import io
import torch
import numpy as np
from datasets import load_dataset
from torch.utils.data import Dataset, DataLoader

# ==================================================================
# 1. Core Decoding Function (Handles the binary packing)
# ==================================================================
def unpack_event_data(item, use_io=True):
    """
    Decodes the custom binary format:
    Header (8 bytes) -> Shape (T, C, H, W) -> Body (Packed Bits)
    """
    if use_io:
        with io.BytesIO(item['data']) as f:
            raw_data = np.load(f)
    else:
        raw_data = np.load(item)
        
    header_size = 4 * 2      # Parse Header (First 8 bytes for 4 uint16 shape values)
    shape_header = raw_data[:header_size].view(np.uint16)
    original_shape = tuple(shape_header) # Returns (T, C, H, W)
    
    packed_body = raw_data[header_size:]    # Parse Body & Bit-unpacking
    unpacked = np.unpackbits(packed_body)
    
    num_elements = np.prod(original_shape)  # Extract valid bits (Handle padding)
    event_flat = unpacked[:num_elements]
    event_data = event_flat.reshape(original_shape).astype(np.float32).copy()
    
    return torch.from_numpy(event_data)

# ==================================================================
# 2. Dataset Wrapper
# ==================================================================
class I2E_Dataset(Dataset):
    def __init__(self, cache_dir, config_name, split='train', transform=None, target_transform=None):
        print(f"🚀 Loading {config_name} [{split}] from Hugging Face...")
        self.ds = load_dataset('UESTC-BICS/I2E', config_name, split=split, cache_dir=cache_dir, keep_in_memory=False)
        self.transform = transform
        self.target_transform = target_transform

    def __len__(self):
        return len(self.ds)

    def __getitem__(self, idx):
        item = self.ds[idx]
        event = unpack_event_data(item)
        label = item['label']
        if self.transform:
            event = self.transform(event)
        if self.target_transform:
            label = self.target_transform(label)
        return event, label

# ==================================================================
# 3. Run Example
# ==================================================================
if __name__ == "__main__":
    import os
    os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'     # Use HF mirror server in some regions

    DATASET_NAME = 'I2E-CIFAR10'                            # Choose your config: 'I2E-CIFAR10', 'I2E-ImageNet', etc.
    MODEL_PATH = 'Your cache path here'                     # e.g., './hf_datasets_cache/'
    
    train_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='train')
    val_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='validation')

    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=32, persistent_workers=True)
    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=32, persistent_workers=True)

    events, labels = next(iter(train_loader))
    print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W]
    print(f"✅ Labels: {labels}")