"""
Claude Tokenizer Implementation

This module provides tokenization functionality for Claude (Anthropic) models.
"""

from typing import List
import re

from . import Token


def claude_tokenizer(text: str) -> List[Token]:
    """
    Tokenize text using Claude (Anthropic) tokenization approximation.
    
    Note: This is an approximation since Anthropic's tokenizer is not publicly available.
    For production use, you would integrate with Anthropic's official tokenizer when available.
    
    Args:
        text: The text to tokenize
    
    Returns:
        List of Token objects with content, start and end positions
    """
    tokens = []
    
    # Claude's tokenizer handles Unicode characters differently and
    # has different subword tokenization patterns than GPT models.
    # This is a simplified approximation.
    
    # Basic tokenization heuristics
    position = 0
    
    # Simplified regex approximating Claude's tokenization approach
    # Note that this is only an approximation
    pattern = r'(\s+|[.,!?;:\'"\(\)\[\]\{\}<>]|\w+|\S+)'
    
    for match in re.finditer(pattern, text):
        content = match.group(0)
        start_position = match.start()
        end_position = match.end()
        
        # Claude seems to tokenize numbers differently
        if re.match(r'^\d+$', content):
            # Tokenize each digit separately for numbers
            for i, digit in enumerate(content):
                tokens.append(Token(
                    content=digit,
                    start_position=start_position + i,
                    end_position=start_position + i + 1
                ))
        # Handle longer words differently than GPT
        elif len(content) > 5 and re.match(r'\w+', content):
            # Claude's subword tokenization tends to split on morpheme boundaries
            # This is a simplified approach
            current_pos = 0
            remaining = content
            
            # Try to split on common morpheme boundaries
            while remaining:
                # Take 3-5 characters at a time, simulating subword splits
                chunk_size = min(len(remaining), 3 + (len(remaining) % 3))
                chunk = remaining[:chunk_size]
                
                tokens.append(Token(
                    content=chunk,
                    start_position=start_position + current_pos,
                    end_position=start_position + current_pos + len(chunk)
                ))
                
                current_pos += len(chunk)
                remaining = remaining[chunk_size:]
        else:
            # Keep shorter words and symbols intact
            tokens.append(Token(
                content=content,
                start_position=start_position,
                end_position=end_position
            ))
    
    return tokens