"""
  Translation service for multi-language query support.
  
  Supports DeepL API for high-quality translations.
  """
  
  import requests
  from typing import Dict, List, Optional
  from utils.cache import DictCache
  

  # Try to import DEEPL_AUTH_KEY, but allow import to fail
  try:
      from config.env_config import DEEPL_AUTH_KEY
  except ImportError:
      DEEPL_AUTH_KEY = None
  

  
  class Translator:
      """Multi-language translator using DeepL API."""
  

      DEEPL_API_URL = "https://api.deepl.com/v2/translate"  # Pro tier

  
      # Language code mapping
      LANG_CODE_MAP = {
          'zh': 'ZH',
          'en': 'EN',
          'ru': 'RU',
          'ar': 'AR',
          'ja': 'JA',
          'es': 'ES',
          'de': 'DE',
          'fr': 'FR',
          'it': 'IT',
          'pt': 'PT',
      }
  
      def __init__(
          self,
          api_key: Optional[str] = None,
          use_cache: bool = True,

          timeout: int = 10,
          glossary_id: Optional[str] = None,
          translation_context: Optional[str] = None

      ):
          """
          Initialize translator.
  
          Args:

              api_key: DeepL API key (or None to use from config/env)

              use_cache: Whether to cache translations
              timeout: Request timeout in seconds

              glossary_id: DeepL glossary ID for custom terminology (optional)
              translation_context: Context hint for translation (e.g., "e-commerce", "product search")

          """

          # Get API key from config if not provided

          if api_key is None and DEEPL_AUTH_KEY:
              api_key = DEEPL_AUTH_KEY

          self.api_key = api_key
          self.timeout = timeout
          self.use_cache = use_cache

          self.glossary_id = glossary_id
          self.translation_context = translation_context or "e-commerce product search"

  
          if use_cache:
              self.cache = DictCache(".cache/translations.json")
          else:
              self.cache = None
  
      def translate(
          self,
          text: str,
          target_lang: str,

          source_lang: Optional[str] = None,
          context: Optional[str] = None

      ) -> Optional[str]:
          """
          Translate text to target language.
  
          Args:
              text: Text to translate
              target_lang: Target language code ('zh', 'en', 'ru', etc.)
              source_lang: Source language code (optional, auto-detect if None)

              context: Additional context for translation (overrides default context)

  
          Returns:
              Translated text or None if translation fails
          """
          if not text or not text.strip():
              return text
  
          # Normalize language codes
          target_lang = target_lang.lower()
          if source_lang:
              source_lang = source_lang.lower()
  

          # Use provided context or default context
          translation_context = context or self.translation_context
  
          # Check cache (include context in cache key for accuracy)

          if self.use_cache:

              cache_key = f"{source_lang or 'auto'}:{target_lang}:{translation_context}:{text}"

              cached = self.cache.get(cache_key, category="translations")
              if cached:
                  return cached
  
          # If no API key, return mock translation (for testing)
          if not self.api_key:
              print(f"[Translator] No API key, returning original text (mock mode)")
              return text
  

          # Translate using DeepL with fallback

          result = self._translate_deepl(text, target_lang, source_lang, translation_context)

          # If translation failed, try fallback to free API
          if result is None and "api.deepl.com" in self.DEEPL_API_URL:
              print(f"[Translator] Pro API failed, trying free API...")

              result = self._translate_deepl_free(text, target_lang, source_lang, translation_context)

  
          # If still failed, return original text with warning
          if result is None:
              print(f"[Translator] Translation failed, returning original text")
              result = text
  

          # Cache result
          if result and self.use_cache:

              cache_key = f"{source_lang or 'auto'}:{target_lang}:{translation_context}:{text}"

              self.cache.set(cache_key, result, category="translations")
  
          return result
  
      def _translate_deepl(
          self,
          text: str,
          target_lang: str,

          source_lang: Optional[str],
          context: Optional[str] = None

      ) -> Optional[str]:

          """
          Translate using DeepL API with context and glossary support.
          
          Args:
              text: Text to translate
              target_lang: Target language code
              source_lang: Source language code (optional)
              context: Context hint for translation (e.g., "e-commerce product search")
          """

          # Map to DeepL language codes
          target_code = self.LANG_CODE_MAP.get(target_lang, target_lang.upper())
  
          headers = {
              "Authorization": f"DeepL-Auth-Key {self.api_key}",
              "Content-Type": "application/json",
          }
  

          # Build text with context for better disambiguation
          # For e-commerce, add context words to help DeepL understand the domain
          # This is especially important for single-word ambiguous terms like "车" (car vs rook)
          text_to_translate, needs_extraction = self._add_ecommerce_context(text, source_lang, context)
  

          payload = {

              "text": [text_to_translate],

              "target_lang": target_code,
          }
  
          if source_lang:
              source_code = self.LANG_CODE_MAP.get(source_lang, source_lang.upper())
              payload["source_lang"] = source_code
  

          # Add glossary if configured
          if self.glossary_id:
              payload["glossary_id"] = self.glossary_id
  
          # Note: DeepL API v2 doesn't have a direct "context" parameter,
          # but we can improve translation by:
          # 1. Using glossary for domain-specific terms (best solution)
          # 2. Adding context words to the text (for single-word queries) - implemented in _add_ecommerce_context
          # 3. Using more specific source language detection
  

          try:
              response = requests.post(
                  self.DEEPL_API_URL,
                  headers=headers,
                  json=payload,
                  timeout=self.timeout
              )
  
              if response.status_code == 200:
                  data = response.json()
                  if "translations" in data and len(data["translations"]) > 0:

                      translated_text = data["translations"][0]["text"]
                      # If we added context, extract just the term from the result
                      if needs_extraction:
                          translated_text = self._extract_term_from_translation(
                              translated_text, text, target_code
                          )
                      return translated_text

              else:
                  print(f"[Translator] DeepL API error: {response.status_code} - {response.text}")
                  return None
  
          except requests.Timeout:
              print(f"[Translator] Translation request timed out")
              return None
          except Exception as e:
              print(f"[Translator] Translation failed: {e}")
              return None
  

      def _translate_deepl_free(
          self,
          text: str,
          target_lang: str,

          source_lang: Optional[str],
          context: Optional[str] = None

      ) -> Optional[str]:

          """
          Translate using DeepL Free API.
          
          Note: Free API may not support glossary_id parameter.
          """

          # Map to DeepL language codes
          target_code = self.LANG_CODE_MAP.get(target_lang, target_lang.upper())
  
          headers = {
              "Authorization": f"DeepL-Auth-Key {self.api_key}",
              "Content-Type": "application/json",
          }
  
          payload = {
              "text": [text],
              "target_lang": target_code,
          }
  
          if source_lang:
              source_code = self.LANG_CODE_MAP.get(source_lang, source_lang.upper())
              payload["source_lang"] = source_code
  

          # Note: Free API typically doesn't support glossary_id
          # But we can still use context hints in the text
  

          try:
              response = requests.post(
                  "https://api-free.deepl.com/v2/translate",
                  headers=headers,
                  json=payload,
                  timeout=self.timeout
              )
  
              if response.status_code == 200:
                  data = response.json()
                  if "translations" in data and len(data["translations"]) > 0:
                      return data["translations"][0]["text"]
              else:
                  print(f"[Translator] DeepL Free API error: {response.status_code} - {response.text}")
                  return None
  
          except requests.Timeout:
              print(f"[Translator] Free API request timed out")
              return None
          except Exception as e:
              print(f"[Translator] Free API translation failed: {e}")
              return None
  

      def translate_multi(
          self,
          text: str,
          target_langs: List[str],

          source_lang: Optional[str] = None,
          context: Optional[str] = None

      ) -> Dict[str, Optional[str]]:
          """
          Translate text to multiple target languages.
  
          Args:
              text: Text to translate
              target_langs: List of target language codes
              source_lang: Source language code (optional)

              context: Context hint for translation (optional)

  
          Returns:
              Dictionary mapping language code to translated text
          """
          results = {}
          for lang in target_langs:

              results[lang] = self.translate(text, lang, source_lang, context)

          return results
  

      def _add_ecommerce_context(
          self,
          text: str,
          source_lang: Optional[str],
          context: Optional[str]
      ) -> tuple:
          """
          Add e-commerce context to text for better disambiguation.
          
          For single-word ambiguous Chinese terms, we add context words that help
          DeepL understand this is an e-commerce/product search context.
          
          Args:
              text: Original text to translate
              source_lang: Source language code
              context: Context hint
              
          Returns:
              Tuple of (text_with_context, needs_extraction)
              - text_with_context: Text to send to DeepL
              - needs_extraction: Whether we need to extract the term from the result
          """
          # Only apply for e-commerce context and Chinese source
          if not context or "e-commerce" not in context.lower():
              return text, False
              
          if not source_lang or source_lang.lower() != 'zh':
              return text, False
              
          # For single-word queries, add context to help disambiguation
          text_stripped = text.strip()
          if len(text_stripped.split()) == 1 and len(text_stripped) <= 2:
              # Common ambiguous Chinese e-commerce terms like "车" (car vs rook)
              # We add a context phrase: "购买 [term]" (buy [term]) or "商品 [term]" (product [term])
              # This helps DeepL understand the e-commerce context
              # We'll need to extract just the term from the translation result
              context_phrase = f"购买 {text_stripped}"
              return context_phrase, True
          
          # For multi-word queries, DeepL usually has enough context
          return text, False
  
      def _extract_term_from_translation(
          self,
          translated_text: str,
          original_text: str,
          target_lang_code: str
      ) -> str:
          """
          Extract the actual term from a translation that included context.
          
          For example, if we translated "购买 车" (buy car) and got "buy car",
          we want to extract just "car".
          
          Args:
              translated_text: Full translation result
              original_text: Original single-word query
              target_lang_code: Target language code (EN, ZH, etc.)
              
          Returns:
              Extracted term or original translation if extraction fails
          """
          # For English target, try to extract the last word (the actual term)
          if target_lang_code == "EN":
              words = translated_text.strip().split()
              if len(words) > 1:
                  # Usually the last word is the term we want
                  # But we need to be smart - if it's "buy car", we want "car"
                  # Common context words to skip: buy, purchase, product, item, etc.
                  context_words = {"buy", "purchase", "product", "item", "commodity", "goods"}
                  # Try to find the term (not a context word)
                  for word in reversed(words):
                      word_lower = word.lower().rstrip('.,!?;:')
                      if word_lower not in context_words:
                          return word_lower
                  # If all words are context words, return the last one
                  return words[-1].lower().rstrip('.,!?;:')
          
          # For other languages or if extraction fails, return as-is
          # The user can configure a glossary for better results
          return translated_text
  

      def get_translation_needs(
          self,
          detected_lang: str,
          supported_langs: List[str]
      ) -> List[str]:
          """
          Determine which languages need translation.
  
          Args:
              detected_lang: Detected query language
              supported_langs: List of supported languages
  
          Returns:
              List of language codes to translate to
          """
          # If detected language is in supported list, translate to others
          if detected_lang in supported_langs:
              return [lang for lang in supported_langs if lang != detected_lang]
  
          # Otherwise, translate to all supported languages
          return supported_langs