src/core/byte_tokenizer_v6.py

"""
Byte-Level Tokenizer V6 - Pure Learning Based
No vocabulary, no language rules - just bytes
"""

import torch
from typing import List, Dict, Union, Optional
import numpy as np


class ByteTokenizerV6:
    """
    Pure byte-level tokenizer
    - No vocabulary needed (bytes are 0-255)
    - No language-specific rules
    - Model learns all patterns from data
    """
    
    def __init__(self, max_seq_len: int = 512):
        """Initialize byte tokenizer"""
        
        self.max_seq_len = max_seq_len
        
        # Special tokens (beyond byte range 0-255)
        self.PAD = 256
        self.BOS = 257  
        self.EOS = 258
        self.MASK = 259
        
        # Total vocabulary size = 256 bytes + 4 special tokens
        self.vocab_size = 260
        
        print(f"Byte tokenizer initialized (vocab_size={self.vocab_size})")
    
    def encode(self, text: str, add_special_tokens: bool = True) -> Dict:
        """
        Encode text to byte IDs
        
        Args:
            text: Input text
            add_special_tokens: Whether to add BOS/EOS
            
        Returns:
            dict with 'input_ids', 'attention_mask', 'length'
        """
        # Convert text to UTF-8 bytes (pure bytes, no rules)
        byte_sequence = list(text.encode('utf-8'))
        
        # Truncate if necessary
        max_len = self.max_seq_len - 2 if add_special_tokens else self.max_seq_len
        if len(byte_sequence) > max_len:
            byte_sequence = byte_sequence[:max_len]
        
        # Add special tokens
        if add_special_tokens:
            input_ids = [self.BOS] + byte_sequence + [self.EOS]
        else:
            input_ids = byte_sequence
        
        # Create attention mask (1 for real tokens, 0 for padding)
        attention_mask = [1] * len(input_ids)
        
        return {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'length': len(input_ids)
        }
    
    def encode_batch(self, texts: List[str], add_special_tokens: bool = True) -> Dict:
        """
        Encode multiple texts with padding
        
        Args:
            texts: List of input texts
            add_special_tokens: Whether to add special tokens
            
        Returns:
            Batched tensors with padding
        """
        encoded_texts = []
        max_length = 0
        
        # Encode each text
        for text in texts:
            encoded = self.encode(text, add_special_tokens)
            encoded_texts.append(encoded)
            max_length = max(max_length, encoded['length'])
        
        # Limit to max sequence length
        max_length = min(max_length, self.max_seq_len)
        
        # Initialize batch tensors
        batch_size = len(texts)
        input_ids = np.full((batch_size, max_length), self.PAD, dtype=np.int64)
        attention_mask = np.zeros((batch_size, max_length), dtype=np.float32)
        
        # Fill batch tensors
        for i, encoded in enumerate(encoded_texts):
            seq_len = min(encoded['length'], max_length)
            input_ids[i, :seq_len] = encoded['input_ids'][:seq_len]
            attention_mask[i, :seq_len] = 1.0
        
        return {
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.float32),
            'lengths': torch.tensor([e['length'] for e in encoded_texts], dtype=torch.long)
        }
    
    def decode(self, input_ids: Union[List[int], torch.Tensor, np.ndarray], 
               skip_special_tokens: bool = True) -> str:
        """
        Decode byte IDs back to text
        
        Args:
            input_ids: Byte ID sequence
            skip_special_tokens: Whether to skip special tokens
            
        Returns:
            Decoded text string
        """
        # Convert to list if needed
        if isinstance(input_ids, torch.Tensor):
            input_ids = input_ids.cpu().numpy().tolist()
        elif isinstance(input_ids, np.ndarray):
            input_ids = input_ids.tolist()
        
        # Filter special tokens if requested
        if skip_special_tokens:
            # Only keep actual bytes (0-255)
            input_ids = [b for b in input_ids if 0 <= b <= 255]
        else:
            # Replace special tokens with readable markers
            processed = []
            for b in input_ids:
                if b == self.PAD:
                    continue  # Skip padding
                elif b == self.BOS:
                    processed.append(ord('['))  # Use [ for BOS
                elif b == self.EOS:
                    processed.append(ord(']'))  # Use ] for EOS
                elif b == self.MASK:
                    processed.append(ord('*'))  # Use * for MASK
                elif 0 <= b <= 255:
                    processed.append(b)
            input_ids = processed
        
        # Convert bytes to text
        try:
            # Try UTF-8 decoding
            byte_array = bytes(input_ids)
            text = byte_array.decode('utf-8', errors='replace')
            return text
        except Exception as e:
            # Fallback: convert directly to chars
            return "".join([chr(b) if b < 128 else '?' for b in input_ids])
    
    def decode_batch(self, input_ids: torch.Tensor, skip_special_tokens: bool = True) -> List[str]:
        """
        Decode a batch of byte sequences
        
        Args:
            input_ids: Batch of byte IDs (batch_size, seq_len)
            skip_special_tokens: Whether to skip special tokens
            
        Returns:
            List of decoded texts
        """
        texts = []
        for i in range(input_ids.shape[0]):
            text = self.decode(input_ids[i], skip_special_tokens)
            texts.append(text)
        return texts
    
    def tokenize(self, text: str) -> List[int]:
        """
        Simple tokenization to byte IDs (no special tokens)
        
        Args:
            text: Input text
            
        Returns:
            List of byte IDs
        """
        return list(text.encode('utf-8'))
    
    def detokenize(self, byte_ids: List[int]) -> str:
        """
        Simple detokenization from byte IDs
        
        Args:
            byte_ids: List of byte IDs
            
        Returns:
            Decoded text
        """
        try:
            return bytes(byte_ids).decode('utf-8', errors='replace')
        except:
            return "".join([chr(b) if b < 128 else '?' for b in byte_ids])
    
    def get_vocab_size(self) -> int:
        """Get vocabulary size"""
        return self.vocab_size
    
    def get_special_tokens(self) -> Dict[str, int]:
        """Get special token IDs"""
        return {
            'pad_id': self.PAD,
            'bos_id': self.BOS,
            'eos_id': self.EOS,
            'mask_id': self.MASK
        }


# Test code
if __name__ == "__main__":
    # Initialize tokenizer
    tokenizer = ByteTokenizerV6()
    
    # Test texts in multiple languages
    test_texts = [
        "Hello World!",
        "안녕하세요",
        "你好世界",
        "こんにちは",
        "مرحبا بالعالم",
        "Здравствуй мир"
    ]
    
    print("=" * 50)
    print("Single Text Encoding/Decoding Test")
    print("=" * 50)
    
    for text in test_texts:
        print(f"\nOriginal: {text}")
        
        # Encode
        encoded = tokenizer.encode(text)
        print(f"Encoded length: {encoded['length']}")
        print(f"First 10 bytes: {encoded['input_ids'][:10]}")
        
        # Decode
        decoded = tokenizer.decode(encoded['input_ids'])
        print(f"Decoded: {decoded}")
        print(f"Match: {decoded == text}")
    
    print("\n" + "=" * 50)
    print("Batch Encoding/Decoding Test")
    print("=" * 50)
    
    # Batch test
    batch_result = tokenizer.encode_batch(test_texts)
    print(f"Batch shape: {batch_result['input_ids'].shape}")
    print(f"Attention mask shape: {batch_result['attention_mask'].shape}")
    
    # Decode batch
    decoded_texts = tokenizer.decode_batch(batch_result['input_ids'])
    print("\nBatch decoding results:")
    for orig, dec in zip(test_texts, decoded_texts):
        print(f"Original: {orig}")
        print(f"Decoded:  {dec}")
        print(f"Match: {orig == dec}")
        print()