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, audio_path=None):
    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, audio_path=None, 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, audio_path=audio_path)


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


def crop_and_pad(image, rect):
    x0, y0, x1, y1 = rect
    h, w = image.shape[:2]

    # 确保坐标在图像范围内
    x0, y0 = max(0, x0), max(0, y0)
    x1, y1 = min(w, x1), min(h, y1)

    # 计算原始框的宽度和高度
    width = x1 - x0
    height = y1 - y0

    # 使用较小的边长作为裁剪正方形的边长
    side_length = min(width, height)

    # 计算正方形框中心点
    center_x = (x0 + x1) // 2
    center_y = (y0 + y1) // 2

    # 重新计算正方形框的坐标
    new_x0 = max(0, center_x - side_length // 2)
    new_y0 = max(0, center_y - side_length // 2)
    new_x1 = min(w, new_x0 + side_length)
    new_y1 = min(h, new_y0 + side_length)

    # 最终裁剪框的尺寸修正（确保是正方形）
    if (new_x1 - new_x0) != (new_y1 - new_y0):
        side_length = min(new_x1 - new_x0, new_y1 - new_y0)
        new_x1 = new_x0 + side_length
        new_y1 = new_y0 + side_length

    # 裁剪图像
    cropped_image = image[new_y0:new_y1, new_x0:new_x1]

    return cropped_image