import {z} from 'zod'

export type GetApiV1ProjectsProjectsIdLocationsLocationsIdModelsArgs = {projectsId: string, locationsId: string, filter?: string | undefined, orderBy?: string | undefined, pageSize?: number | undefined, pageToken?: string | undefined, readMask?: unknown | undefined};

export type GetApiV1ProjectsProjectsIdLocationsLocationsIdModelsOptions = {baseUrl?: string | undefined, Oauth2?: string | undefined, Oauth2c?: string | undefined, fetch?: typeof fetch | undefined};

export const googleCloudAiplatformV1GenieSource = z.object({baseModelUri: z.string().optional()});

export const googleCloudAiplatformV1ModelGardenSource = z.object({publicModelName: z.string().optional(), skipHfModelCache: z.boolean().optional(), versionId: z.string().optional()});

export const googleCloudAiplatformV1ModelBaseModelSource = z.object({genieSource: googleCloudAiplatformV1GenieSource.optional(), modelGardenSource: googleCloudAiplatformV1ModelGardenSource.optional()});

export const googleCloudAiplatformV1Checkpoint = z.object({checkpointId: z.string().optional(), epoch: z.string().optional(), step: z.string().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 googleCloudAiplatformV1ModelDataStats = z.object({testAnnotationsCount: z.string().optional(), testDataItemsCount: z.string().optional(), trainingAnnotationsCount: z.string().optional(), trainingDataItemsCount: z.string().optional(), validationAnnotationsCount: z.string().optional(), validationDataItemsCount: z.string().optional()});

export const googleCloudAiplatformV1DeployedModelRef = z.object({checkpointId: z.string().optional(), deployedModelId: z.string().optional(), endpoint: z.string().optional()});

export const googleCloudAiplatformV1EncryptionSpec = z.object({kmsKeyName: 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 googleCloudAiplatformV1ModelSourceInfo = z.object({copy: z.boolean().optional(), sourceType: z.enum(["MODEL_SOURCE_TYPE_UNSPECIFIED", "AUTOML", "CUSTOM", "BQML", "MODEL_GARDEN", "GENIE", "CUSTOM_TEXT_EMBEDDING", "MARKETPLACE"]).optional()});

export const googleCloudAiplatformV1ModelOriginalModelInfo = z.object({model: z.string().optional()});

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

export const googleCloudAiplatformV1ModelExportFormat = z.object({exportableContents: z.array(z.enum(["EXPORTABLE_CONTENT_UNSPECIFIED", "ARTIFACT", "IMAGE"])).optional(), id: z.string().optional()});

export const googleCloudAiplatformV1Model = z.object({artifactUri: z.string().optional(), baseModelSource: googleCloudAiplatformV1ModelBaseModelSource.optional(), checkpoints: z.array(googleCloudAiplatformV1Checkpoint).optional(), containerSpec: googleCloudAiplatformV1ModelContainerSpec.optional(), createTime: z.string().optional(), dataStats: googleCloudAiplatformV1ModelDataStats.optional(), defaultCheckpointId: z.string().optional(), deployedModels: z.array(googleCloudAiplatformV1DeployedModelRef).optional(), description: z.string().optional(), displayName: z.string().optional(), encryptionSpec: googleCloudAiplatformV1EncryptionSpec.optional(), etag: z.string().optional(), explanationSpec: googleCloudAiplatformV1ExplanationSpec.optional(), labels: z.record(z.string(), z.string()).optional(), metadata: z.unknown(), metadataArtifact: z.string().optional(), metadataSchemaUri: z.string().optional(), modelSourceInfo: googleCloudAiplatformV1ModelSourceInfo.optional(), name: z.string().optional(), originalModelInfo: googleCloudAiplatformV1ModelOriginalModelInfo.optional(), pipelineJob: z.string().optional(), predictSchemata: googleCloudAiplatformV1PredictSchemata.optional(), satisfiesPzi: z.boolean().optional(), satisfiesPzs: z.boolean().optional(), supportedDeploymentResourcesTypes: z.array(z.enum(["DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED", "DEDICATED_RESOURCES", "AUTOMATIC_RESOURCES", "SHARED_RESOURCES"])).optional(), supportedExportFormats: z.array(googleCloudAiplatformV1ModelExportFormat).optional(), supportedInputStorageFormats: z.array(z.string()).optional(), supportedOutputStorageFormats: z.array(z.string()).optional(), trainingPipeline: z.string().optional(), updateTime: z.string().optional(), versionAliases: z.array(z.string()).optional(), versionCreateTime: z.string().optional(), versionDescription: z.string().optional(), versionId: z.string().optional(), versionUpdateTime: z.string().optional()});

export const googleCloudAiplatformV1ListModelsResponse = z.object({models: z.array(googleCloudAiplatformV1Model).optional(), nextPageToken: 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
};

/**
 * Lists Models in a Location.
 * 
 * GET /v1/projects/{projectsId}/locations/{locationsId}/models
 * 
 * Also accepts auth: Oauth2 + Oauth2c
 */
export const getApiV1ProjectsProjectsIdLocationsLocationsIdModels = async (args: GetApiV1ProjectsProjectsIdLocationsLocationsIdModelsArgs, options?: GetApiV1ProjectsProjectsIdLocationsLocationsIdModelsOptions) => {
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 (args.filter != null) query.append("filter", String(args.filter))
if (args.orderBy != null) query.append("orderBy", String(args.orderBy))
if (args.pageSize != null) query.append("pageSize", String(args.pageSize))
if (args.pageToken != null) query.append("pageToken", String(args.pageToken))
if (args.readMask != null) query.append("readMask", String(args.readMask))
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))}/models${query.size > 0 ? `?${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(googleCloudAiplatformV1ListModelsResponse, await res.json())
};