import {z} from 'zod'

export type GetApiV1ProjectsProjectsIdLocationsLocationsIdBatchPredictionJobsBatchPredictionJobsIdArgs = {projectsId: string, locationsId: string, batchPredictionJobsId: string};

export type GetApiV1ProjectsProjectsIdLocationsLocationsIdBatchPredictionJobsBatchPredictionJobsIdOptions = {baseUrl?: string | undefined, Oauth2?: string | undefined, Oauth2c?: string | undefined, fetch?: typeof fetch | undefined};

export const googleCloudAiplatformV1CompletionStats = z.object({failedCount: z.string().optional(), incompleteCount: z.string().optional(), successfulCount: z.string().optional(), successfulForecastPointCount: z.string().optional()});

export const googleCloudAiplatformV1ReservationAffinity = z.object({key: z.string().optional(), reservationAffinityType: z.enum(["TYPE_UNSPECIFIED", "NO_RESERVATION", "ANY_RESERVATION", "SPECIFIC_RESERVATION"]).optional(), values: z.array(z.string()).optional()});

export const googleCloudAiplatformV1MachineSpec = z.object({acceleratorCount: z.number().int().optional(), acceleratorType: z.enum(["ACCELERATOR_TYPE_UNSPECIFIED", "NVIDIA_TESLA_K80", "NVIDIA_TESLA_P100", "NVIDIA_TESLA_V100", "NVIDIA_TESLA_P4", "NVIDIA_TESLA_T4", "NVIDIA_TESLA_A100", "NVIDIA_A100_80GB", "NVIDIA_L4", "NVIDIA_H100_80GB", "NVIDIA_H100_MEGA_80GB", "NVIDIA_H200_141GB", "NVIDIA_B200", "NVIDIA_GB200", "NVIDIA_RTX_PRO_6000", "TPU_V2", "TPU_V3", "TPU_V4_POD", "TPU_V5_LITEPOD"]).optional(), gpuPartitionSize: z.string().optional(), machineType: z.string().optional(), reservationAffinity: googleCloudAiplatformV1ReservationAffinity.optional(), tpuTopology: z.string().optional()});

export const googleCloudAiplatformV1BatchDedicatedResources = z.object({machineSpec: googleCloudAiplatformV1MachineSpec.optional(), maxReplicaCount: z.number().int().optional(), startingReplicaCount: z.number().int().optional()});

export const googleCloudAiplatformV1EncryptionSpec = z.object({kmsKeyName: z.string().optional()});

export const googleRpcStatus = z.object({code: z.number().int().optional(), details: z.array(z.record(z.string(), z.unknown())).optional(), message: z.string().optional()});

export const googleCloudAiplatformV1ExplanationMetadataInputMetadataFeatureValueDomain = z.object({maxValue: z.number().optional(), minValue: z.number().optional(), originalMean: z.number().optional(), originalStddev: z.number().optional()});

export const googleCloudAiplatformV1ExplanationMetadataInputMetadataVisualization = z.object({clipPercentLowerbound: z.number().optional(), clipPercentUpperbound: z.number().optional(), colorMap: z.enum(["COLOR_MAP_UNSPECIFIED", "PINK_GREEN", "VIRIDIS", "RED", "GREEN", "RED_GREEN", "PINK_WHITE_GREEN"]).optional(), overlayType: z.enum(["OVERLAY_TYPE_UNSPECIFIED", "NONE", "ORIGINAL", "GRAYSCALE", "MASK_BLACK"]).optional(), polarity: z.enum(["POLARITY_UNSPECIFIED", "POSITIVE", "NEGATIVE", "BOTH"]).optional(), type: z.enum(["TYPE_UNSPECIFIED", "PIXELS", "OUTLINES"]).optional()});

export const googleCloudAiplatformV1ExplanationMetadataInputMetadata = z.object({denseShapeTensorName: z.string().optional(), encodedBaselines: z.array(z.unknown()).optional(), encodedTensorName: z.string().optional(), encoding: z.enum(["ENCODING_UNSPECIFIED", "IDENTITY", "BAG_OF_FEATURES", "BAG_OF_FEATURES_SPARSE", "INDICATOR", "COMBINED_EMBEDDING", "CONCAT_EMBEDDING"]).optional(), featureValueDomain: googleCloudAiplatformV1ExplanationMetadataInputMetadataFeatureValueDomain.optional(), groupName: z.string().optional(), indexFeatureMapping: z.array(z.string()).optional(), indicesTensorName: z.string().optional(), inputBaselines: z.array(z.unknown()).optional(), inputTensorName: z.string().optional(), modality: z.string().optional(), visualization: googleCloudAiplatformV1ExplanationMetadataInputMetadataVisualization.optional()});

export const googleCloudAiplatformV1ExplanationMetadataOutputMetadata = z.object({displayNameMappingKey: z.string().optional(), indexDisplayNameMapping: z.unknown(), outputTensorName: z.string().optional()});

export const googleCloudAiplatformV1ExplanationMetadata = z.object({featureAttributionsSchemaUri: z.string().optional(), inputs: z.record(z.string(), googleCloudAiplatformV1ExplanationMetadataInputMetadata).optional(), latentSpaceSource: z.string().optional(), outputs: z.record(z.string(), googleCloudAiplatformV1ExplanationMetadataOutputMetadata).optional()});

export const googleCloudAiplatformV1GcsSource = z.object({uris: z.array(z.string()).optional()});

export const googleCloudAiplatformV1ExamplesExampleGcsSource = z.object({dataFormat: z.enum(["DATA_FORMAT_UNSPECIFIED", "JSONL"]).optional(), gcsSource: googleCloudAiplatformV1GcsSource.optional()});

export const googleCloudAiplatformV1Presets = z.object({modality: z.enum(["MODALITY_UNSPECIFIED", "IMAGE", "TEXT", "TABULAR"]).optional(), query: z.enum(["PRECISE", "FAST"]).optional()});

export const googleCloudAiplatformV1Examples = z.object({exampleGcsSource: googleCloudAiplatformV1ExamplesExampleGcsSource.optional(), nearestNeighborSearchConfig: z.unknown(), neighborCount: z.number().int().optional(), presets: googleCloudAiplatformV1Presets.optional()});

export const googleCloudAiplatformV1BlurBaselineConfig = z.object({maxBlurSigma: z.number().optional()});

export const googleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature = z.object({name: z.string().optional(), sigma: z.number().optional()});

export const googleCloudAiplatformV1FeatureNoiseSigma = z.object({noiseSigma: z.array(googleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature).optional()});

export const googleCloudAiplatformV1SmoothGradConfig = z.object({featureNoiseSigma: googleCloudAiplatformV1FeatureNoiseSigma.optional(), noiseSigma: z.number().optional(), noisySampleCount: z.number().int().optional()});

export const googleCloudAiplatformV1IntegratedGradientsAttribution = z.object({blurBaselineConfig: googleCloudAiplatformV1BlurBaselineConfig.optional(), smoothGradConfig: googleCloudAiplatformV1SmoothGradConfig.optional(), stepCount: z.number().int().optional()});

export const googleCloudAiplatformV1SampledShapleyAttribution = z.object({pathCount: z.number().int().optional()});

export const googleCloudAiplatformV1XraiAttribution = z.object({blurBaselineConfig: googleCloudAiplatformV1BlurBaselineConfig.optional(), smoothGradConfig: googleCloudAiplatformV1SmoothGradConfig.optional(), stepCount: z.number().int().optional()});

export const googleCloudAiplatformV1ExplanationParameters = z.object({examples: googleCloudAiplatformV1Examples.optional(), integratedGradientsAttribution: googleCloudAiplatformV1IntegratedGradientsAttribution.optional(), outputIndices: z.array(z.unknown()).optional(), sampledShapleyAttribution: googleCloudAiplatformV1SampledShapleyAttribution.optional(), topK: z.number().int().optional(), xraiAttribution: googleCloudAiplatformV1XraiAttribution.optional()});

export const googleCloudAiplatformV1ExplanationSpec = z.object({metadata: googleCloudAiplatformV1ExplanationMetadata.optional(), parameters: googleCloudAiplatformV1ExplanationParameters.optional()});

export const googleCloudAiplatformV1BigQuerySource = z.object({inputUri: z.string().optional()});

export const googleCloudAiplatformV1VertexMultimodalDatasetSource = z.object({datasetName: z.string().optional()});

export const googleCloudAiplatformV1BatchPredictionJobInputConfig = z.object({bigquerySource: googleCloudAiplatformV1BigQuerySource.optional(), gcsSource: googleCloudAiplatformV1GcsSource.optional(), instancesFormat: z.string().optional(), vertexMultimodalDatasetSource: googleCloudAiplatformV1VertexMultimodalDatasetSource.optional()});

export const googleCloudAiplatformV1BatchPredictionJobInstanceConfig = z.object({excludedFields: z.array(z.string()).optional(), includedFields: z.array(z.string()).optional(), instanceType: z.string().optional(), keyField: z.string().optional()});

export const googleCloudAiplatformV1ManualBatchTuningParameters = z.object({batchSize: z.number().int().optional()});

export const googleCloudAiplatformV1BigQueryDestination = z.object({outputUri: z.string().optional()});

export const googleCloudAiplatformV1GcsDestination = z.object({outputUriPrefix: z.string().optional()});

export const googleCloudAiplatformV1VertexMultimodalDatasetDestination = z.object({bigqueryDestination: googleCloudAiplatformV1BigQueryDestination.optional(), displayName: z.string().optional()});

export const googleCloudAiplatformV1BatchPredictionJobOutputConfig = z.object({bigqueryDestination: googleCloudAiplatformV1BigQueryDestination.optional(), gcsDestination: googleCloudAiplatformV1GcsDestination.optional(), predictionsFormat: z.string().optional(), vertexMultimodalDatasetDestination: googleCloudAiplatformV1VertexMultimodalDatasetDestination.optional()});

export const googleCloudAiplatformV1BatchPredictionJobOutputInfo = z.object({bigqueryOutputDataset: z.string().optional(), bigqueryOutputTable: z.string().optional(), gcsOutputDirectory: z.string().optional(), vertexMultimodalDatasetName: z.string().optional()});

export const googleCloudAiplatformV1ResourcesConsumed = z.object({replicaHours: z.number().optional()});

export const googleCloudAiplatformV1EnvVar = z.object({name: z.string().optional(), value: z.string().optional()});

export const googleCloudAiplatformV1Port = z.object({containerPort: z.number().int().optional()});

export const googleCloudAiplatformV1ProbeExecAction = z.object({command: z.array(z.string()).optional()});

export const googleCloudAiplatformV1ProbeGrpcAction = z.object({port: z.number().int().optional(), service: z.string().optional()});

export const googleCloudAiplatformV1ProbeHttpHeader = z.object({name: z.string().optional(), value: z.string().optional()});

export const googleCloudAiplatformV1ProbeHttpGetAction = z.object({host: z.string().optional(), httpHeaders: z.array(googleCloudAiplatformV1ProbeHttpHeader).optional(), path: z.string().optional(), port: z.number().int().optional(), scheme: z.string().optional()});

export const googleCloudAiplatformV1ProbeTcpSocketAction = z.object({host: z.string().optional(), port: z.number().int().optional()});

export const googleCloudAiplatformV1Probe = z.object({exec: googleCloudAiplatformV1ProbeExecAction.optional(), failureThreshold: z.number().int().optional(), grpc: googleCloudAiplatformV1ProbeGrpcAction.optional(), httpGet: googleCloudAiplatformV1ProbeHttpGetAction.optional(), initialDelaySeconds: z.number().int().optional(), periodSeconds: z.number().int().optional(), successThreshold: z.number().int().optional(), tcpSocket: googleCloudAiplatformV1ProbeTcpSocketAction.optional(), timeoutSeconds: z.number().int().optional()});

export const googleCloudAiplatformV1ModelContainerSpec = z.object({args: z.array(z.string()).optional(), command: z.array(z.string()).optional(), deploymentTimeout: z.string().optional(), env: z.array(googleCloudAiplatformV1EnvVar).optional(), grpcPorts: z.array(googleCloudAiplatformV1Port).optional(), healthProbe: googleCloudAiplatformV1Probe.optional(), healthRoute: z.string().optional(), imageUri: z.string().optional(), invokeRoutePrefix: z.string().optional(), livenessProbe: googleCloudAiplatformV1Probe.optional(), ports: z.array(googleCloudAiplatformV1Port).optional(), predictRoute: z.string().optional(), sharedMemorySizeMb: z.string().optional(), startupProbe: googleCloudAiplatformV1Probe.optional()});

export const googleCloudAiplatformV1PredictSchemata = z.object({instanceSchemaUri: z.string().optional(), parametersSchemaUri: z.string().optional(), predictionSchemaUri: z.string().optional()});

export const googleCloudAiplatformV1UnmanagedContainerModel = z.object({artifactUri: z.string().optional(), containerSpec: googleCloudAiplatformV1ModelContainerSpec.optional(), predictSchemata: googleCloudAiplatformV1PredictSchemata.optional()});

export const googleCloudAiplatformV1BatchPredictionJob = z.object({completionStats: googleCloudAiplatformV1CompletionStats.optional(), createTime: z.string().optional(), dedicatedResources: googleCloudAiplatformV1BatchDedicatedResources.optional(), disableContainerLogging: z.boolean().optional(), displayName: z.string().optional(), encryptionSpec: googleCloudAiplatformV1EncryptionSpec.optional(), endTime: z.string().optional(), error: googleRpcStatus.optional(), explanationSpec: googleCloudAiplatformV1ExplanationSpec.optional(), generateExplanation: z.boolean().optional(), inputConfig: googleCloudAiplatformV1BatchPredictionJobInputConfig.optional(), instanceConfig: googleCloudAiplatformV1BatchPredictionJobInstanceConfig.optional(), labels: z.record(z.string(), z.string()).optional(), manualBatchTuningParameters: googleCloudAiplatformV1ManualBatchTuningParameters.optional(), model: z.string().optional(), modelParameters: z.unknown(), modelVersionId: z.string().optional(), name: z.string().optional(), outputConfig: googleCloudAiplatformV1BatchPredictionJobOutputConfig.optional(), outputInfo: googleCloudAiplatformV1BatchPredictionJobOutputInfo.optional(), partialFailures: z.array(googleRpcStatus).optional(), resourcesConsumed: googleCloudAiplatformV1ResourcesConsumed.optional(), satisfiesPzi: z.boolean().optional(), satisfiesPzs: z.boolean().optional(), serviceAccount: z.string().optional(), startTime: z.string().optional(), state: z.enum(["JOB_STATE_UNSPECIFIED", "JOB_STATE_QUEUED", "JOB_STATE_PENDING", "JOB_STATE_RUNNING", "JOB_STATE_SUCCEEDED", "JOB_STATE_FAILED", "JOB_STATE_CANCELLING", "JOB_STATE_CANCELLED", "JOB_STATE_PAUSED", "JOB_STATE_EXPIRED", "JOB_STATE_UPDATING", "JOB_STATE_PARTIALLY_SUCCEEDED"]).optional(), unmanagedContainerModel: googleCloudAiplatformV1UnmanagedContainerModel.optional(), updateTime: z.string().optional()});

export const validateResponse = async <Output>(
  schema: {
    readonly '~standard': {
      readonly validate: (value: unknown) => ({ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }) | Promise<{ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }>
    }
  },
  value: unknown
): Promise<Output> => {
  const result = await schema['~standard'].validate(value)

  if (result.issues !== undefined) {
    const detail = result.issues
      .map(({ message, path }) =>
        path?.length
          ? `${path.map((segment) => String(typeof segment === 'object' ? segment.key : segment)).join('.')}: ${message}`
          : message
      )
      .join('; ')

    throw new Error(`Response does not match the schema: ${detail}`, { cause: { issues: result.issues } })
  }

  return result.value
};

/**
 * Gets a BatchPredictionJob
 * 
 * GET /v1/projects/{projectsId}/locations/{locationsId}/batchPredictionJobs/{batchPredictionJobsId}
 */
export const getApiV1ProjectsProjectsIdLocationsLocationsIdBatchPredictionJobsBatchPredictionJobsId = async (args: GetApiV1ProjectsProjectsIdLocationsLocationsIdBatchPredictionJobsBatchPredictionJobsIdArgs, options?: GetApiV1ProjectsProjectsIdLocationsLocationsIdBatchPredictionJobsBatchPredictionJobsIdOptions) => {
const {baseUrl = "https://aiplatform.googleapis.com", fetch: fetchFn = fetch, Oauth2, Oauth2c} = options ?? {}

const query = new URLSearchParams()
const headers = new Headers()
headers.set('accept', "application/json")

if (Oauth2 !== undefined) headers.set('authorization', `Bearer ${Oauth2}`)
if (Oauth2c !== undefined) headers.set('authorization', `Bearer ${Oauth2c}`)

const url = `${baseUrl.replace(/\/+$/, '')}/v1/projects/${encodeURIComponent(String(args.projectsId))}/locations/${encodeURIComponent(String(args.locationsId))}/batchPredictionJobs/${encodeURIComponent(String(args.batchPredictionJobsId))}${query.size > 0 ? `?${query}` : ''}`

const res = await fetchFn(url, {method: "GET", headers})

if (!res.ok) throw new Error(`Request failed with status ${res.status}`, {cause: {status: res.status, body: await res.text()}})

return validateResponse(googleCloudAiplatformV1BatchPredictionJob, await res.json())
};