Rocky_Mountain_Vending/.pnpm-store/v10/files/5a/200b907edf753d1d94837a03de855b9e8fd373f7b978aa3fe33ba85a29837f15f8ae296604b5c1f89e28ed8c31e6f2f60ec32952f40d1d3c3866df924fda43

Object.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });

const instrumentation = require('@opentelemetry/instrumentation');
const core = require('@sentry/core');
const constants = require('./constants.js');

// List of patched methods
// From: https://sdk.vercel.ai/docs/ai-sdk-core/telemetry#collected-data
const INSTRUMENTED_METHODS = [
  'generateText',
  'streamText',
  'generateObject',
  'streamObject',
  'embed',
  'embedMany',
] ;

/**
 * Determines whether to record inputs and outputs for Vercel AI telemetry based on the configuration hierarchy.
 *
 * The order of precedence is:
 * 1. The vercel ai integration options
 * 2. The experimental_telemetry options in the vercel ai method calls
 * 3. When telemetry is explicitly enabled (isEnabled: true), default to recording
 * 4. Otherwise, use the sendDefaultPii option from client options
 */
function determineRecordingSettings(
  integrationRecordingOptions,
  methodTelemetryOptions,
  telemetryExplicitlyEnabled,
  defaultRecordingEnabled,
) {
  const recordInputs =
    integrationRecordingOptions?.recordInputs !== undefined
      ? integrationRecordingOptions.recordInputs
      : methodTelemetryOptions.recordInputs !== undefined
        ? methodTelemetryOptions.recordInputs
        : telemetryExplicitlyEnabled === true
          ? true // When telemetry is explicitly enabled, default to recording inputs
          : defaultRecordingEnabled;

  const recordOutputs =
    integrationRecordingOptions?.recordOutputs !== undefined
      ? integrationRecordingOptions.recordOutputs
      : methodTelemetryOptions.recordOutputs !== undefined
        ? methodTelemetryOptions.recordOutputs
        : telemetryExplicitlyEnabled === true
          ? true // When telemetry is explicitly enabled, default to recording inputs
          : defaultRecordingEnabled;

  return { recordInputs, recordOutputs };
}

/**
 * This detects is added by the Sentry Vercel AI Integration to detect if the integration should
 * be enabled.
 *
 * It also patches the `ai` module to enable Vercel AI telemetry automatically for all methods.
 */
class SentryVercelAiInstrumentation extends instrumentation.InstrumentationBase {
   __init() {this._isPatched = false;}
   __init2() {this._callbacks = [];}

   constructor(config = {}) {
    super('@sentry/instrumentation-vercel-ai', core.SDK_VERSION, config);SentryVercelAiInstrumentation.prototype.__init.call(this);SentryVercelAiInstrumentation.prototype.__init2.call(this);  }

  /**
   * Initializes the instrumentation by defining the modules to be patched.
   */
   init() {
    const module = new instrumentation.InstrumentationNodeModuleDefinition('ai', ['>=3.0.0 <5'], this._patch.bind(this));
    return module;
  }

  /**
   * Call the provided callback when the module is patched.
   * If it has already been patched, the callback will be called immediately.
   */
   callWhenPatched(callback) {
    if (this._isPatched) {
      callback();
    } else {
      this._callbacks.push(callback);
    }
  }

  /**
   * Patches module exports to enable Vercel AI telemetry.
   */
   _patch(moduleExports) {
    this._isPatched = true;

    this._callbacks.forEach(callback => callback());
    this._callbacks = [];

    function generatePatch(originalMethod) {
      return (...args) => {
        const existingExperimentalTelemetry = args[0].experimental_telemetry || {};
        const isEnabled = existingExperimentalTelemetry.isEnabled;

        const client = core.getCurrentScope().getClient();
        const integration = client?.getIntegrationByName(constants.INTEGRATION_NAME);
        const integrationOptions = integration?.options;
        const shouldRecordInputsAndOutputs = integration ? Boolean(client?.getOptions().sendDefaultPii) : false;

        const { recordInputs, recordOutputs } = determineRecordingSettings(
          integrationOptions,
          existingExperimentalTelemetry,
          isEnabled,
          shouldRecordInputsAndOutputs,
        );

        args[0].experimental_telemetry = {
          ...existingExperimentalTelemetry,
          isEnabled: isEnabled !== undefined ? isEnabled : true,
          recordInputs,
          recordOutputs,
        };

        return core.handleCallbackErrors(
          () => {
            // @ts-expect-error we know that the method exists
            return originalMethod.apply(this, args);
          },
          error => {
            // This error bubbles up to unhandledrejection handler (if not handled before),
            // where we do not know the active span anymore
            // So to circumvent this, we set the active span on the error object
            // which is picked up by the unhandledrejection handler
            if (error && typeof error === 'object') {
              core.addNonEnumerableProperty(error, '_sentry_active_span', core.getActiveSpan());
            }
          },
        );
      };
    }

    // Is this an ESM module?
    // https://tc39.es/ecma262/#sec-module-namespace-objects
    if (Object.prototype.toString.call(moduleExports) === '[object Module]') {
      // In ESM we take the usual route and just replace the exports we want to instrument
      for (const method of INSTRUMENTED_METHODS) {
        moduleExports[method] = generatePatch(moduleExports[method]);
      }

      return moduleExports;
    } else {
      // In CJS we can't replace the exports in the original module because they
      // don't have setters, so we create a new object with the same properties
      const patchedModuleExports = INSTRUMENTED_METHODS.reduce((acc, curr) => {
        acc[curr] = generatePatch(moduleExports[curr]);
        return acc;
      }, {} );

      return { ...moduleExports, ...patchedModuleExports };
    }
  }
}

exports.SentryVercelAiInstrumentation = SentryVercelAiInstrumentation;
exports.determineRecordingSettings = determineRecordingSettings;
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