import {z} from 'zod'

export type GetApiV1BaseModelsArgs = {supports_training?: (boolean | null) | undefined, supports_inference?: (boolean | null) | undefined, task_type?: (string | null) | undefined};

export type GetApiV1BaseModelsOptions = {baseUrl: string, fetch?: typeof fetch | undefined};

export const benchmarkComponentScore = z.object({benchmark_id: z.string(), label: z.string(), score: z.number()});

export const baseModelResponse = z.object({id: z.string(), label: z.string(), description: z.string(), task_type: z.string(), context_window: z.number().int().nullable().optional(), max_input_tokens: z.number().int().nullable().optional(), max_output_tokens: z.number().int().nullable().optional(), release_month: z.string().nullable().optional(), input_price_per_million: z.number().nullable().optional(), output_price_per_million: z.number().nullable().optional(), cache_read_price_per_million: z.number().nullable().optional(), cache_write_price_per_million: z.number().nullable().optional(), supports_inference: z.boolean(), is_chat_model: z.boolean(), supports_on_demand_inference: z.boolean(), supports_image_input: z.boolean().optional(), supports_zdr: z.boolean().optional(), supports_training: z.boolean(), encoder_features: z.array(z.enum(["entities", "classifications", "structures", "relations", "records", "span_attributes", "constrained_classification", "joint_ie", "wide_span_entities"])).nullable().optional(), default_training_batch_size: z.number().int().nullable().optional(), max_training_batch_size: z.number().int().nullable().optional(), license: z.string().nullable().optional(), tier: z.string().nullable().optional(), featured_rank: z.number().int().nullable().optional(), deprecated: z.boolean().optional(), replacement_model: z.string().nullable().optional(), deprecation_date: z.string().nullable().optional(), sunset_date: z.string().nullable().optional(), coding_benchmark_mean: z.number().nullable().optional(), agentic_benchmark_mean: z.number().nullable().optional(), coding_benchmarks: z.array(benchmarkComponentScore).optional(), agentic_benchmarks: z.array(benchmarkComponentScore).optional(), market_input_price_per_million: z.number().nullable().optional(), market_output_price_per_million: z.number().nullable().optional(), market_price_provider_count: z.number().int().nullable().optional()});

export const baseModelsResponse = z.object({models: z.array(baseModelResponse)});

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 Base Models
 * 
 * GET /v1/base-models
 */
export const getApiV1BaseModels = async (args: GetApiV1BaseModelsArgs, options: GetApiV1BaseModelsOptions) => {
const {baseUrl, fetch: fetchFn = fetch} = options ?? {}

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

if (args.supports_training != null) query.append("supports_training", String(args.supports_training))
if (args.supports_inference != null) query.append("supports_inference", String(args.supports_inference))
if (args.task_type != null) query.append("task_type", String(args.task_type))

const url = `${baseUrl.replace(/\/+$/, '')}/v1/base-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(baseModelsResponse, await res.json())
};