# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from typing import IO, Any, Union

import cv2
import numpy as np
import torch
from einops import rearrange
from PIL import Image as PILImage
from torch import Tensor

from cosmos_predict1.utils import log
from cosmos_predict1.utils.easy_io import easy_io

try:
    import ffmpegcv
except Exception as e:  # ImportError cannot catch all problems
    log.info(e)
    ffmpegcv = None


def save_video(grid, video_name, fps=30):
    grid = (grid * 255).astype(np.uint8)
    grid = np.transpose(grid, (1, 2, 3, 0))
    with ffmpegcv.VideoWriter(video_name, "h264", fps) as writer:
        for frame in grid:
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

            writer.write(frame)


def save_img_or_video(sample_C_T_H_W_in01: Tensor, save_fp_wo_ext: Union[str, IO[Any]], fps: int = 24) -> None:
    """
    Save a tensor as an image or video file based on shape

        Args:
        sample_C_T_H_W_in01 (Tensor): Input tensor with shape (C, T, H, W) in [0, 1] range.
        save_fp_wo_ext (Union[str, IO[Any]]): File path without extension or file-like object.
        fps (int): Frames per second for video. Default is 24.
    """
    assert sample_C_T_H_W_in01.ndim == 4, "Only support 4D tensor"
    assert isinstance(save_fp_wo_ext, str) or hasattr(
        save_fp_wo_ext, "write"
    ), "save_fp_wo_ext must be a string or file-like object"

    if torch.is_floating_point(sample_C_T_H_W_in01):
        sample_C_T_H_W_in01 = sample_C_T_H_W_in01.clamp(0, 1)
    else:
        assert sample_C_T_H_W_in01.dtype == torch.uint8, "Only support uint8 tensor"
        sample_C_T_H_W_in01 = sample_C_T_H_W_in01.float().div(255)

    if sample_C_T_H_W_in01.shape[1] == 1:
        save_obj = PILImage.fromarray(
            rearrange((sample_C_T_H_W_in01.cpu().float().numpy() * 255), "c 1 h w -> h w c").astype(np.uint8),
            mode="RGB",
        )
        ext = ".jpg" if isinstance(save_fp_wo_ext, str) else ""
        easy_io.dump(
            save_obj,
            f"{save_fp_wo_ext}{ext}" if isinstance(save_fp_wo_ext, str) else save_fp_wo_ext,
            file_format="jpg",
            format="JPEG",
            quality=85,
        )
    else:
        save_obj = rearrange((sample_C_T_H_W_in01.cpu().float().numpy() * 255), "c t h w -> t h w c").astype(np.uint8)
        ext = ".mp4" if isinstance(save_fp_wo_ext, str) else ""
        easy_io.dump(
            save_obj,
            f"{save_fp_wo_ext}{ext}" if isinstance(save_fp_wo_ext, str) else save_fp_wo_ext,
            file_format="mp4",
            format="mp4",
            fps=fps,
        )