





'''simple docstring'''
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    a__							:							List[str] =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 				lowercase__   (   __lowercase						:  Any     ) ->      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__							:							List[Any] =parser.parse_args()

    a__							:							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)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase	: Any  				={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=[
						    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMSNModel',
						    'ViTMSNForImageClassification',
						    'ViTMSNPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Union[str, Any]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
