"""Build the FACT offline caches for a whole LeRobot-v2 T-Rex dataset.

* ``--what latents``: Wan2.2 VAE latents of every frame of every episode
  (``<out>/latents/episode_XXXXXX.npy``, float16 [N,48,10,20]).  Shardable
  across devices with ``--shard i --num-shards n``; existing files are skipped.
* ``--what text``: umT5-XXL token embeddings of every task string
  (``<out>/text_cache.npz``).

Observation preprocessing is identical to the baseline path (per-view resize
to 160x320, DreamZero 2x2 grid [head | right ; left | black], resize back to
160x320, single-frame VAE encode).
"""

from __future__ import annotations

import argparse
import json
import time
from pathlib import Path

import numpy as np
import torch

from fact.data import latent_cache_path, load_episode_tasks

VIEW_DIRS = (
    "observation.images.head_left",
    "observation.images.left_wrist",
    "observation.images.right_wrist",
)


def _load_component(module: torch.nn.Module, path: str, min_coverage: float) -> None:
    state = torch.load(path, map_location="cpu")
    if isinstance(state, dict):
        for key in ("state_dict", "model", "module"):
            if isinstance(state.get(key), dict):
                state = state[key]
                break
    state = {k.removeprefix("module."): v for k, v in state.items() if torch.is_tensor(v)}
    current = module.state_dict()
    compatible = {k: v for k, v in state.items() if k in current and current[k].shape == v.shape}
    coverage = sum(v.numel() for v in compatible.values()) / max(1, sum(v.numel() for v in current.values()))
    if coverage < min_coverage:
        raise ValueError(f"checkpoint coverage {coverage:.3f} < {min_coverage} for {path}")
    module.load_state_dict(compatible, strict=False)


def load_vae(path: str, device: torch.device):
    from fact.perception.wan_video_vae import WanVideoVAE38

    vae = WanVideoVAE38(z_dim=48, dim=160, vae_pretrained_path=path)
    _load_component(vae.model, path, 0.99)
    vae = vae.to(device).eval()
    vae.mean = vae.mean.to(device)
    vae.std = vae.std.to(device)
    vae.scale = [vae.mean, vae.std.reciprocal()]
    return vae


def read_all_frames(path: Path) -> np.ndarray:
    import decord

    reader = decord.VideoReader(str(path), num_threads=2)
    return reader.get_batch(list(range(len(reader)))).asnumpy().astype(np.uint8)


def composite(views: list[np.ndarray], height: int, width: int) -> torch.Tensor:
    from torchvision.transforms import InterpolationMode
    from torchvision.transforms import functional as TF

    resized = []
    for frames in views:
        t = torch.from_numpy(frames).permute(0, 3, 1, 2)
        t = TF.resize(t, [height, width], interpolation=InterpolationMode.BILINEAR, antialias=True)
        resized.append(t)
    head, left, right = resized
    n = head.shape[0]
    grid = torch.zeros(n, 3, 2 * height, 2 * width, dtype=torch.uint8)
    grid[:, :, :height, :width] = head
    grid[:, :, height:, :width] = left
    grid[:, :, :height, width:] = right
    grid = grid.float() / 255.0 * 2 - 1
    return torch.nn.functional.interpolate(grid, size=(height, width), mode="bilinear", align_corners=False)


def encode_episode(vae, root: Path, episode: int, device, height: int, width: int, batch: int) -> np.ndarray:
    views = []
    for view in VIEW_DIRS:
        path = root / "videos" / f"chunk-{episode // 1000:03d}" / view / f"episode_{episode:06d}.mp4"
        views.append(read_all_frames(path))
    n = min(v.shape[0] for v in views)
    images = composite([v[:n] for v in views], height, width)
    latents = []
    with torch.no_grad():
        for i in range(0, n, batch):
            chunk = images[i : i + batch].to(device).unsqueeze(2)  # B,3,1,H,W
            z = vae.encode(chunk, tiled=False)
            latents.append(z[:, :, 0].float().cpu())
    return torch.cat(latents).numpy().astype(np.float16)


def build_latents(args, root: Path, out: Path) -> None:
    device = torch.device("cuda")
    tasks = load_episode_tasks(root)
    episodes = sorted(tasks) if not args.episodes else [int(e) for e in args.episodes.split(",")]
    episodes = [e for i, e in enumerate(episodes) if i % args.num_shards == args.shard]
    (out / "latents").mkdir(parents=True, exist_ok=True)
    todo = [e for e in episodes if not latent_cache_path(out, e).exists()]
    print(f"shard {args.shard}/{args.num_shards}: {len(episodes)} episodes, {len(todo)} to encode", flush=True)
    if not todo:
        return
    vae = load_vae(args.vae, device)
    start = time.time()
    for n, episode in enumerate(todo):
        latents = encode_episode(vae, root, episode, device, args.height, args.width, args.batch)
        if not np.isfinite(latents.astype(np.float32)).all():
            raise ValueError(f"non-finite latents for episode {episode}")
        tmp = latent_cache_path(out, episode).with_suffix(".tmp.npy")
        np.save(tmp, latents)
        tmp.rename(latent_cache_path(out, episode))
        if n % 5 == 0:
            print(f"  [{n + 1}/{len(todo)}] episode {episode}: {latents.shape} ({time.time() - start:.0f}s)", flush=True)
    print(f"shard {args.shard} done in {(time.time() - start) / 60:.1f} min", flush=True)


def build_text(args, root: Path, out: Path) -> None:
    from fact.perception.wan_video_text_encoder import WanTextEncoder
    from fact.perception.tokenizer import HuggingfaceTokenizer

    device = torch.device("cuda")
    tasks: dict[int, str] = {}
    with (root / "meta" / "tasks.jsonl").open() as handle:
        for line in handle:
            if line.strip():
                rec = json.loads(line)
                tasks[int(rec["task_index"])] = rec["task"]
    tokenizer = HuggingfaceTokenizer(name=args.tokenizer, seq_len=args.max_text_tokens, clean="whitespace")
    encoder = WanTextEncoder(text_encoder_pretrained_path=args.text_encoder)
    _load_component(encoder, args.text_encoder, 0.99)
    encoder = encoder.to(device=device, dtype=torch.bfloat16).eval()
    indices = sorted(tasks)
    tokens = np.zeros((len(indices), args.max_text_tokens, 4096), dtype=np.float16)
    masks = np.zeros((len(indices), args.max_text_tokens), dtype=bool)
    with torch.no_grad():
        for i in range(0, len(indices), args.batch):
            chunk = indices[i : i + args.batch]
            ids, mask = tokenizer([tasks[t] for t in chunk], return_mask=True)
            emb = encoder(ids.to(device), mask.to(device)).float().cpu().numpy()
            tokens[i : i + len(chunk)] = emb.astype(np.float16)
            masks[i : i + len(chunk)] = mask.numpy().astype(bool)
    out.mkdir(parents=True, exist_ok=True)
    np.savez_compressed(out / "text_cache.npz", tokens=tokens, mask=masks, task_index=np.array(indices))
    print(f"text cache: {len(indices)} tasks x {args.max_text_tokens} tokens -> {out / 'text_cache.npz'}", flush=True)


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset-root", required=True)
    parser.add_argument("--out", required=True, help="cache directory")
    parser.add_argument("--what", choices=["latents", "text"], required=True)
    parser.add_argument("--vae", default=None)
    parser.add_argument("--text-encoder", default=None)
    parser.add_argument("--tokenizer", default=None)
    parser.add_argument("--max-text-tokens", type=int, default=64)
    parser.add_argument("--episodes", default="", help="comma list; default all")
    parser.add_argument("--shard", type=int, default=0)
    parser.add_argument("--num-shards", type=int, default=1)
    parser.add_argument("--height", type=int, default=160)
    parser.add_argument("--width", type=int, default=320)
    parser.add_argument("--batch", type=int, default=16)
    args = parser.parse_args()
    root, out = Path(args.dataset_root), Path(args.out)
    if args.what == "latents":
        if not args.vae:
            parser.error("--vae is required for latents")
        build_latents(args, root, out)
    else:
        if not (args.text_encoder and args.tokenizer):
            parser.error("--text-encoder and --tokenizer are required for text")
        build_text(args, root, out)


if __name__ == "__main__":
    main()