"""
Gemini Tokenizer Implementation

This module provides tokenization functionality for Google's Gemini models.
"""

from typing import List
import re

from . import Token


def gemini_tokenizer(text: str) -> List[Token]:
    """
    Tokenize text using Gemini tokenization approximation.
    
    Note: This is an approximation since Google's tokenizer is not publicly available.
    For production use, you would integrate with Google's official tokenizer when available.
    
    Args:
        text: The text to tokenize
    
    Returns:
        List of Token objects with content, start and end positions
    """
    tokens = []
    
    # Gemini seems to use a SentencePiece-based 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()
        
        # Specialize tokenization based on word structure
        if re.match(r'^\w+$', content) and len(content) > 4:
            # Apply SentencePiece-like segmentation
            segments = []
            current = ""
            
            for char in content:
                current += char
                # Potential break points - this is a heuristic
                if len(current) >= 3 and (len(current) % 3 == 0 or current.endswith('ing') or 
                                         current.endswith('ed') or current.endswith('ly')):
                    segments.append(current)
                    current = ""
            
            if current:  # Add any remaining characters
                segments.append(current)
            
            # Create tokens for each segment
            pos_offset = 0
            for segment in segments:
                tokens.append(Token(
                    content=segment,
                    start_position=start_position + pos_offset,
                    end_position=start_position + pos_offset + len(segment)
                ))
                pos_offset += len(segment)
        else:
            # For simple tokens, keep them intact
            tokens.append(Token(
                content=content,
                start_position=start_position,
                end_position=end_position
            ))
    
    return tokens