
"""simple docstring"""






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					_lowercase							=				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__			(		__magic_name__      )						->List[Any]:
								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)

					_lowercase							=				parser.parse_args()

					_lowercase							=				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"""






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
					import bsa
					from bsa import BeautifulSoup


_lowercase							=				logging.get_logger(__name__)

class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    **_lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										requires_backends(self  ,    ["bs4"]       )
										super().__init__(**_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       []
										__lowercase       =       []
										__lowercase       =       element if element.name else element.parent
										for parent in child.parents:  # type: bs4.element.Tag
													__lowercase       =       parent.find_all(child.name  ,    recursive=_lowerCamelCase       )
													xpath_tags.append(child.name       )
													xpath_subscripts.append(
													    0 if 1 == len(_lowerCamelCase       ) else next(i for i, s in enumerate(_lowerCamelCase  ,    1       ) if s is child       )       )
													__lowercase       =       parent
										xpath_tags.reverse()
										xpath_subscripts.reverse()
										return xpath_tags, xpath_subscripts
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       BeautifulSoup(_lowerCamelCase  ,    "html.parser"       )

										__lowercase       =       []
										__lowercase       =       []
										__lowercase       =       []

										for element in html_code.descendants:
													if type(_lowerCamelCase       ) == bsa.element.NavigableString:
																if type(element.parent       ) != bsa.element.Tag:
																			continue

																__lowercase       =       html.unescape(_lowerCamelCase       ).strip()
																if not text_in_this_tag:
																			continue

																all_doc_strings.append(_lowerCamelCase       )

																__lowercase   ,					__lowercase       =       self.xpath_soup(_lowerCamelCase       )
																stringaxtag_seq.append(_lowerCamelCase       )
																stringaxsubs_seq.append(_lowerCamelCase       )

										if len(_lowerCamelCase       ) != len(_lowerCamelCase       ):
													raise ValueError("Number of doc strings and xtags does not correspond"       )
										if len(_lowerCamelCase       ) != len(_lowerCamelCase       ):
													raise ValueError("Number of doc strings and xsubs does not correspond"       )

										return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       ""
										for tagname, subs in zip(_lowerCamelCase  ,    _lowerCamelCase       ):
													xpath += f'''/{tagname}'''
													if subs != 0:
																xpath += f'''[{subs}]'''
										return xpath





							def __call__(     self  ,    _lowerCamelCase       )			->							BatchFeature:




										'''simple docstring'''
										__lowercase       =       False

										# Check that strings has a valid type
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													__lowercase       =       True
										elif isinstance(_lowerCamelCase  ,    (list, tuple)       ):
													if len(_lowerCamelCase       ) == 0 or isinstance(html_strings[0]  ,    _lowerCamelCase       ):
																__lowercase       =       True

										if not valid_strings:
													raise ValueError(
													    "HTML strings must of type `str`, `List[str]` (batch of examples), "
													    f'''but is of type {type(_lowerCamelCase       )}.'''       )

										__lowercase       =       bool(isinstance(_lowerCamelCase  ,    (list, tuple)       ) and (isinstance(html_strings[0]  ,    _lowerCamelCase       ))       )

										if not is_batched:
													__lowercase       =       [html_strings]

										# Get nodes + xpaths
										__lowercase       =       []
										__lowercase       =       []
										for html_string in html_strings:
													__lowercase   ,					__lowercase   ,					__lowercase       =       self.get_three_from_single(_lowerCamelCase       )
													nodes.append(_lowerCamelCase       )
													__lowercase       =       []
													for node, tag_list, sub_list in zip(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       ):
																__lowercase       =       self.construct_xpath(_lowerCamelCase  ,    _lowerCamelCase       )
																xpath_strings.append(_lowerCamelCase       )
													xpaths.append(_lowerCamelCase       )

										# return as Dict
										__lowercase       =       {"nodes": nodes, "xpaths": xpaths}
										__lowercase       =       BatchFeature(data=_lowerCamelCase  ,    tensor_type=_lowerCamelCase       )

										return encoded_inputs





