import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from pathlib import Path
from datasets import load_dataset

from bit_transformer import (
    BitTransformerLM,
    configure_optimizer,
    expand_model,
    text_to_bits,
)
from bit_transformer.training import train_loop as basic_train


def _build_memmap(lines, path: Path, max_len: int) -> None:
    """Precompute bit tensors into a memory-mapped file."""
    arr = np.memmap(path, mode="w+", shape=(len(lines), max_len), dtype="uint8")
    for idx, text in enumerate(lines):
        bits = text_to_bits(text)[:max_len]
        if len(bits) < max_len:
            bits.extend([0] * (max_len - len(bits)))
        arr[idx] = np.array(bits, dtype="uint8")
    arr.flush()


class MemmapDataset(Dataset):
    """Dataset backed by a memory-mapped array."""

    def __init__(self, path: Path, length: int, max_len: int) -> None:
        self.path = path
        self.length = length
        self.max_len = max_len
        self._arr = np.memmap(path, mode="r", shape=(length, max_len), dtype="uint8")

    def __len__(self) -> int:  # pragma: no cover - trivial
        return self.length

    def __getitem__(self, idx: int) -> torch.Tensor:
        return torch.from_numpy(self._arr[idx].astype("int64"))


def progressive_scale_schedule(steps=12, max_len=64, dataset_size=128):
    """Run deterministic scale-up on WikiText data."""
    ds = load_dataset("wikitext", "wikitext-2-raw-v1")
    train_lines = [t for t in ds["train"]["text"] if t.strip()][:dataset_size]
    valid_lines = [t for t in ds["validation"]["text"] if t.strip()][: dataset_size // 4]

    train_path = Path("wikitext_train.memmap")
    valid_path = Path("wikitext_valid.memmap")
    _build_memmap(train_lines, train_path, max_len)
    _build_memmap(valid_lines, valid_path, max_len)

    train = MemmapDataset(train_path, len(train_lines), max_len)
    valid = torch.from_numpy(
        np.memmap(valid_path, mode="r", shape=(len(valid_lines), max_len), dtype="uint8")
    ).long()

    layers = 1
    width = 32
    params = dict(
        d_model=width,
        nhead=4,
        num_layers=layers,
        dim_feedforward=width * 2,
        max_seq_len=max_len,
        reversible=True,
        chunk_size=max_len,
        use_autocast=True,
        use_act=True,
        act_threshold=0.9,
    )
    model = BitTransformerLM(**params)
    steps_per_epoch = max(1, (len(train) + 7) // 8)
    optimizer, scheduler = configure_optimizer(model, lr=1e-3, total_steps=(steps + 1) * steps_per_epoch)

    results = []
    for step in range(steps + 1):
        basic_train(
            model,
            train,
            epochs=1,
            compress_prob=0.5,
            log=False,
            forward_kwargs=None,
            num_workers=2,
        )

        with torch.no_grad():
            logits, _ = model(valid)
            pred = logits[:, :-1, :].reshape(-1, 2)
            target = valid[:, 1:].reshape(-1)
            val_loss = F.cross_entropy(pred, target).item()
        print(f"Step {step} validation loss: {val_loss:.4f}")
        results.append((step, val_loss))

        if step < steps:
            if step % 2 == 0:
                layers *= 2
            else:
                width *= 2
            params = dict(
                d_model=width,
                nhead=4,
                num_layers=layers,
                dim_feedforward=width * 2,
                max_seq_len=max_len,
                reversible=True,
                chunk_size=max_len,
                use_autocast=True,
                use_act=True,
                act_threshold=0.9,
            )
            model = expand_model(model, params)
            optimizer, scheduler = configure_optimizer(model, lr=1e-3, total_steps=(steps - step) * steps_per_epoch)
            print(f"Scaled model to {layers} layers and width {width}")
    return results


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Deterministic scale-up benchmark")
    parser.add_argument("--steps", type=int, default=12, help="number of scale-up steps")
    parser.add_argument("--max-len", type=int, default=64, help="sequence length")
    parser.add_argument("--dataset-size", type=int, default=128, help="number of training lines")
    args = parser.parse_args()

    progressive_scale_schedule(steps=args.steps, max_len=args.max_len, dataset_size=args.dataset_size)