import {z} from 'zod'

export type GoogleCloudAiplatformV1Tensor = {boolVal?: Array<boolean> | undefined, bytesVal?: Array<string> | undefined, doubleVal?: Array<number> | undefined, dtype?: ('DATA_TYPE_UNSPECIFIED' | 'BOOL' | 'STRING' | 'FLOAT' | 'DOUBLE' | 'INT8' | 'INT16' | 'INT32' | 'INT64' | 'UINT8' | 'UINT16' | 'UINT32' | 'UINT64') | undefined, floatVal?: Array<number> | undefined, int64Val?: Array<unknown> | undefined, intVal?: Array<number> | undefined, listVal?: Array<GoogleCloudAiplatformV1Tensor> | undefined, shape?: Array<unknown> | undefined, stringVal?: Array<string> | undefined, structVal?: {[key: string]: GoogleCloudAiplatformV1Tensor} | undefined, tensorVal?: string | undefined, uint64Val?: Array<unknown> | undefined, uintVal?: Array<number> | undefined};

export type GoogleCloudAiplatformV1StreamingPredictRequest = {inputs?: Array<GoogleCloudAiplatformV1Tensor> | undefined, parameters?: GoogleCloudAiplatformV1Tensor | undefined};

export type CreateApiV1ProjectsProjectsIdLocationsLocationsIdEndpointsEndpointsIdServerStreamingPredictArgs = {projectsId: string, locationsId: string, endpointsId: string, body: GoogleCloudAiplatformV1StreamingPredictRequest};

export type CreateApiV1ProjectsProjectsIdLocationsLocationsIdEndpointsEndpointsIdServerStreamingPredictOptions = {baseUrl?: string | undefined, Oauth2?: string | undefined, Oauth2c?: string | undefined, fetch?: typeof fetch | undefined};

export const googleCloudAiplatformV1Tensor: z.ZodType<GoogleCloudAiplatformV1Tensor> = z.object({boolVal: z.array(z.boolean()).optional(), bytesVal: z.array(z.string()).optional(), doubleVal: z.array(z.number()).optional(), dtype: z.enum(["DATA_TYPE_UNSPECIFIED", "BOOL", "STRING", "FLOAT", "DOUBLE", "INT8", "INT16", "INT32", "INT64", "UINT8", "UINT16", "UINT32", "UINT64"]).optional(), floatVal: z.array(z.number()).optional(), int64Val: z.array(z.string()).optional(), intVal: z.array(z.number().int()).optional(), listVal: z.array(z.lazy(() => googleCloudAiplatformV1Tensor)).optional(), shape: z.array(z.string()).optional(), stringVal: z.array(z.string()).optional(), structVal: z.record(z.string(), z.lazy(() => googleCloudAiplatformV1Tensor)).optional(), tensorVal: z.string().optional(), uint64Val: z.array(z.string()).optional(), uintVal: z.array(z.number().int()).optional()});

export const googleCloudAiplatformV1StreamingPredictResponse = z.object({outputs: z.array(googleCloudAiplatformV1Tensor).optional(), parameters: googleCloudAiplatformV1Tensor.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
};

/**
 * Perform a server-side streaming online prediction request for Vertex LLM streaming.
 * 
 * POST /v1/projects/{projectsId}/locations/{locationsId}/endpoints/{endpointsId}:serverStreamingPredict
 * 
 * Also accepts auth: Oauth2 + Oauth2c, Oauth2 + Oauth2c
 */
export const createApiV1ProjectsProjectsIdLocationsLocationsIdEndpointsEndpointsIdServerStreamingPredict = async (args: CreateApiV1ProjectsProjectsIdLocationsLocationsIdEndpointsEndpointsIdServerStreamingPredictArgs, options?: CreateApiV1ProjectsProjectsIdLocationsLocationsIdEndpointsEndpointsIdServerStreamingPredictOptions) => {
const {baseUrl = "https://aiplatform.googleapis.com", fetch: fetchFn = fetch, Oauth2, Oauth2c} = options ?? {}

const query = new URLSearchParams()
const headers = new Headers()
headers.set('content-type', "application/json")
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))}/endpoints/${encodeURIComponent(String(args.endpointsId))}:serverStreamingPredict${query.size > 0 ? `?${query}` : ''}`

const res = await fetchFn(url, {method: "POST", headers, body: JSON.stringify(args.body)})

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

return validateResponse(googleCloudAiplatformV1StreamingPredictResponse, await res.json())
};