import {z} from 'zod'

export type KerasModelTypeEnum = 'int8' | 'float32' | 'akida' | 'requiresRetrain';

export type KerasVisualLayerType = 'dense' | 'conv1d' | 'conv2d' | 'reshape' | 'flatten' | 'dropout' | 'batchNormalization' | 'transfer_mobilenetv2_a35' | 'transfer_mobilenetv2_a1' | 'transfer_mobilenetv2_a05' | 'transfer_mobilenetv2_160_a1' | 'transfer_mobilenetv2_160_a75' | 'transfer_mobilenetv2_160_a5' | 'transfer_mobilenetv2_160_a35' | 'transfer_mobilenetv1_a25_d100' | 'transfer_mobilenetv1_a2_d100' | 'transfer_mobilenetv1_a1_d100' | 'transfer_kws_mobilenetv1_a1_d100' | 'transfer_kws_mobilenetv2_a35_d100' | 'transfer_kws_syntiant_ndp10x' | 'transfer_kws_conv2d_tiny' | 'object_ssd_mobilenet_v2_fpnlite_320x320' | 'fomo_mobilenet_v2_a01' | 'fomo_mobilenet_v2_a35' | 'transfer_organization' | 'transfer_akidanet_imagenet_160_a100' | 'transfer_akidanet_imagenet_160_a50' | 'transfer_akidanet_imagenet_160_a25' | 'transfer_akidanet_imagenet_224_a100' | 'transfer_akidanet_imagenet_224_a50' | 'transfer_akidanet_imagenet_224_a25' | 'fomo_akidanet_a50' | 'fomo_ad_gmm' | 'fomo_ad_patchcore';

export type KerasVisualLayer = {type: KerasVisualLayerType, neurons?: number | undefined, kernelSize?: number | undefined, dropoutRate?: number | undefined, columns?: number | undefined, stack?: number | undefined, enabled?: boolean | undefined, organizationModelId?: number | undefined};

export type AugmentationPolicyImageEnum = 'none' | 'all';

export type AugmentationPolicySpectrogram = {enabled: boolean, warping?: boolean | undefined, freqMasking?: ('none' | 'low' | 'high') | undefined, timeMasking?: ('none' | 'low' | 'high') | undefined, gaussianNoise?: ('none' | 'low' | 'high') | undefined};

export type AkidaEdgeLearningConfig = {enabled: boolean, additionalClasses?: number | undefined, neuronsPerClass?: number | undefined};

export type AnomalyCapacity = 'low' | 'medium' | 'high';

export type KerasModelVariantEnum = 'int8' | 'float32' | 'akida';

export type BlockParamsVisualAnomalyPatchcore = {backbone?: string | undefined, numLayers?: number | undefined, poolSize?: number | undefined, samplingRatio?: number | undefined, numNearestNeighbors?: number | undefined};

export type BlockParamsVisualAnomalyGmm = {backbone?: string | undefined};

export type BlockParameters = BlockParamsVisualAnomalyPatchcore | BlockParamsVisualAnomalyGmm;

export type ImpulseSpecificGpuTrainingProcessor = 'cpu' | 'gpu';

export type KFoldsValidationConfig = {numFolds: number, includeFinalTrainingRun?: boolean | undefined, extraEpochs?: number | undefined};

export type SetKerasParameterRequest = {mode?: ('expert' | 'visual') | undefined, minimumConfidenceRating?: number | undefined, selectedModelType?: KerasModelTypeEnum | undefined, script?: string | undefined, visualLayers?: Array<KerasVisualLayer> | undefined, trainingCycles?: number | undefined, learningRate?: number | undefined, batchSize?: number | undefined, trainTestSplit?: number | undefined, autoClassWeights?: boolean | undefined, useLearnedOptimizer?: boolean | undefined, augmentationPolicyImage?: AugmentationPolicyImageEnum | undefined, augmentationPolicySpectrogram?: AugmentationPolicySpectrogram | undefined, profileInt8?: boolean | undefined, skipEmbeddingsAndMemory?: boolean | undefined, akidaEdgeLearningConfig?: AkidaEdgeLearningConfig | undefined, customValidationMetadataKey?: string | undefined, showAdvancedTrainingSettings?: boolean | undefined, showAugmentationTrainingSettings?: boolean | undefined, customParameters?: ({[key: string]: string | null}) | undefined, anomalyCapacity?: AnomalyCapacity | undefined, lastShownModelVariant?: KerasModelVariantEnum | undefined, blockParameters?: BlockParameters | undefined, trainingProcessor?: ImpulseSpecificGpuTrainingProcessor | undefined, kFoldsValidationConfig?: KFoldsValidationConfig | undefined, assertGpuUsagePercentage?: (number | null) | undefined};

export type CreateApiApiProjectIdTrainingKerasLearnIdArgs = {projectId: number, learnId: number, body: SetKerasParameterRequest};

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

export const genericApiResponse = z.object({success: z.boolean(), error: z.string().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
};

/**
 * Keras settings
 * 
 * POST /api/{projectId}/training/keras/{learnId}
 * 
 * Also accepts auth: JWTAuthentication, JWTHttpHeaderAuthentication, OAuth2
 */
export const createApiApiProjectIdTrainingKerasLearnId = async (args: CreateApiApiProjectIdTrainingKerasLearnIdArgs, options?: CreateApiApiProjectIdTrainingKerasLearnIdOptions) => {
const {baseUrl = "https://studio.edgeimpulse.com/v1", fetch: fetchFn = fetch, ApiKeyAuthentication} = options ?? {}

const query = new URLSearchParams()
const headers = new Headers()
headers.set('content-type', "application/json")
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))}${query.size > 0 ? `?${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(genericApiResponse, await res.json())
};