y + 2 * MOD - z' - quotient' * MOD = 0\n///\n/// even though `z'` is no longer the canonical representative of `x * y mod MOD`.\n///\n/// The same consideration applies to almost every constrained `BigNum` relation:\n/// whenever a value participates *only* through a modular equality, and no\n/// separate range constraint is imposed on that value, the prover is free to\n/// shift it by an extra `MOD` as long as the resulting limb\n/// encoding still satisfies its bit-bounds. This is inherent in working with\n/// modular constraints; the responsibility for enforcing canonical\n/// representatives lies with the caller when it is required.\n///\n/// This is the same \"extra modulus\" phenomenon as in the `add`/`sub` functions:\n/// the constraints are sound for modular arithmetic, but any caller that\n/// requires canonical outputs in `[0, MOD)` must additionally enforce a\n/// range check (for example via `validate_in_field`) on the relevant terms.\npub(crate) fn evaluate_quadratic_expression(\n params: BigNumParams,\n lhs_terms: [[[u128; N]; LHS_N]; NUM_PRODUCTS],\n lhs_flags: [[bool; LHS_N]; NUM_PRODUCTS],\n rhs_terms: [[[u128; N]; RHS_N]; NUM_PRODUCTS],\n rhs_flags: [[bool; RHS_N]; NUM_PRODUCTS],\n linear_terms: [[u128; N]; ADD_N],\n linear_flags: [bool; ADD_N],\n) {\n assert(NUM_PRODUCTS < 64, f\"evaluate_quadratic_expression overflow in operands count\");\n // NUM_PRODUCTS < 64 is a light bound that tries to ensure each limb sum < 2^{246} so that the 126-bit bound is valid.\n\n lhs_terms.for_each(|lhs_limbs: [[u128; N]; LHS_N]| {\n lhs_limbs.for_each(|term: [u128; N]| validate_in_range::(term))\n });\n rhs_terms.for_each(|rhs_limbs: [[u128; N]; RHS_N]| {\n rhs_limbs.for_each(|term: [u128; N]| validate_in_range::(term))\n });\n linear_terms.for_each(|term: [u128; N]| validate_in_range::(term));\n\n let expression_limbs: [Field; 2 * N - 1] = compute_quadratic_expression_with_modulus::(\n params,\n lhs_terms,\n lhs_flags,\n rhs_terms,\n rhs_flags,\n linear_terms,\n linear_flags,\n );\n validate_expression_is_zero(expression_limbs);\n}\n\n// ------------------------------ UDIV MOD EXPRESSION ------------------------------\n\n/// Given a `udiv_mod` `BigNum` expression that is equal to `0` over integers, compute the borrow flags (unconstrained)\n///\n/// Mirror function of `__compute_quadratic_expression_with_borrow_flags` optimized to compute borrow flags of an expression:\n/// divisor * quotient + remainder - numerator = 0\n/// see `__compute_quadratic_expression_with_borrow_flags` for details\n///\n/// The main differences from it are:\n/// 1. `product_limbs` stores only the least-significant `N` limbs of\n/// `quotient * divisor + remainder`. This is sufficient to compute the\n/// borrow flags for the first `N` limbs of\n/// quotient * divisor + remainder - numerator.\n///\n/// For an honest `udiv_mod` relation we also have\n/// quotient * divisor <= numerator < B^N,\n/// so the true product fits into `N` limbs as an integer.\n/// 2. Instead of subtracting `quotient * MOD` we subtract `numerator`. This is due to the fact that\n/// we no longer work over `MOD`, and we can't really do subtractions as we did previously: `double_modulus - x`\n///\n/// ## Note\n/// We leave the borrow values at 2^{246}, even though we should never reach this bound with just 3 terms\n/// The cases where it can happen are: N >= 64 (middle limb will have 64 additions). And it is a pure completeness issue\n/// But the rest of the library will probably not work with that massive number anyway\nunconstrained fn __compute_udiv_mod_expression_with_borrow_flags(\n numerator: [u128; N],\n divisor: [u128; N],\n quotient: [u128; N],\n remainder: [u128; N],\n) -> [bool; N - 1] {\n let mut product_limbs: [Field; N] = [0; N];\n let mut numerator_field: [Field; N] = [0; N];\n for i in 0..N {\n for j in 0..N - i {\n product_limbs[i + j] += (quotient[i] as Field) * (divisor[j] as Field);\n }\n product_limbs[i] += (remainder[i] as Field);\n\n numerator_field[i] = numerator[i] as Field;\n }\n\n __compute_borrow_flags(product_limbs, numerator_field)\n}\n\n/// Constrained version of `__compute_udiv_mod_expression_with_borrow_flags`\n///\n/// Computes the following expression in-circuit:\n/// quotient * divisor + remainder - numerator = 0\n///\n/// Mirror function of `compute_quadratic_expression_with_modulus`. See it for details.\n///\n/// ## Soundness note\n/// We compute the full convolution `quotient * divisor` into `2N - 1` limbs\n/// and then constrain all limbs with index `i >= N` to be zero. For `i >= N`\n/// the value `expression_limbs_full[i]` is a sum of products\n/// sum_{j+k=i} quotient[j] * divisor[k]\n/// with no contribution from `numerator` or `remainder`.\n///\n/// Every limb of `quotient` and `divisor` is range-constrained to be a 120-bit\n/// integer, so each product term is < 2^{240} and each coefficient of the\n/// convolution is strictly smaller than the field modulus. In this regime,\n/// the constraint `expression_limbs_full[i] == 0` in `Field` coincides with\n/// the same equality over the integers.\n///\n/// Vanishing of all high limbs `i >= N` is therefore an integer statement that\n/// the product has degree < N, i.e.\n/// quotient * divisor < 2^{120 * N},\n/// so `quotient * divisor` fits into `N` 120-bit limbs and does not overflow.\nfn compute_udiv_mod_expression(\n numerator: [u128; N],\n divisor: [u128; N],\n quotient: [u128; N],\n remainder: [u128; N],\n) -> [Field; N] {\n // Safety: use an unconstrained function to compute the value of the quotient and borrow_flags out-of-circuit\n let borrow_flags: [bool; N - 1] = unsafe {\n __compute_udiv_mod_expression_with_borrow_flags::(\n numerator,\n divisor,\n quotient,\n remainder,\n )\n };\n\n let mut expression_limbs_full: [Field; 2 * N - 1] = [0; 2 * N - 1];\n for i in 0..N {\n for j in 0..N {\n expression_limbs_full[i + j] += (quotient[i] as Field) * (divisor[j] as Field);\n }\n expression_limbs_full[i] += (remainder[i] as Field) - (numerator[i] as Field);\n }\n\n let mut expression_limbs: [Field; N] = [0; N];\n for i in 0..N {\n expression_limbs[i] = expression_limbs_full[i];\n }\n for i in N..2 * N - 1 {\n assert(expression_limbs_full[i] == 0);\n }\n\n apply_borrow_flags(expression_limbs, borrow_flags)\n}\n\n/// Constrain a `udiv_mod` `BigNum` expression to be equal to 0\n///\n/// Mirror function of `evaluate_quadratic_expression`\n///\n/// ## Details\n///\n/// The constrained expression, viewed over the integers, is:\n///\n/// quotient * divisor + remainder - numerator = 0\n///\n/// ## Completeness\n/// If an honest prover supplies valid `BigNum` inputs that satisfy the equation,\n/// it can always find `borrow_flags` such that the integer\n/// relation holds and all constraints are satisfied.\n///\n/// The only possibility to break completeness is by providing a `BigNum` with `N >= 64`\n/// See `__compute_udiv_mod_expression_with_borrow_flags` for details\n///\n/// ## Soundness\n/// This function is conditionally sound: it enforces that the degree-2 relation\n/// holds over the integers, but it does not, by itself, enforce that\n/// this relation is unique for given `numerator` and `divisor`\n///\n/// For example, if the true relation is\n/// quotient * divisor + remainder - numerator = 0\n///\n/// then we can set quotient' = quotient - 1, remainder' = remainder + divisor\npub(crate) fn validate_udiv_mod_expression(\n numerator: [u128; N],\n divisor: [u128; N],\n quotient: [u128; N],\n remainder: [u128; N],\n) {\n validate_in_range::(numerator);\n validate_in_range::(divisor);\n validate_in_range::(quotient);\n validate_in_range::(remainder);\n\n let expression_limbs: [Field; N] =\n compute_udiv_mod_expression::(numerator, divisor, quotient, remainder);\n validate_expression_is_zero(expression_limbs);\n}\n" }, "557": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/fns/serialization.nr", "source": "use crate::utils::map::invert_array;\n\n// Conversions between big endian and little endian byte arrays and BigNum instances\n// the byte serialization should have `(MOD_BITS + 7) / 8` bytes.\n// each 120-bit limb is represented by 15 bytes, and there are fewer bytes for covering the most significant limb\n\n/// Construct a `BigNum` value from a big-endian byte array.\n///\n/// The input encodes an integer in base 256, which we split into `N` 120-bit limbs.\n///\n/// ## Note\n/// We only enforce that the value is < 2^MOD_BITS. We do not enforce that it is\n/// reduced modulo the field modulus.\n///\n/// Consistency between `N` and `MOD_BITS` is expected:\n/// - `N * 15 >= num_bytes`\n/// - `num_bytes > (N - 1) * 15`\n///\n/// Enforcing range constraints on each limb is crucial for efficiency.\n/// In principle, accumulating `u8` values already bounds the integer,\n/// but relying on Noir to infer a `u128` from a large linear combination\n/// would trigger a very general (and expensive) range checks\npub fn from_be_bytes(x: [u8; (MOD_BITS + 7) / 8]) -> [u128; N] {\n let num_bytes: u32 = (MOD_BITS + 7) / 8;\n\n let mut result: [u128; N] = [0; N];\n\n let excess_bytes: u32 = N * 15 - num_bytes;\n let last_limb_bytes: u32 = 15 - excess_bytes;\n\n let mut limb: Field = 0;\n for i in 0..last_limb_bytes {\n limb *= 256;\n limb += x[i] as Field;\n }\n\n limb.assert_max_bit_size::<120>();\n result[N - 1] = limb as u128;\n\n let mut byte_ptr: u32 = last_limb_bytes;\n for i in 1..N {\n let mut limb: Field = 0;\n for _ in 0..15 {\n limb *= 256;\n limb += x[byte_ptr] as Field;\n byte_ptr += 1;\n }\n limb.assert_max_bit_size::<120>();\n result[N - i - 1] = limb as u128;\n }\n\n if (MOD_BITS % 8 != 0) {\n let most_significant_byte: Field = x[0] as Field;\n most_significant_byte.assert_max_bit_size::();\n }\n result\n}\n\n/// Construct a big-endian byte array from a `BigNum` value.\n///\n/// The output contains `(MOD_BITS + 7) / 8` bytes. We serialize the most\n/// significant limb first, which may occupy fewer than 15 bytes, followed by\n/// the remaining full 15-byte limbs in big-endian order.\n///\n/// Consistency between `N` and `MOD_BITS` is expected:\n/// - the most significant limb contributes `((MOD_BITS + 7) / 8) - (N - 1) * 15` bytes;\n/// - all other limbs are serialized as full 15-byte chunks.\npub fn to_be_bytes(val: [u128; N]) -> [u8; (MOD_BITS + 7) / 8] {\n let mut result: [u8; (MOD_BITS + 7) / 8] = [0; (MOD_BITS + 7) / 8];\n\n let last_limb_num_bytes: u32 = (MOD_BITS + 7) / 8 - (N - 1) * 15;\n let mut byte_ptr: u32 = last_limb_num_bytes;\n // First we deal with the full limbs\n for i in 0..N - 1 {\n let idx: u32 = (N - 1) - i - 1;\n let limb_bytes: [u8; 15] = (val[idx] as Field).to_be_bytes();\n for j in 0..15 {\n result[byte_ptr] = limb_bytes[j];\n byte_ptr += 1;\n }\n }\n\n let last_limb_bytes: [u8; ((MOD_BITS + 7) / 8 - (N - 1) * 15)] =\n (val[N - 1] as Field).to_be_bytes();\n for i in 0..last_limb_num_bytes {\n result[i] = last_limb_bytes[i];\n }\n\n result\n}\n\n/// Construct a `BigNum` value from little-endian byte array\n///\n/// Reverse an array and apply `from_be_bytes`\n///\n/// See `from_be_bytes` for details\npub fn from_le_bytes(x: [u8; (MOD_BITS + 7) / 8]) -> [u128; N] {\n let be_x: [u8; (MOD_BITS + 7) / 8] = invert_array(x);\n from_be_bytes(be_x)\n}\n\n/// Construct a little-endian byte array from a `BigNum` value\n///\n/// Apply `to_be_bytes` and reverse an array\n///\n/// See `to_be_bytes` for details\npub fn to_le_bytes(val: [u128; N]) -> [u8; (MOD_BITS + 7) / 8] {\n let result_be: [u8; (MOD_BITS + 7) / 8] = to_be_bytes(val);\n invert_array(result_be)\n}\n" }, "558": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/fns/unconstrained_helpers.nr", "source": "// This file contains the unconstrained helpers that are mostly used by unconstrained ops\n\nuse crate::constants::TWO_POW_120;\n\nuse crate::fns::unconstrained_ops::{__eq, __gte, __neg, __pow, __sqr};\n\nuse crate::utils::msb::get_msb;\nuse crate::utils::split_bits::{__normalize_limbs, __split_120_bits};\n\nuse crate::params::BigNumParams;\n\n// ------------------------------ DERIVATION HELPER FUNCTIONS ------------------------------\n\n/// Construct a `1` BigNum value (unconstrained)\npub(crate) unconstrained fn __one() -> [u128; N] {\n let mut limbs: [u128; N] = [0; N];\n limbs[0] = 1;\n limbs\n}\n\n/// Construct a BigNum value from Field (unconstrained)\n///\n/// Split the native `Field` value into `N` 120-bit limbs\npub(crate) unconstrained fn __from_field(val: Field) -> [u128; N] {\n let mut x: Field = val;\n let mut result: [u128; N] = [0; N];\n\n if (N == 1) {\n let (first_limb, _): (u128, Field) = __split_120_bits(x);\n result[0] = first_limb;\n }\n\n if (N == 2) {\n let (first_limb, x): (u128, Field) = __split_120_bits(x);\n let (second_limb, _): (u128, Field) = __split_120_bits(x);\n result[0] = first_limb;\n result[1] = second_limb;\n }\n\n if (N > 2) {\n let (first_limb, x): (u128, Field) = __split_120_bits(x);\n let (second_limb, x): (u128, Field) = __split_120_bits(x);\n let (third_limb, _): (u128, Field) = __split_120_bits(x);\n result[0] = first_limb;\n result[1] = second_limb;\n result[2] = third_limb;\n }\n result\n}\n\n// ------------------------------ ARITHMETIC WITH FLAGS HELPER FUNCTIONS ------------------------------\n// These are the functions that compute modular operations results as well as borrow and carry flags for constraints\n\n/// Compute flags for modular addition (unconstrained)\n///\n/// Given `lhs, rhs, modulus`, computes the carry and borrow flags needed\n/// for `lhs + rhs - (overflow ? modulus : 0)`.\n/// The result is computed in constrained code using `compute_add_result`.\n///\n/// Returns (carry_flags, borrow_flags, overflow) where:\n/// - carry_flags: carries from adding lhs + rhs\n/// - borrow_flags: borrows from subtracting modulus when overflow occurs\n/// - overflow: true if lhs + rhs >= modulus (so we need to subtract modulus)\npub(crate) unconstrained fn compute_add_flags(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n) -> ([bool; N - 1], [bool; N - 1], bool) {\n let mask: u128 = TWO_POW_120 - 1;\n\n let add_res: [u128; N] = __helper_add(lhs, rhs);\n let overflow: bool = __gte(add_res, modulus);\n\n let subtrahend: [u128; N] = if overflow { modulus } else { [0; N] };\n\n let mut borrow_flags: [bool; N - 1] = [false; N - 1];\n let mut carry_flags: [bool; N - 1] = [false; N - 1];\n\n let mut carry: u128 = 0;\n let mut borrow: u128 = 0;\n for i in 0..N {\n let mut add_term: u128 = lhs[i] + rhs[i] + carry;\n carry = add_term >> 120;\n add_term &= mask;\n\n let sub_term: u128 = subtrahend[i] + borrow;\n borrow = (sub_term > add_term) as u128;\n\n // Only set `borrow` and `carry` if they differ\n // And if it's not the last limb\n if (carry != borrow) & (i < N - 1) {\n carry_flags[i] = carry != 0;\n borrow_flags[i] = borrow != 0;\n }\n }\n (carry_flags, borrow_flags, overflow)\n}\n\n/// Compute flags for modular subtraction (unconstrained)\n///\n/// Given `lhs, rhs, modulus`, computes the carry and borrow flags needed\n/// for `lhs - rhs + (underflow ? modulus : 0)`.\n/// The result is computed in constrained code using `compute_sub_result`.\n///\n/// Returns (carry_flags, borrow_flags, underflow) where:\n/// - carry_flags: carries from adding (lhs + modulus) when underflow occurs\n/// - borrow_flags: borrows from subtracting rhs\n/// - underflow: true if lhs < rhs (so we need to add modulus)\npub(crate) unconstrained fn compute_sub_flags(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n) -> ([bool; N - 1], [bool; N - 1], bool) {\n let mask: u128 = TWO_POW_120 - 1;\n\n let underflow: bool = !__gte(lhs, rhs);\n\n let addend: [u128; N] = if underflow { modulus } else { [0; N] };\n\n let mut borrow_flags: [bool; N - 1] = [false; N - 1];\n let mut carry_flags: [bool; N - 1] = [false; N - 1];\n\n let mut carry: u128 = 0;\n let mut borrow: u128 = 0;\n for i in 0..N {\n let mut add_term: u128 = lhs[i] + addend[i] + carry;\n carry = add_term >> 120;\n add_term &= mask;\n\n let sub_term: u128 = rhs[i] + borrow;\n borrow = (sub_term > add_term) as u128;\n\n // Only set `borrow` and `carry` if they differ\n // And if it's not the last limb\n if (carry != borrow) & (i < N - 1) {\n carry_flags[i] = carry != 0;\n borrow_flags[i] = borrow != 0;\n }\n }\n (carry_flags, borrow_flags, underflow)\n}\n\n/// Compute borrow flags for lhs - rhs (unconstrained)\n///\n/// The result is computed in constrained code using `compute_gte_result`\npub(crate) unconstrained fn compute_borrow_flags(\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [bool; N - 1] {\n let mut borrow_flags: [bool; N - 1] = [false; N - 1];\n borrow_flags[0] = lhs[0] < rhs[0];\n for i in 1..N - 1 {\n borrow_flags[i] = lhs[i] < rhs[i] + (borrow_flags[i - 1] as u128);\n }\n borrow_flags\n}\n\n/// Compute underflow and borrow flags for gte comparison (unconstrained)\n///\n/// Returns (underflow, borrow_flags) where:\n/// - underflow is true if lhs < rhs\n/// - borrow_flags correspond to max(lhs, rhs) - min(lhs, rhs)\npub(crate) unconstrained fn compute_gte_flags(\n lhs: [u128; N],\n rhs: [u128; N],\n) -> (bool, [bool; N - 1]) {\n let underflow: bool = !__gte(lhs, rhs);\n // swap if underflow so we're computing borrow flags for larger - smaller\n let (a, b): ([u128; N], [u128; N]) = if underflow { (rhs, lhs) } else { (lhs, rhs) };\n\n let borrow_flags = compute_borrow_flags(a, b);\n (underflow, borrow_flags)\n}\n\n// ------------------------------ BARRETT REDUCTION ------------------------------\n\n/// `BARRETT_REDUCTION_OVERFLOW_BITS` defines how large an input to barrett reduction can be\n///\n/// maximum value = modulus^2 << BARRETT_REDUCTION_OVERFLOW_BITS\n/// see __barrett_reduction for more details\nglobal BARRETT_REDUCTION_OVERFLOW_BITS: u32 = 6;\n\n/// Optimized modular multiplication (unconstrained)\n///\n/// The trick is to approximate 1/p with m/2**r, because division by 2**r is much cheaper\n/// In our case m = redc_param = floor(2^{MOD_BITS * 2 + BARRET_REDUCTION_OVERFLOW_BITS} / p)\n/// r = MOD_BITS * 2 + BARRET_REDUCTION_OVERFLOW_BITS\n///\n/// When we apply the barrett reduction, the maximum value of the output will be <= p * (1 + x/2^{2k})\n/// where p = modulus,\n/// x = reduction input\n///\n/// If x > p * p, we need s to be larger than modulus_bits()\n/// We hardcode s = BARRET_REDUCTION_OVERFLOW_BITS = 6, which means that the maximum value of x is approx. 64 * p * p\n/// This should be larger than most values put into `evaluate_quadratic_expression`\n///\n/// ## TODO\n/// Detect cases where x might be too large at comptime\n///\n/// ## Note\n/// very niche edge case error that we need to be aware of:\n/// N must be large enough to cover the modulus *plus* BARRETT_REDUCTION_OVERFLOW_BITS\n/// i.e. a 359-bit prime needs (I think) 4 limbs to represent or we may overflow\n/// when calling __barrett_reduction\n///\n/// ## Note on final reduction\n///\n/// Assumptions:\n/// - k = ceil(log2 p), so p <= 2^k\n/// - s = 6, m = redc_param = floor(2^{2*k + s}/p)\n/// - x < 64 * p^2 (x < 2^{2 * k + s})\n///\n/// Let m' = 2^{2*k + s} / p, and write m = m' - \\epsilon, \\epsilon \\in [0, 1)\n//\n/// quo = floor(x * m / 2^{2 * k + s}) = floor(x * m' / 2^{2 * k + s} - x * \\epsilon / 2^{2 * k + s}) =\n/// floor(x / p - x * \\epsilon / 2^{2 * k + s})\n///\n/// Bounds:\n/// quo <= floor(x / p)\n///\n/// floor(a - b) >= floor(a) - ceil(b) (known identity) =>\n/// quo >= floor(x / p) - ceil(x * \\epsilon / 2^{2 * k + s})\n/// >= floor(x / p) - ceil(x / 2^{2 * k + s}) (epsilon < 1)\n///\n/// x / 2^{2 * k + s} < C * p^2 / 2^{2 * k + s} <= C * 2^{2 * k} / 2^{2 * k + s} = C / 2^s\n///\n/// When the assumption holds (C = 64), ceil(x / 2^{2 * k + s}) = 1, for x > 0\n/// Therefore quo = {floor(x/p), floor(x/p) - 1}\n///\n/// In first case: rem = x - quo * p = x - floor(x/p) * p < p\n/// In second case: rem = x - (floor(x/p) - 1) * p = (x - floor(x/p) * p) + p -> need 1 subtraction\n///\n/// ### Note\n/// In the worst case, we will have the input > 64 * p^2\n/// (for example (a1 + b1) * (c1 + d1) + ... 64 times)\n/// This is highly unlikely though, but there should be more reductions in that case.\npub(crate) unconstrained fn __barrett_reduction(\n x: [u128; 2 * N],\n redc_param: [u128; N],\n k: u32,\n modulus: [u128; N],\n) -> ([u128; N], [u128; N]) {\n // TODO: switch to __helper_mul, once the compiler is smart enough to handle this\n let mut mulout_field: [Field; 3 * N] = [0; 3 * N];\n for i in 0..(2 * N) {\n for j in 0..N {\n mulout_field[i + j] += (x[i] as Field) * (redc_param[j] as Field);\n }\n }\n let mulout: [u128; 3 * N] = __normalize_limbs(mulout_field);\n\n let quotient: [u128; 3 * N] = __shr(mulout, (k + k + BARRETT_REDUCTION_OVERFLOW_BITS));\n\n // Remove a bunch of zeros from the end\n let mut smaller_quotient: [u128; N] = [0; N];\n for i in 0..N {\n smaller_quotient[i] = quotient[i] as u128;\n }\n\n // long_quotient_mul_modulus can never exceed input value `x` so can fit into size-2 array\n let long_quotient_mul_modulus: [u128; 2 * N] = __helper_mul(smaller_quotient, modulus);\n let long_remainder: [u128; 2 * N] = __helper_sub(x, long_quotient_mul_modulus);\n\n // Remove a bunch of zeros from the end\n let mut remainder: [u128; N] = [0; N];\n for i in 0..N {\n remainder[i] = long_remainder[i];\n }\n\n if (__gte(remainder, modulus)) {\n remainder = __helper_sub(remainder, modulus);\n smaller_quotient = __increment(smaller_quotient);\n }\n\n (smaller_quotient, remainder)\n}\n\n// ------------------------------ ARITHMETIC HELPER FUNCTIONS ------------------------------\n// These are the functions that operate on limbs as if they were just big integers\n\n/// Adds `1` to the BigNum value without modular reduction (unconstrained)\n///\n/// ## Note\n/// The `carry` must be `0` at the end of the loop.\n/// No explicit assertion is made, as this condition is validated during evaluation.\npub(crate) unconstrained fn __increment(val: [u128; N]) -> [u128; N] {\n let mask: u128 = TWO_POW_120 - 1;\n\n let mut result: [u128; N] = [0; N];\n let mut carry: u128 = 1;\n for i in 0..N {\n let add_term: u128 = val[i] + carry;\n carry = add_term >> 120;\n result[i] = add_term & mask;\n }\n result\n}\n\n/// Adds two `BigNum` values without modular reduction (unconstrained).\n///\n/// ## Note\n/// The `carry` must be `0` at the end of the loop.\n/// No explicit assertion is made, as this condition is validated during evaluation.\npub(crate) unconstrained fn __helper_add(lhs: [u128; N], rhs: [u128; N]) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n\n let mut carry: u128 = 0;\n let mask: u128 = TWO_POW_120 - 1;\n\n for i in 0..N {\n let add_term: u128 = lhs[i] + rhs[i] + carry;\n carry = add_term >> 120;\n result[i] = add_term & mask;\n }\n result\n}\n\n/// Subtracts two `BigNum` values without modular reduction (unconstrained).\n///\n/// ## Note\n/// The `borrow` must be `0` at the end of the loop.\n/// No explicit assertion is made, as this condition is validated during evaluation.\npub(crate) unconstrained fn __helper_sub(lhs: [u128; N], rhs: [u128; N]) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n\n let mut borrow: u128 = 0;\n for i in 0..N {\n let subtrahend: u128 = rhs[i] + borrow;\n borrow = (subtrahend > lhs[i]) as u128;\n result[i] = (borrow << 120) + lhs[i] - subtrahend;\n }\n result\n}\n\n/// Multiplies two `BigNum` values without modular reduction (unconstrained).\n///\n/// Computes the full schoolbook product of two N-limb little-endian arrays\n///\n/// ## Note\n/// The mathematical product fits in `2 * N - 1` limbs, but we keep `2 * N`\n/// limbs intentionally as the extra high limb safely absorbs a possible single limb overflow\n/// for moduli close to `120 * N` bits.\npub(crate) unconstrained fn __helper_mul(\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; 2 * N] {\n let mut result: [Field; 2 * N] = [0; 2 * N];\n for i in 0..N {\n for j in 0..N {\n result[i + j] += (lhs[i] as Field) * (rhs[j] as Field);\n }\n }\n __normalize_limbs(result)\n}\n\n/// Computes `x * 2^{-1} (mod MOD)` (unconstrained).\n///\n/// For odd `MOD`, this is equivalent to:\n/// - `x/2` if `x` is even\n/// - `(x + MOD)/2` if `x` is odd (since `x + MOD` is even)\n///\n/// ## Note\n/// - `MOD` must be odd.\npub(crate) unconstrained fn __half_mod_odd(\n modulus: [u128; N],\n x: [u128; N],\n) -> [u128; N] {\n let temp = if __is_even::(x) {\n x\n } else {\n __helper_add(x, modulus)\n };\n __shr1(temp)\n}\n\n// ------------------------------ LOGIC HELPER FUNCTIONS ------------------------------\n// These are the functions that operate on limbs as if they were just big integers\n\n/// Left-shifts a `BigNum` value by `shift` bits (unconstrained).\n///\n/// Performs a bitwise left shift across limbs.\n///\n/// ## Note\n/// The most significant limb is truncated to 120 bits after the shift.\n///\n/// No bounds check is performed on `num_shifted_limbs`.\n/// However, we use it only in `__udiv_mod`, where it is not possible to reach\n/// `num_shifted_limbs` > `N`\npub(crate) unconstrained fn __shl(input: [u128; N], shift: u32) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n\n let num_shifted_limbs: u32 = shift / 120;\n let limb_shift: u128 = (shift % 120) as u128;\n let remainder_shift: u128 = 120 - limb_shift;\n\n let mask: u128 = TWO_POW_120 - 1;\n let mut remainder: u128 = input[0] >> remainder_shift;\n\n result[num_shifted_limbs] = (input[0] << limb_shift) & mask;\n\n for i in 1..(N - num_shifted_limbs) {\n let value: u128 = input[i];\n let upshift: u128 = ((value << limb_shift) | remainder) & mask;\n result[i + num_shifted_limbs] = upshift;\n remainder = value >> remainder_shift;\n }\n result\n}\n\n/// Right-shifts a `BigNum` value by `shift` bits (unconstrained).\n///\n/// Performs a bitwise right shift across limbs.\n///\n/// # Note\n/// No bounds check is performed on `num_shifted_limbs`.\n/// However, we use it only in `__tonelli_shanks_sqrt`, where it is not possible to reach\n/// `num_shifted_limbs` > `N`\npub(crate) unconstrained fn __shr(input: [u128; N], shift: u32) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n\n let num_shifted_limbs: u32 = shift / 120;\n let limb_shift: u128 = (shift % 120) as u128;\n\n let remainder_shift: u128 = 120 - limb_shift;\n let low_mask: u128 = (1 as u128 << limb_shift) - 1;\n\n result[0] = input[num_shifted_limbs] >> limb_shift;\n for i in 1..(N - num_shifted_limbs) {\n let value: u128 = input[i + num_shifted_limbs];\n\n let carry: u128 = (value & low_mask) << remainder_shift;\n result[i - 1] |= carry;\n\n result[i] = value >> limb_shift;\n }\n result\n}\n\n/// Right-shifts a `BigNum` value by `1` bit (unconstrained)\n///\n/// # Note\n/// All the operations on limbs are executed in place\n/// to save opcodes\npub(crate) unconstrained fn __shr1(mut input: [u128; N]) -> [u128; N] {\n let value: u128 = input[N - 1];\n let mut remainder: u128 = (value & 1) << 119;\n input[N - 1] >>= 1;\n\n for i in 1..N {\n let value: u128 = input[N - 1 - i];\n input[N - 1 - i] = (value >> 1) | remainder;\n remainder = (value & 1) << 119;\n }\n input\n}\n\n/// Returns the index of the most significant set bit in a `BigNum` value (unconstrained).\npub(crate) unconstrained fn __get_msb(val: [u128; N]) -> u32 {\n let mut count: u32 = 0;\n for i in 0..N {\n let idx: u32 = N - 1 - i;\n let v: u128 = val[idx];\n if (v > 0) {\n count = 120 * idx + get_msb(v);\n break;\n }\n }\n count\n}\n\n/// Returns `true` if the bit at position `bit` is set in the `BigNum` (unconstrained).\n///\n/// ## Note\n/// No bounds check is performed on `bit`\npub(crate) fn __get_bit(input: [u128; N], bit: u32) -> bool {\n let segment_index: u32 = bit / 120;\n let uint_index: u128 = (bit % 120) as u128;\n\n let limb: u128 = input[segment_index];\n let value: u128 = (limb >> uint_index) & 1;\n value == 1\n}\n\n/// Returns `true` if the `BigNum` value is even (unconstrained)\npub(crate) unconstrained fn __is_even(x: [u128; N]) -> bool {\n (x[0] & 1) == 0\n}\n\n// ------------------------------ SQRT HELPER FUNCTIONS ------------------------------\n// These are the functions that are used during taking a square root\n\n/// Compute the maximal power of 2 that divides the group order (unconstrained)\n///\n/// Find the maximum value s such that `MOD = 2^s * q + 1`, where `q` is odd\n/// This is needed for our Tonelli-Shanks sqrt algorithm\npub(crate) unconstrained fn __primitive_root_log_size(modulus: [u128; N]) -> u32 {\n let mut target: [u128; N] = __helper_sub(modulus, __one());\n let mut result: u32 = 0;\n while !__get_bit(target, result) {\n result += 1;\n }\n result\n}\n\n/// Find a quadratic non-residue `g` where `g` is the smallest such value (unconstrained)\n/// i.e. smallest `g` such that `g^{(p - 1)/2} = -1 (mod MOD)`\n/// or smallest `g`, such that `x^2 - g = 0 (mod MOD)` has no solutions\n///\n/// ## Note\n/// WARNING If the field is not prime, this function will enter an infinite loop!\npub(crate) unconstrained fn __quadratic_non_residue(\n params: BigNumParams,\n) -> [u128; N] {\n let one: [u128; N] = __one();\n let neg_one: [u128; N] = __neg(params.modulus, one);\n\n let p_minus_one_over_two: [u128; N] = __shr1(__helper_sub(params.modulus, __one()));\n\n // We start with 2\n let mut target: [u128; N] = [0; N];\n target[0] = 2;\n\n let mut expd: [u128; N] = __pow(params, target, p_minus_one_over_two);\n while !__eq(expd, neg_one) {\n target = __increment(target);\n expd = __pow(params, target, p_minus_one_over_two);\n }\n target\n}\n\n/// Compute the smallest `i`, such that `t^{2^i} = 1, t^{2^{i-1}} = -1 (mod MOD)` (unconstrained)\n///\n/// ## Note\n/// Multiplicative order of t must divide 2^v2(MOD-1), otherwise you'll end up in an infinite loop!\npub(crate) unconstrained fn __tonelli_shanks_sqrt_find_i(\n params: BigNumParams,\n t: [u128; N],\n) -> u32 {\n let one: [u128; N] = __one();\n let mut c: [u128; N] = t;\n\n let mut i: u32 = 0;\n // Compute t^{2^k} until it hits 1 for the first time\n while !__eq(c, one) {\n c = __sqr::(params, c);\n i += 1;\n }\n i\n}\n" }, "559": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/fns/unconstrained_ops.nr", "source": "// This file contains the unconstrained operations that are used directly by BigNum class\n\nuse crate::fns::constrained_ops::derive_from_seed;\n\nuse crate::fns::unconstrained_helpers::{\n __barrett_reduction, __get_bit, __get_msb, __half_mod_odd, __helper_add, __helper_mul,\n __helper_sub, __increment, __is_even, __one, __primitive_root_log_size, __quadratic_non_residue,\n __shl, __shr, __shr1, __tonelli_shanks_sqrt_find_i,\n};\n\nuse crate::constants::TWO_POW_120;\n\nuse crate::params::BigNumParams;\n\n// ------------------------------ DERIVATION FUNCTIONS ------------------------------\n\n/// Deterministically derives a `BigNum` from a seed value (unconstrained)\n///\n/// Takes a seed byte array and generates a `BigNum` in the range [0, modulus-1].\n///\n/// See more information in `constrained_ops.nr`: `derive_from_seed`\npub unconstrained fn __derive_from_seed(\n params: BigNumParams,\n seed: [u8; SeedBytes],\n) -> [u128; N] {\n derive_from_seed::(params, seed)\n}\n\n// ------------------------------ COMPARISON FUNCTIONS ------------------------------\n\n/// Compare two limb arrays for equality (unconstrained)\npub unconstrained fn __eq(lhs: [u128; N], rhs: [u128; N]) -> bool {\n lhs == rhs\n}\n\n/// Compare a limb array to a zero array (unconstrained)\npub unconstrained fn __is_zero(limbs: [u128; N]) -> bool {\n limbs == [0; N]\n}\n\n/// Compare two little-endian limb arrays for `lhs >= rhs` over integers (unconstrained)\n///\n/// Starts from the most significant limb (`N - 1`) and returns true\n/// if `lhs` is greater or equal to `rhs`\npub(crate) unconstrained fn __gte(lhs: [u128; N], rhs: [u128; N]) -> bool {\n let mut result: bool = true;\n for i in 0..N {\n let idx: u32 = N - 1 - i;\n if (lhs[idx] != rhs[idx]) {\n result = lhs[idx] > rhs[idx];\n break;\n }\n }\n result\n}\n\n// ------------------------------ ARITHMETIC FUNCTIONS ------------------------------\n\n/// Negates a `BigNum` value, returning `m - x` (unconstrained)\n///\n/// ## Note\n/// The input is assumed to be less than modulus\npub unconstrained fn __neg(modulus: [u128; N], limbs: [u128; N]) -> [u128; N] {\n __helper_sub(modulus, limbs)\n}\n\n/// Adds two `BigNum` values with modular reduction (unconstrained)\n///\n/// Sums the limbs one by one, keeping the carry.\n/// In case the result overflows the modulus, the modulus is subtracted once\n///\n/// ## Note\n/// The `carry` must be `0` at the end of the loop.\n/// No explicit assertion is made, as this condition is validated during evaluation.\npub unconstrained fn __add(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n let mut result: [u128; N] = [0; N];\n let mut carry: u128 = 0;\n let mask: u128 = TWO_POW_120 - 1;\n\n for i in 0..N {\n let add_term: u128 = (lhs[i] + rhs[i] + carry);\n carry = add_term >> 120;\n result[i] = add_term & mask;\n }\n\n // check if the result is greater than the modulus\n if __gte(result, modulus) {\n __helper_sub(result, modulus)\n } else {\n result\n }\n}\n\n/// Subtracts two `BigNum` values with modular reduction (unconstrained).\n///\n/// Computes `x + (m - y)` (mod m)\npub unconstrained fn __sub(\n modulus: [u128; N],\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n __add(modulus, lhs, __neg(modulus, rhs))\n}\n\n/// Multiply `x` and `y` and reduce via Barrett, returning (Q, R) (unconstrained).\n///\n/// For `BigNum` values `x` and `y` compute (`Q`, `R`) such that:\n/// x * y = R + Q * m, 0 <= R < m\n/// See `__barrett_reduction` for details.\npub(crate) unconstrained fn __mul_with_quotient(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> ([u128; N], [u128; N]) {\n let to_reduce: [u128; N * 2] = __helper_mul(lhs, rhs);\n let (q, r): ([u128; N], [u128; N]) =\n __barrett_reduction(to_reduce, params.redc_param, MOD_BITS, params.modulus);\n (q, r)\n}\n\n/// Multiplies two `BigNum` values with modular reduction (unconstrained).\npub unconstrained fn __mul(\n params: BigNumParams,\n lhs: [u128; N],\n rhs: [u128; N],\n) -> [u128; N] {\n __mul_with_quotient::(params, lhs, rhs).1\n}\n\n/// Squares a `BigNum` value with modular reduction (unconstrained).\npub unconstrained fn __sqr(\n params: BigNumParams,\n val: [u128; N],\n) -> [u128; N] {\n __mul_with_quotient::(params, val, val).1\n}\n\n/// Modular exponentiation via square-and-multiply. LSB-first (unconstrained).\n///\n/// Computes `x^e mod m`\n///\n/// ## Note\n/// For the loop, we are using `MOD_BITS` instead of `__get_msb`\n/// because it is much much cheaper\npub unconstrained fn __pow(\n params: BigNumParams,\n val: [u128; N],\n exponent: [u128; N],\n) -> [u128; N] {\n let mut accumulator: [u128; N] = __one();\n let mut x: [u128; N] = val;\n let num_bits: u32 = MOD_BITS + 1;\n\n for i in 0..num_bits {\n if __get_bit(exponent, i) {\n accumulator = __mul::(params, accumulator, x);\n }\n x = __sqr::(params, x);\n }\n accumulator\n}\n\n/// Given a `BigNum` value `x` compute x^{-1} (mod MOD) (unconstrained)\n///\n/// This is an alternative to Fermat inversion(`__pow_invmod`) (`val^(modulus-2) mod MOD`) that uses\n/// the (extended) binary GCD / Stein algorithm.\n/// See [Binary GCD algorithm](https://en.wikipedia.org/wiki/Binary_GCD_algorithm)\n///\n/// ## Note\n/// - `modulus` must be odd (required by `__half_mod_odd`)\n/// - `gcd(val, modulus) == 1` (i.e. `val` is invertible modulo `modulus`)\npub unconstrained fn __invmod(\n params: BigNumParams,\n val: [u128; N],\n) -> [u128; N] {\n assert(params.has_multiplicative_inverse);\n if (__is_zero(val) | __eq(val, params.modulus)) {\n [0; N]\n } else {\n let mut u: [u128; N] = val;\n let mut v: [u128; N] = params.modulus;\n\n let one: [u128; N] = __one::();\n\n let mut x1: [u128; N] = one;\n let mut x2: [u128; N] = [0; N];\n\n while (!__eq(u, one) & !__eq(v, one)) {\n // Get rid of the even part of u\n while __is_even(u) {\n u = __shr1(u);\n x1 = __half_mod_odd(params.modulus, x1);\n }\n\n // Get rid of the even part of v\n while __is_even(v) {\n v = __shr1(v);\n x2 = __half_mod_odd(params.modulus, x2);\n }\n\n // Update the intermediate values after both (u, v) are odd\n if __gte(u, v) {\n u = __helper_sub(u, v);\n x1 = __sub(params.modulus, x1, x2);\n } else {\n v = __helper_sub(v, u);\n x2 = __sub(params.modulus, x2, x1);\n }\n }\n\n if __eq(u, one) {\n x1\n } else {\n x2\n }\n }\n}\n\n/// Divides two `BigNum` values with modular reduction (unconstrained).\n///\n/// Computes `x * y^{-1}` (mod m)\n///\n/// ## Note\n/// The divisor must be nonzero\n/// No explicit assertion is made, as this condition is validated during evaluation\npub unconstrained fn __div(\n params: BigNumParams,\n numerator: [u128; N],\n divisor: [u128; N],\n) -> [u128; N] {\n let inv_divisor: [u128; N] = __invmod::(params, divisor);\n __mul::(params, numerator, inv_divisor)\n}\n\n/// Given the `BigNum` inputs `x, y`, compute integer division x / y (unconstrained)\n///\n/// This function implements binary long division and outputs (`quotient`, `remainder`) such that:\n/// 1. floor(numerator / divisor) = quotient\n/// 2. numerator % divisor = remainder\n/// 3. divisor * quotient + remainder = numerator\n///\n/// ## Note\n/// The divisor must be nonzero\n/// No explicit assertion is made, as this condition is validated during evaluation\npub unconstrained fn __udiv_mod(\n numerator: [u128; N],\n divisor: [u128; N],\n) -> ([u128; N], [u128; N]) {\n let mut quotient: [u128; N] = [0; N];\n let mut remainder: [u128; N] = numerator;\n let b: [u128; N] = divisor;\n\n let numerator_msb: u32 = __get_msb(numerator);\n let divisor_msb: u32 = __get_msb(divisor);\n if divisor_msb > numerator_msb {\n ([0; N], numerator)\n } else {\n let mut bit_difference: u32 = __get_msb(remainder) - __get_msb(divisor);\n let mut divisor: [u128; N] = __shl(divisor, bit_difference);\n let mut accumulator: [u128; N] = __shl(__one(), bit_difference);\n\n // The same as divisor > remainder\n if (__gte(divisor, __increment(remainder))) {\n divisor = __shr1(divisor);\n accumulator = __shr1(accumulator);\n }\n\n for _ in 0..(N * 120) {\n if (__gte(remainder, b) == false) {\n break;\n }\n // we've shunted 'divisor' up to have the same bit length as our remainder.\n // If remainder >= divisor, then a is at least '1 << bit_difference' multiples of b\n if (__gte(remainder, divisor)) {\n remainder = __helper_sub(remainder, divisor);\n // we can use OR here instead of +, as\n // accumulator is always a nice power of two\n quotient = __helper_add(quotient, accumulator);\n }\n divisor = __shr1(divisor);\n accumulator = __shr1(accumulator);\n }\n\n (quotient, remainder)\n }\n}\n\n/// Batch modular inversion of `BigNum` values in an array (unconstrained)\n///\n/// Given values v[0..M), returns inv[0...M) with inv[i] = v[i]^{-1} (mod m)\n///\n/// We use the Montgomery trick:\n/// First we compute the partial products:\n/// T0 = 1\n/// T1 = v1,\n/// T2 = v1 * v2,\n/// ...\n/// T_{m - 1} = T_{m - 2} * v_{m - 1} = v1 * ... * v_{m - 1}\n///\n/// P = T_{m-1} * v_m = v1 * v2 * ... * v_m\n///\n/// Then we calculate a single inverse P^-1 = v1^-1 * v2^-1 * ... * v_m^-1\n/// Finally we compute\n/// v_m^-1 = (P^-1 * T_{m-1})\n/// v_{m - 1}^-1 = (P^-1 * v_m * T_{m - 2})\n/// ....\n/// v_2^-1 = (P^-1 * v_m * ... * v_3 * T_1)\n/// v_1^-1 = (P^-1 * v_m * ... * v_2 * T_0)\n///\n/// ## Note\n/// Zero elements are allowed and are left unchanged in the resulting array\n///\n/// This interacts poorly with `neg(zero)`:\n/// Calling `neg` on `zero` yields `modulus` rather than `0`.\n/// A value in this form will **not** satisfy\n/// the `__is_zero` check and will lead to incorrect results.\n///\n/// This edge case should be rare, but it's worth keeping in mind when\n/// composing operations or debugging unexpected behavior\npub(crate) unconstrained fn batch_invert(\n params: BigNumParams,\n vals: [[u128; N]; M],\n) -> [[u128; N]; M] {\n let mut accumulator: [u128; N] = __one();\n let mut temporaries: [[u128; N]; M] = [[0; N]; M];\n\n for i in 0..M {\n temporaries[i] = accumulator;\n if (!__is_zero(vals[i])) {\n accumulator = __mul::(params, accumulator, vals[i]);\n }\n }\n\n let mut result: [[u128; N]; M] = [[0; N]; M];\n accumulator = __invmod::(params, accumulator);\n for i in 0..M {\n let idx: u32 = M - 1 - i;\n if (!__is_zero(vals[idx])) {\n let T0: [u128; N] = __mul::(params, accumulator, temporaries[idx]);\n accumulator = __mul::(params, accumulator, vals[idx]);\n result[idx] = T0;\n }\n }\n result\n}\n\n/// Batch modular inversion of `BigNum` values in a slice (unconstrained)\n///\n/// See `batch_invert` for details\npub(crate) unconstrained fn batch_invert_slice(\n params: BigNumParams,\n vals: [[u128; N]],\n) -> [[u128; N]] {\n let mut accumulator: [u128; N] = __one();\n let mut temporaries: [[u128; N]] = &[];\n\n let M: u32 = vals.len();\n\n for i in 0..M {\n temporaries = temporaries.push_back(accumulator);\n if (!__is_zero(vals[i])) {\n accumulator = __mul::(params, accumulator, vals[i]);\n }\n }\n\n let mut result: [[u128; N]] = [];\n accumulator = __invmod::(params, accumulator);\n for i in 0..M {\n let idx: u32 = M - 1 - i;\n if (!__is_zero(vals[idx])) {\n let T0: [u128; N] = __mul::(params, accumulator, temporaries[idx]);\n accumulator = __mul::(params, accumulator, vals[idx]);\n result = result.push_front(T0);\n } else {\n result = result.push_front([0; N]);\n }\n }\n\n result\n}\n\n/// Compute a modular square root in a prime field (unconstrained)\npub unconstrained fn __sqrt(\n params: BigNumParams,\n input: [u128; N],\n) -> std::option::Option<[u128; N]> {\n assert(\n params.has_multiplicative_inverse,\n \"BigNum::__sqrt: Must be a field to take square roots\",\n );\n\n if (__is_zero(input)) {\n Option::some(input)\n } else if (params.modulus[0] % 4 == 3) {\n __easy_sqrt(params, input)\n } else {\n __tonelli_shanks_sqrt(params, input)\n }\n}\n\n/// Compute a modular square root using the Tonelli-Shanks algorithm (unconstrained)\n///\n/// Solves `x^2 = a (mod MOD)` for odd prime MOD\n///\n/// ## Algorithm\n///\n/// Here p = MOD\n///\n/// Tonelli-Shanks setup\n///\n/// Write `p - 1 = 2^s * Q`, `Q` - odd\n/// Define:\n/// `R = a^{(Q+1)/2}`\n/// `t = a^Q`\n///\n/// so that `R^2 = a^{Q + 1} = a * a^Q = a * t`\n/// If t = 1, we are done\n///\n/// By Euler's criterion, `a` is a quadratic reside iff `a^{(p - 1)/2} = 1`\n/// Since `t = a^Q` and `(p - 1) / 2 = Q * 2^{s-1}` we have:\n/// `t^{2^{s-1}} = a^{Q * 2^{s-1}} = a^{(p-1)/2} = 1`, assuming `a` is a q.r.\n///\n/// To proceed with computing our square root, we want to transfer `t` into a smaller subgroup,\n/// specifically, the `2^(s-2)`'th roots of unity or lower.\n///\n/// We do this by finding some value `b`, such that\n/// `(t * b^2)^{2^{s-2}} = 1` and `R' = R * b`\n/// Finding such a `b` is trivial, because from Euler's criterion, we know that,\n/// for any quadratic non-residue `z`, `z^{(p - 1) / 2} = -1`\n/// i.e. `z^{Q * 2^{s-1}} = -1`\n/// => `z^Q` is a `2^{s-1}`'th root of `-1`\n/// => `z^{2 * Q}` is a `2^{s-2}`'th root of `-1`\n///\n/// Since `t^{2^{s-1}} = 1`, we know that for some `i`, `i <= s - 2: t^{2^{i-1}} = -1`\n/// => `t * z^{2 * Q}` is a `2^{s - 2}`'th root of unity.\n/// We can iteratively transform `t` into ever smaller subgroups, until `t = 1`.\n/// At each iteration, we need to find a new value for `b`, which we can obtain\n/// by repeatedly squaring `z^Q`\n///\n/// ## Note\n/// Only use for prime fields! Function may infinite loop if used for non-prime fields\n///\n/// The input is assumed to be a nonzero value\npub(crate) unconstrained fn __tonelli_shanks_sqrt(\n params: BigNumParams,\n input: [u128; N],\n) -> std::option::Option<[u128; N]> {\n let mut result: Option<[u128; N]> = Option::none();\n\n let one: [u128; N] = __one();\n let s: u32 = __primitive_root_log_size(params.modulus); // p - 1 = 2^s * Q, where Q is odd\n let Q: [u128; N] = __shr(__helper_sub(params.modulus, one), s);\n let Q_minus_one_over_two: [u128; N] = __shr1(__helper_sub(Q, one)); // (Q - 1) / 2\n\n let z: [u128; N] = __quadratic_non_residue::(params);\n\n // Initialize:\n // b = a^{(Q - 1)/2}\n // R = a * b = a^{(Q + 1) / 2} => R^2 = a * a^Q\n // t = R * b = a^Q\n let mut b: [u128; N] = __pow::(params, input, Q_minus_one_over_two);\n let mut r: [u128; N] = __mul::(params, input, b);\n let mut t: [u128; N] = __mul::(params, r, b);\n\n let mut check: [u128; N] = t;\n // Assure t^{2^{s - 1}} = a^{(p -1)/2} = 1, otherwise we have met a non-residue\n for _ in 0..s - 1 {\n check = __sqr::(params, check);\n }\n if (__eq(check, one)) {\n let mut m: u32 = s;\n let mut c: [u128; N] = __pow::(params, z, Q); // z^Q - proper 2^{M}'th root of unity\n\n // Tonelli-Shanks main loop\n\n // At the beginning of each iteration we have:\n // M - current exponent such that t lies in the subgroup of order 2^m\n // t - element, whose order divides 2^m\n // c - the proper 2^M'th root of unity\n // R - accumulator with R^2 = a * t\n\n // If t == 1, we are done and R is a square root\n //\n // Otherwise\n // 1. We compute 1 <= i < M, such that t^{2^i} = 1, t^{2^{i - 1}} = -1\n // 2. Set b = c^{2^{M - i - 1}}, so it becomes a proper 2^(i+1)'th root of unity\n // Then b^2 has order 2^i which matches the order of t\n //\n // 3. Update the state values:\n // R <- R * b\n // t <- t * b^2\n // c <- b^2 = c^{2^{M - i}}\n // M <- i\n // reduces the order of t from 2^i to at most 2^{i - 1} and preserves R^2 = a * t\n //\n // The loop runs at most s times because M strictly decreases\n for _ in 0..s {\n if (__eq(t, one)) {\n result = Option::some(r);\n break;\n }\n let i: u32 = __tonelli_shanks_sqrt_find_i::(params, t);\n let mut j: u32 = m - i - 1;\n b = c;\n for _ in 0..j {\n b = __sqr(params, b);\n }\n\n let b2: [u128; N] = __sqr::(params, b);\n c = b2;\n t = __mul::(params, t, b2);\n r = __mul::(params, r, b);\n m = i;\n }\n }\n result\n}\n\n/// Compute a modular square root for MOD = 3 (mod 4) (unconstrained)\n///\n/// In case MOD = 3 (mod 4), the square root can be computed using the formula:\n/// `R = a^{(MOD + 1) / 4} (mod MOD)`\n///\n/// Then R^2 = a^{(MOD + 1)/ 4 * 2} = a^{(MOD + 1) / 2} = a^{(MOD - 1) / 2 + 1} = a\n///\n/// ## Note\n/// The input is assumed to be a nonzero value\n///\n/// This is much cheaper than `__tonelli_shanks_sqrt`\npub(crate) unconstrained fn __easy_sqrt(\n params: BigNumParams,\n input: [u128; N],\n) -> std::option::Option<[u128; N]> {\n let mut result: Option<[u128; N]> = Option::none();\n\n let one: [u128; N] = __one();\n let p_minus_one_over_two: [u128; N] = __shr1(__helper_sub(params.modulus, one));\n let mut check: [u128; N] = __pow(params, input, p_minus_one_over_two);\n if (__eq(check, one)) {\n // a = (MOD - 1) / 2\n // b = (a + 1) / 2 = ((MOD - 1) / 2 + 1) / 2 = (MOD + 1) / 4\n let p_plus_one_over_four: [u128; N] = __shr1(__increment(p_minus_one_over_two));\n result = Option::some(__pow(params, input, p_plus_one_over_four));\n }\n result\n}\n\nmod test_invmod {\n use crate::bignum::BigNum;\n use crate::fields::bls12_381Fq::BLS12_381_Fq;\n use crate::fields::bn254Fq::BN254_Fq;\n use crate::fields::ed25519Fq::ED25519_Fq;\n\n /// test inverting neg(zero)\n unconstrained fn helper_invmod_neg_zero() {\n let zero: BN = BN::zero();\n let neg_zero: BN = zero.__neg();\n let modulus: BN = BN::modulus();\n\n assert(neg_zero == modulus, \"neg(zero) should equal modulus\");\n\n let result: BN = neg_zero.__invmod();\n\n assert(result.__is_zero(), \"invmod(modulus) should return zero\");\n }\n\n #[test]\n unconstrained fn test_invmod_neg_zero() {\n helper_invmod_neg_zero::();\n helper_invmod_neg_zero::();\n helper_invmod_neg_zero::();\n }\n\n /// test inverting modulus\n unconstrained fn helper_invmod_on_modulus_value() {\n let modulus: BN = BN::modulus();\n let result: BN = modulus.__invmod();\n assert(result.__is_zero(), \"invmod(modulus) should return zero\");\n }\n\n #[test]\n unconstrained fn test_invmod_on_modulus_value() {\n helper_invmod_on_modulus_value::();\n helper_invmod_on_modulus_value::();\n helper_invmod_on_modulus_value::();\n }\n\n /// test that zero returns zero\n unconstrained fn helper_invmod_zero_returns_zero() {\n let zero: BN = BN::zero();\n let result: BN = zero.__invmod();\n assert(result.__is_zero(), \"invmod(0) should return 0\");\n }\n\n #[test]\n unconstrained fn test_invmod_zero_returns_zero() {\n helper_invmod_zero_returns_zero::();\n helper_invmod_zero_returns_zero::();\n helper_invmod_zero_returns_zero::();\n }\n\n /// test invmod via division\n unconstrained fn helper_invmod_via_division(x: [u8; 4], y: [u8; 4]) {\n let a: BN = BN::derive_from_seed(x);\n let b: BN = BN::derive_from_seed(y);\n\n // a / b should equal a * invmod(b)\n let div_result: BN = a.__div(b);\n let inv_b: BN = b.__invmod();\n let mul_result: BN = a.__mul(inv_b);\n assert(b.__mul(inv_b) == BN::one(), \"b * invmod(b) should equal 1\");\n\n assert(div_result == mul_result, \"division should equal multiply by inverse\");\n }\n\n #[test]\n unconstrained fn test_invmod_via_division(x: [u8; 4], y: [u8; 4]) {\n helper_invmod_via_division::(x, y);\n helper_invmod_via_division::(x, y);\n helper_invmod_via_division::(x, y);\n }\n}\n" }, "568": { "path": "/home/runner/nargo/github.com/noir-lang/noir-bignum/v0.9.2/src/utils/msb.nr", "source": "use std::ops::WrappingMul;\n\nglobal MUL_DE_BRUIJN_BIT: [u32; 128] = [\n 1, 14, 2, 15, 26, 20, 3, 16, 68, 80, 27, 21, 56, 50, 4, 17, 65, 96, 69, 81, 105, 99, 28, 22, 86,\n 90, 57, 51, 72, 42, 5, 126, 18, 66, 48, 94, 97, 84, 70, 124, 82, 122, 106, 100, 114, 108, 29,\n 23, 77, 102, 87, 91, 119, 116, 58, 52, 61, 110, 73, 37, 43, 31, 6, 127, 13, 25, 19, 67, 79, 55,\n 49, 64, 95, 104, 98, 85, 89, 71, 41, 125, 47, 93, 83, 123, 121, 113, 107, 76, 101, 118, 115, 60,\n 109, 36, 30, 12, 24, 78, 54, 63, 103, 88, 40, 46, 92, 120, 112, 75, 117, 59, 35, 11, 53, 62, 39,\n 45, 111, 74, 34, 10, 38, 44, 33, 9, 32, 8, 7, 128,\n];\n\n/// Get the most significant bit position of a `val` (unconstrained)\n///\n/// Bit hack that uses De Bruijn sequence to calculate msb position in log(N)\n/// See [IntegerLog2DeBruijn](https://graphics.stanford.edu/~seander/bithacks.html#IntegerLogDeBruijn)\npub(crate) unconstrained fn get_msb(x: u128) -> u32 {\n let result: u32 = if x == 0 {\n 0\n } else {\n let mut v: u128 = x;\n v |= v >> 1;\n v |= v >> 2;\n v |= v >> 4;\n v |= v >> 8;\n v |= v >> 16;\n v |= v >> 32;\n v |= v >> 64;\n let index: u128 = (v.wrapping_mul(0x1FC10C2FBCF471B913B14CD2595D6D5)) >> 121;\n MUL_DE_BRUIJN_BIT[index as u32]\n };\n result\n}\n\nmod tests {\n use crate::constants::{TWO_POW_120, TWO_POW_60};\n use crate::fns::unconstrained_helpers::__get_msb;\n use super::get_msb as get_msb128;\n use std::ops::WrappingMul;\n\n fn assert_msb_equal(x: u64) {\n // Safety: test code\n let msb64 = unsafe { get_msb64(x) };\n // Safety: test code\n let msb128 = unsafe { get_msb128(x as u128) };\n assert_eq(msb64, msb128);\n }\n\n #[test]\n /// Check that the msb functions are equivalent with de bruijn sequence for 64 bits and 128 bits\n fn test_get_msb() {\n // Test case 1: MSB at position 7\n let x: u64 = 0x80; // binary: 10000000\n assert_msb_equal(x);\n\n // Test case 2: MSB at position 0\n let x: u64 = 0x1; // binary: 00000001\n assert_msb_equal(x);\n\n // Test case 3: MSB at position 63\n let x: u64 = 0x8000000000000000; // binary: 1000...0000 (63 zeros)\n assert_msb_equal(x);\n\n // Test case 4: Zero input\n let x: u64 = 0x0;\n assert_msb_equal(x);\n\n // Test case 5: All bits set\n let x: u64 = 0xFFFFFFFFFFFFFFFF;\n assert_msb_equal(x);\n }\n\n /// Multiple entires in the `MUL_DE_BRUIJN_BIT` list do not map to a valid output of `v * 0x6c04f118e9966f6b`.\n /// This is a dummy value to fill the gaps in the map.\n global n1: u32 = 0xffffffff;\n\n global MUL_DE_BRUIJN_BIT_64: [u32; 128] = [\n 0, // change to 1 if you want bitSize(0) = 1\n 48, n1, n1, 31, n1, 15, 51, n1, 63, 5, n1, n1, n1, 19, n1, 23, 28, n1, n1, n1, 40, 36, 46,\n n1, 13, n1, n1, n1, 34, n1, 58, n1, 60, 2, 43, 55, n1, n1, n1, 50, 62, 4, n1, 18, 27, n1,\n 39, 45, n1, n1, 33, 57, n1, 1, 54, n1, 49, n1, 17, n1, n1, 32, n1, 53, n1, 16, n1, n1, 52,\n n1, n1, n1, 64, 6, 7, 8, n1, 9, n1, n1, n1, 20, 10, n1, n1, 24, n1, 29, n1, n1, 21, n1, 11,\n n1, n1, 41, n1, 25, 37, n1, 47, n1, 30, 14, n1, n1, n1, n1, 22, n1, n1, 35, 12, n1, n1, n1,\n 59, 42, n1, n1, 61, 3, 26, 38, 44, n1, 56,\n ];\n\n pub(crate) unconstrained fn get_msb64(x: u64) -> u32 {\n let mut v: u64 = x;\n v |= v >> 1;\n v |= v >> 2;\n v |= v >> 4;\n v |= v >> 8;\n v |= v >> 16;\n v |= v >> 32;\n let index: u64 = (v.wrapping_mul(0x6c04f118e9966f6b)) >> 57;\n (index as Field).assert_max_bit_size::<32>();\n MUL_DE_BRUIJN_BIT_64[index as u32]\n }\n\n unconstrained fn __get_msb64(val: [u128; N]) -> u32 {\n let mut count: u32 = 0;\n for i in 0..N {\n let v: u128 = val[((N) - 1 - i)];\n let v_low: u64 = v as u64 % TWO_POW_60 as u64;\n let v_high: u64 = ((v - v_low as u128) / TWO_POW_60) as u64;\n if (v_high > 0) {\n count = 60 * ((2 * N) - 1 - (i * 2)) + get_msb64(v_high);\n break;\n }\n if (v_low > 0) {\n count = 60 * ((2 * N) - 1 - (i * 2 + 1)) + get_msb64(v_low);\n break;\n }\n }\n count\n }\n\n #[test]\n // Check that the msb functions are equivalent with De Bruijn sequence for 64 bits and 128 bits\n unconstrained fn test_get_msb_equivalence() {\n // Test single limb (64-bit number)\n let x: Field = 0x8000000000000000;\n let arr: [u128; 4] = [0, 0, x as u128, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n\n // Test multiple limbs (120-bit number)\n let x: Field = 0x800000000000000000000000000000; // 120 bits number, 2^119\n let arr: [u128; 4] = [0, 0, x as u128, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n\n // Test zero\n let arr: [u128; 4] = [0, 0, 0, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n\n // Test all bits set (120 bits)\n let x: Field = 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF; // 120 bits, 2^120 - 1\n let arr: [u128; 4] = [0, x as u128, 0, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n\n // Test systematic bit positions\n for i in 0..120 {\n let x: u128 = 1;\n let shifted: u128 = x << i;\n let arr: [u128; 4] = [0, shifted, 0, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n }\n\n // Test random-like patterns (multiple bits set)\n let patterns: [Field; 7] = [\n 0xAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA, // alternating bits\n 0x555555555555555555555555555555, // alternating bits (opposite)\n 0x1234567890ABCDEF1234567890ABCD, // some pattern\n 0xFEDCBA0987654321FEDCBA09876543, // some pattern\n 0x800000000000000000000000000001, // highest and lowest bits\n 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFE, // all bits except lowest\n 0x7FFFFFFFFFFFFFFFFFFFFFFFFFFFFF, // all bits except highest\n ];\n for i in 0..patterns.len() {\n let arr: [u128; 4] = [0, patterns[i] as u128, 0, 0];\n let msb1: u32 = __get_msb64(arr);\n let msb2: u32 = __get_msb(arr);\n assert_eq(msb1, msb2);\n }\n\n // Test with MSB in different array positions (120 bits)\n let x: Field = 0x800000000000000000000000000000; // 120 bits\n let arr1: [u128; 4] = [x as u128, 0, 0, 0];\n let arr2: [u128; 4] = [0, x as u128, 0, 0];\n let arr3: [u128; 4] = [0, 0, x as u128, 0];\n let arr4: [u128; 4] = [0, 0, 0, x as u128];\n let msb1_1: u32 = __get_msb64(arr1);\n let msb2_1: u32 = __get_msb(arr1);\n assert_eq(msb1_1, msb2_1);\n\n let msb1_2: u32 = __get_msb64(arr2);\n let msb2_2: u32 = __get_msb(arr2);\n assert_eq(msb1_2, msb2_2);\n\n let msb1_3: u32 = __get_msb64(arr3);\n let msb2_3: u32 = __get_msb(arr3);\n assert_eq(msb1_3, msb2_3);\n\n let msb1_4: u32 = __get_msb64(arr4);\n let msb2_4: u32 = __get_msb(arr4);\n assert_eq(msb1_4, msb2_4);\n }\n\n #[test]\n unconstrained fn fuzz_get_msb(seed: [u128; 5]) {\n let mut seed_copy: [u128; 5] = seed;\n for i in 0..5 {\n seed_copy[i] = seed_copy[i] & (TWO_POW_120 - 1);\n }\n let msb1: u32 = __get_msb64(seed_copy);\n let msb2: u32 = __get_msb(seed_copy);\n assert_eq(msb1, msb2);\n }\n}\n" }, "580": { "path": "/home/runner/nargo/github.com/noir-lang/sha512/master/src/encoding.nr", "source": "use crate::tables::{\n BASE4_ENCODE_1BIT_TABLE, BASE4_ENCODE_3BIT_TABLE, BASE4_ENCODE_4BIT_TABLE,\n BASE4_ENCODE_5BIT_TABLE, BASE4_ENCODE_6BIT_TABLE, BASE4_ENCODE_7BIT_TABLE,\n BASE4_ENCODE_8BIT_TABLE, BASE4_ENCODE_9BIT_TABLE, BASE4_MAJORITY_DECODE_5BIT_TABLE,\n BASE4_PARTIAL_CHOOSE_DECODE_5BIT_TABLE, BASE4_POWERS, BASE4_XOR_DECODE_5BIT_TABLE,\n};\n\n// W2_POWER_SUM represents scalars that each base4_encoded_slices member must be multiplied by, in order to produce\n// `encode(raw.rotate19) + encode(raw.rotate61) + encode(raw >> 6)`\n\nglobal W2_POWER_SUM: [Field; 12] = [\n 0x040000000000000000000040,\n 0x100000000000000000000100,\n 0x40000000000000000000040001,\n 0x0100000000000000000000100004,\n 0x0400000000000000000000400010,\n 0x04000000000000000000004000100000,\n 0x100004000001,\n 0x1000040000010000,\n 0x10000400000100000000,\n 0x100004000001000000000000,\n 0x4000100000040000000000000000,\n 0x4000001000000000000000000001,\n];\n\n// W15_POWER_SUM represents scalars that each base4_encoded_slices member must be multiplied by, in order to produce\n// `encode(raw.rotate1) + encode(raw.rotate8) + encode(raw >> 7)`\nglobal W15_POWER_SUM: [Field; 12] = [\n 0x40010000000000000000000000000000,\n 0x040000000000000000000000000001,\n 0x10000000000000000000000000000400,\n 0x40000000000000000000000000001001,\n 0x4005,\n 0x40050000,\n 0x1001400000,\n 0x10014000000000,\n 0x100140000000000000,\n 0x1001400000000000000000,\n 0x40050000000000000000000000,\n 0x01001400000000000000000000000000,\n];\n\n/// Encodes the u64 inputs to the `Majority` function in an extended Base4 represeentation\n///\n/// *param* raw: the input 64-bit limb\n/// *param* encoded: `raw` but where every bit is represented as a base4 value (e.g. encoded = \\sum 4^i b_i, where `b_i` represents the i'th bit of `raw`)\n/// *param* rotate_sum: the base-4 encoding of `encode(raw.rotate28) + encode(raw.rotate34) + encode(raw.rotate39)`\n/// we use the fact that the map between rotate_sum and `raw.rotate28 ^ raw.rotate34 ^ raw.rotate39` is injective\npub(crate) struct EncodedMajority {\n pub(crate) raw: Field,\n pub(crate) encoded: Field,\n pub(crate) rotate_sum: Field,\n}\nimpl EncodedMajority {\n pub(crate) fn new() -> Self {\n EncodedMajority { raw: 0, encoded: 0, rotate_sum: 0 }\n }\n}\n\n/// encodes the u64 inputs to the `Choose` function in an extended Base4 represeentation\n/// *param* raw: the input 64-bit limb\n/// *param* encoded: `raw` but where every bit is represented as a base4 value (e.g. encoded = \\sum 4^i b_i, where `b_i` represents the i'th bit of `raw`)\n/// *param* rotate_sum: the base-4 encoding of `encode(raw.rotate14) + encode(raw.rotate18) + encode(raw.rotate41)`\n/// we use the fact that the map between rotate_sum and `raw.rotate14 ^ raw.rotate18 ^ raw.rotate41` is injective\npub(crate) struct EncodedChoose {\n pub(crate) raw: Field,\n pub(crate) encoded: Field,\n pub(crate) rotate_sum: Field,\n}\nimpl EncodedChoose {\n pub(crate) fn new() -> Self {\n EncodedChoose { raw: 0, encoded: 0, rotate_sum: 0 }\n }\n}\n\n/// Encodes the u64 message limbs in a base4 representation, as well as the sum of the rotated components required for witness extension\n\n/// *param* raw : the input 64-bit limb\n/// *param* w : `raw` but where every bit is represented as a base4 value (e.g. encoded = \\sum 4^i b_i, where `b_i` represents the i'th bit of `raw`)\n/// *param* w2_rotate_sum : the base-4 encoding of `encode(raw.rotate19) + encode(raw.rotate61) + encode(raw >> 6)`\n/// *param* w15_rotate_sum : the base-4 encoding of `encode(raw.rotate1) + encode(raw.rotate8) + encode(raw >> 7)`\n/// like with EncodedChoose and EncodedMajority, there is an injective map between `w2_rotate_sum`/`w15_rotate_sum` and the base-2 bitwise operations required for message extension\npub(crate) struct EncodedWitness {\n pub(crate) raw: Field,\n pub(crate) w: Field,\n pub(crate) w2_rotate_sum: Field,\n pub(crate) w15_rotate_sum: Field,\n}\n\nimpl EncodedWitness {\n pub(crate) fn new() -> Self {\n EncodedWitness { raw: 0, w: 0, w2_rotate_sum: 0, w15_rotate_sum: 0 }\n }\n}\n\n/// Take an input that is a u64 encoded via base4, and split it into 5-bit slices encoded via base64\nunconstrained fn __split_into_base4_5bit_slices(encoded: Field) -> [u32; 13] {\n let mut acc = encoded as u128;\n\n let mut slices: [u32; 13] = [0; 13];\n\n for i in 0..13 {\n slices[i] = (acc & 1023) as u32;\n acc >>= 10;\n }\n\n slices\n}\n\n/// Split an input Field element `e` (known to be 64 bits) into slices that can be efficiently composed to produce inputs to `EncodedChoose`\nunconstrained fn __decompose_e(e: Field) -> [u32; 9] {\n let mut r: [u32; 9] = [0; 9];\n let mut bytes = e.to_le_bytes::<8>();\n let b8to14 = bytes[1] & 63;\n let b14to16 = bytes[1] >> 6;\n let b16to18 = bytes[2] & 3;\n let b18to24 = bytes[2] >> 2;\n let b40to41 = bytes[5] & 1;\n let b41to48 = bytes[5] >> 1;\n\n r[0] = bytes[0] as u32; // 0-8\n r[1] = b8to14 as u32; // 8-14\n r[2] = (b14to16 + (b16to18 << 2)) as u32; // 14-18\n r[3] = b18to24 as u32;\n r[4] = bytes[3] as u32;\n r[5] = bytes[4] as u32 + (b40to41 as u32) * 256;\n r[6] = b41to48 as u32;\n r[7] = bytes[6] as u32;\n r[8] = bytes[7] as u32;\n r[0] = bytes[0] as u32; // 0-8\n r[1] = b8to14 as u32; // 8-14\n r[2] = (b14to16 + (b16to18 << 2)) as u32; // 14-18\n r[3] = b18to24 as u32;\n r[4] = bytes[3] as u32;\n r[5] = bytes[4] as u32 + (b40to41 as u32) * 256;\n r[6] = b41to48 as u32;\n r[7] = bytes[6] as u32;\n r[8] = bytes[7] as u32;\n r\n}\n\n/// Split an input Field element `a` (known to be 64 bits) into slices that can be efficiently composed to produce inputs to `EncodedMajority`\nunconstrained fn __decompose_a(a: Field) -> [u32; 9] {\n let mut r: [u32; 9] = [0; 9];\n let mut bytes = a.to_le_bytes::<8>();\n let b24to28 = bytes[3] & 15;\n let b28to32 = bytes[3] >> 4;\n let b32to34 = bytes[4] & 3;\n let b34to39 = (bytes[4] >> 2) & 31;\n let b39to40 = bytes[4] >> 7;\n r[0] = bytes[0] as u32;\n r[1] = bytes[1] as u32;\n r[2] = bytes[2] as u32;\n r[3] = b24to28 as u32;\n r[4] = (b28to32 + (b32to34 << 4)) as u32;\n r[5] = b34to39 as u32;\n r[6] = b39to40 as u32 + (bytes[5] as u32 * 2);\n r[7] = bytes[6] as u32;\n r[8] = bytes[7] as u32;\n r\n}\n\n/// Split an input Field element `w` (known to be 64 bits) into slices that can be efficiently composed to produce inputs to `EncodedWitness`\nunconstrained fn __decompose_witness(w: Field) -> [u32; 12] {\n // bit slice structure is:\n // 1, 5, 1, 1, 8, 3, 8, 8, 8, 9, 9, 3\n let mut acc: u64 = w as u64;\n let mut r: [u32; 12] = [0; 12];\n let mut r: [u32; 12] = [0; 12];\n\n r[0] = (acc & 1) as u32;\n r[0] = (acc & 1) as u32;\n acc >>= 1;\n r[1] = (acc & 31) as u32;\n r[1] = (acc & 31) as u32;\n acc >>= 5;\n r[2] = (acc & 1) as u32;\n r[2] = (acc & 1) as u32;\n acc >>= 1;\n r[3] = (acc & 1) as u32;\n r[3] = (acc & 1) as u32;\n acc >>= 1;\n r[4] = (acc & 255) as u32;\n r[4] = (acc & 255) as u32;\n acc >>= 8;\n r[5] = (acc & 7) as u32;\n r[5] = (acc & 7) as u32;\n acc >>= 3;\n r[6] = (acc & 255) as u32;\n r[6] = (acc & 255) as u32;\n acc >>= 8;\n r[7] = (acc & 255) as u32;\n r[7] = (acc & 255) as u32;\n acc >>= 8;\n r[8] = (acc & 255) as u32;\n r[8] = (acc & 255) as u32;\n acc >>= 8;\n r[9] = (acc & 511) as u32;\n r[9] = (acc & 511) as u32;\n acc >>= 9;\n r[10] = (acc & 511) as u32;\n r[10] = (acc & 511) as u32;\n acc >>= 9;\n r[11] = (acc & 7) as u32;\n r\n}\n\n/// Take an input field element `w`, known to be 64-bits, and convert into an EncodedWitness member\n///\n/// # Cost\n/// 40 gates\npub(crate) fn encode_message_extension(w: Field) -> EncodedWitness {\n //Safety: split input into slices with the following bit sizes:\n // 1, 5, 1, 1, 8, 3, 8, 8, 8, 9, 9, 3\n // We need to validate the correctness of these slice claims by asserting their sum equals `w`,\n // and we also need to validate the bit range of each slice\n // (we get an implicit range constraint for free by indexing our BASE4_ENCODE_TABLE lookup tables)\n let s: [u32; 12] = unsafe { __decompose_witness(w) };\n let s: [u32; 12] = unsafe { __decompose_witness(w) };\n\n // Validate the sum of the slices equals the original input `w`\n // Note: we range constrain the slices later on in the function\n let mut reconstructed = s[11] as Field;\n reconstructed *= 512;\n reconstructed += s[10] as Field;\n reconstructed *= 512;\n reconstructed += s[9] as Field;\n reconstructed *= 256;\n reconstructed += s[8] as Field;\n reconstructed *= 256;\n reconstructed += s[7] as Field;\n reconstructed *= 256;\n reconstructed += s[6] as Field;\n reconstructed *= 8;\n reconstructed += s[5] as Field;\n reconstructed *= 256;\n reconstructed += s[4] as Field;\n reconstructed *= 2;\n reconstructed += s[3] as Field;\n reconstructed *= 2;\n reconstructed += s[2] as Field;\n reconstructed *= 32;\n reconstructed += s[1] as Field;\n reconstructed *= 2;\n reconstructed += s[0] as Field;\n assert_eq(reconstructed, w);\n\n let mut base4_encoded_slices: [Field; 12] = [0; 12];\n\n // convert our input slices `s` into base4-encoded slices\n // Note: indexing each lookup table provides an implicit range check on the elements of `s`\n base4_encoded_slices[0] = BASE4_ENCODE_1BIT_TABLE[s[0]];\n base4_encoded_slices[1] = BASE4_ENCODE_5BIT_TABLE[s[1]];\n base4_encoded_slices[2] = BASE4_ENCODE_1BIT_TABLE[s[2]];\n base4_encoded_slices[3] = BASE4_ENCODE_1BIT_TABLE[s[3]];\n base4_encoded_slices[4] = BASE4_ENCODE_8BIT_TABLE[s[4]];\n base4_encoded_slices[5] = BASE4_ENCODE_3BIT_TABLE[s[5]];\n base4_encoded_slices[6] = BASE4_ENCODE_8BIT_TABLE[s[6]];\n base4_encoded_slices[7] = BASE4_ENCODE_8BIT_TABLE[s[7]];\n base4_encoded_slices[8] = BASE4_ENCODE_8BIT_TABLE[s[8]];\n base4_encoded_slices[9] = BASE4_ENCODE_9BIT_TABLE[s[9]];\n base4_encoded_slices[10] = BASE4_ENCODE_9BIT_TABLE[s[10]];\n base4_encoded_slices[11] = BASE4_ENCODE_3BIT_TABLE[s[11]];\n\n // 4 gates\n let mut w2_rotate_sum: Field = base4_encoded_slices[0] * W2_POWER_SUM[0];\n w2_rotate_sum += base4_encoded_slices[1] * W2_POWER_SUM[1];\n w2_rotate_sum += base4_encoded_slices[2] * W2_POWER_SUM[2];\n w2_rotate_sum += base4_encoded_slices[3] * W2_POWER_SUM[3];\n w2_rotate_sum += base4_encoded_slices[4] * W2_POWER_SUM[4];\n w2_rotate_sum += base4_encoded_slices[5] * W2_POWER_SUM[5];\n w2_rotate_sum += base4_encoded_slices[6] * W2_POWER_SUM[6];\n w2_rotate_sum += base4_encoded_slices[7] * W2_POWER_SUM[7];\n w2_rotate_sum += base4_encoded_slices[8] * W2_POWER_SUM[8];\n w2_rotate_sum += base4_encoded_slices[9] * W2_POWER_SUM[9];\n w2_rotate_sum += base4_encoded_slices[10] * W2_POWER_SUM[10];\n w2_rotate_sum += base4_encoded_slices[11] * W2_POWER_SUM[11];\n\n // 4 gates\n let mut w15_rotate_sum: Field = base4_encoded_slices[0] * W15_POWER_SUM[0];\n w15_rotate_sum += base4_encoded_slices[1] * W15_POWER_SUM[1];\n w15_rotate_sum += base4_encoded_slices[2] * W15_POWER_SUM[2];\n w15_rotate_sum += base4_encoded_slices[3] * W15_POWER_SUM[3];\n w15_rotate_sum += base4_encoded_slices[4] * W15_POWER_SUM[4];\n w15_rotate_sum += base4_encoded_slices[5] * W15_POWER_SUM[5];\n w15_rotate_sum += base4_encoded_slices[6] * W15_POWER_SUM[6];\n w15_rotate_sum += base4_encoded_slices[7] * W15_POWER_SUM[7];\n w15_rotate_sum += base4_encoded_slices[8] * W15_POWER_SUM[8];\n w15_rotate_sum += base4_encoded_slices[9] * W15_POWER_SUM[9];\n w15_rotate_sum += base4_encoded_slices[10] * W15_POWER_SUM[10];\n w15_rotate_sum += base4_encoded_slices[11] * W15_POWER_SUM[11];\n\n // 4 gates\n let mut ror0: Field = base4_encoded_slices[0];\n ror0 += base4_encoded_slices[1] * BASE4_POWERS[1];\n ror0 += base4_encoded_slices[2] * BASE4_POWERS[6];\n ror0 += base4_encoded_slices[3] * BASE4_POWERS[7];\n ror0 += base4_encoded_slices[4] * BASE4_POWERS[8];\n ror0 += base4_encoded_slices[5] * BASE4_POWERS[16];\n ror0 += base4_encoded_slices[6] * BASE4_POWERS[19];\n ror0 += base4_encoded_slices[7] * BASE4_POWERS[27];\n ror0 += base4_encoded_slices[8] * BASE4_POWERS[35];\n ror0 += base4_encoded_slices[9] * BASE4_POWERS[43];\n ror0 += base4_encoded_slices[10] * BASE4_POWERS[52];\n ror0 += base4_encoded_slices[11] * BASE4_POWERS[61];\n\n EncodedWitness { raw: w, w: ror0, w2_rotate_sum, w15_rotate_sum }\n}\n\n/// Take an input field element `e`, known to be 64-bits, and convert into an EncodedChoose member\n///\n/// # Cost\n/// 27 gates\npub(crate) fn encode_e(e: Field) -> EncodedChoose {\n //Safety: split input into slices with the following bit sizes:\n // 8,6,4,6,8,9,7,8,8\n // We need to validate the correctness of these slice claims by asserting their sum equals `a`,\n // and we also need to validate the bit range of each slice\n // (we get an implicit range constraint for free by indexing our BASE4_ENCODE_TABLE lookup tables)\n let s = unsafe { __decompose_e(e) };\n\n // Validate the sum of the slices equals the original input `e`\n // Note: we range constrain the slices later on in the function\n let mut reconstructed = s[8] as Field;\n reconstructed *= 256;\n reconstructed += s[7] as Field;\n reconstructed *= 128;\n reconstructed += s[6] as Field;\n reconstructed *= 512;\n reconstructed += s[5] as Field;\n reconstructed *= 256;\n reconstructed += s[4] as Field;\n reconstructed *= 64;\n reconstructed += s[3] as Field;\n reconstructed *= 16;\n reconstructed += s[2] as Field;\n reconstructed *= 64;\n reconstructed += s[1] as Field;\n reconstructed *= 256;\n reconstructed += s[0] as Field;\n assert_eq(reconstructed, e); // 4 gates?\n\n // convert our input slices `s` into base4-encoded slices\n // Note: indexing each lookup table provides an implicit range check on the elements of `s`\n let mut base4_encoded_slices: [Field; 9] = [0; 9];\n base4_encoded_slices[0] = BASE4_ENCODE_8BIT_TABLE[s[0]];\n base4_encoded_slices[1] = BASE4_ENCODE_6BIT_TABLE[s[1]];\n base4_encoded_slices[2] = BASE4_ENCODE_4BIT_TABLE[s[2]];\n base4_encoded_slices[3] = BASE4_ENCODE_6BIT_TABLE[s[3]];\n base4_encoded_slices[4] = BASE4_ENCODE_8BIT_TABLE[s[4]];\n base4_encoded_slices[5] = BASE4_ENCODE_9BIT_TABLE[s[5]];\n base4_encoded_slices[6] = BASE4_ENCODE_7BIT_TABLE[s[6]];\n base4_encoded_slices[7] = BASE4_ENCODE_8BIT_TABLE[s[7]];\n base4_encoded_slices[8] = BASE4_ENCODE_8BIT_TABLE[s[8]];\n\n let mut e_ror0: Field = base4_encoded_slices[0];\n e_ror0 += base4_encoded_slices[1] * BASE4_POWERS[8];\n e_ror0 += base4_encoded_slices[2] * BASE4_POWERS[14];\n e_ror0 += base4_encoded_slices[3] * BASE4_POWERS[18];\n e_ror0 += base4_encoded_slices[4] * BASE4_POWERS[24];\n e_ror0 += base4_encoded_slices[5] * BASE4_POWERS[32];\n e_ror0 += base4_encoded_slices[6] * BASE4_POWERS[41];\n e_ror0 += base4_encoded_slices[7] * BASE4_POWERS[48];\n e_ror0 += base4_encoded_slices[8] * BASE4_POWERS[56];\n\n std::as_witness(e_ror0);\n\n // RSUMPOWERS represents scalars that each base4_encoded_slices member must be multiplied by, in order to produce\n // `encode(raw.rotate14) + encode(raw.rotate18) + encode(raw.rotate41)`\n let RSUMPOWERS = [\n 0x10100000000000400000000000,\n 0x101000000000004000000000000000,\n 0x01000000000004000000000000000001,\n 0x0400000000000000000101,\n 0x400000000000000000101000,\n 0x4000000000000000001010000000,\n 0x40400000000001,\n 0x101000000000004000,\n 0x1010000000000040000000,\n ];\n let mut e_ror_sum = base4_encoded_slices[0] * RSUMPOWERS[0];\n e_ror_sum += base4_encoded_slices[1] * RSUMPOWERS[1];\n e_ror_sum += base4_encoded_slices[2] * RSUMPOWERS[2];\n e_ror_sum += base4_encoded_slices[3] * RSUMPOWERS[3];\n e_ror_sum += base4_encoded_slices[4] * RSUMPOWERS[4];\n e_ror_sum += base4_encoded_slices[5] * RSUMPOWERS[5];\n e_ror_sum += base4_encoded_slices[6] * RSUMPOWERS[6];\n e_ror_sum += base4_encoded_slices[7] * RSUMPOWERS[7];\n e_ror_sum += base4_encoded_slices[8] * RSUMPOWERS[8];\n std::as_witness(e_ror_sum);\n\n EncodedChoose { raw: e, encoded: e_ror0, rotate_sum: e_ror_sum }\n}\n\n/// Take an input field element `a`, known to be 64-bits, and convert into an EncodedMajority member\n///\n/// # Cost\n/// 27 gates\npub(crate) fn encode_a(a: Field) -> EncodedMajority {\n //Safety: split input into slices with the following bit sizes:\n // 8,8,8,4,6,5,9,8,8\n // We need to validate the correctness of these slice claims by asserting their sum equals `a`,\n // and we also need to validate the bit range of each slice\n // (we get an implicit range constraint for free by indexing our BASE4_ENCODE_TABLE lookup tables)\n let s = unsafe { __decompose_a(a) };\n\n // Validate the sum of the slices equals the original input `e`\n // Note: we range constrain the slices later on in the function\n let mut reconstructed = s[8] as Field;\n reconstructed *= 256;\n reconstructed += s[7] as Field;\n reconstructed *= 512;\n reconstructed += s[6] as Field;\n reconstructed *= 32;\n reconstructed += s[5] as Field;\n reconstructed *= 64;\n reconstructed += s[4] as Field;\n reconstructed *= 16;\n reconstructed += s[3] as Field;\n reconstructed *= 256;\n reconstructed += s[2] as Field;\n reconstructed *= 256;\n reconstructed += s[1] as Field;\n reconstructed *= 256;\n reconstructed += s[0] as Field;\n assert_eq(reconstructed, a);\n\n let mut base4_encoded_slices: [Field; 9] = [0; 9];\n\n // convert our input slices `s` into base4-encoded slices\n // Note: indexing each lookup table provides an implicit range check on the elements of `s`\n base4_encoded_slices[0] = BASE4_ENCODE_8BIT_TABLE[s[0]];\n base4_encoded_slices[1] = BASE4_ENCODE_8BIT_TABLE[s[1]];\n base4_encoded_slices[2] = BASE4_ENCODE_8BIT_TABLE[s[2]];\n base4_encoded_slices[3] = BASE4_ENCODE_4BIT_TABLE[s[3]];\n base4_encoded_slices[4] = BASE4_ENCODE_6BIT_TABLE[s[4]];\n base4_encoded_slices[5] = BASE4_ENCODE_5BIT_TABLE[s[5]];\n base4_encoded_slices[6] = BASE4_ENCODE_9BIT_TABLE[s[6]];\n base4_encoded_slices[7] = BASE4_ENCODE_8BIT_TABLE[s[7]];\n base4_encoded_slices[8] = BASE4_ENCODE_8BIT_TABLE[s[8]];\n\n // RSUMPOWERS represents scalars that each base4_encoded_slices member must be multiplied by, in order to produce\n // `encode(raw.rotate28) + encode(raw.rotate34) + encode(raw.rotate39)`\n let RSUMPOWERS: [Field; 9] = [\n 0x01001004000000000000,\n 0x010010040000000000000000,\n 0x0100100400000000000000000000,\n 0x01001004000000000000000000000000,\n 0x100400000000000000000000000001,\n 0x400000000000000000000000001001,\n 0x400401,\n 0x010010040000,\n 0x0100100400000000,\n ];\n let mut a_ror_sum = base4_encoded_slices[0] * RSUMPOWERS[0];\n a_ror_sum += base4_encoded_slices[1] * RSUMPOWERS[1];\n a_ror_sum += base4_encoded_slices[2] * RSUMPOWERS[2];\n a_ror_sum += base4_encoded_slices[3] * RSUMPOWERS[3];\n a_ror_sum += base4_encoded_slices[4] * RSUMPOWERS[4];\n a_ror_sum += base4_encoded_slices[5] * RSUMPOWERS[5];\n a_ror_sum += base4_encoded_slices[6] * RSUMPOWERS[6];\n a_ror_sum += base4_encoded_slices[7] * RSUMPOWERS[7];\n a_ror_sum += base4_encoded_slices[8] * RSUMPOWERS[8];\n std::as_witness(a_ror_sum);\n // // 8,8,8,4,6,5,9,8,8\n let mut a_ror0 = base4_encoded_slices[0];\n a_ror0 += base4_encoded_slices[1] * BASE4_POWERS[8];\n a_ror0 += base4_encoded_slices[2] * BASE4_POWERS[16];\n a_ror0 += base4_encoded_slices[3] * BASE4_POWERS[24];\n a_ror0 += base4_encoded_slices[4] * BASE4_POWERS[28];\n a_ror0 += base4_encoded_slices[5] * BASE4_POWERS[34];\n a_ror0 += base4_encoded_slices[6] * BASE4_POWERS[39];\n a_ror0 += base4_encoded_slices[7] * BASE4_POWERS[48];\n a_ror0 += base4_encoded_slices[8] * BASE4_POWERS[56];\n std::as_witness(a_ror0);\n EncodedMajority { raw: a, encoded: a_ror0, rotate_sum: a_ror_sum }\n}\n\n/// Given three Field inputs `a, b, c`, known to be 64-bit values encoded as base4 numbers,\n/// evaluate the SHA384/512 majority function `(a and b) xor (a and c) xor (b and c)`\n///\n/// Note: the return parameter is a base-2 value\npub(crate) fn decode_majority(a: Field, b: Field, c: Field) -> Field {\n // adding base4 basis numbers produces an injective map vs computing (a&b)^(a&c)^(b&c) in the base2 basis\n // e.g. each base4 \"bit\" in `encoded` will be either 0, 1, 2 or 3\n // We use a lookup table `BASE4_MAJORITY_DECODE_5BIT_TABLE` to create the following map for each bit:\n // | a + b + c (base4) | (a&b)^(a&c)^(b&c) (base2) |\n // | ----------------- | ------------------------- |\n // | 0 | 0 |\n // | 1 | 0 |\n // | 2 | 1 |\n // | 3 | 1 |\n // note: BASE4_MAJORITY_DECODE_5BIT_TABLE is size 4^5 because we apply the above map to 5 bits at a time\n let encoded = a + b + c;\n // Safety: split input into base4 5bit slices (i.e. 10 bit slices)\n // we validate the sum of the slices equals the input.\n // indexing `BASE4_MAJORITY_DECODE_5BIT_TABLE` by each slice is an implicit range check\n let slices = unsafe { __split_into_base4_5bit_slices(encoded) };\n let mut reconstructed: Field = 0;\n let mut decoded: Field = 0;\n\n // 13 lookups = 26 gates\n for i in 0..13 {\n reconstructed *= 1024;\n reconstructed += slices[12 - i] as Field;\n decoded *= 32;\n decoded += BASE4_MAJORITY_DECODE_5BIT_TABLE[slices[12 - i]];\n }\n // reconstructed = 13 chunks, encoded = 3 chunks = 5 gates?\n assert_eq(reconstructed, encoded);\n\n // Tadaa. the output is our base2 XOR result.\n decoded\n}\n\n/// Given three Field inputs `e, f, g`, known to be 64-bit values encoded as base4 numbers,\n/// evaluate the SHA384/512 choose function `(e & f) ^ (!e & g)`\n///\n/// Note: the return parameter is a base-2 value\npub(crate) fn decode_choose(e: Field, f: Field, g: Field) -> Field {\n // Note: unlike in `decode_majority`, there is not a nice injective map between\n // a linear relation between base4-encoded values and the bitwise operation over base2 values.\n // The smallest linear relation that works is base7, and we cannot fit a base7 basis representation of a u64 into a Field element (too many bits).\n\n // What we do instead is we create two maps and sum the result together\n // We create an injective map between: `e + e + g` (base4) and `(!e & g)` (base2)\n // and: `e + f` (base4) and `(e & f)` (base2)\n // It is not possible for both (!e & g) AND (e & f) to both equal 1 for a single bit.\n // So we can sum together the two decoded outputs\n let two_e_plus_g = e + e + g;\n let e_plus_f = e + f;\n // Safety: split input into base4 5bit slices (i.e. 10 bit slices)\n // we validate the sum of the slices equals the input.\n // indexing `BASE4_PARTIAL_CHOOSE_DECODE_5BIT_TABLE` by each slice is an implicit range check\n let lhs_slices = unsafe { __split_into_base4_5bit_slices(two_e_plus_g) };\n let mut reconstructed: Field = 0;\n let mut decoded_lhs: Field = 0;\n\n // adding `2e + g` base4 basis numbers produces an injective map vs computing (!e & g) in the base2 basis\n // e.g. each base4 \"bit\" in `encoded` will be either 0, 1, 2 or 3\n // We use a lookup table `BASE4_MAJORITY_DECODE_5BIT_TABLE` to create the following map for each bit:\n // | 2e + g (base4) | (!e & g) (base2) |\n // | -------------- | ---------------- |\n // | 0 | 0 |\n // | 1 | 1 |\n // | 2 | 0 |\n // | 3 | 0 |\n // note: BASE4_PARTIAL_CHOOSE_DECODE_5BIT_TABLE is size 4^5 because we apply the above map to 5 bits at a time\n for i in 0..13 {\n reconstructed *= 1024;\n reconstructed += lhs_slices[12 - i] as Field;\n decoded_lhs *= 32;\n decoded_lhs += BASE4_PARTIAL_CHOOSE_DECODE_5BIT_TABLE[lhs_slices[12 - i]];\n }\n // relation has 15 inputs = 5 gates\n assert_eq(reconstructed, two_e_plus_g);\n\n // Safety: split input into base4 5bit slices (i.e. 10 bit slices)\n // we validate the sum of the slices equals the input.\n // indexing `BASE4_MAJORITY_DECODE_5BIT_TABLE` by each slice is an implicit range check\n let rhs_slices = unsafe { __split_into_base4_5bit_slices(e_plus_f) };\n let mut reconstructed: Field = 0;\n let mut decoded_rhs: Field = 0;\n\n // adding `2e + f` base4 basis numbers produces an injective map vs computing (!e & f) in the base2 basis\n // e.g. each base4 \"bit\" in `encoded` will be either 0, 1, 2 or 3\n // We use a lookup table `BASE4_MAJORITY_DECODE_5BIT_TABLE` to create the following map for each bit:\n // | e + f (base4) | (e & f) (base2) |\n // | -------------- | ---------------- |\n // | 0 | 0 |\n // | 1 | 0 |\n // | 2 | 1 |\n // | 3 | 0 |\n // note: BASE4_PARTIAL_CHOOSE_DECODE_5BIT_TABLE is size 4^5 because we apply the above map to 5 bits at a time\n for i in 0..13 {\n reconstructed *= 1024;\n reconstructed += rhs_slices[12 - i] as Field;\n decoded_rhs *= 32;\n decoded_rhs += BASE4_MAJORITY_DECODE_5BIT_TABLE[rhs_slices[12 - i]];\n }\n // relation has 15 inputs = 5 gates\n assert_eq(reconstructed, e_plus_f);\n\n decoded_lhs + decoded_rhs\n}\n\n/// Given an input `encoded` that is a linear sum of base4 elements, map into a base2 number which is a linear XOR of the same elements\n///\n/// e.g. if `encoded = a + b + c` in base4, the return value will be a ^ b ^ c in base2\npub(crate) fn decode_xor(encoded: Field) -> Field {\n //Safety: split input into \"5-bit\" slices.\n // Because the input is in base4, we actually obtain 10-bit slices\n // We need to validate the correctness of these slice claims by asserting their sum equals `encoded`,\n // and we also need to validate the bit range of each slice\n // (we get an implicit range constraint for free by indexing our BASE4_XOR_DECODE_5BIT_TABLE lookup table)\n let slices = unsafe { __split_into_base4_5bit_slices(encoded) };\n\n let mut reconstructed: Field = 0;\n let mut decoded: Field = 0;\n\n // We use a lookup table `BASE4_XOR_DECODE_5BIT_TABLE` to create the following map for each bit:\n // | encoded | xor result (base2) |\n // | -------------- | ------------------ |\n // | 0 | 0 |\n // | 1 | 1 |\n // | 2 | 0 |\n // | 3 | 1 |\n // note: BASE4_XOR_DECODE_5BIT_TABLE is size 4^5 because we apply the above map to 5 bits at a time\n for i in 0..13 {\n reconstructed *= 1024;\n reconstructed += slices[12 - i] as Field;\n decoded *= 32;\n decoded += BASE4_XOR_DECODE_5BIT_TABLE[slices[12 - i]];\n }\n // decoded = 13 adds = 5 gates\n // reconstructed vs encoded = 14 vars = 5 gates\n assert_eq(reconstructed, encoded);\n\n decoded\n}\n\n#[test]\nfn test_encode_message_extension() {\n let expected: u64 = 0x7ffffaffeffff2ff;\n let encoded = encode_message_extension(expected as Field);\n\n let w_ror1: u64 = (expected >> 1) + ((expected & 1) << (64 - 1));\n let w_ror8 = (expected >> 8) + ((expected & 255) << (64 - 8));\n let w_rshift7 = (expected >> 7);\n let w_ror19 = (expected >> 19) + ((expected & 0x7ffff) << (64 - 19));\n let w_ror61 = (expected >> 61) + ((expected & 0x1fffffffffffffff) << (64 - 61));\n let w_rshift6 = (expected >> 6);\n\n let result = decode_xor(encoded.w);\n\n let decoded_w2_rotate_sum: Field = decode_xor(encoded.w2_rotate_sum);\n let decoded_w15_rotate_sum: Field = decode_xor(encoded.w15_rotate_sum);\n\n assert_eq(result, expected as Field);\n assert_eq(decoded_w2_rotate_sum, (w_ror19 ^ w_ror61 ^ w_rshift6) as Field);\n assert_eq(decoded_w15_rotate_sum, (w_ror1 ^ w_ror8 ^ w_rshift7) as Field);\n}\n\n#[test]\nfn test_encode_e() {\n let expected: u64 = 0x7ffffaffeffff2ff;\n let e_encoded = encode_e(expected as Field);\n\n let ror_14 = (expected >> 14) + ((expected & 0x3fff) << (64 - 14));\n let ror_18 = (expected >> 18) + ((expected & 0x3ffff) << (64 - 18));\n let ror_41 = (expected >> 41) + ((expected & 0x1ffffffffff) << (64 - 41));\n\n let result = decode_xor(e_encoded.encoded);\n let decoded_rotate_sum: Field = decode_xor(e_encoded.rotate_sum);\n\n assert_eq(result, expected as Field);\n assert_eq(decoded_rotate_sum, (ror_14 ^ ror_18 ^ ror_41) as Field);\n}\n\n#[test]\nfn test_encode_a() {\n let expected: u64 = 0x7ffffaffeffff2ff; // two pow 52\n let a_encoded = encode_a(expected as Field);\n\n let ror_28 = (expected >> 28) + ((expected & 0x0fffffff) << (64 - 28));\n let ror_34 = (expected >> 34) + ((expected & 0x3ffffffff) << (64 - 34));\n let ror_39 = (expected >> 39) + ((expected & 0x7fffffffff) << (64 - 39));\n\n let result = decode_xor(a_encoded.encoded);\n let decoded_ror_sum: Field = decode_xor(a_encoded.rotate_sum);\n\n assert_eq(result, expected as Field);\n assert_eq(decoded_ror_sum, (ror_28 ^ ror_34 ^ ror_39) as Field);\n}\n\n#[test]\nfn test_majority() {\n let a: u64 = 0x7ffffaffeffff2ff;\n let b: u64 = 0xf23ff3900baa22ff;\n let c: u64 = 0x2439823847ffeeaa;\n\n let a_enc = encode_a(a as Field);\n let b_enc = encode_a(b as Field);\n let c_enc = encode_a(c as Field);\n\n let result = decode_majority(a_enc.encoded, b_enc.encoded, c_enc.encoded);\n\n let expected = ((a & b) ^ (a & c) ^ (b & c)) as Field;\n\n assert_eq(result, expected);\n}\n\n#[test]\nfn test_choose() {\n let e: u64 = 0x7ffffaffeffff2ff;\n let f: u64 = 0xf23ff3900baa22ff;\n let g: u64 = 0xf439823847ffeeaa;\n\n let e_enc = encode_e(e as Field);\n let f_enc = encode_e(f as Field);\n let g_enc = encode_e(g as Field);\n\n let result = decode_choose(e_enc.encoded, f_enc.encoded, g_enc.encoded);\n\n let expected = ((!e & g) ^ (e & f)) as Field;\n\n assert_eq(result, expected);\n}\n" }, "581": { "path": "/home/runner/nargo/github.com/noir-lang/sha512/master/src/formatting.nr", "source": "/// Convert 128-byte array to array of 16 u64s\npub(crate) fn msg_u8_to_u64(msg: [u8; 128]) -> [u64; 16] {\n let mut msg64: [u64; 16] = [0; 16];\n\n for i in 0..16 {\n let mut msg_field: Field = 0;\n for j in 0..8 {\n msg_field = msg_field * 256 + msg[128 - 8 * (i + 1) + j] as Field;\n }\n\n msg64[15 - i] = to_u64(msg_field);\n }\n\n msg64\n}\n\n/// Convert an input `Field` element into a `u64`\n///\n/// # Notes\n/// This fn requires that `input` is < 2^{64} or unsatisfiable constraints will be created.\n/// This fn is cheaper than `as u64` because the overflow case is not handled\npub(crate) fn to_u64(input: Field) -> u64 {\n // Safety: plucking u64 out of an unconstrained fn and then validating its correctness\n // is cheaper than `as u64`, because `as u64` handles overflow cases, which we can simply forbid with a range check\n let converted = unsafe { __to_u64(input) };\n assert_eq(converted as Field, input);\n input.assert_max_bit_size::<64>();\n converted\n}\n\n/// Given an input `Field` element known to be less than 2^72, return the component of `input` that is less than 2^64\npub(crate) fn reduce_modulo_64(input: Field) -> Field {\n // Safety: obtain result and then validate result is correct\n let (modulus, remainder) = unsafe { __reduce_modulo_64(input) };\n\n // 1.25 gates\n modulus.assert_max_bit_size::<8>();\n // 64 bits = 14/// 4 + 8 = 3.25\n remainder.assert_max_bit_size::<64>();\n assert_eq(modulus * 0x10000000000000000 + remainder, input);\n\n remainder\n}\n\n// used by `to_u64`\nunconstrained fn __to_u64(input: Field) -> u64 {\n input as u64\n}\n\n// use by `reduce_modulo_64`\nunconstrained fn __reduce_modulo_64(input: Field) -> (Field, Field) {\n let input128 = input as u128;\n\n let remainder = input128 & 0xffffffffffffffff;\n let modulus = input128 >> 64;\n\n (modulus as Field, remainder as Field)\n}\n" }, "583": { "path": "/home/runner/nargo/github.com/noir-lang/sha512/master/src/lib.nr", "source": "mod permutation;\nmod tables;\nmod generate_tables;\nmod encoding;\nmod benchmarks;\nmod formatting;\nmod oracle_tests;\n\nuse crate::encoding::{encode_a, encode_e};\nuse crate::formatting::{msg_u8_to_u64, reduce_modulo_64, to_u64};\nuse crate::permutation::{compress, extend_message, RoundState};\nuse crate::tables::{SHA384_INITIAL_HASH_VALUES, SHA512_INITIAL_HASH_VALUES};\nuse std::ops::WrappingAdd;\npub global SHA512_LENGTH_PARAMETER_BYTES: u32 = 16;\n\n/// Size of the message block when packed as 8-byte integer array.\npub global SHA512_BLOCK_SIZE: u32 = 128;\n\nglobal INT_BLOCK_SIZE: u32 = 16;\ntype INT_BLOCK = [u64; INT_BLOCK_SIZE];\n\n/// The state accumulates the blocks.\n/// Its overall size is the same as the `HASH`.\ntype STATE = [u64; 8];\n\n/// SHA512 compression function\n///\n/// We expose this as a public method because the compression fn is needed if the user desires to evaluate a partial hash.\n/// Interface is compatible with the sha256 blackbox fns\npub fn sha512_compression(_input: INT_BLOCK, _state: STATE) -> STATE {\n let mut message_input: [Field; 16] = [0; 16];\n for i in 0..16 {\n message_input[i] = _input[i] as Field;\n }\n\n let mut state: RoundState = RoundState {\n a: encode_a(_state[0] as Field),\n b: encode_a(_state[1] as Field),\n c: encode_a(_state[2] as Field),\n d: encode_a(_state[3] as Field),\n e: encode_e(_state[4] as Field),\n f: encode_e(_state[5] as Field),\n g: encode_e(_state[6] as Field),\n h: encode_e(_state[7] as Field),\n };\n\n let w: [Field; 80] = extend_message(message_input);\n compress(&mut state, w);\n\n let mut output: STATE = [0; 8];\n output[0] = to_u64(state.a.raw);\n output[1] = to_u64(state.b.raw);\n output[2] = to_u64(state.c.raw);\n output[3] = to_u64(state.d.raw);\n output[4] = to_u64(state.e.raw);\n output[5] = to_u64(state.f.raw);\n output[6] = to_u64(state.g.raw);\n output[7] = to_u64(state.h.raw);\n output\n}\n\n/// The SHA384/SHA512 hash function\n///\n/// Input is the message to be hashed, produces a 64-byte output\n/// The `IS_SHA512` generic is used to toggle whether a SHA384 or SHA512 hash is desired\n/// (the only diff between SHA384/SHA512 are different initial starting constants, and the SHA384 hash output is truncated)\nfn digest(msg: [u8; N]) -> [u8; 64] {\n // noir-fmt:ignore\n let mut h: [u64; 8] = if IS_SHA512 == 1 {\n SHA512_INITIAL_HASH_VALUES\n } else {\n SHA384_INITIAL_HASH_VALUES\n };\n let mut c: [u64; 8] = [0; 8]; // Compression of current message block as sequence of u64\n let mut out_h: [u8; 64] = [0; 64]; // Digest as sequence of bytes\n\n let num_blocks = (N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE;\n\n let num_padded_bytes = num_blocks * SHA512_BLOCK_SIZE;\n let mut padded_msg\n : [u8; ((N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE) * SHA512_BLOCK_SIZE] = [\n 0; ((N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE)\n * SHA512_BLOCK_SIZE\n ];\n\n for i in 0..N {\n padded_msg[i] = msg[i];\n }\n padded_msg[N] = 128;\n // N.B. assuming the message length (in bits) can be covered in 32 bits (approx 16MB message!)\n let length_bytes = (N as Field * 8).to_le_bytes::<4>();\n padded_msg[num_padded_bytes - 1] = length_bytes[0];\n padded_msg[num_padded_bytes - 2] = length_bytes[1];\n padded_msg[num_padded_bytes - 3] = length_bytes[2];\n padded_msg[num_padded_bytes - 4] = length_bytes[3];\n\n let mut block_bytes: [u8; 128] = [0; 128];\n for i in 0..num_blocks {\n for j in 0..128 {\n block_bytes[j] = padded_msg[i * 128 + j];\n }\n c = sha512_compression(msg_u8_to_u64(block_bytes), h);\n for j in 0..8 {\n h[j] = to_u64(reduce_modulo_64(h[j] as Field + c[j] as Field));\n }\n }\n\n // Return final hash as byte array\n for j in 0..8 {\n let h_bytes: [u8; 8] = (h[7 - j] as Field).to_le_bytes();\n for k in 0..8 {\n out_h[63 - 8 * j - k] = h_bytes[k];\n }\n }\n out_h\n}\n\n/// The SHA384/SHA512 hash function for a *variable length* input (up to a bounded maximum)\n///\n/// Input is the message to be hashed, produces a 64-byte output\n/// The `IS_SHA512` generic is used to toggle whether a SHA384 or SHA512 hash is desired\n///\n/// (the only diff between SHA384/SHA512 are different initial starting constants, and the SHA384 hash output is truncated)\nfn digest_var(msg: BoundedVec) -> [u8; 64] {\n // noir-fmt:ignore\n let mut h: [u64; 8] = if IS_SHA512 == 1 {\n SHA512_INITIAL_HASH_VALUES\n } else {\n SHA384_INITIAL_HASH_VALUES\n };\n let mut c: [u64; 8] = [0; 8]; // Compression of current message block as sequence of u64\n let mut out_h: [u8; 64] = [0; 64]; // Digest as sequence of bytes\n\n let num_blocks = (N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE;\n\n let mut padded_msg\n : [u8; ((N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE) * SHA512_BLOCK_SIZE] = [\n 0; ((N + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE)\n * SHA512_BLOCK_SIZE\n ];\n\n let msg_text = msg.storage();\n for i in 0..N {\n padded_msg[i] = msg_text[i];\n }\n\n let msg_length = msg.len();\n\n let num_used_blocks =\n (msg_length + SHA512_LENGTH_PARAMETER_BYTES + SHA512_BLOCK_SIZE) / SHA512_BLOCK_SIZE;\n\n let num_used_padded_bytes = num_used_blocks * SHA512_BLOCK_SIZE;\n // N.B. this line turns `padded_msg` into a RAM array, which is expensive!\n // The cost of this is roughly 7* N gates.\n // There are cheaper ways of doing this (e.g. pack into 8-byte field elements first, then update msg length),\n // however the cost of the RAM array is small relative to the total cost of a hash, so not really worth the extra complexity\n padded_msg[msg_length] = 128;\n // N.B. we assume the message length (in bits) can be covered in 32 bits (approx 16MB message!)\n let length_bytes = (msg_length as Field * 8).to_le_bytes::<4>();\n padded_msg[num_used_padded_bytes - 1] = length_bytes[0];\n padded_msg[num_used_padded_bytes - 2] = length_bytes[1];\n padded_msg[num_used_padded_bytes - 3] = length_bytes[2];\n padded_msg[num_used_padded_bytes - 4] = length_bytes[3];\n\n let mut output_h: [u64; 8] = h;\n let mut block_bytes: [u8; 128] = [0; 128];\n for i in 0..num_blocks {\n for j in 0..128 {\n block_bytes[j] = padded_msg[i * 128 + j];\n }\n c = sha512_compression(msg_u8_to_u64(block_bytes), h);\n for j in 0..8 {\n h[j] = h[j].wrapping_add(c[j]);\n }\n if (i < num_used_blocks) {\n output_h = h;\n }\n }\n\n // Return final hash as byte array\n for j in 0..8 {\n let h_bytes: [u8; 8] = (output_h[7 - j] as Field).to_le_bytes();\n for k in 0..8 {\n out_h[63 - 8 * j - k] = h_bytes[k];\n }\n }\n out_h\n}\n\n/// methods here are interface-compatible with sha256 blackbox fns\npub mod sha512 {\n pub fn digest(msg: [u8; N]) -> [u8; 64] {\n crate::digest::<_, 1_u1>(msg)\n }\n\n pub fn sha512_var(msg: BoundedVec) -> [u8; 64] {\n crate::digest_var::<_, 1_u1>(msg)\n }\n\n // Fuzz testing the sha512 implementation\n\n #[export]\n fn test_sha512_1(input: BoundedVec) -> [u8; 64] {\n sha512_var(input)\n }\n\n #[export]\n fn test_sha512_100(input: BoundedVec) -> [u8; 64] {\n sha512_var(input)\n }\n\n #[export]\n fn test_sha512_256(input: BoundedVec) -> [u8; 64] {\n sha512_var(input)\n }\n\n #[test]\n fn test_empty_string() {\n let input: [u8; 0] = [];\n let result = digest(input);\n\n let expected: [u8; 64] = [\n 0xcf, 0x83, 0xe1, 0x35, 0x7e, 0xef, 0xb8, 0xbd, 0xf1, 0x54, 0x28, 0x50, 0xd6, 0x6d,\n 0x80, 0x07, 0xd6, 0x20, 0xe4, 0x05, 0x0b, 0x57, 0x15, 0xdc, 0x83, 0xf4, 0xa9, 0x21,\n 0xd3, 0x6c, 0xe9, 0xce, 0x47, 0xd0, 0xd1, 0x3c, 0x5d, 0x85, 0xf2, 0xb0, 0xff, 0x83,\n 0x18, 0xd2, 0x87, 0x7e, 0xec, 0x2f, 0x63, 0xb9, 0x31, 0xbd, 0x47, 0x41, 0x7a, 0x81,\n 0xa5, 0x38, 0x32, 0x7a, 0xf9, 0x27, 0xda, 0x3e,\n ];\n\n assert_eq(result, expected);\n }\n\n #[test]\n fn test_abc() {\n let input = \"abc\".as_bytes();\n let result = digest(input);\n let result_var = sha512_var(BoundedVec::::from_array(input));\n let expected: [u8; 64] = [\n 0xdd, 0xaf, 0x35, 0xa1, 0x93, 0x61, 0x7a, 0xba, 0xcc, 0x41, 0x73, 0x49, 0xae, 0x20,\n 0x41, 0x31, 0x12, 0xe6, 0xfa, 0x4e, 0x89, 0xa9, 0x7e, 0xa2, 0x0a, 0x9e, 0xee, 0xe6,\n 0x4b, 0x55, 0xd3, 0x9a, 0x21, 0x92, 0x99, 0x2a, 0x27, 0x4f, 0xc1, 0xa8, 0x36, 0xba,\n 0x3c, 0x23, 0xa3, 0xfe, 0xeb, 0xbd, 0x45, 0x4d, 0x44, 0x23, 0x64, 0x3c, 0xe8, 0x0e,\n 0x2a, 0x9a, 0xc9, 0x4f, 0xa5, 0x4c, 0xa4, 0x9f,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n #[test]\n fn test_418_bit_msg() {\n let input = \"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq\".as_bytes();\n let result = digest(input);\n let result_var = sha512_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 64] = [\n 0x20, 0x4a, 0x8f, 0xc6, 0xdd, 0xa8, 0x2f, 0x0a, 0x0c, 0xed, 0x7b, 0xeb, 0x8e, 0x08,\n 0xa4, 0x16, 0x57, 0xc1, 0x6e, 0xf4, 0x68, 0xb2, 0x28, 0xa8, 0x27, 0x9b, 0xe3, 0x31,\n 0xa7, 0x03, 0xc3, 0x35, 0x96, 0xfd, 0x15, 0xc1, 0x3b, 0x1b, 0x07, 0xf9, 0xaa, 0x1d,\n 0x3b, 0xea, 0x57, 0x78, 0x9c, 0xa0, 0x31, 0xad, 0x85, 0xc7, 0xa7, 0x1d, 0xd7, 0x03,\n 0x54, 0xec, 0x63, 0x12, 0x38, 0xca, 0x34, 0x45,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n #[test]\n fn test_two_block_msg() {\n let input = \"abcdefghbcdefghicdefghijdefghijkefghijklfghijklmghijklmnhijklmnoijklmnopjklmnopqklmnopqrlmnopqrsmnopqrstnopqrstu\"\n .as_bytes();\n let result = digest(input);\n let result_var = sha512_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 64] = [\n 0x8e, 0x95, 0x9b, 0x75, 0xda, 0xe3, 0x13, 0xda, 0x8c, 0xf4, 0xf7, 0x28, 0x14, 0xfc,\n 0x14, 0x3f, 0x8f, 0x77, 0x79, 0xc6, 0xeb, 0x9f, 0x7f, 0xa1, 0x72, 0x99, 0xae, 0xad,\n 0xb6, 0x88, 0x90, 0x18, 0x50, 0x1d, 0x28, 0x9e, 0x49, 0x00, 0xf7, 0xe4, 0x33, 0x1b,\n 0x99, 0xde, 0xc4, 0xb5, 0x43, 0x3a, 0xc7, 0xd3, 0x29, 0xee, 0xb6, 0xdd, 0x26, 0x54,\n 0x5e, 0x96, 0xe5, 0x5b, 0x87, 0x4b, 0xe9, 0x09,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n}\n\npub mod sha384 {\n pub fn digest(msg: [u8; N]) -> [u8; 48] {\n let full_result = crate::digest::<_, 0_u1>(msg);\n let mut r: [u8; 48] = [0; 48];\n for i in 0..48 {\n r[i] = full_result[i];\n }\n r\n }\n\n pub fn sha384_var(msg: BoundedVec) -> [u8; 48] {\n let full_result = crate::digest_var::<_, 0_u1>(msg);\n let mut r: [u8; 48] = [0; 48];\n for i in 0..48 {\n r[i] = full_result[i];\n }\n r\n }\n\n // Fuzz testing the sha384 implementation\n\n #[export]\n fn test_sha384_1(input: BoundedVec) -> [u8; 48] {\n sha384_var(input)\n }\n\n #[export]\n fn test_sha384_100(input: BoundedVec) -> [u8; 48] {\n sha384_var(input)\n }\n\n #[export]\n fn test_sha384_256(input: BoundedVec) -> [u8; 48] {\n sha384_var(input)\n }\n\n #[test]\n fn test_empty_string() {\n let input: [u8; 0] = [];\n let result = digest(input);\n let result_var = sha384_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 48] = [\n 0x38, 0xb0, 0x60, 0xa7, 0x51, 0xac, 0x96, 0x38, 0x4c, 0xd9, 0x32, 0x7e, 0xb1, 0xb1,\n 0xe3, 0x6a, 0x21, 0xfd, 0xb7, 0x11, 0x14, 0xbe, 0x07, 0x43, 0x4c, 0x0c, 0xc7, 0xbf,\n 0x63, 0xf6, 0xe1, 0xda, 0x27, 0x4e, 0xde, 0xbf, 0xe7, 0x6f, 0x65, 0xfb, 0xd5, 0x1a,\n 0xd2, 0xf1, 0x48, 0x98, 0xb9, 0x5b,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n #[test]\n fn test_abc() {\n let input = \"abc\".as_bytes();\n let result = digest(input);\n let result_var = sha384_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 48] = [\n 0xcb, 0x00, 0x75, 0x3f, 0x45, 0xa3, 0x5e, 0x8b, 0xb5, 0xa0, 0x3d, 0x69, 0x9a, 0xc6,\n 0x50, 0x07, 0x27, 0x2c, 0x32, 0xab, 0x0e, 0xde, 0xd1, 0x63, 0x1a, 0x8b, 0x60, 0x5a,\n 0x43, 0xff, 0x5b, 0xed, 0x80, 0x86, 0x07, 0x2b, 0xa1, 0xe7, 0xcc, 0x23, 0x58, 0xba,\n 0xec, 0xa1, 0x34, 0xc8, 0x25, 0xa7,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n #[test]\n fn test_418_bit_msg() {\n let input = \"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq\".as_bytes();\n let result = digest(input);\n let result_var = sha384_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 48] = [\n 0x33, 0x91, 0xfd, 0xdd, 0xfc, 0x8d, 0xc7, 0x39, 0x37, 0x07, 0xa6, 0x5b, 0x1b, 0x47,\n 0x09, 0x39, 0x7c, 0xf8, 0xb1, 0xd1, 0x62, 0xaf, 0x05, 0xab, 0xfe, 0x8f, 0x45, 0x0d,\n 0xe5, 0xf3, 0x6b, 0xc6, 0xb0, 0x45, 0x5a, 0x85, 0x20, 0xbc, 0x4e, 0x6f, 0x5f, 0xe9,\n 0x5b, 0x1f, 0xe3, 0xc8, 0x45, 0x2b,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n\n #[test]\n fn test_two_block_msg() {\n let input = \"abcdefghbcdefghicdefghijdefghijkefghijklfghijklmghijklmnhijklmnoijklmnopjklmnopqklmnopqrlmnopqrsmnopqrstnopqrstu\"\n .as_bytes();\n let result = digest(input);\n let result_var = sha384_var(BoundedVec::::from_array(input));\n\n let expected: [u8; 48] = [\n 0x09, 0x33, 0x0c, 0x33, 0xf7, 0x11, 0x47, 0xe8, 0x3d, 0x19, 0x2f, 0xc7, 0x82, 0xcd,\n 0x1b, 0x47, 0x53, 0x11, 0x1b, 0x17, 0x3b, 0x3b, 0x05, 0xd2, 0x2f, 0xa0, 0x80, 0x86,\n 0xe3, 0xb0, 0xf7, 0x12, 0xfc, 0xc7, 0xc7, 0x1a, 0x55, 0x7e, 0x2d, 0xb9, 0x66, 0xc3,\n 0xe9, 0xfa, 0x91, 0x74, 0x60, 0x39,\n ];\n\n assert_eq(result, expected);\n assert_eq(result_var, expected);\n }\n}\n" }, "585": { "path": "/home/runner/nargo/github.com/noir-lang/sha512/master/src/permutation.nr", "source": "use crate::encoding::{\n decode_choose, decode_majority, decode_xor, encode_a, encode_e, encode_message_extension,\n EncodedChoose, EncodedMajority, EncodedWitness,\n};\nuse crate::formatting::reduce_modulo_64;\nuse crate::tables::SHA512_ROUND_CONSTANTS;\n\n/// Represent the SHA384/512 permutation state as base4-encoded variables\npub(crate) struct RoundState {\n pub(crate) a: EncodedMajority,\n pub(crate) b: EncodedMajority,\n pub(crate) c: EncodedMajority,\n pub(crate) d: EncodedMajority,\n pub(crate) e: EncodedChoose,\n pub(crate) f: EncodedChoose,\n pub(crate) g: EncodedChoose,\n pub(crate) h: EncodedChoose,\n}\n\n/// Perform message extension, expanding 16 words of the message schedule into 80 words\npub(crate) fn extend_message(witness: [Field; 16]) -> [Field; 80] {\n let mut w: [EncodedWitness; 80] = [EncodedWitness::new(); 80];\n\n for i in 0..16 {\n w[i] = encode_message_extension(witness[i]);\n }\n for i in 16..78 {\n // s0 = (w[i-15] rightrotate 7) xor (w[i-15] rightrotate 18) xor (w[i-15] rightshift 3)\n // When we encoded `w`, w15_rotate_sum represents the base4-encoded linear sum of the above\n // all we need to do is decode back into base2\n let s0: Field = decode_xor(w[i - 15].w15_rotate_sum);\n\n // s1 = (w[i-2] rightrotate 17) xor (w[i-2] rightrotate 19) xor (w[i-2] rightshift 10)\n // When we encoded `w`, w2_rotate_sum represents the base4-encoded linear sum of the above\n // all we need to do is decode back into base2\n let s1 = decode_xor(w[i - 2].w2_rotate_sum);\n\n // evaluating the sum as Field elements and then reducing modulo 64 is more efficient than `u64` arithmetic\n let new_witness = reduce_modulo_64(w[i - 16].raw + s0 + w[i - 7].raw + s1);\n\n // take the output and encode into the extended base4 representation\n w[i] = encode_message_extension(new_witness);\n }\n\n let mut result: [Field; 80] = [0; 80];\n for i in 0..78 {\n result[i] = w[i].raw;\n }\n\n // For the final 2 iterations, we do not need to encode the output, as we will only access these variables in their native base2 representation\n for i in 78..80 {\n let s0: Field = decode_xor(w[i - 15].w15_rotate_sum);\n\n let s1 = decode_xor(w[i - 2].w2_rotate_sum);\n\n result[i] = reduce_modulo_64(w[i - 16].raw + s0 + w[i - 7].raw + s1);\n }\n result\n}\n\n/// Evaluate the SHA384/512 compression function\n///\n/// # Cost\n/// Each iteration costs 243 gates\npub(crate) fn compress(state: &mut RoundState, w: [Field; 80]) {\n for i in 0..80 {\n // S0 = (a rightrotate 2) xor (a rightrotate 13) xor (a rightrotate 22)\n // When we encoded `a`, a_rotrate_sum represents the base4-encoded linear sum of the above\n // all we need to do is decode back into base2\n let S0 = decode_xor(state.a.rotate_sum);\n let majority = decode_majority(state.a.encoded, state.b.encoded, state.c.encoded);\n let temp2 = S0 + majority;\n\n // S1 = (e rightrotate 6) xor (e rightrotate 11) xor (e rightrotate 25)\n // When we encoded `a`, a_rotrate_sum represents the base4-encoded linear sum of the above\n // all we need to do is decode back into base2\n let S1 = decode_xor(state.e.rotate_sum);\n let choose_output = decode_choose(state.e.encoded, state.f.encoded, state.g.encoded);\n let temp1 = state.h.raw + S1 + choose_output + (SHA512_ROUND_CONSTANTS[i] as Field) + w[i];\n // temp1 is a linar sum of ~50 inputs. Without std::as_witness the addition gates required to eval are duplicated\n // for both `raw_e` and `raw_a`\n std::as_witness(temp1); // temp1 = 106 gates\n\n // 6.5 gates\n let raw_e = reduce_modulo_64(state.d.raw + temp1);\n\n // 6.5 gates\n let raw_a = reduce_modulo_64(temp1 + temp2);\n\n state.h = state.g;\n state.g = state.f;\n state.f = state.e;\n state.d = state.c;\n state.c = state.b;\n state.b = state.a;\n\n if (i < 79) {\n // encode the new `a, e` vars in the extended base4 representation\n let encoded_e = encode_e(raw_e); // 27 gates\n let encoded_a = encode_a(raw_a); // 27 gates\n state.e = encoded_e;\n state.a = encoded_a;\n } else {\n // if this is the last iteration, no need to encode `a` and `e` as we only care about the raw value\n state.a = EncodedMajority { encoded: 0, raw: raw_a, rotate_sum: 0 };\n state.e = EncodedChoose { encoded: 0, raw: raw_e, rotate_sum: 0 };\n }\n }\n}\n\nmod test {\n unconstrained fn __extend_message_native(witness: [Field; 16]) -> [u64; 80] {\n let mut w: [u64; 80] = [0; 80];\n for i in 0..16 {\n w[i] = witness[i] as u64;\n }\n\n for i in 16..80 {\n let w15 = w[i - 15];\n let w2 = w[i - 2];\n\n let ror1: u64 = (w15 >> 1) + ((w15 & 1) << (64 - 1));\n let ror8 = (w15 >> 8) + ((w15 & 255) << (64 - 8));\n let rshift7 = (w15 >> 7);\n let ror19 = (w2 >> 19) + ((w2 & 0x7ffff) << (64 - 19));\n let ror61 = (w2 >> 61) + ((w2 & 0x1fffffffffffffff) << (64 - 61));\n let rshift6 = (w2 >> 6);\n\n let s0 = ror1 ^ ror8 ^ rshift7;\n let s1 = ror19 ^ ror61 ^ rshift6;\n\n let wout =\n ((w[i - 16] as Field) + (s0 as Field) + (w[i - 7] as Field) + (s1 as Field)) as u64;\n w[i] = wout;\n }\n w\n }\n unconstrained fn __compress_native(witness: [Field; 80]) -> [Field; 8] {\n let mut expected: [Field; 8] = [0; 8];\n\n let mut a: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[0];\n let mut b: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[1];\n let mut c: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[2];\n let mut d: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[3];\n let mut e: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[4];\n let mut f: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[5];\n let mut g: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[6];\n let mut h: u64 = crate::tables::SHA512_INITIAL_HASH_VALUES[7];\n for i in 0..80 {\n let ror14 = (e >> 14) + ((e & 0x3fff) << (64 - 14));\n let ror18 = (e >> 18) + ((e & 0x3ffff) << (64 - 18));\n let ror41 = (e >> 41) + ((e & 0x1ffffffffff) << (64 - 41));\n\n let ror28 = (a >> 28) + ((a & 0x0fffffff) << (64 - 28));\n let ror34 = (a >> 34) + ((a & 0x3ffffffff) << (64 - 34));\n let ror39 = (a >> 39) + ((a & 0x7fffffffff) << (64 - 39));\n\n let S1 = ror14 ^ ror18 ^ ror41;\n let ch = (e & f) ^ ((!e) & g);\n let mut temp1 = h as Field + S1 as Field;\n temp1 += ch as Field;\n temp1 += crate::tables::SHA512_ROUND_CONSTANTS[i] as Field;\n temp1 += witness[i];\n let temp1 = temp1 as u64;\n let S0 = ror28 ^ ror34 ^ ror39;\n let maj = (a & b) ^ (a & c) ^ (b & c);\n let temp2 = (S0 as Field + maj as Field) as u64;\n\n h = g;\n g = f;\n f = e;\n e = (d as Field + temp1 as Field) as u64;\n d = c;\n c = b;\n b = a;\n a = (temp1 as Field + temp2 as Field) as u64;\n }\n expected[0] = a as Field;\n expected[1] = b as Field;\n expected[2] = c as Field;\n expected[3] = d as Field;\n expected[4] = e as Field;\n expected[5] = f as Field;\n expected[6] = g as Field;\n expected[7] = h as Field;\n\n expected\n }\n\n #[test]\n fn test_compress() {\n let mut witness: [Field; 80] = [0; 80];\n for i in 0..80 {\n witness[i] = crate::tables::SHA512_ROUND_CONSTANTS[i] as Field;\n }\n let mut state: crate::permutation::RoundState = crate::permutation::RoundState {\n a: crate::encoding::encode_a(crate::tables::SHA512_INITIAL_HASH_VALUES[0] as Field),\n b: crate::encoding::encode_a(crate::tables::SHA512_INITIAL_HASH_VALUES[1] as Field),\n c: crate::encoding::encode_a(crate::tables::SHA512_INITIAL_HASH_VALUES[2] as Field),\n d: crate::encoding::encode_a(crate::tables::SHA512_INITIAL_HASH_VALUES[3] as Field),\n e: crate::encoding::encode_e(crate::tables::SHA512_INITIAL_HASH_VALUES[4] as Field),\n f: crate::encoding::encode_e(crate::tables::SHA512_INITIAL_HASH_VALUES[5] as Field),\n g: crate::encoding::encode_e(crate::tables::SHA512_INITIAL_HASH_VALUES[6] as Field),\n h: crate::encoding::encode_e(crate::tables::SHA512_INITIAL_HASH_VALUES[7] as Field),\n };\n\n crate::permutation::compress(&mut state, witness);\n // Safety: get native result to compare against\n let expected = unsafe { __compress_native(witness) };\n assert_eq(state.a.raw, expected[0]);\n assert_eq(state.b.raw, expected[1]);\n assert_eq(state.c.raw, expected[2]);\n assert_eq(state.d.raw, expected[3]);\n assert_eq(state.e.raw, expected[4]);\n assert_eq(state.f.raw, expected[5]);\n assert_eq(state.g.raw, expected[6]);\n assert_eq(state.h.raw, expected[7]);\n }\n\n #[test]\n fn test_extend_message() {\n let mut initial_witness: [Field; 16] = [0; 16];\n for i in 0..16 {\n initial_witness[i] = crate::tables::SHA512_ROUND_CONSTANTS[i] as Field;\n }\n\n let result = crate::permutation::extend_message(initial_witness);\n // Safety: get native result to compare against\n let expected = unsafe { __extend_message_native(initial_witness) };\n\n for i in 0..80 {\n assert_eq(result[i], expected[i] as Field);\n }\n }\n}\n" } }, "functions": [ { "abi": { "error_types": { "14415304921900233953": { "error_kind": "string", "string": "Initializer address is not the contract deployer" }, "15764276373176857197": { "error_kind": "string", "string": "Stack too deep" }, "9967937311635654895": { "error_kind": "string", "string": "Initialization hash does not match" } }, "parameters": [ { "name": "escrow_class_id", "type": { "kind": "field" }, "visibility": "private" } ], "return_type": null }, "bytecode": 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"is_unconstrained": true, "name": "constructor" }, { "abi": { "error_types": { "11084314168516775404": { "error_kind": "string", "string": "assert_is_not_zero_integer fail" }, "11217216648848338406": { "error_kind": "string", "string": "The provided public data tree leaf preimage is not the correct leaf for the requested slot" }, "12327971061804302172": { "error_kind": "fmtstring", "item_types": [], "length": 98 }, "12469291177396340830": { "error_kind": "string", "string": "call to assert_max_bit_size" }, "14990209321349310352": { "error_kind": "string", "string": "attempt to add with overflow" }, "15711892660910782274": { "error_kind": "string", "string": "Hint values do not match hash" }, "15764276373176857197": { "error_kind": "string", "string": "Stack too deep" }, "15835548349546956319": { "error_kind": "string", "string": "Field failed to decompose into specified 32 limbs" }, "16216212843441549037": { "error_kind": "string", "string": "Proving public value inclusion failed" }, "16431471497789672479": { "error_kind": "string", "string": "Index out of bounds" }, "1998584279744703196": { "error_kind": "string", "string": "attempt to subtract with overflow" }, "2014890719615096298": { "error_kind": "string", "string": "Field failed to decompose into specified 40 limbs" }, "361444214588792908": { "error_kind": "string", "string": "attempt to multiply with overflow" }, "819864067177566446": { "error_kind": "string", "string": "Field failed to decompose into specified 8 limbs" }, "8228371833434187583": { "error_kind": "string", "string": "Trying to read from uninitialized PublicImmutable" }, "9589626482238399944": { "error_kind": "string", "string": "Non-zero hint for zero hash" }, "9703767922216001139": { "error_kind": "string", "string": "Can't read a pending nullifier with a zero contract address" } }, "parameters": [ { "name": "inputs", "type": { "fields": [ { "name": "call_context", "type": { "fields": [ { "name": "msg_sender", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::aztec_address::AztecAddress" } }, { "name": "contract_address", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::aztec_address::AztecAddress" } }, { "name": "function_selector", "type": { "fields": [ { "name": "inner", "type": { "kind": "integer", "sign": "unsigned", "width": 32 } } ], "kind": "struct", "path": "aztec::protocol_types::abis::function_selector::FunctionSelector" } }, { "name": "is_static_call", "type": { "kind": "boolean" } } ], "kind": "struct", "path": "aztec::protocol_types::abis::call_context::CallContext" } }, { "name": "anchor_block_header", "type": { "fields": [ { "name": "last_archive", "type": { "fields": [ { "name": "root", "type": { "kind": "field" } }, { "name": "next_available_leaf_index", "type": { "kind": "field" } } ], "kind": "struct", "path": 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"sign": "unsigned", "width": 64 } }, { "name": "coinbase", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::eth_address::EthAddress" } }, { "name": "fee_recipient", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::aztec_address::AztecAddress" } }, { "name": "gas_fees", "type": { "fields": [ { "name": "fee_per_da_gas", "type": { "kind": "integer", "sign": "unsigned", "width": 128 } }, { "name": "fee_per_l2_gas", "type": { "kind": "integer", "sign": "unsigned", "width": 128 } } ], "kind": "struct", "path": "aztec::protocol_types::abis::gas_fees::GasFees" } } ], "kind": "struct", "path": "aztec::protocol_types::abis::global_variables::GlobalVariables" } }, { "name": "total_fees", "type": { "kind": "field" } }, { "name": "total_mana_used", "type": { "kind": "field" } } ], "kind": "struct", "path": 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", 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}, { "abi": { "error_types": { "12913276134398371456": { "error_kind": "string", "string": "push out of bounds" }, "14990209321349310352": { "error_kind": "string", "string": "attempt to add with overflow" }, "15764276373176857197": { "error_kind": "string", "string": "Stack too deep" }, "16431471497789672479": { "error_kind": "string", "string": "Index out of bounds" }, "9530675838293881722": { "error_kind": "string", "string": "Writer did not write all data" }, "992401946138144806": { "error_kind": "string", "string": "Attempted to read past end of BoundedVec" } }, "parameters": [ { "name": "messages", "type": { "fields": [ { "name": "storage", "type": { "kind": "array", "length": 16, "type": { "fields": [ { "name": "ciphertext", "type": { "fields": [ { "name": "storage", "type": { "kind": "array", "length": 15, "type": { "kind": "field" } } }, { "name": "len", "type": { "kind": "integer", "sign": "unsigned", "width": 32 } } ], "kind": "struct", "path": "std::collections::bounded_vec::BoundedVec" } }, { "name": "recipient", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::aztec_address::AztecAddress" } }, { "name": "tx_hash", "type": { "fields": [ { "name": "_is_some", "type": { "kind": "boolean" } }, { "name": "_value", "type": { "kind": "field" } } ], "kind": "struct", "path": "std::option::Option" } }, { "name": "anchor_block_timestamp", "type": { "kind": "integer", "sign": "unsigned", "width": 64 } } ], "kind": "struct", "path": "aztec::messages::processing::offchain::OffchainMessage" } } }, { "name": "len", "type": { "kind": "integer", "sign": "unsigned", "width": 32 } } ], "kind": "struct", "path": "std::collections::bounded_vec::BoundedVec" }, "visibility": "private" } ], "return_type": null }, "bytecode": 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"is_unconstrained": true, "name": "offchain_receive" }, { "abi": { "error_types": { "12511970388699677811": { "error_kind": "fmtstring", "item_types": [ { "kind": "field" } ], "length": 27 }, "14415304921900233953": { "error_kind": "string", "string": "Initializer address is not the contract deployer" }, "14990209321349310352": { "error_kind": "string", "string": "attempt to add with overflow" }, "15764276373176857197": { "error_kind": "string", "string": "Stack too deep" }, "16431471497789672479": { "error_kind": "string", "string": "Index out of bounds" }, "1998584279744703196": { "error_kind": "string", "string": "attempt to subtract with overflow" }, "361444214588792908": { "error_kind": "string", "string": "attempt to multiply with overflow" }, "9967937311635654895": { "error_kind": "string", "string": "Initialization hash does not match" } }, "parameters": [ { "name": "selector", "type": { "kind": "field" }, "visibility": "private" } ], "return_type": null }, "bytecode": 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subtract with overflow" }, "2967937905572420042": { "error_kind": "fmtstring", "item_types": [ { "kind": "field" }, { "kind": "field" } ], "length": 61 }, "3330370348214585450": { "error_kind": "fmtstring", "item_types": [ { "kind": "field" }, { "kind": "field" } ], "length": 48 }, "361444214588792908": { "error_kind": "string", "string": "attempt to multiply with overflow" }, "7158692485152407286": { "error_kind": "fmtstring", "item_types": [ { "kind": "integer", "sign": "unsigned", "width": 32 } ], "length": 101 }, "9530675838293881722": { "error_kind": "string", "string": "Writer did not write all data" }, "9791669845391776238": { "error_kind": "string", "string": "0 has a square root; you cannot claim it is not square" }, "9885968605480832328": { "error_kind": "string", "string": "Attempted to read past the length of a CapsuleArray" }, "992401946138144806": { "error_kind": "string", "string": "Attempted to read past end of BoundedVec" } }, "parameters": [ { "name": "scope", "type": { "fields": [ { "name": "inner", "type": { "kind": "field" } } ], "kind": "struct", "path": "aztec::protocol_types::address::aztec_address::AztecAddress" }, "visibility": "private" } ], "return_type": null }, "bytecode": 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