import {z} from 'zod'

export type AiFineTuningDatasetGenerateForm = {projectIds?: Array<number> | undefined, tmIds?: Array<number> | undefined, purpose?: ('training' | 'validation') | undefined, dateFrom?: string | undefined, dateTo?: string | undefined, maxFileSize?: number | undefined, minExamplesCount?: number | undefined, maxExamplesCount?: number | undefined};

export type CreateApiUsersUserIdAiPromptsAiPromptIdFineTuningDatasetsArgs = {userId: number, aiPromptId: number, body: AiFineTuningDatasetGenerateForm};

export type CreateApiUsersUserIdAiPromptsAiPromptIdFineTuningDatasetsOptions = {baseUrl?: string | undefined, fetch?: typeof fetch | undefined};

export const aiFineTuningDataset = z.object({identifier: z.string().optional(), status: z.string().optional(), progress: z.number().int().optional(), attributes: z.object({projectIds: z.array(z.number().int()).nullable().optional(), tmIds: z.array(z.number().int()).nullable().optional(), purpose: z.enum(["training", "validation"]).optional(), dateFrom: z.string().nullable().optional(), dateTo: z.string().nullable().optional(), maxFileSize: z.number().int().nullable().optional(), minExamplesCount: z.number().int().nullable().optional(), maxExamplesCount: z.number().int().nullable().optional()}).optional(), createdAt: z.string().optional(), updatedAt: z.string().optional(), startedAt: z.string().optional(), finishedAt: z.string().optional()});

export const aiFineTuningDatasetResource = z.object({data: aiFineTuningDataset});

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
};

/**
 * Generate AI Prompt Fine-Tuning Dataset
 * 
 * POST /users/{userId}/ai/prompts/{aiPromptId}/fine-tuning/datasets
 */
export const createApiUsersUserIdAiPromptsAiPromptIdFineTuningDatasets = async (args: CreateApiUsersUserIdAiPromptsAiPromptIdFineTuningDatasetsArgs, options?: CreateApiUsersUserIdAiPromptsAiPromptIdFineTuningDatasetsOptions) => {
const {baseUrl = "https://api.crowdin.com/api/v2", fetch: fetchFn = fetch} = options ?? {}

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

const url = `${baseUrl.replace(/\/+$/, '')}/users/${encodeURIComponent(String(args.userId))}/ai/prompts/${encodeURIComponent(String(args.aiPromptId))}/fine-tuning/datasets${query.size > 0 ? `?${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(aiFineTuningDatasetResource, await res.json())
};