"""
Dataset for embedding inversion training.
Reads pre-converted numpy .npy files for instant loading.
"""

import os
import glob
import bisect
import json
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer

class EmbeddingInversionDataset(Dataset):
    """
    Dataset đọc dữ liệu từ file .pt đã được preprocess.
    """
    def __init__(self, pt_file_path, tokenizer, max_seq_len=32, val=False, val_split=0.01):
        self.tokenizer = tokenizer
        self.max_seq_len = max_seq_len
        
        # Load dữ liệu từ file .pt
        print(f"Loading data from {pt_file_path}...")
        self.raw_data = torch.load(pt_file_path) # List[Dict]
        
        # Tạo mapping để truy cập chunk theo index phẳng (flat index)
        # Vì mỗi cuốn sách có số lượng chunk khác nhau
        self.flat_indices = []
        for book_idx, book in enumerate(self.raw_data):
            num_chunks = len(book["chunks_text"])
            for chunk_idx in range(num_chunks):
                self.flat_indices.append((book_idx, chunk_idx))
        
        self.total_rows = len(self.flat_indices)

        # Chia Train/Val
        val_count = int(self.total_rows * val_split)
        if val:
            self.start_idx = self.total_rows - val_count
            self.length = val_count
        else:
            self.start_idx = 0
            self.length = self.total_rows - val_count

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        global_idx = self.start_idx + idx
        book_idx, chunk_in_book_idx = self.flat_indices[global_idx]
        
        # Lấy dữ liệu
        book_data = self.raw_data[book_idx]
        text = book_data["chunks_text"][chunk_in_book_idx]
        embedding = book_data["embeddings"][chunk_in_book_idx] # Tensor đã có sẵn

        # Tokenize lại text để lấy token_ids (vì file .pt chỉ lưu text)
        encoding = self.tokenizer(
            text,
            max_length=self.max_seq_len,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        )
        
        token_ids = encoding["input_ids"].squeeze(0) # (max_seq_len)
        
        # Tạo padding mask
        # Thông thường 0 hoặc 1 tùy model, Qwen thường dùng padding_mask từ tokenizer
        padding_mask = (token_ids == self.tokenizer.pad_token_id)

        return {
            "token_ids": token_ids.long(),
            "embedding": embedding.float(),
            "padding_mask": padding_mask,
        }

# /kaggle/embedding-inversion-demo/dataset.py

def create_dataloaders(config):
    dc = config["data"]
    tc = config["training"]
    mc = config["model"]

    # Sửa lỗi KeyError: Thử lấy 'encoder_model', nếu không có thì lấy 'encoder_model_name'
    model_name = mc.get("encoder_model") or mc.get("encoder_model_name")
    
    if not model_name:
        raise KeyError("Config file thiếu key 'encoder_model' hoặc 'encoder_model_name' trong phần 'model'")

    print(f"Initializing tokenizer from: {model_name}")
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    pt_path = dc["data_dir"]

    train_ds = EmbeddingInversionDataset(
        pt_path, tokenizer, 
        max_seq_len=mc["max_seq_len"],
        val=False, val_split=dc["val_split"]
    )
    val_ds = EmbeddingInversionDataset(
        pt_path, tokenizer, 
        max_seq_len=mc["max_seq_len"],
        val=True, val_split=dc["val_split"]
    )

    train_loader = DataLoader(
        train_ds, batch_size=tc["batch_size"], shuffle=True,
        num_workers=tc["num_workers"], pin_memory=True, drop_last=True
    )
    val_loader = DataLoader(
        val_ds, batch_size=tc["batch_size"], shuffle=False,
        num_workers=tc["num_workers"], pin_memory=True
    )

    return train_loader, val_loader