import {z} from 'zod'

export type InputSourceType = 'payload' | 'literal' | 'vector' | 'blob';

export type InputMapping = {input_key: string, source_type?: (InputSourceType | null) | undefined, path?: (string | null) | undefined, override: unknown};

export type EnrichmentMergeMode = 'replace' | 'append';

export type EnrichmentField = {field_path: string, target_field?: (string | null) | undefined, merge_mode?: EnrichmentMergeMode | undefined};

export type SourceCollectionInput = {collection_id: string, enrichment_fields?: (Array<EnrichmentField> | null) | undefined};

export type StepKeySource = 'assignment_label' | 'assignment_node_id' | 'field_path';

export type CovariateType = 'categorical' | 'numeric' | 'embedding' | 'cluster_id';

export type CovariateConfig = {field_path: string, covariate_type: CovariateType, name: string, binning_strategy?: ('quartiles' | 'deciles' | 'custom' | null) | undefined, clustering_method?: ('kmeans' | 'hdbscan' | null) | undefined, n_clusters?: (number | null) | undefined};

export type StepAnalyticsConfigInput = {timestamp_field: string, sequence_id_field: string, step_key_source?: StepKeySource | undefined, step_key_field_path?: (string | null) | undefined, covariates?: Array<CovariateConfig> | undefined, max_sequence_duration_days?: (number | null) | undefined};

export type FlatTaxonomyConfigInput = {taxonomy_type?: 'flat' | undefined, retriever_id: string, input_mappings: Array<InputMapping>, source_collection: SourceCollectionInput, top_k?: number | undefined, step_analytics?: (StepAnalyticsConfigInput | null) | undefined};

export type HierarchyInferenceStrategy = 'schema' | 'cluster' | 'llm';

export type HierarchicalNodeInput = {collection_id: string, parent_collection_id?: (string | null) | undefined, label?: (string | null) | undefined, summary?: (string | null) | undefined, keywords?: (Array<string> | null) | undefined, retriever_id?: (string | null) | undefined, enrichment_fields?: (Array<EnrichmentField> | null) | undefined, input_mappings?: (Array<InputMapping> | null) | undefined};

export type HierarchicalTaxonomyConfigInput = {taxonomy_type?: 'hierarchical' | undefined, retriever_id?: (string | null) | undefined, input_mappings?: (Array<InputMapping> | null) | undefined, inference_strategy?: (HierarchyInferenceStrategy | null) | undefined, inference_collections?: (Array<string> | null) | undefined, llm_provider?: ('openai_chat_v1' | null) | undefined, llm_model?: (string | null) | undefined, llm_prompt_template?: (string | null) | undefined, llm_sample_size?: number | undefined, cluster_ids?: (Array<string> | null) | undefined, cluster_overlap_threshold?: number | undefined, hierarchical_nodes?: (Array<HierarchicalNodeInput> | null) | undefined, step_analytics?: (StepAnalyticsConfigInput | null) | undefined, path_field?: (string | null) | undefined, path_separator?: string | undefined};

export type CreateTaxonomyRequest = {taxonomy_name: string, description?: (string | null) | undefined, config: FlatTaxonomyConfigInput | HierarchicalTaxonomyConfigInput, metadata?: ({[key: string]: unknown} | null) | undefined};

export type CreateApiV1TaxonomiesArgs = {body: CreateTaxonomyRequest};

export type CreateApiV1TaxonomiesOptions = {baseUrl?: string | undefined, BearerAuth?: string | undefined, NamespaceHeader?: string | undefined, fetch?: typeof fetch | undefined};

export const inputSourceType = z.enum(["payload", "literal", "vector", "blob"]);

export const inputMapping = z.object({input_key: z.string(), source_type: inputSourceType.nullable().optional(), path: z.string().nullable().optional(), override: z.unknown()});

export const enrichmentMergeMode = z.enum(["replace", "append"]);

export const enrichmentField = z.object({field_path: z.string(), target_field: z.string().nullable().optional(), merge_mode: enrichmentMergeMode.optional()});

export const sourceCollectionOutput = z.object({collection_id: z.string(), enrichment_fields: z.array(enrichmentField).nullable().optional()});

export const stepKeySource = z.enum(["assignment_label", "assignment_node_id", "field_path"]);

export const covariateType = z.enum(["categorical", "numeric", "embedding", "cluster_id"]);

export const covariateConfig = z.object({field_path: z.string(), covariate_type: covariateType, name: z.string().min(1).max(100), binning_strategy: z.enum(["quartiles", "deciles", "custom"]).nullable().optional(), clustering_method: z.enum(["kmeans", "hdbscan"]).nullable().optional(), n_clusters: z.number().int().gte(2).lte(100).nullable().optional()});

export const stepAnalyticsConfigOutput = z.object({timestamp_field: z.string(), sequence_id_field: z.string(), step_key_source: stepKeySource.optional(), step_key_field_path: z.string().nullable().optional(), covariates: z.array(covariateConfig).optional(), max_sequence_duration_days: z.number().int().gte(1).lte(365).nullable().optional()});

export const flatTaxonomyConfigOutput = z.object({taxonomy_type: z.literal("flat").optional(), retriever_id: z.string(), input_mappings: z.array(inputMapping), source_collection: sourceCollectionOutput, top_k: z.number().int().gte(1).lte(50).optional(), step_analytics: stepAnalyticsConfigOutput.nullable().optional()});

export const hierarchyInferenceStrategy = z.enum(["schema", "cluster", "llm"]);

export const hierarchicalNodeOutput = z.object({collection_id: z.string(), parent_collection_id: z.string().nullable().optional(), label: z.string().nullable().optional(), summary: z.string().nullable().optional(), keywords: z.array(z.string()).nullable().optional(), retriever_id: z.string().nullable().optional(), enrichment_fields: z.array(enrichmentField).nullable().optional(), input_mappings: z.array(inputMapping).nullable().optional()});

export const hierarchicalTaxonomyConfigOutput = z.object({taxonomy_type: z.literal("hierarchical").optional(), retriever_id: z.string().nullable().optional(), input_mappings: z.array(inputMapping).nullable().optional(), inference_strategy: hierarchyInferenceStrategy.nullable().optional(), inference_collections: z.array(z.string()).nullable().optional(), llm_provider: z.literal("openai_chat_v1").nullable().optional(), llm_model: z.string().nullable().optional(), llm_prompt_template: z.string().nullable().optional(), llm_sample_size: z.number().int().optional(), cluster_ids: z.array(z.string()).nullable().optional(), cluster_overlap_threshold: z.number().optional(), hierarchical_nodes: z.array(hierarchicalNodeOutput).nullable().optional(), step_analytics: stepAnalyticsConfigOutput.nullable().optional(), path_field: z.string().nullable().optional(), path_separator: z.string().min(1).optional()});

export const taxonomyPathNode = z.object({node_id: z.string(), label: z.string(), path: z.array(z.string()), parent_id: z.string().nullable().optional(), level: z.number().int().gte(0)});

export const taxonomyResponse = z.object({taxonomy_id: z.string().optional(), version: z.number().int().gte(1).optional(), taxonomy_name: z.string(), description: z.string().nullable().optional(), retriever_id: z.string().nullable().optional(), input_mappings: z.array(inputMapping).nullable().optional(), config: z.discriminatedUnion("taxonomy_type", [flatTaxonomyConfigOutput, hierarchicalTaxonomyConfigOutput]), nodes: z.array(taxonomyPathNode).nullable().optional(), ready: z.boolean().optional(), created_at: z.string().optional(), metadata: z.record(z.string(), z.unknown()).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
};

/**
 * Create Taxonomy
 * 
 * POST /v1/taxonomies
 */
export const createApiV1Taxonomies = async (args: CreateApiV1TaxonomiesArgs, options?: CreateApiV1TaxonomiesOptions) => {
const {baseUrl = "https://api.mixpeek.com", fetch: fetchFn = fetch, BearerAuth, NamespaceHeader} = options ?? {}

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

if (BearerAuth !== undefined) headers.set('authorization', `Bearer ${BearerAuth}`)
if (NamespaceHeader !== undefined) headers.set("X-Namespace", NamespaceHeader)

const url = `${baseUrl.replace(/\/+$/, '')}/v1/taxonomies${query.size > 0 ? `?${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(taxonomyResponse, await res.json())
};