dataset.py

import torch
from torch.utils.data import Dataset
import json
import random


class MTPDataset(Dataset):
    """Dataset optimizado para corpus con formato instruction-context-response"""
    
    def __init__(self, corpus_path, tokenizer, max_seq_len=2048, 
                 use_augmentation=True, augmentation_prob=0.3):
        self.tokenizer = tokenizer
        self.max_seq_len = max_seq_len
        self.use_augmentation = use_augmentation
        self.augmentation_prob = augmentation_prob
        self.data = []
        
        # Load corpus
        print(f"   → Cargando corpus: {corpus_path}")
        valid_count = 0
        with open(corpus_path, 'r', encoding='utf-8') as f:
            for line_num, line in enumerate(f, 1):
                line = line.strip()
                if not line:
                    continue
                    
                try:
                    entry = json.loads(line)
                    
                    # Validar campos requeridos
                    if 'instruction' not in entry or 'response' not in entry:
                        print(f"   ⚠ Línea {line_num}: Falta 'instruction' o 'response'")
                        continue
                    
                    instruction = entry['instruction'].strip()
                    response = entry['response'].strip()
                    
                    if not instruction or not response:
                        print(f"   ⚠ Línea {line_num}: Campos vacíos")
                        continue
                    
                    # Context puede estar vacío o no existir
                    context = entry.get('context', '').strip()
                    
                    self.data.append({
                        'instruction': instruction,
                        'context': context,
                        'response': response
                    })
                    valid_count += 1
                    
                except json.JSONDecodeError as e:
                    print(f"   ❌ Línea {line_num}: JSON inválido - {e}")
                    continue
        
        print(f"   ✓ Cargados {valid_count} ejemplos válidos de {line_num} líneas")
        if use_augmentation:
            print(f"   ✓ Augmentación activada (prob={augmentation_prob})")
    
    def __len__(self):
        return len(self.data)
    
    def augment_text(self, text):
        """Augmentación mejorada de texto"""
        if not self.use_augmentation or random.random() > self.augmentation_prob or not text:
            return text
        
        # 1. Variación en espacios
        text = text.strip()
        
        # 2. Variación en puntuación final
        if random.random() < 0.25:
            if text.endswith('.'):
                if random.random() < 0.5:
                    text = text[:-1]
            elif not text.endswith(('.', '!', '?', ':')):
                if random.random() < 0.5:
                    text = text + '.'
        
        # 3. Variación en mayúsculas iniciales
        if random.random() < 0.1 and len(text) > 0:
            if text[0].isupper():
                text = text[0].lower() + text[1:]
            elif text[0].islower():
                text = text[0].upper() + text[1:]
        
        return text
    
    def __getitem__(self, idx):
        entry = self.data[idx]
        
        instruction = entry['instruction']
        context = entry['context']
        response = entry['response']
        
        # Aplicar augmentación
        instruction = self.augment_text(instruction)
        context = self.augment_text(context)
        response = self.augment_text(response)
        
        # Formato optimizado para entrenamiento con contexto opcional
        if context:
            full_text = f"### Instrucción:\n{instruction}\n\n### Contexto:\n{context}\n\n### Respuesta:\n{response}"
        else:
            full_text = f"### Instrucción:\n{instruction}\n\n### Respuesta:\n{response}"
        
        # Tokenize
        tokens = self.tokenizer.encode(full_text)
        
        # Add BOS and EOS
        tokens = [self.tokenizer.bos_id()] + tokens + [self.tokenizer.eos_id()]
        
        # Truncate if too long (mantener BOS y EOS)
        if len(tokens) > self.max_seq_len:
            tokens = [tokens[0]] + tokens[1:self.max_seq_len-1] + [self.tokenizer.eos_id()]
        
        input_ids = torch.tensor(tokens[:-1], dtype=torch.long)
        target_ids = torch.tensor(tokens[1:], dtype=torch.long)
        
        return input_ids, target_ids


def collate_fn(batch, pad_id=0):
    """Collate function optimizada con padding dinámico"""
    input_ids = [item[0] for item in batch]
    target_ids = [item[1] for item in batch]
    
    # Find max length in this batch (dynamic padding)
    max_len = max(len(ids) for ids in input_ids)
    
    # Pad sequences
    input_ids_padded = []
    target_ids_padded = []
    
    for inp, tgt in zip(input_ids, target_ids):
        pad_len = max_len - len(inp)
        
        # Pad input with pad_id
        input_ids_padded.append(
            torch.cat([inp, torch.full((pad_len,), pad_id, dtype=torch.long)])
        )
        
        # Pad target with -100 (ignore_index in CrossEntropyLoss)
        target_ids_padded.append(
            torch.cat([tgt, torch.full((pad_len,), -100, dtype=torch.long)])
        )
    
    return torch.stack(input_ids_padded), torch.stack(target_ids_padded)