from __future__ import annotations

import shutil
import subprocess
import tempfile
import hashlib
import os
from pathlib import Path

import numpy as np
import torch
from PIL import Image

from .rife_source import prepare_rife_source


def _prepare_rife_checkpoint() -> Path:
    prepare_rife_source()
    import vfi_utils

    vfi_utils.config["ckpts_path"] = os.environ.get("RIFE_CHECKPOINT_ROOT", "/tmp/rife-checkpoints")
    checkpoint = Path(vfi_utils.load_file_from_github_release("rife", "rife49.pth"))
    if checkpoint.stat().st_size != 21_345_274:
        raise RuntimeError("Dimensione inattesa del checkpoint RIFE rife49.pth.")
    digest = hashlib.sha256(checkpoint.read_bytes()).hexdigest()
    if digest != "e55fd00f3cc184e3c65961f4bb827a9da022e78eed36b055242c0ac30000d533":
        raise RuntimeError("SHA-256 inatteso del checkpoint RIFE rife49.pth.")
    return checkpoint


def prepare_rife_model() -> None:
    """Materialize the small seam model once during global bootstrap."""
    prepare_rife_source()
    from comfy.model_management import get_torch_device
    from vfi_models import rife as rife_module
    from vfi_models.rife.rife_arch import IFNet

    cache_key = ("rife49.pth", "float32", False)
    if cache_key in rife_module._model_cache:
        return
    checkpoint = _prepare_rife_checkpoint()
    model = IFNet(arch_ver="4.7")
    state = torch.load(checkpoint, map_location="cpu", weights_only=True)
    model.load_state_dict(state, strict=True)
    del state
    rife_module._model_cache[cache_key] = model.eval().requires_grad_(False).to(get_torch_device())


def close_rife_model() -> None:
    prepare_rife_source()
    from vfi_models import rife as rife_module

    rife_module._model_cache.clear()


def apply_rife_seam(frames: torch.Tensor, seam_frames: int = 4) -> torch.Tensor:
    """Replace the cyclic seam with RIFE intermediates without changing length."""
    seam_frames = int(seam_frames)
    if seam_frames <= 0 or frames.shape[0] < seam_frames + 2:
        return frames
    prepare_rife_model()
    from vfi_models.rife import RIFE_VFI

    tail_count = max(1, seam_frames // 2)
    head_count = max(1, seam_frames - tail_count)
    anchors = torch.stack((frames[-tail_count - 1], frames[head_count])).float()
    interpolated = RIFE_VFI().vfi(
        ckpt_name="rife49.pth",
        frames=anchors,
        clear_cache_after_n_frames=1,
        multiplier=seam_frames + 1,
        fast_mode=True,
        ensemble=False,
        scale_factor=1.0,
        keep_output_on_device=True,
    )[0][1:-1]
    if interpolated.shape[0] != seam_frames:
        raise RuntimeError(
            f"RIFE ha restituito {interpolated.shape[0]} frame intermedi; attesi {seam_frames}."
        )
    result = frames.clone()
    result[-tail_count:] = interpolated[:tail_count].to(result)
    result[:head_count] = interpolated[tail_count:].to(result)
    return result


def save_frame_bundle(frames: torch.Tensor) -> str:
    frames = frames.detach().cpu().float().clamp(0.0, 1.0)
    bundle = tempfile.NamedTemporaryFile(prefix="wan-loop-", suffix=".npz", delete=False)
    bundle.close()
    np.savez_compressed(bundle.name, frames=(frames.numpy() * 255.0).round().astype(np.uint8))
    return bundle.name


def _crossfade(frames: np.ndarray) -> np.ndarray:
    if len(frames) < 2:
        return frames
    result = frames.copy()
    # Preserve the initial conditioning frame; soften only the final boundary.
    result[-1] = np.rint(
        result[-1].astype(np.float32) * 0.5 + result[0].astype(np.float32) * 0.5
    ).clip(0, 255).astype(np.uint8)
    return result


OUTPUT_FORMATS = {
    "mkv": {
        "suffix": ".mkv",
        "encoder_args": [
            "-c:v", "libsvtav1", "-preset", "6", "-crf", "45", "-pix_fmt", "yuv420p",
        ],
    },
    "mp4": {
        "suffix": ".mp4",
        "encoder_args": [
            "-c:v", "libx264", "-preset", "slow", "-crf", "28", "-tune", "film",
            "-pix_fmt", "yuv420p", "-movflags", "+faststart",
        ],
    },
}


def _encode_frame_sequence(work: Path, ffmpeg: str, fps: int, output_format: str) -> str:
    output_format = str(output_format).strip().lower()
    if output_format not in OUTPUT_FORMATS:
        raise ValueError(f"Unsupported output format: {output_format!r}")
    format_spec = OUTPUT_FORMATS[output_format]
    output_handle = tempfile.NamedTemporaryFile(
        prefix="wan-loop-", suffix=format_spec["suffix"], delete=False
    )
    output = Path(output_handle.name)
    output_handle.close()
    try:
        command = [
            ffmpeg,
            "-hide_banner",
            "-loglevel",
            "error",
            "-y",
            "-framerate",
            str(int(fps)),
            "-i",
            str(work / "frame_%05d.png"),
            *format_spec["encoder_args"],
            str(output),
        ]
        subprocess.run(command, check=True)
        return str(output)
    except Exception:
        output.unlink(missing_ok=True)
        raise


def _encode_bundle(
    bundle_path: str,
    fps: int,
    output_formats: tuple[str, ...],
    crossfade: bool,
) -> dict[str, str]:
    source = Path(bundle_path)
    if not source.is_file():
        raise FileNotFoundError(f"Bundle frame non trovato: {source}")
    normalized_formats = tuple(dict.fromkeys(str(item).strip().lower() for item in output_formats))
    if not normalized_formats or any(item not in OUTPUT_FORMATS for item in normalized_formats):
        raise ValueError(f"Unsupported output formats: {normalized_formats!r}")
    ffmpeg = shutil.which("ffmpeg")
    if not ffmpeg:
        raise RuntimeError("ffmpeg non è installato nello Space.")
    work = Path(tempfile.mkdtemp(prefix="wan-loop-frames-"))
    outputs: dict[str, str] = {}
    try:
        with np.load(source) as data:
            frames = data["frames"]
        if crossfade:
            frames = _crossfade(frames)
        for index, frame in enumerate(frames):
            Image.fromarray(frame, mode="RGB").save(work / f"frame_{index:05d}.png")
        for output_format in normalized_formats:
            outputs[output_format] = _encode_frame_sequence(work, ffmpeg, fps, output_format)
        return outputs
    except Exception:
        for output in outputs.values():
            Path(output).unlink(missing_ok=True)
        raise
    finally:
        source.unlink(missing_ok=True)
        shutil.rmtree(work, ignore_errors=True)


def encode_video(
    bundle_path: str,
    fps: int,
    output_format: str = "mkv",
    crossfade: bool = True,
) -> str:
    output_format = str(output_format).strip().lower()
    return _encode_bundle(bundle_path, fps, (output_format,), crossfade)[output_format]


def encode_video_with_preview(
    bundle_path: str,
    fps: int,
    output_format: str = "mkv",
    crossfade: bool = True,
) -> tuple[str, str]:
    """Return browser-compatible MP4 preview and the selected download."""
    output_format = str(output_format).strip().lower()
    formats = (output_format,) if output_format == "mp4" else (output_format, "mp4")
    outputs = _encode_bundle(bundle_path, fps, formats, crossfade)
    return outputs["mp4"], outputs[output_format]


def encode_mp4(bundle_path: str, fps: int, crossfade: bool = True) -> str:
    """Compatibility wrapper for callers that explicitly require MP4."""
    return encode_video(bundle_path, fps=fps, output_format="mp4", crossfade=crossfade)