"""
Mistral Tokenizer Implementation

This module provides tokenization functionality for Mistral AI models.
"""

from typing import List
import re

from . import Token


def mistral_tokenizer(text: str) -> List[Token]:
    """
    Tokenize text using Mistral tokenization approximation.
    
    Note: This is an approximation since Mistral's exact tokenizer implementation may not be publicly available.
    For production use, you would integrate with Mistral's official tokenizer when available.
    
    Args:
        text: The text to tokenize
    
    Returns:
        List of Token objects with content, start and end positions
    """
    tokens = []
    
    # Mistral uses a custom BPE tokenizer
    # This is a simplified approximation
    
    # Basic tokenization heuristics
    position = 0
    
    # Simplified regex pattern for tokenization
    pattern = r'(\s+|[.,!?;:\'"\(\)\[\]\{\}<>]|\w+|\S+)'
    
    for match in re.finditer(pattern, text):
        content = match.group(0)
        start_position = match.start()
        end_position = match.end()
        
        # Handle whitespace
        if re.match(r'^\s+$', content):
            tokens.append(Token(
                content=content,
                start_position=start_position,
                end_position=end_position
            ))
            continue
            
        # Handle special characters
        if re.match(r'^[.,!?;:\'"\(\)\[\]\{\}<>]$', content):
            tokens.append(Token(
                content=content,
                start_position=start_position,
                end_position=end_position
            ))
            continue
            
        # For words, apply BPE-like tokenization
        if re.match(r'^\w+$', content):
            # BPE tends to split at subword boundaries
            if len(content) <= 5:
                # Short words often stay as single tokens
                tokens.append(Token(
                    content=content,
                    start_position=start_position,
                    end_position=end_position
                ))
            else:
                # Apply some heuristic rules similar to BPE
                current_pos = 0
                remaining = content
                
                # Common prefixes in Mistral's vocabulary
                prefixes = ["un", "re", "in", "de", "con", "dis", "pre", "im", "ex", "sub"]
                # Common suffixes in Mistral's vocabulary
                suffixes = ["ing", "ed", "ly", "tion", "ment", "ness", "able", "ible", "ful", "less"]
                
                # Check for common prefixes
                prefix_match = None
                for prefix in prefixes:
                    if remaining.lower().startswith(prefix) and len(remaining) > len(prefix):
                        prefix_match = prefix
                        break
                        
                if prefix_match:
                    tokens.append(Token(
                        content=remaining[:len(prefix_match)],
                        start_position=start_position + current_pos,
                        end_position=start_position + current_pos + len(prefix_match)
                    ))
                    current_pos += len(prefix_match)
                    remaining = remaining[len(prefix_match):]
                
                # Check for common suffixes
                suffix_match = None
                for suffix in suffixes:
                    if remaining.lower().endswith(suffix) and len(remaining) > len(suffix):
                        suffix_match = suffix
                        break
                        
                if suffix_match:
                    # Add stem
                    stem = remaining[:-len(suffix_match)]
                    tokens.append(Token(
                        content=stem,
                        start_position=start_position + current_pos,
                        end_position=start_position + current_pos + len(stem)
                    ))
                    
                    # Add suffix
                    tokens.append(Token(
                        content=remaining[-len(suffix_match):],
                        start_position=start_position + current_pos + len(stem),
                        end_position=end_position
                    ))
                else:
                    # If no suffix match, break at syllable-like boundaries
                    while remaining:
                        # Approximate syllable segmentation
                        chunk_size = min(4, len(remaining))
                        tokens.append(Token(
                            content=remaining[:chunk_size],
                            start_position=start_position + current_pos,
                            end_position=start_position + current_pos + chunk_size
                        ))
                        current_pos += chunk_size
                        remaining = remaining[chunk_size:]
        else:
            # For other tokens, keep them intact
            tokens.append(Token(
                content=content,
                start_position=start_position,
                end_position=end_position
            ))
    
    return tokens