import os
from pathlib import Path

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

import imageio

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 save_videos_from_pil(pil_images, path, fps=8):
    save_fmt = Path(path).suffix
    os.makedirs(os.path.dirname(path), exist_ok=True)

    if save_fmt == ".mp4":
        with imageio.get_writer(path, fps=fps) as writer:
            for img in pil_images:
                img_array = np.array(img)  # Convert PIL Image to numpy array
                writer.append_data(img_array)

    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,
            optimize=False,
            lossless=True
        )
    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 i, x in enumerate(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)