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#!/usr/bin/env python3
"""
Optimized dataset class for morphological reinflection task
"""
import torch
from torch.utils.data import Dataset
from typing import List, Tuple, Dict
import re
import numpy as np
class MorphologicalDataset(Dataset):
"""Optimized dataset for morphological reinflection task"""
def __init__(self, src_file: str, tgt_file: str, src_vocab: Dict[str, int],
tgt_vocab: Dict[str, int], max_length: int = 100):
self.src_file = src_file
self.tgt_file = tgt_file
self.src_vocab = src_vocab
self.tgt_vocab = tgt_vocab
self.max_length = max_length
# Load and preprocess data for faster access
self.data = self._load_and_preprocess_data()
def _load_and_preprocess_data(self) -> List[Tuple[List[int], List[int], List[int], List[int]]]:
"""Load and preprocess data with tokenization for faster access"""
data = []
# Pre-compute special token indices
pad_idx = self.src_vocab['<PAD>']
unk_idx = self.src_vocab['<UNK>']
bos_idx = self.src_vocab['<BOS>']
eos_idx = self.src_vocab['<EOS>']
with open(self.src_file, 'r', encoding='utf-8') as src_f, \
open(self.tgt_file, 'r', encoding='utf-8') as tgt_f:
for src_line, tgt_line in zip(src_f, tgt_f):
src_tokens = src_line.strip().split()
tgt_tokens = tgt_line.strip().split()
# Filter out empty lines
if src_tokens and tgt_tokens:
# Pre-tokenize sequences
src_indices, src_mask = self._tokenize_sequence_fast(
src_tokens, self.src_vocab, pad_idx, unk_idx, bos_idx, eos_idx
)
tgt_indices, tgt_mask = self._tokenize_sequence_fast(
tgt_tokens, self.tgt_vocab, pad_idx, unk_idx, bos_idx, eos_idx
)
data.append((src_indices, src_mask, tgt_indices, tgt_mask))
return data
def _tokenize_sequence_fast(self, tokens: List[str], vocab: Dict[str, int],
pad_idx: int, unk_idx: int, bos_idx: int, eos_idx: int) -> Tuple[List[int], List[int]]:
"""Fast tokenization with pre-computed indices"""
# Convert tokens to indices using vectorized operations
indices = []
for token in tokens:
indices.append(vocab.get(token, unk_idx))
# Add BOS and EOS
indices = [bos_idx] + indices + [eos_idx]
# Truncate or pad to max_length
if len(indices) > self.max_length:
indices = indices[:self.max_length]
else:
indices = indices + [pad_idx] * (self.max_length - len(indices))
# Create mask (1 for real tokens, 0 for padding)
mask = [1 if idx != pad_idx else 0 for idx in indices]
return indices, mask
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, idx: int) -> Tuple[List[int], List[int], List[int], List[int]]:
return self.data[idx]
def build_vocabulary(data_files: List[str], min_freq: int = 1) -> Dict[str, int]:
"""Build vocabulary from data files with optimized processing"""
token_freq = {}
for file_path in data_files:
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
tokens = line.strip().split()
for token in tokens:
token_freq[token] = token_freq.get(token, 0) + 1
# Filter by frequency and sort
filtered_tokens = [(token, freq) for token, freq in token_freq.items()
if freq >= min_freq]
filtered_tokens.sort(key=lambda x: x[1], reverse=True)
# Build vocabulary
vocab = {'<PAD>': 0, '<UNK>': 1, '<BOS>': 2, '<EOS>': 3}
# Add tokens to vocabulary
for token, _ in filtered_tokens:
if token not in vocab:
vocab[token] = len(vocab)
return vocab
def tokenize_sequence(tokens: List[str], vocab: Dict[str, int],
max_length: int, add_bos_eos: bool = True) -> Tuple[List[int], List[int]]:
"""Tokenize a sequence and create masks"""
# Convert tokens to indices
indices = []
for token in tokens:
if token in vocab:
indices.append(vocab[token])
else:
indices.append(vocab['<UNK>'])
# Add BOS and EOS if requested
if add_bos_eos:
indices = [vocab['<BOS>']] + indices + [vocab['<EOS>']]
# Truncate or pad to max_length
if len(indices) > max_length:
indices = indices[:max_length]
else:
indices = indices + [vocab['<PAD>']] * (max_length - len(indices))
# Create mask (1 for real tokens, 0 for padding)
mask = [1 if idx != vocab['<PAD>'] else 0 for idx in indices]
return indices, mask
def collate_fn(batch: List[Tuple[List[int], List[int], List[int], List[int]]],
src_vocab: Dict[str, int], tgt_vocab: Dict[str, int],
max_length: int = 100) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Optimized collate function for DataLoader with vectorized operations"""
# Pre-allocate lists for better memory management
batch_size = len(batch)
src_batch = []
src_masks = []
tgt_batch = []
tgt_masks = []
# Extract pre-tokenized data
for src_indices, src_mask, tgt_indices, tgt_mask in batch:
src_batch.append(src_indices)
src_masks.append(src_mask)
tgt_batch.append(tgt_indices)
tgt_masks.append(tgt_mask)
# Convert to tensors using stack for better performance
src_batch = torch.stack([torch.tensor(seq, dtype=torch.long) for seq in src_batch])
src_masks = torch.stack([torch.tensor(seq, dtype=torch.long) for seq in src_masks])
tgt_batch = torch.stack([torch.tensor(seq, dtype=torch.long) for seq in tgt_batch])
tgt_masks = torch.stack([torch.tensor(seq, dtype=torch.long) for seq in tgt_masks])
# Transpose for transformer input format [seq_len, batch_size]
src_batch = src_batch.t()
src_masks = src_masks.t()
tgt_batch = tgt_batch.t()
tgt_masks = tgt_masks.t()
return src_batch, src_masks, tgt_batch, tgt_masks
def analyze_vocabulary(data_files: List[str]) -> Dict:
"""Analyze vocabulary statistics"""
token_freq = {}
total_tokens = 0
total_sequences = 0
for file_path in data_files:
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
tokens = line.strip().split()
total_sequences += 1
for token in tokens:
token_freq[token] = token_freq.get(token, 0) + 1
total_tokens += 1
# Analyze special tokens (features)
feature_tokens = [token for token in token_freq.keys()
if token.startswith('<') and token.endswith('>')]
# Analyze character tokens
char_tokens = [token for token in token_freq.keys()
if not token.startswith('<') and len(token) == 1]
return {
'total_tokens': total_tokens,
'total_sequences': total_sequences,
'unique_tokens': len(token_freq),
'feature_tokens': len(feature_tokens),
'char_tokens': len(char_tokens),
'avg_seq_length': total_tokens / total_sequences if total_sequences > 0 else 0,
'feature_examples': feature_tokens[:10], # First 10 features
'char_examples': char_tokens[:10] # First 10 characters
}
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