




"""simple docstring"""


import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    A_      			=       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_12,
        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       UpperCAmelCase__ (snake_case__							:      Optional[int]					):
      """simple docstring"""

      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_      			=       parser.parse_args()

    A_      			=       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 typing import Any


def       UpperCAmelCase__ (snake_case__							:      list					):
  """simple docstring"""

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


