"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
     _a   :	int 					=     argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )
     # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
     parser.add_argument(
         '--original_config_file',
         default=None,
         type=str,
         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(
         '--scheduler_type',
         default='pndm',
         type=str,
         help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
     )
     parser.add_argument(
         '--pipeline_type',
         default=None,
         type=str,
         help=(
             'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
             '. If `None` pipeline will be automatically inferred.'
         ),
     )
     parser.add_argument(
         '--image_size',
         default=None,
         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(
         '--prediction_type',
         default=None,
         type=str,
         help=(
             'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
             ' Diffusion v2 Base. Use \'v_prediction\' 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.)')
     parser.add_argument(
         '--stable_unclip',
         type=str,
         default=None,
         required=False,
         help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
     )
     parser.add_argument(
         '--stable_unclip_prior',
         type=str,
         default=None,
         required=False,
         help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
     )
     parser.add_argument(
         '--clip_stats_path',
         type=str,
         help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
         required=False,
     )
     parser.add_argument(
         '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
     )
     parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
     parser.add_argument(
         '--vae_path',
         type=str,
         default=None,
         required=False,
         help='Set to a path, hub id to an already converted vae to not convert it again.',
     )
     _a   :	Dict 					=     parser.parse_args()

     _a   :	List[str] 					=     download_from_original_stable_diffusion_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         prediction_type=args.prediction_type,
         model_type=args.pipeline_type,
         extract_ema=args.extract_ema,
         scheduler_type=args.scheduler_type,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         stable_unclip=args.stable_unclip,
         stable_unclip_prior=args.stable_unclip_prior,
         clip_stats_path=args.clip_stats_path,
         controlnet=args.controlnet,
         vae_path=args.vae_path,
     )

     if args.half:
          pipe.to(torch_dtype=torch.floataa)

     if args.controlnet:
          # only save the controlnet model
          pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
     else:
          pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


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




