# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import Union

import mmcv
import numpy as np
import torch
from torch import Tensor


def imrenormalize(img: Union[Tensor, np.ndarray], img_norm_cfg: dict,
                  new_img_norm_cfg: dict) -> Union[Tensor, np.ndarray]:
    """Re-normalize the image.

    Args:
        img (Tensor | ndarray): Input image. If the input is a Tensor, the
            shape is (1, C, H, W). If the input is a ndarray, the shape
            is (H, W, C).
        img_norm_cfg (dict): Original configuration for the normalization.
        new_img_norm_cfg (dict): New configuration for the normalization.

    Returns:
        Tensor | ndarray: Output image with the same type and shape of
        the input.
    """
    if isinstance(img, torch.Tensor):
        assert img.ndim == 4 and img.shape[0] == 1
        new_img = img.squeeze(0).cpu().numpy().transpose(1, 2, 0)
        new_img = _imrenormalize(new_img, img_norm_cfg, new_img_norm_cfg)
        new_img = new_img.transpose(2, 0, 1)[None]
        return torch.from_numpy(new_img).to(img)
    else:
        return _imrenormalize(img, img_norm_cfg, new_img_norm_cfg)


def _imrenormalize(img: Union[Tensor, np.ndarray], img_norm_cfg: dict,
                   new_img_norm_cfg: dict) -> Union[Tensor, np.ndarray]:
    """Re-normalize the image."""
    img_norm_cfg = img_norm_cfg.copy()
    new_img_norm_cfg = new_img_norm_cfg.copy()
    for k, v in img_norm_cfg.items():
        if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
            img_norm_cfg[k] = np.array(v, dtype=img.dtype)
    # reverse cfg
    if 'bgr_to_rgb' in img_norm_cfg:
        img_norm_cfg['rgb_to_bgr'] = img_norm_cfg['bgr_to_rgb']
        img_norm_cfg.pop('bgr_to_rgb')
    for k, v in new_img_norm_cfg.items():
        if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
            new_img_norm_cfg[k] = np.array(v, dtype=img.dtype)
    img = mmcv.imdenormalize(img, **img_norm_cfg)
    img = mmcv.imnormalize(img, **new_img_norm_cfg)
    return img