"""
  Field type definitions for the search engine configuration system.
  
  This module defines all supported field types, analyzers, and their
  corresponding Elasticsearch mapping configurations.
  """
  
  from enum import Enum
  from typing import Dict, Any, Optional
  from dataclasses import dataclass
  
  
  class FieldType(Enum):
      """Supported field types in the search engine."""
      TEXT = "text"
      KEYWORD = "keyword"
      TEXT_EMBEDDING = "text_embedding"
      IMAGE_EMBEDDING = "image_embedding"
      INT = "int"
      LONG = "long"
      FLOAT = "float"
      DOUBLE = "double"
      DATE = "date"
      BOOLEAN = "boolean"
      JSON = "json"
  
  
  class AnalyzerType(Enum):
      """Supported analyzer types for text fields."""
      # E-commerce general analysis - Chinese
      CHINESE_ECOMMERCE = "index_ansj"
      CHINESE_ECOMMERCE_QUERY = "query_ansj"
  
      # Standard language analyzers
      ENGLISH = "english"
      ARABIC = "arabic"
      SPANISH = "spanish"
      RUSSIAN = "russian"
      JAPANESE = "japanese"
  
      # Standard analyzers
      STANDARD = "standard"
      KEYWORD = "keyword"
  
  
  class SimilarityType(Enum):
      """Supported similarity algorithms for text fields."""
      BM25 = "BM25"
      BM25_CUSTOM = "BM25_custom"  # Modified BM25 with b=0.0, k1=0.0
  
  
  @dataclass
  class FieldConfig:
      """Configuration for a single field."""
      name: str
      field_type: FieldType

      analyzer: Optional[AnalyzerType] = None
      search_analyzer: Optional[AnalyzerType] = None
      required: bool = False
      multi_language: bool = False  # If true, field has language variants
      languages: Optional[list] = None  # ['zh', 'en', 'ru']
      boost: float = 1.0
      store: bool = False
      index: bool = True

      return_in_source: bool = True  # Whether to include this field in search result _source

  
      # For embedding fields
      embedding_dims: int = 1024
      embedding_similarity: str = "dot_product"  # dot_product, cosine, l2_norm
  
      # For nested fields (like image embeddings)
      nested: bool = False
      nested_properties: Optional[Dict[str, Any]] = None
  

      # Hybrid Keyword Text (HKText) support
      keyword_subfield: bool = False
      keyword_ignore_above: int = 256
  

  
  def get_es_mapping_for_field(field_config: FieldConfig) -> Dict[str, Any]:
      """
      Generate Elasticsearch mapping configuration for a field.
  
      Args:
          field_config: Field configuration object
  
      Returns:
          Dictionary containing ES mapping for the field
      """
      mapping = {}
  
      if field_config.field_type == FieldType.TEXT:
          mapping = {
              "type": "text",
              "store": field_config.store,
              "index": field_config.index
          }
  
          if field_config.analyzer:
              if field_config.analyzer == AnalyzerType.CHINESE_ECOMMERCE:
                  mapping["analyzer"] = "index_ansj"
                  mapping["search_analyzer"] = "query_ansj"
              else:
                  mapping["analyzer"] = field_config.analyzer.value
  
          if field_config.search_analyzer:
              mapping["search_analyzer"] = field_config.search_analyzer.value
  

          if field_config.keyword_subfield:
              mapping.setdefault("fields", {})
              mapping["fields"]["keyword"] = {
                  "type": "keyword",
                  "ignore_above": field_config.keyword_ignore_above
              }
  

      elif field_config.field_type == FieldType.KEYWORD:
          mapping = {
              "type": "keyword",
              "store": field_config.store,
              "index": field_config.index
          }
  
      elif field_config.field_type == FieldType.TEXT_EMBEDDING:
          mapping = {
              "type": "dense_vector",
              "dims": field_config.embedding_dims,
              "index": True,
              "similarity": field_config.embedding_similarity
          }
  
      elif field_config.field_type == FieldType.IMAGE_EMBEDDING:
          if field_config.nested:
              mapping = {
                  "type": "nested",
                  "properties": {
                      "vector": {
                          "type": "dense_vector",
                          "dims": field_config.embedding_dims,
                          "index": True,
                          "similarity": field_config.embedding_similarity
                      },
                      "url": {
                          "type": "keyword"
                      }
                  }
              }
          else:
              # Simple vector field
              mapping = {
                  "type": "dense_vector",
                  "dims": field_config.embedding_dims,
                  "index": True,
                  "similarity": field_config.embedding_similarity
              }
  
      elif field_config.field_type in [FieldType.INT, FieldType.LONG]:
          mapping = {
              "type": "long",
              "store": field_config.store,
              "index": field_config.index
          }
  
      elif field_config.field_type in [FieldType.FLOAT, FieldType.DOUBLE]:
          mapping = {
              "type": "float",
              "store": field_config.store,
              "index": field_config.index
          }
  
      elif field_config.field_type == FieldType.DATE:
          mapping = {
              "type": "date",
              "store": field_config.store,
              "index": field_config.index
          }
  
      elif field_config.field_type == FieldType.BOOLEAN:
          mapping = {
              "type": "boolean",
              "store": field_config.store,
              "index": field_config.index
          }
  
      elif field_config.field_type == FieldType.JSON:

          if field_config.nested and field_config.nested_properties:
              # Nested type with properties (e.g., variants)
              mapping = {
                  "type": "nested",
                  "properties": {}
              }
              # Generate mappings for nested properties
              for prop_name, prop_config in field_config.nested_properties.items():
                  prop_type = prop_config.get("type", "keyword")
                  prop_mapping = {"type": prop_type}
                  
                  # Add analyzer for text fields
                  if prop_type == "text" and "analyzer" in prop_config:

                      analyzer_str = prop_config["analyzer"]
                      # Convert chinese_ecommerce to index_ansj/query_ansj
                      if analyzer_str == "chinese_ecommerce":
                          prop_mapping["analyzer"] = "index_ansj"
                          prop_mapping["search_analyzer"] = "query_ansj"
                      else:
                          prop_mapping["analyzer"] = analyzer_str

                  
                  # Add other properties
                  if "index" in prop_config:
                      prop_mapping["index"] = prop_config["index"]
                  if "store" in prop_config:
                      prop_mapping["store"] = prop_config["store"]
                  
                  mapping["properties"][prop_name] = prop_mapping
          else:
              # Simple object type
              mapping = {
                  "type": "object",
                  "enabled": True
              }

  
      return mapping
  
  
  def get_default_analyzers() -> Dict[str, Any]:
      """
      Get default analyzer definitions for the index.
  
      Returns:
          Dictionary of analyzer configurations
      """
      return {
          "analysis": {
              "analyzer": {
                  "index_ansj": {
                      "type": "custom",
                      "tokenizer": "standard",
                      "filter": ["lowercase", "asciifolding"]
                  },
                  "query_ansj": {
                      "type": "custom",
                      "tokenizer": "standard",
                      "filter": ["lowercase", "asciifolding"]
                  }
              }
          }
      }
  
  
  def get_default_similarity() -> Dict[str, Any]:
      """
      Get default similarity configuration (modified BM25).
  
      Returns:
          Dictionary of similarity configurations
      """
      return {
          "similarity": {
              "default": {
                  "type": "BM25",
                  "b": 0.0,
                  "k1": 0.0
              }
          }
      }
  
  
  # Mapping of field type strings to FieldType enum
  FIELD_TYPE_MAP = {
      "text": FieldType.TEXT,
      "TEXT": FieldType.TEXT,

      "HKText": FieldType.TEXT,
      "hktext": FieldType.TEXT,
      "HKTEXT": FieldType.TEXT,

      "keyword": FieldType.KEYWORD,
      "KEYWORD": FieldType.KEYWORD,
      "LITERAL": FieldType.KEYWORD,
      "text_embedding": FieldType.TEXT_EMBEDDING,
      "TEXT_EMBEDDING": FieldType.TEXT_EMBEDDING,
      "EMBEDDING": FieldType.TEXT_EMBEDDING,
      "image_embedding": FieldType.IMAGE_EMBEDDING,
      "IMAGE_EMBEDDING": FieldType.IMAGE_EMBEDDING,
      "int": FieldType.INT,
      "INT": FieldType.INT,
      "long": FieldType.LONG,
      "LONG": FieldType.LONG,
      "float": FieldType.FLOAT,
      "FLOAT": FieldType.FLOAT,
      "double": FieldType.DOUBLE,
      "DOUBLE": FieldType.DOUBLE,
      "date": FieldType.DATE,
      "DATE": FieldType.DATE,
      "boolean": FieldType.BOOLEAN,
      "BOOLEAN": FieldType.BOOLEAN,
      "json": FieldType.JSON,
      "JSON": FieldType.JSON,
  }
  
  
  # Mapping of analyzer strings to AnalyzerType enum
  ANALYZER_MAP = {
      "chinese": AnalyzerType.CHINESE_ECOMMERCE,
      "chinese_ecommerce": AnalyzerType.CHINESE_ECOMMERCE,
      "index_ansj": AnalyzerType.CHINESE_ECOMMERCE,
      "english": AnalyzerType.ENGLISH,
      "arabic": AnalyzerType.ARABIC,
      "spanish": AnalyzerType.SPANISH,
      "russian": AnalyzerType.RUSSIAN,
      "japanese": AnalyzerType.JAPANESE,
      "standard": AnalyzerType.STANDARD,
      "keyword": AnalyzerType.KEYWORD,
  }