{"version":3,"file":"index.cjs","names":["BaseChatOpenAI","getChatOpenAIModelParams","ChatOpenAIResponses","ChatOpenAICompletions","isBuiltInTool","isOpenAICustomTool","isCustomTool","_modelPrefersResponsesAPI"],"sources":["../../src/chat_models/index.ts"],"sourcesContent":["import { CallbackManagerForLLMRun } from \"@langchain/core/callbacks/manager\";\nimport { AIMessageChunk, type BaseMessage } from \"@langchain/core/messages\";\nimport { ChatGenerationChunk, type ChatResult } from \"@langchain/core/outputs\";\nimport { type BaseLanguageModelInput } from \"@langchain/core/language_models/base\";\nimport { Runnable } from \"@langchain/core/runnables\";\nimport { type OpenAICallOptions, type OpenAIChatInput } from \"../types.js\";\nimport {\n  _convertToOpenAITool,\n  isBuiltInTool,\n  isCustomTool,\n  isOpenAICustomTool,\n} from \"../utils/tools.js\";\nimport { _modelPrefersResponsesAPI } from \"../utils/misc.js\";\nimport { _convertOpenAIResponsesUsageToLangChainUsage } from \"../utils/output.js\";\nimport {\n  ChatOpenAICompletions,\n  ChatOpenAICompletionsCallOptions,\n} from \"./completions.js\";\nimport {\n  ChatOpenAIResponses,\n  ChatOpenAIResponsesCallOptions,\n} from \"./responses.js\";\nimport {\n  BaseChatOpenAI,\n  BaseChatOpenAIFields,\n  getChatOpenAIModelParams,\n} from \"./base.js\";\n\nexport type { OpenAICallOptions, OpenAIChatInput };\n\nexport type ChatOpenAICallOptions = ChatOpenAICompletionsCallOptions &\n  ChatOpenAIResponsesCallOptions;\n\nexport interface ChatOpenAIFields extends BaseChatOpenAIFields {\n  /**\n   * Whether to use the responses API for all requests. If `false` the responses API will be used\n   * only when required in order to fulfill the request.\n   */\n  useResponsesApi?: boolean;\n  /**\n   * The completions chat instance\n   * @internal\n   */\n  completions?: ChatOpenAICompletions;\n  /**\n   * The responses chat instance\n   * @internal\n   */\n  responses?: ChatOpenAIResponses;\n}\n\n/**\n * OpenAI chat model integration.\n *\n * To use with Azure, import the `AzureChatOpenAI` class.\n *\n * Setup:\n * Install `@langchain/openai` and set an environment variable named `OPENAI_API_KEY`.\n *\n * ```bash\n * npm install @langchain/openai\n * export OPENAI_API_KEY=\"your-api-key\"\n * ```\n *\n * ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html#constructor)\n *\n * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.html)\n *\n * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.\n * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:\n *\n * ```typescript\n * // When calling `.withConfig`, call options should be passed via the first argument\n * const llmWithArgsBound = llm.withConfig({\n *   stop: [\"\\n\"],\n *   tools: [...],\n * });\n *\n * // When calling `.bindTools`, call options should be passed via the second argument\n * const llmWithTools = llm.bindTools(\n *   [...],\n *   {\n *     tool_choice: \"auto\",\n *   }\n * );\n * ```\n *\n * ## Examples\n *\n * <details open>\n * <summary><strong>Instantiate</strong></summary>\n *\n * ```typescript\n * import { ChatOpenAI } from '@langchain/openai';\n *\n * const llm = new ChatOpenAI({\n *   model: \"gpt-4o-mini\",\n *   temperature: 0,\n *   maxTokens: undefined,\n *   timeout: undefined,\n *   maxRetries: 2,\n *   // apiKey: \"...\",\n *   // configuration: {\n *   //   baseURL: \"...\",\n *   // }\n *   // organization: \"...\",\n *   // other params...\n * });\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Invoking</strong></summary>\n *\n * ```typescript\n * const input = `Translate \"I love programming\" into French.`;\n *\n * // Models also accept a list of chat messages or a formatted prompt\n * const result = await llm.invoke(input);\n * console.log(result);\n * ```\n *\n * ```txt\n * AIMessage {\n *   \"id\": \"chatcmpl-9u4Mpu44CbPjwYFkTbeoZgvzB00Tz\",\n *   \"content\": \"J'adore la programmation.\",\n *   \"response_metadata\": {\n *     \"tokenUsage\": {\n *       \"completionTokens\": 5,\n *       \"promptTokens\": 28,\n *       \"totalTokens\": 33\n *     },\n *     \"finish_reason\": \"stop\",\n *     \"system_fingerprint\": \"fp_3aa7262c27\"\n *   },\n *   \"usage_metadata\": {\n *     \"input_tokens\": 28,\n *     \"output_tokens\": 5,\n *     \"total_tokens\": 33\n *   }\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Streaming Chunks</strong></summary>\n *\n * ```typescript\n * for await (const chunk of await llm.stream(input)) {\n *   console.log(chunk);\n * }\n * ```\n *\n * ```txt\n * AIMessageChunk {\n *   \"id\": \"chatcmpl-9u4NWB7yUeHCKdLr6jP3HpaOYHTqs\",\n *   \"content\": \"\"\n * }\n * AIMessageChunk {\n *   \"content\": \"J\"\n * }\n * AIMessageChunk {\n *   \"content\": \"'adore\"\n * }\n * AIMessageChunk {\n *   \"content\": \" la\"\n * }\n * AIMessageChunk {\n *   \"content\": \" programmation\",,\n * }\n * AIMessageChunk {\n *   \"content\": \".\",,\n * }\n * AIMessageChunk {\n *   \"content\": \"\",\n *   \"response_metadata\": {\n *     \"finish_reason\": \"stop\",\n *     \"system_fingerprint\": \"fp_c9aa9c0491\"\n *   },\n * }\n * AIMessageChunk {\n *   \"content\": \"\",\n *   \"usage_metadata\": {\n *     \"input_tokens\": 28,\n *     \"output_tokens\": 5,\n *     \"total_tokens\": 33\n *   }\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Aggregate Streamed Chunks</strong></summary>\n *\n * ```typescript\n * import { AIMessageChunk } from '@langchain/core/messages';\n * import { concat } from '@langchain/core/utils/stream';\n *\n * const stream = await llm.stream(input);\n * let full: AIMessageChunk | undefined;\n * for await (const chunk of stream) {\n *   full = !full ? chunk : concat(full, chunk);\n * }\n * console.log(full);\n * ```\n *\n * ```txt\n * AIMessageChunk {\n *   \"id\": \"chatcmpl-9u4PnX6Fy7OmK46DASy0bH6cxn5Xu\",\n *   \"content\": \"J'adore la programmation.\",\n *   \"response_metadata\": {\n *     \"prompt\": 0,\n *     \"completion\": 0,\n *     \"finish_reason\": \"stop\",\n *   },\n *   \"usage_metadata\": {\n *     \"input_tokens\": 28,\n *     \"output_tokens\": 5,\n *     \"total_tokens\": 33\n *   }\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Bind tools</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const GetWeather = {\n *   name: \"GetWeather\",\n *   description: \"Get the current weather in a given location\",\n *   schema: z.object({\n *     location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n *   }),\n * }\n *\n * const GetPopulation = {\n *   name: \"GetPopulation\",\n *   description: \"Get the current population in a given location\",\n *   schema: z.object({\n *     location: z.string().describe(\"The city and state, e.g. San Francisco, CA\")\n *   }),\n * }\n *\n * const llmWithTools = llm.bindTools(\n *   [GetWeather, GetPopulation],\n *   {\n *     // strict: true  // enforce tool args schema is respected\n *   }\n * );\n * const aiMsg = await llmWithTools.invoke(\n *   \"Which city is hotter today and which is bigger: LA or NY?\"\n * );\n * console.log(aiMsg.tool_calls);\n * ```\n *\n * ```txt\n * [\n *   {\n *     name: 'GetWeather',\n *     args: { location: 'Los Angeles, CA' },\n *     type: 'tool_call',\n *     id: 'call_uPU4FiFzoKAtMxfmPnfQL6UK'\n *   },\n *   {\n *     name: 'GetWeather',\n *     args: { location: 'New York, NY' },\n *     type: 'tool_call',\n *     id: 'call_UNkEwuQsHrGYqgDQuH9nPAtX'\n *   },\n *   {\n *     name: 'GetPopulation',\n *     args: { location: 'Los Angeles, CA' },\n *     type: 'tool_call',\n *     id: 'call_kL3OXxaq9OjIKqRTpvjaCH14'\n *   },\n *   {\n *     name: 'GetPopulation',\n *     args: { location: 'New York, NY' },\n *     type: 'tool_call',\n *     id: 'call_s9KQB1UWj45LLGaEnjz0179q'\n *   }\n * ]\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Structured Output</strong></summary>\n *\n * ```typescript\n * import { z } from 'zod';\n *\n * const Joke = z.object({\n *   setup: z.string().describe(\"The setup of the joke\"),\n *   punchline: z.string().describe(\"The punchline to the joke\"),\n *   rating: z.number().nullable().describe(\"How funny the joke is, from 1 to 10\")\n * }).describe('Joke to tell user.');\n *\n * const structuredLlm = llm.withStructuredOutput(Joke, {\n *   name: \"Joke\",\n *   strict: true, // Optionally enable OpenAI structured outputs\n * });\n * const jokeResult = await structuredLlm.invoke(\"Tell me a joke about cats\");\n * console.log(jokeResult);\n * ```\n *\n * ```txt\n * {\n *   setup: 'Why was the cat sitting on the computer?',\n *   punchline: 'Because it wanted to keep an eye on the mouse!',\n *   rating: 7\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>JSON Object Response Format</strong></summary>\n *\n * ```typescript\n * const jsonLlm = llm.withConfig({ response_format: { type: \"json_object\" } });\n * const jsonLlmAiMsg = await jsonLlm.invoke(\n *   \"Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]\"\n * );\n * console.log(jsonLlmAiMsg.content);\n * ```\n *\n * ```txt\n * {\n *   \"randomInts\": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Multimodal</strong></summary>\n *\n * ```typescript\n * import { HumanMessage } from '@langchain/core/messages';\n *\n * const imageUrl = \"https://example.com/image.jpg\";\n * const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());\n * const base64Image = Buffer.from(imageData).toString('base64');\n *\n * const message = new HumanMessage({\n *   content: [\n *     { type: \"text\", text: \"describe the weather in this image\" },\n *     {\n *       type: \"image_url\",\n *       image_url: { url: `data:image/jpeg;base64,${base64Image}` },\n *     },\n *   ]\n * });\n *\n * const imageDescriptionAiMsg = await llm.invoke([message]);\n * console.log(imageDescriptionAiMsg.content);\n * ```\n *\n * ```txt\n * The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Usage Metadata</strong></summary>\n *\n * ```typescript\n * const aiMsgForMetadata = await llm.invoke(input);\n * console.log(aiMsgForMetadata.usage_metadata);\n * ```\n *\n * ```txt\n * { input_tokens: 28, output_tokens: 5, total_tokens: 33 }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Logprobs</strong></summary>\n *\n * ```typescript\n * const logprobsLlm = new ChatOpenAI({ model: \"gpt-4o-mini\", logprobs: true });\n * const aiMsgForLogprobs = await logprobsLlm.invoke(input);\n * console.log(aiMsgForLogprobs.response_metadata.logprobs);\n * ```\n *\n * ```txt\n * {\n *   content: [\n *     {\n *       token: 'J',\n *       logprob: -0.000050616763,\n *       bytes: [Array],\n *       top_logprobs: []\n *     },\n *     {\n *       token: \"'\",\n *       logprob: -0.01868736,\n *       bytes: [Array],\n *       top_logprobs: []\n *     },\n *     {\n *       token: 'ad',\n *       logprob: -0.0000030545007,\n *       bytes: [Array],\n *       top_logprobs: []\n *     },\n *     { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },\n *     {\n *       token: ' la',\n *       logprob: -0.515404,\n *       bytes: [Array],\n *       top_logprobs: []\n *     },\n *     {\n *       token: ' programm',\n *       logprob: -0.0000118755715,\n *       bytes: [Array],\n *       top_logprobs: []\n *     },\n *     { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },\n *     {\n *       token: '.',\n *       logprob: -0.0000037697225,\n *       bytes: [Array],\n *       top_logprobs: []\n *     }\n *   ],\n *   refusal: null\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Response Metadata</strong></summary>\n *\n * ```typescript\n * const aiMsgForResponseMetadata = await llm.invoke(input);\n * console.log(aiMsgForResponseMetadata.response_metadata);\n * ```\n *\n * ```txt\n * {\n *   tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },\n *   finish_reason: 'stop',\n *   system_fingerprint: 'fp_3aa7262c27'\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>JSON Schema Structured Output</strong></summary>\n *\n * ```typescript\n * const llmForJsonSchema = new ChatOpenAI({\n *   model: \"gpt-4o-2024-08-06\",\n * }).withStructuredOutput(\n *   z.object({\n *     command: z.string().describe(\"The command to execute\"),\n *     expectedOutput: z.string().describe(\"The expected output of the command\"),\n *     options: z\n *       .array(z.string())\n *       .describe(\"The options you can pass to the command\"),\n *   }),\n *   {\n *     method: \"jsonSchema\",\n *     strict: true, // Optional when using the `jsonSchema` method\n *   }\n * );\n *\n * const jsonSchemaRes = await llmForJsonSchema.invoke(\n *   \"What is the command to list files in a directory?\"\n * );\n * console.log(jsonSchemaRes);\n * ```\n *\n * ```txt\n * {\n *   command: 'ls',\n *   expectedOutput: 'A list of files and subdirectories within the specified directory.',\n *   options: [\n *     '-a: include directory entries whose names begin with a dot (.).',\n *     '-l: use a long listing format.',\n *     '-h: with -l, print sizes in human readable format (e.g., 1K, 234M, 2G).',\n *     '-t: sort by time, newest first.',\n *     '-r: reverse order while sorting.',\n *     '-S: sort by file size, largest first.',\n *     '-R: list subdirectories recursively.'\n *   ]\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Audio Outputs</strong></summary>\n *\n * ```typescript\n * import { ChatOpenAI } from \"@langchain/openai\";\n *\n * const modelWithAudioOutput = new ChatOpenAI({\n *   model: \"gpt-4o-audio-preview\",\n *   // You may also pass these fields to `.withConfig` as a call argument.\n *   modalities: [\"text\", \"audio\"], // Specifies that the model should output audio.\n *   audio: {\n *     voice: \"alloy\",\n *     format: \"wav\",\n *   },\n * });\n *\n * const audioOutputResult = await modelWithAudioOutput.invoke(\"Tell me a joke about cats.\");\n * const castMessageContent = audioOutputResult.content[0] as Record<string, any>;\n *\n * console.log({\n *   ...castMessageContent,\n *   data: castMessageContent.data.slice(0, 100) // Sliced for brevity\n * })\n * ```\n *\n * ```txt\n * {\n *   id: 'audio_67117718c6008190a3afad3e3054b9b6',\n *   data: 'UklGRqYwBgBXQVZFZm10IBAAAAABAAEAwF0AAIC7AAACABAATElTVBoAAABJTkZPSVNGVA4AAABMYXZmNTguMjkuMTAwAGRhdGFg',\n *   expires_at: 1729201448,\n *   transcript: 'Sure! Why did the cat sit on the computer? Because it wanted to keep an eye on the mouse!'\n * }\n * ```\n * </details>\n *\n * <br />\n *\n * <details>\n * <summary><strong>Audio Outputs</strong></summary>\n *\n * ```typescript\n * import { ChatOpenAI } from \"@langchain/openai\";\n *\n * const modelWithAudioOutput = new ChatOpenAI({\n *   model: \"gpt-4o-audio-preview\",\n *   // You may also pass these fields to `.withConfig` as a call argument.\n *   modalities: [\"text\", \"audio\"], // Specifies that the model should output audio.\n *   audio: {\n *     voice: \"alloy\",\n *     format: \"wav\",\n *   },\n * });\n *\n * const audioOutputResult = await modelWithAudioOutput.invoke(\"Tell me a joke about cats.\");\n * const castAudioContent = audioOutputResult.additional_kwargs.audio as Record<string, any>;\n *\n * console.log({\n *   ...castAudioContent,\n *   data: castAudioContent.data.slice(0, 100) // Sliced for brevity\n * })\n * ```\n *\n * ```txt\n * {\n *   id: 'audio_67117718c6008190a3afad3e3054b9b6',\n *   data: 'UklGRqYwBgBXQVZFZm10IBAAAAABAAEAwF0AAIC7AAACABAATElTVBoAAABJTkZPSVNGVA4AAABMYXZmNTguMjkuMTAwAGRhdGFg',\n *   expires_at: 1729201448,\n *   transcript: 'Sure! Why did the cat sit on the computer? Because it wanted to keep an eye on the mouse!'\n * }\n * ```\n * </details>\n *\n * <br />\n */\nexport class ChatOpenAI<\n  CallOptions extends ChatOpenAICallOptions = ChatOpenAICallOptions,\n> extends BaseChatOpenAI<CallOptions> {\n  /**\n   * Whether to use the responses API for all requests. If `false` the responses API will be used\n   * only when required in order to fulfill the request.\n   */\n  useResponsesApi = false;\n\n  protected responses: ChatOpenAIResponses;\n\n  protected completions: ChatOpenAICompletions;\n\n  get lc_serializable_keys(): string[] {\n    return [...super.lc_serializable_keys, \"useResponsesApi\"];\n  }\n\n  get callKeys(): string[] {\n    return [...super.callKeys, \"useResponsesApi\"];\n  }\n\n  protected fields?: ChatOpenAIFields;\n\n  constructor(model: string, fields?: Omit<ChatOpenAIFields, \"model\">);\n  constructor(fields?: ChatOpenAIFields);\n  constructor(\n    modelOrFields?: string | ChatOpenAIFields,\n    fieldsArg?: Omit<ChatOpenAIFields, \"model\">\n  ) {\n    const fields = getChatOpenAIModelParams(modelOrFields, fieldsArg);\n    super(fields);\n    this.fields = fields;\n    this.useResponsesApi = fields?.useResponsesApi ?? false;\n    this.responses = fields?.responses ?? new ChatOpenAIResponses(fields);\n    this.completions = fields?.completions ?? new ChatOpenAICompletions(fields);\n  }\n\n  protected _useResponsesApi(options: this[\"ParsedCallOptions\"] | undefined) {\n    const usesBuiltInTools = options?.tools?.some(isBuiltInTool);\n    const hasResponsesOnlyKwargs =\n      options?.previous_response_id != null ||\n      options?.text != null ||\n      options?.truncation != null ||\n      options?.include != null ||\n      options?.reasoning?.summary != null ||\n      this.reasoning?.summary != null;\n    const hasCustomTools =\n      options?.tools?.some(isOpenAICustomTool) ||\n      options?.tools?.some(isCustomTool);\n\n    return (\n      this.useResponsesApi ||\n      usesBuiltInTools ||\n      hasResponsesOnlyKwargs ||\n      hasCustomTools ||\n      _modelPrefersResponsesAPI(this.model)\n    );\n  }\n\n  override getLsParams(options: this[\"ParsedCallOptions\"]) {\n    const optionsWithDefaults = this._combineCallOptions(options);\n    if (this._useResponsesApi(options)) {\n      return this.responses.getLsParams(optionsWithDefaults);\n    }\n    return this.completions.getLsParams(optionsWithDefaults);\n  }\n\n  override invocationParams(options?: this[\"ParsedCallOptions\"]) {\n    const optionsWithDefaults = this._combineCallOptions(options);\n    if (this._useResponsesApi(options)) {\n      return this.responses.invocationParams(optionsWithDefaults);\n    }\n    return this.completions.invocationParams(optionsWithDefaults);\n  }\n\n  /** @ignore */\n  override async _generate(\n    messages: BaseMessage[],\n    options: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): Promise<ChatResult> {\n    if (this._useResponsesApi(options)) {\n      return this.responses._generate(messages, options, runManager);\n    }\n    return this.completions._generate(messages, options, runManager);\n  }\n\n  override async *_streamResponseChunks(\n    messages: BaseMessage[],\n    options: this[\"ParsedCallOptions\"],\n    runManager?: CallbackManagerForLLMRun\n  ): AsyncGenerator<ChatGenerationChunk> {\n    if (this._useResponsesApi(options)) {\n      yield* this.responses._streamResponseChunks(\n        messages,\n        this._combineCallOptions(options),\n        runManager\n      );\n      return;\n    }\n    yield* this.completions._streamResponseChunks(\n      messages,\n      this._combineCallOptions(options),\n      runManager\n    );\n  }\n\n  override withConfig(\n    config: Partial<CallOptions>\n  ): Runnable<BaseLanguageModelInput, AIMessageChunk, CallOptions> {\n    const newModel = new ChatOpenAI<CallOptions>(this.fields);\n    newModel.defaultOptions = { ...this.defaultOptions, ...config };\n    return newModel;\n  }\n}\n"],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AA+kBA,IAAa,aAAb,MAAa,mBAEHA,aAAAA,eAA4B;;;;;CAKpC,kBAAkB;CAElB;CAEA;CAEA,IAAI,uBAAiC;AACnC,SAAO,CAAC,GAAG,MAAM,sBAAsB,kBAAkB;;CAG3D,IAAI,WAAqB;AACvB,SAAO,CAAC,GAAG,MAAM,UAAU,kBAAkB;;CAG/C;CAIA,YACE,eACA,WACA;EACA,MAAM,SAASC,aAAAA,yBAAyB,eAAe,UAAU;AACjE,QAAM,OAAO;AACb,OAAK,SAAS;AACd,OAAK,kBAAkB,QAAQ,mBAAmB;AAClD,OAAK,YAAY,QAAQ,aAAa,IAAIC,kBAAAA,oBAAoB,OAAO;AACrE,OAAK,cAAc,QAAQ,eAAe,IAAIC,oBAAAA,sBAAsB,OAAO;;CAG7E,iBAA2B,SAAgD;EACzE,MAAM,mBAAmB,SAAS,OAAO,KAAKC,cAAAA,cAAc;EAC5D,MAAM,yBACJ,SAAS,wBAAwB,QACjC,SAAS,QAAQ,QACjB,SAAS,cAAc,QACvB,SAAS,WAAW,QACpB,SAAS,WAAW,WAAW,QAC/B,KAAK,WAAW,WAAW;EAC7B,MAAM,iBACJ,SAAS,OAAO,KAAKC,cAAAA,mBAAmB,IACxC,SAAS,OAAO,KAAKC,cAAAA,aAAa;AAEpC,SACE,KAAK,mBACL,oBACA,0BACA,kBACAC,aAAAA,0BAA0B,KAAK,MAAM;;CAIzC,YAAqB,SAAoC;EACvD,MAAM,sBAAsB,KAAK,oBAAoB,QAAQ;AAC7D,MAAI,KAAK,iBAAiB,QAAQ,CAChC,QAAO,KAAK,UAAU,YAAY,oBAAoB;AAExD,SAAO,KAAK,YAAY,YAAY,oBAAoB;;CAG1D,iBAA0B,SAAqC;EAC7D,MAAM,sBAAsB,KAAK,oBAAoB,QAAQ;AAC7D,MAAI,KAAK,iBAAiB,QAAQ,CAChC,QAAO,KAAK,UAAU,iBAAiB,oBAAoB;AAE7D,SAAO,KAAK,YAAY,iBAAiB,oBAAoB;;;CAI/D,MAAe,UACb,UACA,SACA,YACqB;AACrB,MAAI,KAAK,iBAAiB,QAAQ,CAChC,QAAO,KAAK,UAAU,UAAU,UAAU,SAAS,WAAW;AAEhE,SAAO,KAAK,YAAY,UAAU,UAAU,SAAS,WAAW;;CAGlE,OAAgB,sBACd,UACA,SACA,YACqC;AACrC,MAAI,KAAK,iBAAiB,QAAQ,EAAE;AAClC,UAAO,KAAK,UAAU,sBACpB,UACA,KAAK,oBAAoB,QAAQ,EACjC,WACD;AACD;;AAEF,SAAO,KAAK,YAAY,sBACtB,UACA,KAAK,oBAAoB,QAAQ,EACjC,WACD;;CAGH,WACE,QAC+D;EAC/D,MAAM,WAAW,IAAI,WAAwB,KAAK,OAAO;AACzD,WAAS,iBAAiB;GAAE,GAAG,KAAK;GAAgB,GAAG;GAAQ;AAC/D,SAAO"}