

"""simple docstring"""





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							__UpperCamelCase  :      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   __SCREAMING_SNAKE_CASE  (      A_ ):
								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)

							__UpperCamelCase  :      Optional[int]							      =		parser.parse_args()

							__UpperCamelCase  :      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)





import warnings

from ..trainer import Trainer
from ..utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     warnings.warn(
         """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
         """instead."""       ,	_lowercase       ,	)
     super().__init__(args=_lowercase       ,	**_lowercase      )
