



"""simple docstring"""
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      A:   Tuple								=		argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
      )
      parser.add_argument(
          "--original_config_file",
          type=str,
          required=True,
          help="The YAML config file corresponding to the original architecture.",
      )
      parser.add_argument(
          "--num_in_channels",
          default=None,
          type=int,
          help="The number of input channels. If `None` number of input channels will be automatically inferred.",
      )
      parser.add_argument(
          "--image_size",
          default=5_1_2,
          type=int,
          help=(
              "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
              " Base. Use 768 for Stable Diffusion v2."
          ),
      )
      parser.add_argument(
          "--extract_ema",
          action="store_true",
          help=(
              "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
              " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
              " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
          ),
      )
      parser.add_argument(
          "--upcast_attention",
          action="store_true",
          help=(
              "Whether the attention computation should always be upcasted. This is necessary when running stable"
              " diffusion 2.1."
          ),
      )
      parser.add_argument(
          "--from_safetensors",
          action="store_true",
          help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
      )
      parser.add_argument(
          "--to_safetensors",
          action="store_true",
          help="Whether to store pipeline in safetensors format or not.",
      )
      parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
      parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

      def       _snake_case						(						UpperCamelCase						:		int						):
       if string == "True":
        return True
       elif string == "False":
        return False
       else:
        raise ValueError(F"could not parse string as bool {string}"						)

      parser.add_argument(
          "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
      )

      parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

      A:   Union[str, Any]								=		parser.parse_args()

      A:   Tuple								=		download_controlnet_from_original_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          extract_ema=args.extract_ema,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          use_linear_projection=args.use_linear_projection,
          cross_attention_dim=args.cross_attention_dim,
      )

      controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

"""simple docstring"""






from PIL import Image



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							(2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))

				def contrast(lowerCamelCase       ) -> int:
								return int(1_2_8 + factor * (c - 1_2_8)       )

				return img.point(lowerCamelCase       )


if __name__ == "__main__":
						# Load image
						with Image.open('image_data/lena.jpg') as img:
												# Change contrast to 170
												lowerCAmelCase__  :  Any						     =	change_contrast(img, 170)
												cont_img.save('image_data/lena_high_contrast.png', format='png')



