import {z} from 'zod'

export type GetApiApiUnstableLlmObsConfigEvaluatorsCustomOptions = {baseUrl?: string | undefined, apiKeyAuth?: string | undefined, appKeyAuth?: string | undefined, fetch?: typeof fetch | undefined};

export const lLMObsCustomEvalConfigUser = z.object({email: z.string().optional()});

export const lLMObsCustomEvalConfigAssessmentCriteria = z.object({max_threshold: z.number().nullable().optional(), min_threshold: z.number().nullable().optional(), pass_values: z.array(z.string()).nullable().optional(), pass_when: z.boolean().nullable().optional()});

export const lLMObsCustomEvalConfigInferenceParams = z.object({frequency_penalty: z.number().optional(), max_tokens: z.number().int().optional(), presence_penalty: z.number().optional(), temperature: z.number().optional(), top_k: z.number().int().optional(), top_p: z.number().optional()});

export const lLMObsCustomEvalConfigParsingType = z.enum(["structured_output", "json", "keyword_search", "typed_questions"]);

export const lLMObsCustomEvalConfigPromptToolCall = z.object({arguments: z.string().optional(), id: z.string().optional(), name: z.string().optional(), type: z.string().optional()});

export const lLMObsCustomEvalConfigPromptToolResult = z.object({name: z.string().optional(), result: z.string().optional(), tool_id: z.string().optional(), type: z.string().optional()});

export const lLMObsCustomEvalConfigPromptContentValue = z.object({text: z.string().optional(), tool_call: lLMObsCustomEvalConfigPromptToolCall.optional(), tool_call_result: lLMObsCustomEvalConfigPromptToolResult.optional()});

export const lLMObsCustomEvalConfigPromptContent = z.object({type: z.string(), value: lLMObsCustomEvalConfigPromptContentValue});

export const lLMObsCustomEvalConfigPromptMessage = z.object({content: z.string().optional(), contents: z.array(lLMObsCustomEvalConfigPromptContent).optional(), role: z.string()});

export const lLMObsCustomEvalConfigLLMJudgeConfig = z.object({assessment_criteria: lLMObsCustomEvalConfigAssessmentCriteria.optional(), context_query: z.string().nullable().optional(), inference_params: lLMObsCustomEvalConfigInferenceParams, last_used_library_prompt_template_name: z.string().nullable().optional(), modified_library_prompt_template: z.boolean().nullable().optional(), output_schema: z.record(z.string(), z.unknown()).nullable().optional(), parsing_type: lLMObsCustomEvalConfigParsingType.optional(), prompt_template: z.array(lLMObsCustomEvalConfigPromptMessage).optional(), target_query: z.string().nullable().optional(), user_specified_json_post_processing_function: z.string().nullable().optional()});

export const lLMObsCustomEvalConfigBedrockOptions = z.object({inference_profile: z.string().optional(), region: z.string().optional()});

export const lLMObsCustomEvalConfigIntegrationProvider = z.enum(["openai", "amazon_bedrock", "anthropic", "azure_openai", "vertex_ai", "llmproxy", "deterministic", "typesafe", "datadog", "datadog_ai_gateway", "insights_sponsored"]);

export const lLMObsCustomEvalConfigVertexAIOptions = z.object({location: z.string().optional(), project: z.string().optional()});

export const lLMObsCustomEvalConfigLLMProvider = z.object({bedrock: lLMObsCustomEvalConfigBedrockOptions.optional(), integration_account_id: z.string().optional(), integration_provider: lLMObsCustomEvalConfigIntegrationProvider.optional(), model_name: z.string().optional(), vertex_ai: lLMObsCustomEvalConfigVertexAIOptions.optional()});

export const lLMObsCustomEvalConfigEvalScope = z.enum(["span", "trace", "trace_fan_out", "session"]);

export const lLMObsCustomEvalConfigTarget = z.object({application_name: z.string(), enabled: z.boolean(), eval_scope: lLMObsCustomEvalConfigEvalScope.optional(), experiment_project_ids: z.array(z.string()).optional(), filter: z.string().nullable().optional(), root_spans_only: z.boolean().nullable().optional(), sampling_percentage: z.number().nullable().optional()});

export const lLMObsCustomEvalConfigAttributes = z.object({category: z.string().optional(), created_at: z.string(), created_by: lLMObsCustomEvalConfigUser.optional(), eval_name: z.string(), last_updated_by: lLMObsCustomEvalConfigUser.optional(), llm_judge_config: lLMObsCustomEvalConfigLLMJudgeConfig.optional(), llm_provider: lLMObsCustomEvalConfigLLMProvider.optional(), target: lLMObsCustomEvalConfigTarget.optional(), updated_at: z.string()});

export const lLMObsCustomEvalConfigType = z.literal("evaluator_config");

export const lLMObsCustomEvalConfigData = z.object({attributes: lLMObsCustomEvalConfigAttributes, id: z.string(), type: lLMObsCustomEvalConfigType});

export const lLMObsCustomEvalConfigListResponse = z.object({data: z.array(lLMObsCustomEvalConfigData)});

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 custom evaluator configurations
 * 
 * GET /api/unstable/llm-obs/config/evaluators/custom
 * 
 * Also accepts auth: AuthZ
 */
export const getApiApiUnstableLlmObsConfigEvaluatorsCustom = async (options?: GetApiApiUnstableLlmObsConfigEvaluatorsCustomOptions) => {
const {baseUrl = "https://api.datadoghq.com", fetch: fetchFn = fetch, apiKeyAuth, appKeyAuth} = options ?? {}

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

if (apiKeyAuth !== undefined) headers.set("DD-API-KEY", apiKeyAuth)
if (appKeyAuth !== undefined) headers.set("DD-APPLICATION-KEY", appKeyAuth)

const url = `${baseUrl.replace(/\/+$/, '')}/api/unstable/llm-obs/config/evaluators/custom${query.size > 0 ? `?${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(lLMObsCustomEvalConfigListResponse, await res.json())
};