import {z} from 'zod'

export type GetApiApiProjectIdTrainingKerasLearnIdDataExplorerFeaturesArgs = {projectId: number, learnId: number};

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

export const inputBlockType = z.enum(["time-series", "image", "features"]);

export const imageInputResizeMode = z.enum(["squash", "fit-short", "fit-long", "crop"]);

export const impulseInputBlockDatasetSubset = z.object({includePercentage: z.number(), seed: z.number().int().optional()});

export const impulseInputBlock = z.object({id: z.number().int().gte(1), type: inputBlockType, name: z.string(), title: z.string(), axes: z.array(z.string()).optional(), windowSizeMs: z.number().int().optional(), windowIncreaseMs: z.number().int().optional(), frequencyHz: z.number().optional(), classificationWindowIncreaseMs: z.number().int().optional(), padZeros: z.boolean().optional(), labelingMethodMultiLabel: z.object({type: z.enum(["end-of-window", "anywhere-in-window", "anywhere-in-window-min-percentage", "majority-in-window"]), labels: z.array(z.string()).optional(), minPercentage: z.number().optional()}).optional(), imageWidth: z.number().int().optional(), imageHeight: z.number().int().optional(), resizeMode: imageInputResizeMode.optional(), resizeMethod: z.enum(["lanczos3", "nearest"]).optional(), cropAnchor: z.enum(["top-left", "top-center", "top-right", "middle-left", "middle-center", "middle-right", "bottom-left", "bottom-center", "bottom-right"]).optional(), createdBy: z.string().optional(), createdAt: z.string().optional(), datasetSubset: impulseInputBlockDatasetSubset.optional()});

export const getDataExplorerFeaturesResponse = z.object({success: z.boolean(), error: z.string().optional(), hasFeatures: z.boolean(), data: z.array(z.object({X: z.record(z.string(), z.number()), y: z.number().int(), yLabel: z.string(), sample: z.object({id: z.number(), name: z.string(), startMs: z.number(), endMs: z.number(), category: z.enum(["training", "testing", "validation"])}).optional()})), inputBlock: impulseInputBlock.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
};

/**
 * Get data explorer features
 * 
 * GET /api/{projectId}/training/keras/{learnId}/data-explorer/features
 * 
 * Also accepts auth: JWTAuthentication, JWTHttpHeaderAuthentication, OAuth2
 */
export const getApiApiProjectIdTrainingKerasLearnIdDataExplorerFeatures = async (args: GetApiApiProjectIdTrainingKerasLearnIdDataExplorerFeaturesArgs, options?: GetApiApiProjectIdTrainingKerasLearnIdDataExplorerFeaturesOptions) => {
const {baseUrl = "https://studio.edgeimpulse.com/v1", fetch: fetchFn = fetch, ApiKeyAuthentication} = options ?? {}

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

if (ApiKeyAuthentication !== undefined) headers.set("x-api-key", ApiKeyAuthentication)

const url = `${baseUrl.replace(/\/+$/, '')}/api/${encodeURIComponent(String(args.projectId))}/training/keras/${encodeURIComponent(String(args.learnId))}/data-explorer/features${query.size > 0 ? `?${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(getDataExplorerFeaturesResponse, await res.json())
};