import {z} from 'zod'

export type CreateApiV20LargelanguagemodelsLargeLanguageModelIdCloneArgs = {Accept?: ('application/json' | 'application/hal+json' | 'application/xml' | 'text/xml' | 'text/csv') | undefined, largeLanguageModelId: string};

export type CreateApiV20LargelanguagemodelsLargeLanguageModelIdCloneOptions = {baseUrl?: string | undefined, APIKeyHeader?: string | undefined, fetch?: typeof fetch | undefined};

export const createApiV20LargelanguagemodelsLargeLanguageModelIdCloneResponse = z.union([z.object({name: z.string(), description: z.string().optional(), modelType: z.enum(["gpt-3.5-turbo", "gpt-3.5-turbo-instruct", "gpt-4", "gpt-4o", "gpt-4o-mini", "gpt-4.1", "gpt-4.1-mini", "gpt-4.1-nano", "gpt-5", "gpt-5-nano", "gpt-5-mini", "gpt-5.4-mini", "gpt-5.4-nano", "gpt-5-chat-latest", "gpt-5.1", "gpt-5.2", "gpt-5.4", "gpt-5.5", "gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", "text-embedding-ada-002", "luminous-extended-control", "luminous-embedding-128", "Pharia-1-Embedding-4608", "gemini-embedding-001", "gemini-embedding-2", "claude-3-opus-20240229", "claude-sonnet-4-6", "custom-model", "custom-embedding-model", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", "gemini-3.1-pro-preview", "gemini-3-flash-preview", "gemini-3.5-flash", "gemini-3.1-flash-lite-preview", "gemini-3.1-flash-lite", "mistral-large-2411", "mistral-small-2503", "pixtral-large-2411", "pixtral-12b-2409"]), modelGroup: z.enum(["chat", "completion", "embedding"]).optional(), apiType: z.enum(["chatCompletion", "responses"]).optional(), isCustomModel: z.boolean().optional(), provider: z.enum(["azureOpenAI", "openAI", "anthropic", "googleVertexAI", "googleGemini", "googleGenAI", "alephAlpha", "awsBedrock", "mistral"]), connectionId: z.string().min(36).max(36), openAI: z.object({customModel: z.string().optional()}).optional(), anthropic: z.object({customModel: z.string().optional()}).optional(), azureOpenAI: z.object({resourceName: z.string().optional(), deploymentName: z.string().optional(), apiVersion: z.string().optional(), baseCustomUrl: z.string().optional()}).optional(), googleVertexAI: z.object({location: z.string().optional(), apiEndpoint: z.string().optional(), publisher: z.string().optional()}).optional(), googleGemini: z.object({location: z.string().optional()}).optional(), googleGenAI: z.object({location: z.string().optional()}).optional(), alephAlpha: z.object({customModel: z.string().optional(), baseCustomUrl: z.string().optional()}).optional(), openAICompatible: z.object({customModel: z.string().optional(), baseCustomUrl: z.string().optional(), customAuthHeader: z.string().optional()}).optional(), resourceLevel: z.literal("project").optional(), isDefault: z.boolean().optional(), fallbacks: z.array(z.object({isFallbackEnabled: z.boolean().optional(), fallbackLLMReferenceId: z.string().min(36).max(36).optional(), immediateFallBack: z.object({failedRequests: z.number().optional(), durationInMinutes: z.number().optional(), emailNotificationList: z.array(z.string()).optional()}).optional()})).optional(), referenceId: z.string().optional(), _id: z.string().min(24).max(24).optional(), createdAt: z.number().int().gte(0).lte(2147483647).optional(), lastChanged: z.number().int().gte(0).lte(2147483647).optional(), createdBy: z.string().min(24).max(24).optional(), lastChangedBy: z.string().min(24).max(24).optional()}), z.object({name: z.string(), description: z.string().optional(), modelType: z.enum(["gpt-3.5-turbo", "gpt-3.5-turbo-instruct", "gpt-4", "gpt-4o", "gpt-4o-mini", "gpt-4.1", "gpt-4.1-mini", "gpt-4.1-nano", "gpt-5", "gpt-5-nano", "gpt-5-mini", "gpt-5.4-mini", "gpt-5.4-nano", "gpt-5-chat-latest", "gpt-5.1", "gpt-5.2", "gpt-5.4", "gpt-5.5", "gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", "text-embedding-ada-002", "luminous-extended-control", "luminous-embedding-128", "Pharia-1-Embedding-4608", "gemini-embedding-001", "gemini-embedding-2", "claude-3-opus-20240229", "claude-sonnet-4-6", "custom-model", "custom-embedding-model", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.5-flash-lite", "gemini-3.1-pro-preview", "gemini-3-flash-preview", "gemini-3.5-flash", "gemini-3.1-flash-lite-preview", "gemini-3.1-flash-lite", "mistral-large-2411", "mistral-small-2503", "pixtral-large-2411", "pixtral-12b-2409"]), modelGroup: z.enum(["chat", "completion", "embedding"]).optional(), apiType: z.enum(["chatCompletion", "responses"]).optional(), isCustomModel: z.boolean().optional(), provider: z.enum(["azureOpenAI", "openAI", "anthropic", "googleVertexAI", "googleGemini", "googleGenAI", "alephAlpha", "awsBedrock", "mistral"]), connectionId: z.string().min(36).max(36), openAI: z.object({customModel: z.string().optional()}).optional(), anthropic: z.object({customModel: z.string().optional()}).optional(), azureOpenAI: z.object({resourceName: z.string().optional(), deploymentName: z.string().optional(), apiVersion: z.string().optional(), baseCustomUrl: z.string().optional()}).optional(), googleVertexAI: z.object({location: z.string().optional(), apiEndpoint: z.string().optional(), publisher: z.string().optional()}).optional(), googleGemini: z.object({location: z.string().optional()}).optional(), googleGenAI: z.object({location: z.string().optional()}).optional(), alephAlpha: z.object({customModel: z.string().optional(), baseCustomUrl: z.string().optional()}).optional(), openAICompatible: z.object({customModel: z.string().optional(), baseCustomUrl: z.string().optional(), customAuthHeader: z.string().optional()}).optional(), resourceLevel: z.literal("organisation"), assignedToProjects: z.array(z.string()).optional(), referenceId: z.string().optional(), _id: z.string().min(24).max(24).optional(), createdAt: z.number().int().gte(0).lte(2147483647).optional(), lastChanged: z.number().int().gte(0).lte(2147483647).optional(), createdBy: z.string().min(24).max(24).optional(), lastChangedBy: z.string().min(24).max(24).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
};

/**
 * Clone a large language model
 * 
 * POST /v2.0/largelanguagemodels/{largeLanguageModelId}/clone
 * 
 * Also accepts auth: APIKeyQueryParam, CXoneTokenHeader, OAuth2
 */
export const createApiV20LargelanguagemodelsLargeLanguageModelIdClone = async (args: CreateApiV20LargelanguagemodelsLargeLanguageModelIdCloneArgs, options?: CreateApiV20LargelanguagemodelsLargeLanguageModelIdCloneOptions) => {
const {baseUrl = "https://api-trial.cognigy.ai/testing", fetch: fetchFn = fetch, APIKeyHeader} = options ?? {}

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

if (args.Accept != null) headers.set("Accept", String(args.Accept))
if (APIKeyHeader !== undefined) headers.set("X-API-Key", APIKeyHeader)

const url = `${baseUrl.replace(/\/+$/, '')}/v2.0/largelanguagemodels/${encodeURIComponent(String(args.largeLanguageModelId))}/clone${query.size > 0 ? `?${query}` : ''}`

const res = await fetchFn(url, {method: "POST", headers})

if (!res.ok) throw new Error(`Request failed with status ${res.status}`, {cause: {status: res.status, body: await res.text()}})

return validateResponse(createApiV20LargelanguagemodelsLargeLanguageModelIdCloneResponse, await res.json())
};