import {z} from 'zod'

export type GetApiV1InferencesArgs = {limit?: number | undefined, offset?: number | undefined, model_id?: (string | null) | undefined, task?: (string | null) | undefined, project_id?: (string | null) | undefined, training_job_id?: (string | null) | undefined, latency_min?: (number | null) | undefined, latency_max?: (number | null) | undefined, llmaj_score_min?: (number | null) | undefined, llmaj_score_max?: (number | null) | undefined, since?: (string | null) | undefined, until?: (string | null) | undefined};

export type GetApiV1InferencesOptions = {baseUrl: string, ApiKeyAuth?: string | undefined, fetch?: typeof fetch | undefined};

export const inferenceRecord = z.object({id: z.string(), user_id: z.string(), model_id: z.string(), model_name: z.string().nullable().optional(), task: z.string().nullable().optional(), input: z.string(), output: z.unknown(), latency_ms: z.number().int().nullable().optional(), ttft_ms: z.number().int().nullable().optional(), tokens: z.number().int().nullable().optional(), input_tokens: z.number().int().nullable().optional(), output_tokens: z.number().int().nullable().optional(), cache_read_tokens: z.number().int().nullable().optional(), cache_write_tokens: z.number().int().nullable().optional(), source: z.string().optional(), status: z.string().optional(), error_type: z.string().nullable().optional(), error_message: z.string().nullable().optional(), created_at: z.string(), project_id: z.string().nullable().optional(), training_job_id: z.string().nullable().optional(), provider: z.string().nullable().optional(), base_model: z.string().nullable().optional(), metadata: z.record(z.string(), z.unknown()).nullable().optional(), human_verdict: z.string().nullable().optional(), human_corrected_output: z.unknown(), human_feedback_notes: z.string().nullable().optional(), human_feedback_at: z.string().nullable().optional(), llmaj_verdict: z.enum(["pass", "fail", "uncertain"]).nullable().optional(), llmaj_score: z.number().nullable().optional(), llmaj_judged_at: z.string().nullable().optional(), llmaj_reasoning: z.string().nullable().optional()});

export const inferenceListResponse = z.object({inferences: z.array(inferenceRecord), total: z.number().int(), limit: z.number().int(), offset: z.number().int()});

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
};

/**
 * List Inference History
 * 
 * GET /v1/inferences
 * 
 * Also accepts auth: BearerAuth
 */
export const getApiV1Inferences = async (args: GetApiV1InferencesArgs, options: GetApiV1InferencesOptions) => {
const {baseUrl, fetch: fetchFn = fetch, ApiKeyAuth} = options ?? {}

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

if (args.limit != null) query.append("limit", String(args.limit))
if (args.offset != null) query.append("offset", String(args.offset))
if (args.model_id != null) query.append("model_id", String(args.model_id))
if (args.task != null) query.append("task", String(args.task))
if (args.project_id != null) query.append("project_id", String(args.project_id))
if (args.training_job_id != null) query.append("training_job_id", String(args.training_job_id))
if (args.latency_min != null) query.append("latency_min", String(args.latency_min))
if (args.latency_max != null) query.append("latency_max", String(args.latency_max))
if (args.llmaj_score_min != null) query.append("llmaj_score_min", String(args.llmaj_score_min))
if (args.llmaj_score_max != null) query.append("llmaj_score_max", String(args.llmaj_score_max))
if (args.since != null) query.append("since", String(args.since))
if (args.until != null) query.append("until", String(args.until))
if (ApiKeyAuth !== undefined) headers.set("X-API-Key", ApiKeyAuth)

const url = `${baseUrl.replace(/\/+$/, '')}/v1/inferences${query.size > 0 ? `?${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(inferenceListResponse, await res.json())
};