#!/usr/bin/env python3
"""Load the FineWeb-Edu Byte-Level dataset from HuggingFace."""

import os
import numpy as np
import torch
from torch.utils.data import IterableDataset, DataLoader, get_worker_info


BOS_ID = 257
EOS_ID = 258
PAD_ID = 256


class FineWebEduByteLevelDataset(IterableDataset):
    def __init__(self, data_dir, seq_len=2048, rank=0, world_size=1):
        self.seq_len = seq_len
        self.data_dir = data_dir
        self.rank = rank
        self.world_size = world_size
        self._files = self._discover_files()

    def _discover_files(self):
        import glob as _glob
        files = sorted(_glob.glob(os.path.join(self.data_dir, "**/*.bin"), recursive=True))
        return [f for i, f in enumerate(files) if i % self.world_size == self.rank]

    def __iter__(self):
        worker = get_worker_info()
        num_workers = worker.num_workers if worker else 1
        worker_id = worker.id if worker else 0
        files = [f for i, f in enumerate(self._files) if i % num_workers == worker_id]
        for filepath in files:
            arr = np.memmap(filepath, dtype=np.uint16, mode='r')
            pos = 0
            while pos + self.seq_len + 1 <= len(arr):
                chunk = arr[pos:pos + self.seq_len + 1]
                pos += self.seq_len + 1
                x = torch.tensor(chunk[:-1], dtype=torch.long)
                y = torch.tensor(chunk[1:], dtype=torch.long)
                y[y == PAD_ID] = -100
                yield x, y


def encode(text: str) -> list:
    return [BOS_ID] + list(text.encode('utf-8')) + [EOS_ID]


def decode(ids: list) -> str:
    return bytes(i for i in ids if 0 <= i <= 255).decode('utf-8', errors='replace')


if __name__ == "__main__":
    from huggingface_hub import snapshot_download
    data_dir = snapshot_download("CLIWorks/Spider-FLEXITOKENS-FP8", repo_type="dataset", allow_patterns=["data/*.bin", "data/metadata.json"])
    data_dir = os.path.join(data_dir, "data")
    ds = FineWebEduByteLevelDataset(data_dir, seq_len=2048)
    loader = DataLoader(ds, batch_size=4, num_workers=0, pin_memory=True)
    for i, (x, y) in enumerate(loader):
        print(f"Batch {i}: x={x.shape}, y={y.shape}")
        if i >= 2:
            break