import logging
from typing import List, Optional, Union
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForMaskedLM

from .base import SpellCheckerBase

logger = logging.getLogger(__name__)

class ContextualLMSpellChecker(SpellCheckerBase):
    """
    Spell checker implementation using a contextual language model (e.g., BERT, BETO).
    (Currently a skeleton implementation).
    """

    DEFAULT_MODEL = "dccuchile/bert-base-spanish-wwm-uncased"

    def __init__(self,
                 model_name: str = DEFAULT_MODEL,
                 device: Optional[Union[str, int]] = None,
                 **kwargs):
        """
        Initializes the contextual language model spell checker (skeleton).

        Args:
            model_name (str): Name or path of the pre-trained masked language model.
            device (Optional[Union[str, int]]): Device ('cpu', 'cuda', cuda index). Auto-detects if None.
            **kwargs: Additional arguments passed to the base class.
        """
        super().__init__(**kwargs)
        self.model_name = model_name
        self.device = device
        # Placeholder for actual model loading
        self.pipeline = None
        self.tokenizer = None
        logger.info(f"ContextualLMSpellChecker skeleton initialized (model: {model_name}). Implementation pending.")
        # In a real implementation, you would load the model/tokenizer/pipeline here
        # try:
        #     # ... model loading logic ...
        #     logger.info("ContextualLMSpellChecker initialized successfully.")
        # except Exception as e:
        #     logger.error(f"Failed to initialize components for model '{model_name}': {e}", exc_info=True)
        #     # Handle initialization failure

    def is_correct(self, word: str) -> bool:
        """
        Checks if a word is considered correctly spelled (skeleton).
        """
        logger.warning("ContextualLMSpellChecker.is_correct() not implemented.")
        # A basic implementation might check against tokenizer vocab, but it's weak.
        # return word in self.tokenizer.vocab if self.tokenizer else True
        return True # Placeholder

    def suggest(self, word: str) -> List[str]:
        """
        Suggests corrections for a potentially misspelled word (skeleton).
        """
        logger.warning("ContextualLMSpellChecker.suggest() not implemented.")
        return [] # Placeholder

    def correct_word(self, word: str) -> str:
        """
        Returns the most likely correction for a single word (skeleton).
        """
        logger.warning("ContextualLMSpellChecker.correct_word() not implemented.")
        return word # Placeholder

    def correct_text(self, text: str) -> str:
        """
        Attempts to automatically correct an entire text (skeleton).
        """
        # Override base method to indicate it's not fully functional yet
        logger.warning("ContextualLMSpellChecker.correct_text() not implemented, returning original text.")
        return text # Placeholder - does not attempt correction

    def get_implementation_details(self) -> str:
         details = f"Using implementation: {self.__class__.__name__} (Skeleton)\n"
         details += f"  LM Model: {self.model_name} (Not fully loaded/used)"
         return details
