watsonx-ai-provider/dist/index.mjs.map

IBM watsonx.ai provider for the Vercel AI SDK - access Granite, OpenAI, Llama, Mistral, and other foundation models

{"version":3,"sources":["../src/watsonx-provider.ts","../src/watsonx-chat-language-model.ts","../src/watsonx-chat-settings.ts","../src/watsonx-config.ts","../src/watsonx-iam.ts","../src/watsonx-schemas.ts","../src/watsonx-error.ts","../src/watsonx-chat-messages.ts","../src/watsonx-prepare-tools.ts","../src/watsonx-chat-helpers.ts","../src/watsonx-embedding-model.ts","../src/version.ts"],"sourcesContent":["import {\n  type LanguageModelV3,\n  type EmbeddingModelV3,\n  NoSuchModelError,\n  type ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n  type FetchFunction,\n  withUserAgentSuffix,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport { WatsonxChatLanguageModel } from './watsonx-chat-language-model';\nimport type { WatsonxChatModelId } from './watsonx-chat-settings';\nimport { WatsonxEmbeddingModel } from './watsonx-embedding-model';\nimport type { WatsonxEmbeddingModelId, WatsonxEmbeddingSettings } from './watsonx-embedding-settings';\nimport { VERSION as PROVIDER_VERSION } from './version';\n\nexport interface WatsonxProviderSettings {\n  /**\n   * IBM Cloud API key. Defaults to WATSONX_AI_APIKEY env variable.\n   * Note: validation is lazy — a missing key does not throw until the first\n   * model call, so `createWatsonx()` succeeds even without credentials.\n   */\n  apiKey?: string;\n\n  /**\n   * watsonx.ai project ID. Defaults to WATSONX_AI_PROJECT_ID env variable.\n   * Lazily validated; see apiKey note.\n   */\n  projectId?: string;\n\n  /**\n   * Base URL for the watsonx.ai API.\n   * Defaults to https://us-south.ml.cloud.ibm.com\n   */\n  baseURL?: string;\n\n  /**\n   * Custom headers to include in requests.\n   */\n  headers?: Record<string, string>;\n\n  /**\n   * Custom fetch implementation. Use to intercept requests for testing,\n   * middleware, proxying, or telemetry.\n   */\n  fetch?: FetchFunction;\n\n  /**\n   * Custom ID generator for stream text/reasoning parts. Use to inject a\n   * deterministic generator in tests; defaults to provider-utils `generateId`.\n   */\n  generateId?: () => string;\n}\n\nexport interface WatsonxProvider extends ProviderV3 {\n  (modelId: WatsonxChatModelId): LanguageModelV3;\n\n  chat(modelId: WatsonxChatModelId): LanguageModelV3;\n\n  languageModel(modelId: WatsonxChatModelId): LanguageModelV3;\n\n  embeddingModel(modelId: WatsonxEmbeddingModelId): EmbeddingModelV3;\n\n  /** @deprecated Use `embeddingModel` instead. */\n  textEmbeddingModel(\n    modelId: WatsonxEmbeddingModelId,\n    settings?: WatsonxEmbeddingSettings\n  ): EmbeddingModelV3;\n}\n\nexport function createWatsonx(options: WatsonxProviderSettings = {}): WatsonxProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? 'https://us-south.ml.cloud.ibm.com';\n\n  const getApiKey = () => {\n    const apiKey = options.apiKey ?? process.env.WATSONX_AI_APIKEY;\n    if (!apiKey) {\n      throw new Error(\n        'IBM Cloud API key is required. Set WATSONX_AI_APIKEY env variable or pass apiKey option.'\n      );\n    }\n    return apiKey;\n  };\n\n  const getProjectId = () => {\n    const projectId = options.projectId ?? process.env.WATSONX_AI_PROJECT_ID;\n    if (!projectId) {\n      throw new Error(\n        'watsonx.ai project ID is required. Set WATSONX_AI_PROJECT_ID env variable or pass projectId option.'\n      );\n    }\n    return projectId;\n  };\n\n  const getHeaders = () =>\n    withUserAgentSuffix(\n      { ...options.headers },\n      `ai-sdk/watsonx/${PROVIDER_VERSION}`\n    );\n\n  const createChatModel = (modelId: WatsonxChatModelId): LanguageModelV3 => {\n    return new WatsonxChatLanguageModel(modelId, {\n      provider: 'watsonx.chat',\n      baseURL,\n      apiKey: getApiKey,\n      projectId: getProjectId,\n      headers: getHeaders,\n      fetch: options.fetch,\n      generateId: options.generateId,\n    });\n  };\n\n  const createEmbeddingModel = (\n    modelId: WatsonxEmbeddingModelId,\n    settings: WatsonxEmbeddingSettings = {}\n  ): EmbeddingModelV3 => {\n    return new WatsonxEmbeddingModel(modelId, settings, {\n      provider: 'watsonx.embedding',\n      baseURL,\n      apiKey: getApiKey,\n      projectId: getProjectId,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n  };\n\n  const provider = function (modelId: WatsonxChatModelId): LanguageModelV3 {\n    if (new.target) {\n      throw new Error(\n        'The watsonx model function cannot be called with the `new` keyword.'\n      );\n    }\n    return createChatModel(modelId);\n  };\n\n  provider.specificationVersion = 'v3' as const;\n  provider.chat = createChatModel;\n  provider.languageModel = createChatModel;\n  provider.embeddingModel = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.imageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n\n  return provider as WatsonxProvider;\n}\n\n/**\n * Default watsonx provider instance. Reads credentials from WATSONX_AI_APIKEY\n * and WATSONX_AI_PROJECT_ID env variables.\n */\nexport const watsonx = createWatsonx();\n","import {\n  type LanguageModelV3,\n  type LanguageModelV3CallOptions,\n  type LanguageModelV3GenerateResult,\n  type LanguageModelV3StreamResult,\n  type LanguageModelV3StreamPart,\n  type LanguageModelV3Content,\n  type LanguageModelV3FinishReason,\n  type LanguageModelV3Usage,\n  type SharedV3Warning,\n  APICallError,\n} from '@ai-sdk/provider';\nimport {\n  postJsonToApi,\n  createJsonResponseHandler,\n  createEventSourceResponseHandler,\n  generateId as defaultGenerateId,\n  parseProviderOptions,\n  combineHeaders,\n  type ParseResult,\n} from '@ai-sdk/provider-utils';\nimport {\n  type WatsonxChatModelId,\n  watsonxLanguageModelOptions,\n} from './watsonx-chat-settings';\nimport { type WatsonxConfig, WATSONX_API_VERSION } from './watsonx-config';\nimport { getIAMToken, invalidateIAMToken } from './watsonx-iam';\nimport { watsonxChatResponseSchema, watsonxChatChunkSchema } from './watsonx-schemas';\nimport { z } from 'zod';\nimport { watsonxErrorHandler } from './watsonx-error';\nimport { convertToWatsonxMessages } from './watsonx-chat-messages';\nimport { prepareWatsonxTools } from './watsonx-prepare-tools';\nimport {\n  convertFinishReason,\n  convertUsage,\n  getResponseMetadata,\n  parseToolCallArgs,\n} from './watsonx-chat-helpers';\n\n// --- Main Model Class ---\n\nexport class WatsonxChatLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3' as const;\n\n  readonly modelId: WatsonxChatModelId;\n  private readonly config: WatsonxConfig;\n\n  // Vision-capable wx models accept https image URLs directly; non-vision\n  // models will surface an error from the backend when sent one.\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    'image/*': [/^https?:\\/\\/.*$/],\n  };\n\n  constructor(modelId: WatsonxChatModelId, config: WatsonxConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private get generateId(): () => string {\n    return this.config.generateId ?? defaultGenerateId;\n  }\n\n  private async getArgs(options: LanguageModelV3CallOptions): Promise<{\n    body: Record<string, unknown>;\n    warnings: SharedV3Warning[];\n  }> {\n    const warnings: SharedV3Warning[] = [];\n\n    // Check for unsupported features\n    if (options.frequencyPenalty != null) {\n      warnings.push({ type: 'unsupported', feature: 'frequencyPenalty' });\n    }\n\n    if (options.presencePenalty != null) {\n      warnings.push({ type: 'unsupported', feature: 'presencePenalty' });\n    }\n\n    if (options.seed != null) {\n      warnings.push({ type: 'unsupported', feature: 'seed' });\n    }\n\n    const watsonxOptions = await parseProviderOptions({\n      provider: 'watsonx',\n      providerOptions: options.providerOptions,\n      schema: watsonxLanguageModelOptions,\n    });\n\n    // Build messages\n    const messages = convertToWatsonxMessages(options);\n\n    // Build base request body\n    const body: Record<string, unknown> = {\n      model_id: this.modelId,\n      project_id: this.config.projectId(),\n      messages,\n      max_tokens: options.maxOutputTokens,\n    };\n\n    if (options.temperature != null) {\n      body.temperature = options.temperature;\n    }\n    if (options.topP != null) {\n      body.top_p = options.topP;\n    }\n    if (options.topK != null) {\n      body.top_k = options.topK;\n    }\n    if (options.stopSequences && options.stopSequences.length > 0) {\n      // wx.ai typically caps stop_sequences around 6; warn past a conservative 4.\n      const WX_STOP_SEQUENCE_SOFT_CAP = 4;\n      if (options.stopSequences.length > WX_STOP_SEQUENCE_SOFT_CAP) {\n        warnings.push({\n          type: 'other',\n          message: `stopSequences has ${options.stopSequences.length} entries; wx.ai may reject more than ${WX_STOP_SEQUENCE_SOFT_CAP} depending on the model family.`,\n        });\n      }\n      body.stop_sequences = options.stopSequences;\n    }\n    if (watsonxOptions?.timeLimit != null) {\n      body.time_limit = watsonxOptions.timeLimit;\n    }\n    // Reasoning models (e.g. openai/gpt-oss-*) accept the OpenAI-style flat\n    // `reasoning_effort` field. Verified to scale reasoning length monotonically\n    // (baseline → low → medium → high) on gpt-oss-120b. Quarkus's `thinking`\n    // block is silently ignored on wx.ai-hosted models, so we use this shape.\n    if (watsonxOptions?.reasoningEffort != null) {\n      body.reasoning_effort = watsonxOptions.reasoningEffort;\n    }\n\n    // responseFormat: wx.ai supports { type: 'json_object' } and json_schema.\n    if (options.responseFormat?.type === 'json') {\n      if (options.responseFormat.schema != null) {\n        body.response_format = {\n          type: 'json_schema',\n          json_schema: {\n            name: options.responseFormat.name ?? 'response',\n            description: options.responseFormat.description,\n            schema: options.responseFormat.schema,\n          },\n        };\n      } else {\n        body.response_format = { type: 'json_object' };\n      }\n    }\n\n    const { tools, toolChoice, toolWarnings } = prepareWatsonxTools(options);\n    warnings.push(...toolWarnings);\n    if (tools && tools.length > 0) {\n      body.tools = tools;\n      if (toolChoice) Object.assign(body, toolChoice);\n      if (watsonxOptions?.parallelToolCalls != null) {\n        body.parallel_tool_calls = watsonxOptions.parallelToolCalls;\n      }\n    }\n\n    return { body, warnings };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { body, warnings } = await this.getArgs(options);\n    const apiKey = this.config.apiKey();\n\n    const call = async () => {\n      const token = await getIAMToken(apiKey);\n      return postJsonToApi({\n        url: `${this.config.baseURL}/ml/v1/text/chat?version=${WATSONX_API_VERSION}`,\n        headers: combineHeaders(\n          { Authorization: `Bearer ${token}` },\n          this.config.headers(),\n          options.headers\n        ),\n        body,\n        failedResponseHandler: watsonxErrorHandler,\n        successfulResponseHandler: createJsonResponseHandler(watsonxChatResponseSchema),\n        abortSignal: options.abortSignal,\n        fetch: this.config.fetch,\n      });\n    };\n\n    let value: Awaited<ReturnType<typeof call>>['value'];\n    let rawValue: Awaited<ReturnType<typeof call>>['rawValue'];\n    let responseHeaders: Awaited<ReturnType<typeof call>>['responseHeaders'];\n    try {\n      ({ value, rawValue, responseHeaders } = await call());\n    } catch (error) {\n      // One-shot retry on 401 with a fresh IAM token — cached token may have\n      // been revoked mid-TTL.\n      if (\n        APICallError.isInstance(error) &&\n        error.statusCode === 401\n      ) {\n        invalidateIAMToken(apiKey);\n        ({ value, rawValue, responseHeaders } = await call());\n      } else {\n        throw error;\n      }\n    }\n    const response = value;\n\n    const choice = response.choices[0];\n    if (!choice) {\n      throw new APICallError({\n        message: 'No choices returned from watsonx.ai',\n        url: `${this.config.baseURL}/ml/v1/text/chat?version=${WATSONX_API_VERSION}`,\n        requestBodyValues: body,\n        isRetryable: false,\n      });\n    }\n\n    // Build content array\n    const content: LanguageModelV3Content[] = [];\n\n    // Reasoning models (e.g. openai/gpt-oss-*) surface chain-of-thought as\n    // reasoning_content alongside the final answer. Emit it before text so\n    // the SDK delivers it as a `reasoning` content part — same handling as\n    // streaming `reasoning-delta` events.\n    if (choice.message.reasoning_content) {\n      content.push({ type: 'reasoning', text: choice.message.reasoning_content });\n    }\n\n    if (choice.message.content) {\n      content.push({ type: 'text', text: choice.message.content });\n    }\n\n    if (choice.message.tool_calls) {\n      for (const tc of choice.message.tool_calls) {\n        content.push({\n          type: 'tool-call',\n          toolCallId: tc.id,\n          toolName: tc.function.name,\n          input: parseToolCallArgs(tc.function.arguments, this.modelId),\n        });\n      }\n    }\n\n    const hasToolCalls = (choice.message.tool_calls?.length ?? 0) > 0;\n\n    return {\n      content,\n      finishReason: convertFinishReason(choice.finish_reason ?? 'stop', hasToolCalls),\n      usage: convertUsage(response.usage),\n      request: { body },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawValue ?? response,\n      },\n      warnings,\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions\n  ): Promise<LanguageModelV3StreamResult> {\n    const { body, warnings } = await this.getArgs(options);\n    const apiKey = this.config.apiKey();\n    const generateId = this.generateId;\n\n    // Stall watchdog: aborts the upstream fetch if no SSE chunk arrives within\n    // the window. wx.ai can silently hang on SSE; we need a side-channel kicker.\n    // Composed with the caller's signal so the SDK's abort flow sees both.\n    const INITIAL_STALL_MS = 120_000; // reasoning models think before streaming\n    const STREAMING_STALL_MS = 30_000; // between chunks once content has started\n    const stallController = new AbortController();\n    const composedSignal = options.abortSignal\n      ? AbortSignal.any([options.abortSignal, stallController.signal])\n      : stallController.signal;\n\n    const call = async () => {\n      const token = await getIAMToken(apiKey);\n      return postJsonToApi({\n        url: `${this.config.baseURL}/ml/v1/text/chat_stream?version=${WATSONX_API_VERSION}`,\n        headers: combineHeaders(\n          { Authorization: `Bearer ${token}` },\n          this.config.headers(),\n          options.headers\n        ),\n        body,\n        failedResponseHandler: watsonxErrorHandler,\n        successfulResponseHandler: createEventSourceResponseHandler(\n          watsonxChatChunkSchema\n        ),\n        abortSignal: composedSignal,\n        fetch: this.config.fetch,\n      });\n    };\n\n    let result: Awaited<ReturnType<typeof call>>;\n    try {\n      result = await call();\n    } catch (error) {\n      if (APICallError.isInstance(error) && error.statusCode === 401) {\n        invalidateIAMToken(apiKey);\n        result = await call();\n      } else {\n        throw error;\n      }\n    }\n    const { value: source, responseHeaders } = result;\n\n    let usage: LanguageModelV3Usage = {\n      inputTokens: { total: 0, noCache: undefined, cacheRead: undefined, cacheWrite: undefined },\n      outputTokens: { total: 0, text: undefined, reasoning: undefined },\n    };\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    const toolCallsInProgress = new Map<\n      number,\n      { id: string; name: string; args: string }\n    >();\n    let textId: string | null = null;\n    let reasoningId: string | null = null;\n    let sentResponseMetadata = false;\n    let receivedContent = false;\n    let finalized = false;\n    let responseId: string | undefined;\n    let receivedFinishReason = false;\n\n    let stallTimer: ReturnType<typeof setTimeout> | undefined;\n    const armStall = () => {\n      if (stallTimer) clearTimeout(stallTimer);\n      const ms = receivedContent ? STREAMING_STALL_MS : INITIAL_STALL_MS;\n      stallTimer = setTimeout(() => {\n        stallController.abort(\n          new Error(\n            `watsonx stream stalled: no data for ${ms / 1000}s (contentStarted=${receivedContent})`\n          )\n        );\n      }, ms);\n    };\n    const clearStall = () => {\n      if (stallTimer) {\n        clearTimeout(stallTimer);\n        stallTimer = undefined;\n      }\n    };\n\n    const finalize = (\n      controller: TransformStreamDefaultController<LanguageModelV3StreamPart>\n    ) => {\n      if (finalized) return;\n      finalized = true;\n      clearStall();\n\n      if (reasoningId !== null) {\n        controller.enqueue({ type: 'reasoning-end', id: reasoningId });\n      }\n      if (textId !== null) {\n        controller.enqueue({ type: 'text-end', id: textId });\n      }\n\n      for (const [, tc] of toolCallsInProgress) {\n        controller.enqueue({ type: 'tool-input-end', id: tc.id });\n        controller.enqueue({\n          type: 'tool-call',\n          toolCallId: tc.id,\n          toolName: tc.name,\n          input: parseToolCallArgs(tc.args, this.modelId),\n        });\n      }\n\n      // Override finish reason when tool calls are present — gpt-oss reports\n      // 'stop' instead of 'tool_calls' for tool-call responses.\n      const finalFinishReason =\n        toolCallsInProgress.size > 0 && finishReason.unified !== 'tool-calls'\n          ? { unified: 'tool-calls' as const, raw: finishReason.raw }\n          : finishReason;\n\n      // Anomaly: wx.ai billed completion tokens but nothing reached the\n      // consumer. Known symptom of mistral-medium-2505 streaming tool-calls.\n      // Always log so production instances surface it for bug reports.\n      const completionTokens = usage.outputTokens?.total ?? 0;\n      if (!receivedContent && completionTokens > 0) {\n        console.warn(\n          `[watsonx] stream anomaly: model=${this.modelId} response_id=${responseId ?? 'unknown'} ` +\n            `reported completion_tokens=${completionTokens} but streamed no content/tool_calls. ` +\n            `Likely a wx.ai streaming bug. Finish reason received: ${receivedFinishReason}.`\n        );\n      }\n\n      controller.enqueue({ type: 'finish', finishReason: finalFinishReason, usage });\n    };\n\n    const stream = source.pipeThrough(\n      new TransformStream<\n        ParseResult<z.infer<typeof watsonxChatChunkSchema>>,\n        LanguageModelV3StreamPart\n      >({\n        start(controller) {\n          controller.enqueue({ type: 'stream-start', warnings });\n          armStall();\n        },\n\n        transform(chunk, controller) {\n          if (finalized) return;\n          armStall();\n\n          // Opt-in per-chunk trace for bug reports. Set WATSONX_DEBUG_STREAM=1\n          // to capture raw + validated chunk shapes to the server console.\n          if (process.env.WATSONX_DEBUG_STREAM) {\n            if (chunk.success) {\n              console.log('[watsonx chunk]', JSON.stringify(chunk.rawValue));\n            } else {\n              console.log(\n                '[watsonx chunk parse-fail]',\n                chunk.error,\n                'raw:',\n                JSON.stringify(chunk.rawValue)\n              );\n            }\n          }\n\n          if (options.includeRawChunks) {\n            controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n          }\n\n          if (!chunk.success) {\n            // Schema drift or malformed chunk. Surface as a stream error rather\n            // than silently dropping — consumers can decide how to react.\n            controller.enqueue({ type: 'error', error: chunk.error });\n            return;\n          }\n\n          const value = chunk.value;\n\n          if (!sentResponseMetadata) {\n            sentResponseMetadata = true;\n            const meta = getResponseMetadata(value);\n            responseId = meta.id;\n            controller.enqueue({\n              type: 'response-metadata',\n              ...meta,\n              timestamp: meta.timestamp ?? new Date(),\n            });\n          }\n\n          if (value.usage != null) {\n            usage = convertUsage(value.usage);\n          }\n\n          const choice = value.choices?.[0];\n          if (!choice) return;\n\n          if (choice.delta?.reasoning_content) {\n            receivedContent = true;\n            if (reasoningId === null) {\n              reasoningId = `reasoning-${generateId()}`;\n              controller.enqueue({ type: 'reasoning-start', id: reasoningId });\n            }\n            controller.enqueue({\n              type: 'reasoning-delta',\n              id: reasoningId,\n              delta: choice.delta.reasoning_content,\n            });\n          }\n\n          if (choice.delta?.content) {\n            receivedContent = true;\n            if (textId === null) {\n              textId = `text-${generateId()}`;\n              controller.enqueue({ type: 'text-start', id: textId });\n            }\n            controller.enqueue({\n              type: 'text-delta',\n              id: textId,\n              delta: choice.delta.content,\n            });\n          }\n\n          if (choice.delta?.tool_calls) {\n            receivedContent = true;\n            for (const tc of choice.delta.tool_calls) {\n              const toolIndex = tc.index ?? 0;\n              let existing = toolCallsInProgress.get(toolIndex);\n              if (!existing) {\n                existing = {\n                  id: tc.id ?? `tool-${toolIndex}`,\n                  name: '',\n                  args: '',\n                };\n                toolCallsInProgress.set(toolIndex, existing);\n              }\n              if (tc.id) existing.id = tc.id;\n              if (tc.function?.name && existing.name === '') {\n                existing.name = tc.function.name;\n                controller.enqueue({\n                  type: 'tool-input-start',\n                  id: existing.id,\n                  toolName: existing.name,\n                });\n              }\n              if (tc.function?.arguments) {\n                existing.args += tc.function.arguments;\n                controller.enqueue({\n                  type: 'tool-input-delta',\n                  id: existing.id,\n                  delta: tc.function.arguments,\n                });\n              }\n            }\n          }\n\n          if (choice.finish_reason) {\n            finishReason = convertFinishReason(choice.finish_reason);\n            receivedFinishReason = true;\n            // Emit cleanup and finish now — some wx models don't close the SSE\n            // connection after finish_reason. terminate() closes the readable\n            // side cleanly so consumers see done without waiting for timeout.\n            finalize(controller);\n            controller.terminate();\n          }\n        },\n\n        flush(controller) {\n          finalize(controller);\n        },\n      })\n    );\n\n    return {\n      stream,\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n","import { z } from 'zod';\n\nexport type WatsonxChatModelId =\n  // OpenAI\n  | 'openai/gpt-oss-120b'\n  // IBM Granite models\n  | 'ibm/granite-4-h-small'\n  | 'ibm/granite-8b-code-instruct'\n  // Meta Llama models\n  | 'meta-llama/llama-3-3-70b-instruct'\n  | 'meta-llama/llama-3-2-11b-vision-instruct'\n  | 'meta-llama/llama-3-2-3b-instruct'\n  | 'meta-llama/llama-3-2-1b-instruct'\n  // Mistral models\n  // Note: mistral-medium-2505 has a known wx.ai streaming bug where tool-call\n  // tokens are billed but never transmitted in SSE deltas. Route mistral\n  // tool-calling workloads through doGenerate (non-streaming) or pick a\n  // different family until IBM fixes it.\n  | 'mistralai/mistral-medium-2505'\n  | 'mistralai/mistral-small-3-1-24b-instruct-2503'\n  | 'mistralai/pixtral-12b'\n  // Allow any model ID\n  | (string & {});\n\n/**\n * watsonx.ai-specific options passed via `providerOptions.watsonx` on a call.\n * Standard generation parameters (temperature, topP, topK, maxOutputTokens,\n * stopSequences) live on LanguageModelV3CallOptions and should be passed there.\n */\nexport const watsonxLanguageModelOptions = z.object({\n  /**\n   * Maximum wall-clock time in milliseconds the server will spend on this\n   * request before aborting. Unique to watsonx.ai.\n   */\n  timeLimit: z.number().optional(),\n\n  /**\n   * Whether the model may call multiple tools in parallel within one response.\n   * Forwarded to wx.ai as `parallel_tool_calls`. Defaults to the model's native\n   * behavior (usually true) when unset.\n   */\n  parallelToolCalls: z.boolean().optional(),\n\n  /**\n   * Reasoning effort for reasoning-capable models. Forwarded to wx.ai as\n   * `reasoning_effort`. Verified to scale chain-of-thought length on\n   * `openai/gpt-oss-120b` (HIGH produces ~3× the reasoning of LOW).\n   *\n   * Models without reasoning capability (e.g. `ibm/granite-4-h-small`)\n   * accept the field but produce no `reasoning_content` regardless.\n   */\n  reasoningEffort: z.enum(['low', 'medium', 'high']).optional(),\n});\n\nexport type WatsonxLanguageModelOptions = z.infer<\n  typeof watsonxLanguageModelOptions\n>;\n","import type { FetchFunction } from '@ai-sdk/provider-utils';\n\n// API version for watsonx.ai endpoints. Matches the value shipped in the\n// ibm-watsonx-ai Python SDK (utils/API_VERSION_PARAM); bump when IBM publishes\n// a newer release we want to opt into.\nexport const WATSONX_API_VERSION = '2026-04-20';\n\nexport interface WatsonxConfig {\n  provider: string;\n  baseURL: string;\n  apiKey: () => string;\n  projectId: () => string;\n  headers: () => Record<string, string>;\n  fetch?: FetchFunction;\n  generateId?: () => string;\n}\n","import { LoadAPIKeyError } from '@ai-sdk/provider';\n\n// Cache tokens per API key to support multiple provider instances\nconst tokenCache = new Map<string, { token: string; expiresAt: number }>();\n\n// Track in-flight token requests to prevent duplicate fetches\nconst pendingRequests = new Map<string, Promise<string>>();\n\nexport async function getIAMToken(apiKey: string): Promise<string> {\n  // Check cache with 5 min buffer\n  const cached = tokenCache.get(apiKey);\n  if (cached && cached.expiresAt > Date.now() + 5 * 60 * 1000) {\n    return cached.token;\n  }\n\n  // Check if there's already a pending request for this API key\n  const pending = pendingRequests.get(apiKey);\n  if (pending) {\n    return pending;\n  }\n\n  // Create and track the token fetch promise\n  const fetchPromise = fetchIAMToken(apiKey);\n  pendingRequests.set(apiKey, fetchPromise);\n\n  try {\n    return await fetchPromise;\n  } finally {\n    pendingRequests.delete(apiKey);\n  }\n}\n\nasync function fetchIAMToken(apiKey: string): Promise<string> {\n  const controller = new AbortController();\n  const timeoutId = setTimeout(() => controller.abort(), 15000);\n  let response: Response;\n  try {\n    response = await fetch('https://iam.cloud.ibm.com/identity/token', {\n      method: 'POST',\n      headers: { 'Content-Type': 'application/x-www-form-urlencoded' },\n      body: new URLSearchParams({\n        grant_type: 'urn:ibm:params:oauth:grant-type:apikey',\n        apikey: apiKey,\n      }),\n      signal: controller.signal,\n    });\n  } finally {\n    clearTimeout(timeoutId);\n  }\n\n  if (!response.ok) {\n    const text = await response.text();\n    throw new LoadAPIKeyError({\n      message: `Failed to get IBM IAM token: ${response.status} - ${text}`,\n    });\n  }\n\n  const data = (await response.json()) as {\n    access_token: string;\n    expires_in: number;\n  };\n\n  tokenCache.set(apiKey, {\n    token: data.access_token,\n    expiresAt: Date.now() + data.expires_in * 1000,\n  });\n\n  return data.access_token;\n}\n\n// For testing - allows clearing the cache\nexport function clearTokenCache(): void {\n  tokenCache.clear();\n}\n\n// Invalidate the cached token for a specific API key. Call when the server\n// returns 401 so the next getIAMToken() re-fetches instead of re-sending the\n// revoked token.\nexport function invalidateIAMToken(apiKey: string): void {\n  tokenCache.delete(apiKey);\n}\n","import { z } from 'zod';\n\n// --- Error Schema (shared) ---\n\nexport const watsonxErrorSchema = z\n  .object({\n    errors: z\n      .array(\n        z\n          .object({\n            code: z.string(),\n            message: z.string(),\n            more_info: z.string().optional(),\n          })\n          .loose()\n      )\n      .optional(),\n    error: z.string().optional(),\n    message: z.string().optional(),\n    // wx.ai variants return either; coerce to one via preprocess.\n    statusCode: z.number().optional(),\n  })\n  .loose()\n  .transform((val) => {\n    const raw = val as { status_code?: number };\n    return { ...val, statusCode: val.statusCode ?? raw.status_code };\n  });\n\n// --- Chat Response Schema ---\n// Top-level schema is strict so unknown fields surface in dev; nested objects\n// remain .loose() to tolerate forward-compatible additions from wx.ai.\n\nexport const watsonxChatResponseSchema = z.object({\n  id: z.string().nullish(),\n  model_id: z.string().nullish(),\n  created: z.number().nullish(),\n  choices: z.array(\n    z\n      .object({\n        index: z.number(),\n        message: z\n          .object({\n            role: z.literal('assistant'),\n            content: z.string().nullish(),\n            reasoning_content: z.string().nullish(),\n            tool_calls: z\n              .array(\n                z\n                  .object({\n                    id: z.string(),\n                    type: z.literal('function'),\n                    function: z\n                      .object({\n                        name: z.string(),\n                        arguments: z.string(),\n                      })\n                      .loose(),\n                  })\n                  .loose()\n              )\n              .optional(),\n          })\n          .loose(),\n        finish_reason: z.string().nullish(),\n      })\n      .loose()\n  ),\n  usage: z\n    .object({\n      prompt_tokens: z.number(),\n      completion_tokens: z.number(),\n      total_tokens: z.number(),\n      completion_tokens_details: z\n        .object({ reasoning_tokens: z.number().optional() })\n        .loose()\n        .optional(),\n    })\n    .loose(),\n});\n\n// --- Chat Stream Chunk Schema ---\n// Validates each SSE `data: {...}` payload. Fields are mostly optional because\n// deltas are sparse — a single chunk may carry only usage, only content, only\n// a tool_calls fragment, or only a finish_reason.\n\nexport const watsonxChatChunkSchema = z.object({\n  id: z.string().nullish(),\n  model_id: z.string().nullish(),\n  created: z.number().nullish(),\n  choices: z\n    .array(\n      z\n        .object({\n          index: z.number().optional(),\n          delta: z\n            .object({\n              role: z.string().optional(),\n              content: z.string().nullish(),\n              reasoning_content: z.string().nullish(),\n              tool_calls: z\n                .array(\n                  z\n                    .object({\n                      index: z.number().optional(),\n                      id: z.string().optional(),\n                      type: z.string().optional(),\n                      function: z\n                        .object({\n                          name: z.string().optional(),\n                          arguments: z.string().optional(),\n                        })\n                        .loose()\n                        .optional(),\n                    })\n                    .loose()\n                )\n                .optional(),\n            })\n            .loose()\n            .optional(),\n          finish_reason: z.string().nullish(),\n        })\n        .loose()\n    )\n    .optional(),\n  usage: z\n    .object({\n      prompt_tokens: z.number(),\n      completion_tokens: z.number(),\n      total_tokens: z.number().optional(),\n      completion_tokens_details: z\n        .object({ reasoning_tokens: z.number().optional() })\n        .loose()\n        .optional(),\n    })\n    .loose()\n    .optional(),\n});\n\n// --- Embedding Response Schema ---\n\nexport const watsonxEmbeddingResponseSchema = z.object({\n  model_id: z.string().nullish(),\n  results: z.array(\n    z\n      .object({\n        embedding: z.array(z.number()),\n        input_token_count: z.number().optional(),\n      })\n      .loose()\n  ),\n  input_token_count: z.number().optional(),\n});\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { watsonxErrorSchema } from './watsonx-schemas';\n\nexport const watsonxErrorHandler = createJsonErrorResponseHandler({\n  errorSchema: watsonxErrorSchema,\n  errorToMessage: (error) => {\n    if (error.errors?.[0]?.message) {\n      return error.errors[0].message;\n    }\n    return error.message ?? error.error ?? 'Unknown watsonx.ai error';\n  },\n  isRetryable: (response, error) => {\n    // Rate limiting\n    if (response.status === 429) return true;\n    // Server errors\n    if (response.status >= 500) return true;\n    // Check error codes\n    const code = error?.errors?.[0]?.code;\n    if (code === 'rate_limit_exceeded' || code === 'service_unavailable')\n      return true;\n    return false;\n  },\n});\n","import {\n  type LanguageModelV3CallOptions,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/provider-utils';\n\n// --- API Types ---\n\ninterface WatsonxContentPart {\n  type: 'text' | 'image_url';\n  text?: string;\n  image_url?: { url: string };\n}\n\nexport interface WatsonxMessage {\n  role: 'system' | 'user' | 'assistant' | 'tool';\n  content?: string | WatsonxContentPart[];\n  tool_calls?: Array<{\n    id: string;\n    type: 'function';\n    function: { name: string; arguments: string };\n  }>;\n  tool_call_id?: string;\n}\n\n// --- Message Conversion ---\n\nexport function convertToWatsonxMessages(\n  options: LanguageModelV3CallOptions\n): WatsonxMessage[] {\n  const messages: WatsonxMessage[] = [];\n\n  for (const message of options.prompt) {\n    switch (message.role) {\n      case 'system':\n        messages.push({ role: 'system', content: message.content });\n        break;\n\n      case 'user': {\n        const parts: WatsonxContentPart[] = [];\n        for (const part of message.content) {\n          if (part.type === 'text') {\n            parts.push({ type: 'text', text: part.text });\n          } else if (part.type === 'file') {\n            if (!part.mediaType.startsWith('image/')) {\n              throw new UnsupportedFunctionalityError({\n                functionality: `file parts with media type ${part.mediaType}`,\n                message:\n                  'watsonx.ai chat only supports image file parts; other media types are not supported.',\n              });\n            }\n            let imageUrl: string;\n            if (part.data instanceof URL) {\n              imageUrl = part.data.toString();\n            } else if (typeof part.data === 'string') {\n              imageUrl =\n                part.data.startsWith('data:') || part.data.startsWith('http')\n                  ? part.data\n                  : `data:${part.mediaType};base64,${part.data}`;\n            } else if (part.data instanceof Uint8Array) {\n              const base64 = convertUint8ArrayToBase64(part.data);\n              imageUrl = `data:${part.mediaType};base64,${base64}`;\n            } else {\n              throw new UnsupportedFunctionalityError({\n                functionality: 'file part with unknown data shape',\n              });\n            }\n            parts.push({ type: 'image_url', image_url: { url: imageUrl } });\n          }\n        }\n        // Collapse to a plain string when there's only one text part — keeps\n        // requests compatible with wx models that don't accept content arrays.\n        if (parts.length === 1 && parts[0].type === 'text') {\n          messages.push({ role: 'user', content: parts[0].text ?? '' });\n        } else {\n          messages.push({ role: 'user', content: parts });\n        }\n        break;\n      }\n\n      case 'assistant': {\n        const toolCalls: WatsonxMessage['tool_calls'] = [];\n        let textContent = '';\n        for (const part of message.content) {\n          if (part.type === 'text') {\n            textContent += part.text;\n          } else if (part.type === 'tool-call') {\n            toolCalls.push({\n              id: part.toolCallId,\n              type: 'function',\n              function: {\n                name: part.toolName,\n                arguments:\n                  typeof part.input === 'string'\n                    ? part.input\n                    : JSON.stringify(part.input),\n              },\n            });\n          }\n        }\n        const msg: WatsonxMessage = { role: 'assistant' };\n        if (textContent) msg.content = textContent;\n        if (toolCalls.length > 0) msg.tool_calls = toolCalls;\n        messages.push(msg);\n        break;\n      }\n\n      case 'tool':\n        for (const part of message.content) {\n          if (part.type === 'tool-result') {\n            let resultContent: string;\n            if (part.output.type === 'text') {\n              resultContent = part.output.value;\n            } else if (part.output.type === 'json') {\n              resultContent = JSON.stringify(part.output.value);\n            } else if (part.output.type === 'error-text') {\n              resultContent = part.output.value;\n            } else if (part.output.type === 'error-json') {\n              resultContent = JSON.stringify(part.output.value);\n            } else {\n              resultContent = JSON.stringify(part.output);\n            }\n            messages.push({\n              role: 'tool',\n              tool_call_id: part.toolCallId,\n              content: resultContent,\n            });\n          }\n        }\n        break;\n    }\n  }\n\n  return messages;\n}\n","import {\n  type LanguageModelV3CallOptions,\n  type LanguageModelV3FunctionTool,\n  type SharedV3Warning,\n} from '@ai-sdk/provider';\n\ninterface WatsonxToolDefinition {\n  type: 'function';\n  function: {\n    name: string;\n    description?: string;\n    parameters: unknown;\n    strict?: boolean;\n  };\n}\n\ntype WatsonxToolChoice =\n  | { tool_choice_option: 'auto' | 'none' | 'required' }\n  | {\n      tool_choice: {\n        type: 'function';\n        function: { name: string };\n      };\n    };\n\nexport interface PreparedTools {\n  tools?: WatsonxToolDefinition[];\n  toolChoice?: WatsonxToolChoice;\n  toolWarnings: SharedV3Warning[];\n}\n\n/**\n * Maps the SDK's `tools` and `toolChoice` call options into the wx.ai request\n * shape. Surfaces warnings for non-function tools (wx.ai only supports\n * function tools) and forwards the optional `strict` flag when present.\n */\nexport function prepareWatsonxTools(\n  options: Pick<LanguageModelV3CallOptions, 'tools' | 'toolChoice'>\n): PreparedTools {\n  const toolWarnings: SharedV3Warning[] = [];\n\n  if (!options.tools || options.tools.length === 0) {\n    return { toolWarnings };\n  }\n\n  for (const tool of options.tools) {\n    if (tool.type !== 'function') {\n      toolWarnings.push({\n        type: 'unsupported',\n        feature: `tool type '${tool.type}'`,\n        details: 'watsonx.ai only supports function tools',\n      });\n    }\n  }\n\n  const tools: WatsonxToolDefinition[] = options.tools\n    .filter((tool): tool is LanguageModelV3FunctionTool => tool.type === 'function')\n    .map((tool) => {\n      const fn: WatsonxToolDefinition['function'] = {\n        name: tool.name,\n        description: tool.description,\n        parameters: tool.inputSchema,\n      };\n      // Forward strict when present — wx.ai honours OpenAI's strict schema flag.\n      const strict = (tool as { strict?: boolean }).strict;\n      if (strict != null) fn.strict = strict;\n      return { type: 'function', function: fn };\n    });\n\n  let toolChoice: WatsonxToolChoice | undefined;\n  if (options.toolChoice) {\n    switch (options.toolChoice.type) {\n      case 'auto':\n      case 'none':\n      case 'required':\n        toolChoice = { tool_choice_option: options.toolChoice.type };\n        break;\n      case 'tool':\n        toolChoice = {\n          tool_choice: {\n            type: 'function',\n            function: { name: options.toolChoice.toolName },\n          },\n        };\n        break;\n    }\n  }\n\n  return { tools, toolChoice, toolWarnings };\n}\n","import type {\n  LanguageModelV3FinishReason,\n  LanguageModelV3ResponseMetadata,\n  LanguageModelV3Usage,\n} from '@ai-sdk/provider';\n\n/**\n * Extracts response metadata (id, modelId, timestamp) from a watsonx.ai chat\n * response or stream chunk. Both shapes include `id`, `model_id`, and `created`\n * (unix seconds). Missing fields surface as `undefined`.\n */\nexport function getResponseMetadata(value: {\n  id?: string | null;\n  model_id?: string | null;\n  created?: number | null;\n}): LanguageModelV3ResponseMetadata {\n  return {\n    id: value.id ?? undefined,\n    modelId: value.model_id ?? undefined,\n    timestamp:\n      typeof value.created === 'number'\n        ? new Date(value.created * 1000)\n        : undefined,\n  };\n}\n\nexport function convertFinishReason(\n  reason: string,\n  hasToolCalls = false\n): LanguageModelV3FinishReason {\n  let unified: LanguageModelV3FinishReason['unified'];\n  switch (reason) {\n    case 'stop':\n    case 'eos_token':\n      unified = 'stop';\n      break;\n    case 'length':\n    case 'max_tokens':\n      unified = 'length';\n      break;\n    case 'tool_calls':\n      unified = 'tool-calls';\n      break;\n    case 'content_filter':\n      unified = 'content-filter';\n      break;\n    default:\n      unified = 'other';\n  }\n  // Infer tool-calls if tools are present but reason is unclear\n  if (unified === 'other' && hasToolCalls) {\n    unified = 'tool-calls';\n  }\n  return { unified, raw: reason };\n}\n\n// Granite models occasionally return tool-call arguments as a JSON-encoded\n// string wrapping the real JSON (e.g. \"\\\"{\\\\\\\"foo\\\\\\\": 1}\\\"\"). Unwrap once,\n// only for IBM Granite models — other providers can legitimately return a\n// JSON-encoded string that we shouldn't mutate.\n//\n// Despite the name, this does not fully parse the arguments — the SDK expects\n// a string/JSON value that it parses itself.\nexport function parseToolCallArgs(args: string, modelId?: string): string {\n  if (modelId == null || !modelId.startsWith('ibm/granite')) {\n    return args;\n  }\n  try {\n    const parsed = JSON.parse(args);\n    if (typeof parsed === 'string') {\n      return parsed;\n    }\n  } catch {\n    // Not valid JSON, keep original\n  }\n  return args;\n}\n\nexport function convertUsage(\n  usage:\n    | {\n        prompt_tokens: number;\n        completion_tokens: number;\n        completion_tokens_details?: { reasoning_tokens?: number };\n      }\n    | null\n    | undefined\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: 0,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: { total: 0, text: undefined, reasoning: undefined },\n    };\n  }\n\n  const reasoningTokens = usage.completion_tokens_details?.reasoning_tokens;\n  const textTokens =\n    reasoningTokens != null\n      ? Math.max(0, usage.completion_tokens - reasoningTokens)\n      : usage.completion_tokens;\n\n  return {\n    inputTokens: {\n      total: usage.prompt_tokens,\n      noCache: undefined,\n      cacheRead: undefined,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: usage.completion_tokens,\n      text: textTokens,\n      reasoning: reasoningTokens,\n    },\n    raw: {\n      prompt_tokens: usage.prompt_tokens,\n      completion_tokens: usage.completion_tokens,\n    },\n  };\n}\n","import {\n  type EmbeddingModelV3,\n  type EmbeddingModelV3CallOptions,\n  type EmbeddingModelV3Result,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  postJsonToApi,\n  createJsonResponseHandler,\n  combineHeaders,\n} from '@ai-sdk/provider-utils';\nimport type {\n  WatsonxEmbeddingModelId,\n  WatsonxEmbeddingSettings,\n} from './watsonx-embedding-settings';\nimport { type WatsonxConfig, WATSONX_API_VERSION } from './watsonx-config';\nimport { getIAMToken } from './watsonx-iam';\nimport { watsonxEmbeddingResponseSchema } from './watsonx-schemas';\nimport { watsonxErrorHandler } from './watsonx-error';\n\n// --- Main Embedding Model Class ---\n\nexport class WatsonxEmbeddingModel implements EmbeddingModelV3 {\n  readonly specificationVersion = 'v3' as const;\n\n  readonly modelId: WatsonxEmbeddingModelId;\n  readonly settings: WatsonxEmbeddingSettings;\n  private readonly config: WatsonxConfig;\n\n  /**\n   * Maximum number of embeddings per API call.\n   * watsonx.ai supports batching multiple texts in a single request.\n   */\n  readonly maxEmbeddingsPerCall = 100;\n\n  /**\n   * Whether the model supports parallel embedding calls.\n   */\n  readonly supportsParallelCalls = true;\n\n  constructor(\n    modelId: WatsonxEmbeddingModelId,\n    settings: WatsonxEmbeddingSettings,\n    config: WatsonxConfig\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  async doEmbed(options: EmbeddingModelV3CallOptions): Promise<EmbeddingModelV3Result> {\n    if (options.values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values: options.values,\n      });\n    }\n\n    const token = await getIAMToken(this.config.apiKey());\n\n    const body: Record<string, unknown> = {\n      model_id: this.modelId,\n      project_id: this.config.projectId(),\n      inputs: options.values,\n    };\n\n    // Add truncation parameter if enabled\n    if (this.settings.truncateInputTokens) {\n      body.parameters = {\n        truncate_input_tokens: true,\n      };\n    }\n\n    const { value: response, rawValue, responseHeaders } = await postJsonToApi({\n      url: `${this.config.baseURL}/ml/v1/text/embeddings?version=${WATSONX_API_VERSION}`,\n      headers: combineHeaders(\n        { Authorization: `Bearer ${token}` },\n        this.config.headers(),\n        options.headers\n      ),\n      body,\n      failedResponseHandler: watsonxErrorHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        watsonxEmbeddingResponseSchema\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    // Calculate total tokens - prefer top-level count, fallback to summing results\n    let totalTokens: number;\n    if (response.input_token_count != null) {\n      totalTokens = response.input_token_count;\n    } else {\n      totalTokens = 0;\n      for (const result of response.results) {\n        totalTokens += result.input_token_count ?? 0;\n      }\n    }\n\n    return {\n      embeddings: response.results.map((r) => r.embedding),\n      usage: { tokens: totalTokens },\n      response: {\n        headers: responseHeaders,\n        body: rawValue ?? response,\n      },\n      warnings: [],\n    };\n  }\n}\n","// Version of the watsonx-ai-provider package, injected at build time by tsup\n// (see tsup.config.ts `define`). Falls back to '0.0.0-test' when running\n// straight from source (tests, dev) where the constant isn't substituted.\ndeclare const __PACKAGE_VERSION__: string | undefined;\n\nexport const VERSION: string =\n  typeof __PACKAGE_VERSION__ !== 'undefined' ? __PACKAGE_VERSION__ : '0.0.0-test';\n"],"mappings":";AAAA;AAAA,EAGE;AAAA,OAEK;AACP;AAAA,EAEE;AAAA,EACA;AAAA,OACK;;;ACVP;AAAA,EAUE;AAAA,OACK;AACP;AAAA,EACE;AAAA,EACA;AAAA,EACA;AAAA,EACA,cAAc;AAAA,EACd;AAAA,EACA;AAAA,OAEK;;;ACpBP,SAAS,SAAS;AA6BX,IAAM,8BAA8B,EAAE,OAAO;AAAA;AAAA;AAAA;AAAA;AAAA,EAKlD,WAAW,EAAE,OAAO,EAAE,SAAS;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA,EAO/B,mBAAmB,EAAE,QAAQ,EAAE,SAAS;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA;AAAA,EAUxC,iBAAiB,EAAE,KAAK,CAAC,OAAO,UAAU,MAAM,CAAC,EAAE,SAAS;AAC9D,CAAC;;;AC/CM,IAAM,sBAAsB;;;ACLnC,SAAS,uBAAuB;AAGhC,IAAM,aAAa,oBAAI,IAAkD;AAGzE,IAAM,kBAAkB,oBAAI,IAA6B;AAEzD,eAAsB,YAAY,QAAiC;AAEjE,QAAM,SAAS,WAAW,IAAI,MAAM;AACpC,MAAI,UAAU,OAAO,YAAY,KAAK,IAAI,IAAI,IAAI,KAAK,KAAM;AAC3D,WAAO,OAAO;AAAA,EAChB;AAGA,QAAM,UAAU,gBAAgB,IAAI,MAAM;AAC1C,MAAI,SAAS;AACX,WAAO;AAAA,EACT;AAGA,QAAM,eAAe,cAAc,MAAM;AACzC,kBAAgB,IAAI,QAAQ,YAAY;AAExC,MAAI;AACF,WAAO,MAAM;AAAA,EACf,UAAE;AACA,oBAAgB,OAAO,MAAM;AAAA,EAC/B;AACF;AAEA,eAAe,cAAc,QAAiC;AAC5D,QAAM,aAAa,IAAI,gBAAgB;AACvC,QAAM,YAAY,WAAW,MAAM,WAAW,MAAM,GAAG,IAAK;AAC5D,MAAI;AACJ,MAAI;AACF,eAAW,MAAM,MAAM,4CAA4C;AAAA,MACjE,QAAQ;AAAA,MACR,SAAS,EAAE,gBAAgB,oCAAoC;AAAA,MAC/D,MAAM,IAAI,gBAAgB;AAAA,QACxB,YAAY;AAAA,QACZ,QAAQ;AAAA,MACV,CAAC;AAAA,MACD,QAAQ,WAAW;AAAA,IACrB,CAAC;AAAA,EACH,UAAE;AACA,iBAAa,SAAS;AAAA,EACxB;AAEA,MAAI,CAAC,SAAS,IAAI;AAChB,UAAM,OAAO,MAAM,SAAS,KAAK;AACjC,UAAM,IAAI,gBAAgB;AAAA,MACxB,SAAS,gCAAgC,SAAS,M