





"""simple docstring"""






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				lowerCAmelCase_			=							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=512,
				    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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Tuple:






											"""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)

				lowerCAmelCase_			=							parser.parse_args()

				lowerCAmelCase_			=							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"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number | (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number & ~(1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number ^ (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       bool:






       """simple docstring"""




       return ((number >> position) & 1) == 1

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






