


"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		a__				:							Any						=     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    UpperCAmelCase__       (lowerCAmelCase_   ):




							'''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__				:							List[str]						=     parser.parse_args()

		a__				:							Optional[int]						=     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 abc import ABC, abstractmethod
from argparse import ArgumentParser




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				ArgumentParser   )       ->	int:
						raise NotImplementedError()




	@abstractmethod
	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[int]:
						raise NotImplementedError()
