






"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = "cpu" ,	SCREAMING_SNAKE_CASE = None					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    torch.load(__SCREAMING_SNAKE_CASE ,	map_location=__SCREAMING_SNAKE_CASE					)
    for k, v in tqdm(state_dict.items()					):
        if not isinstance(__SCREAMING_SNAKE_CASE ,	torch.Tensor					):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'					)
        UpperCamelCase__         =    v.half()
    if save_path is None:  # overwrite src_path
        UpperCamelCase__         =    src_path
    torch.save(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  fire.Fire(convert)











"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




