






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							A__	:			Union[str, 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_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   a			(						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__	:			int			   = parser.parse_args()

							A__	:			Optional[Any]			   = 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 pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.dir_cache is None:
												lowercase__    =			{}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																lowercase__    =			{
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(lowerCamelCase  ): {'''name''': str(lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			hf_hub_url(self.repo_info.id,  lowerCamelCase,  revision=self.repo_info.sha  )
								return fsspec.open(
								    lowerCamelCase,  mode=lowerCamelCase,  headers=get_authentication_headers_for_url(lowerCamelCase,  use_auth_token=self.token  ),  client_kwargs={'''trust_env''': True},  ).open()




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




