import importlib
import os
import os.path as osp
import shutil
import sys
from pathlib import Path

# import av
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image


def seed_everything(seed):
    import random

    import numpy as np

    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed % (2**32))
    random.seed(seed)


def import_filename(filename):
    spec = importlib.util.spec_from_file_location("mymodule", filename)
    module = importlib.util.module_from_spec(spec)
    sys.modules[spec.name] = module
    spec.loader.exec_module(module)
    return module


def delete_additional_ckpt(base_path, num_keep):
    dirs = []
    for d in os.listdir(base_path):
        if d.startswith("checkpoint-"):
            dirs.append(d)
    num_tot = len(dirs)
    if num_tot <= num_keep:
        return
    # ensure ckpt is sorted and delete the ealier!
    del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep]
    for d in del_dirs:
        path_to_dir = osp.join(base_path, d)
        if osp.exists(path_to_dir):
            shutil.rmtree(path_to_dir)


def save_videos_from_pil(pil_images, path, fps=8):
    import av

    save_fmt = Path(path).suffix
    os.makedirs(os.path.dirname(path), exist_ok=True)
    width, height = pil_images[0].size

    if save_fmt == ".mp4":
        codec = "libx264"
        container = av.open(path, "w")
        stream = container.add_stream(codec, rate=fps)

        stream.width = width
        stream.height = height

        for pil_image in pil_images:
            # pil_image = Image.fromarray(image_arr).convert("RGB")
            av_frame = av.VideoFrame.from_image(pil_image)
            container.mux(stream.encode(av_frame))
        container.mux(stream.encode())
        container.close()

    elif save_fmt == ".gif":
        pil_images[0].save(
            fp=path,
            format="GIF",
            append_images=pil_images[1:],
            save_all=True,
            duration=(1 / fps * 1000),
            loop=0,
        )
    else:
        raise ValueError("Unsupported file type. Use .mp4 or .gif.")


def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    height, width = videos.shape[-2:]
    outputs = []

    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)  # (c h w)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)  # (h w c)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        x = Image.fromarray(x)

        outputs.append(x)

    os.makedirs(os.path.dirname(path), exist_ok=True)

    save_videos_from_pil(outputs, path, fps)


def read_frames(video_path):
    container = av.open(video_path)

    video_stream = next(s for s in container.streams if s.type == "video")
    frames = []
    for packet in container.demux(video_stream):
        for frame in packet.decode():
            image = Image.frombytes(
                "RGB",
                (frame.width, frame.height),
                frame.to_rgb().to_ndarray(),
            )
            frames.append(image)

    return frames


def get_fps(video_path):
    container = av.open(video_path)
    video_stream = next(s for s in container.streams if s.type == "video")
    fps = video_stream.average_rate
    container.close()
    return fps