



"""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


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Optional[int]			,**_snake_case						:  List[str]						)					->		List[str]:






				"""simple docstring"""



				requires_backends(self			,['''bs4''']						)
				super().__init__(**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			[]
				lowercase__   :    int				=			[]
				lowercase__   :    Optional[int]				=			element if element.name else element.parent
				for parent in child.parents:  # type: bs4.element.Tag
						lowercase__   :    Union[str, Any]				=			parent.find_all(child.name			,recursive=_snake_case						)
						xpath_tags.append(child.name						)
						xpath_subscripts.append(
						    0 if 1 == len(_snake_case						) else next(i for i, s in enumerate(_snake_case			,1						) if s is child						)						)
						lowercase__   :    List[str]				=			parent
				xpath_tags.reverse()
				xpath_subscripts.reverse()
				return xpath_tags, xpath_subscripts







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Any						)					->		int:






				"""simple docstring"""



				lowercase__   :    str				=			BeautifulSoup(_snake_case			,'''html.parser'''						)

				lowercase__   :    Union[str, Any]				=			[]
				lowercase__   :    Tuple				=			[]
				lowercase__   :    str				=			[]

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

								lowercase__   :    Tuple				=			html.unescape(_snake_case						).strip()
								if not text_in_this_tag:
										continue

								all_doc_strings.append(_snake_case						)

								lowercase__		,			lowercase__   :    List[str]				=			self.xpath_soup(_snake_case						)
								stringaxtag_seq.append(_snake_case						)
								stringaxsubs_seq.append(_snake_case						)

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

				return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  int						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    int				=			''''''
				for tagname, subs in zip(_snake_case			,_snake_case						):
						xpath += f"""/{tagname}"""
						if subs != 0:
								xpath += f"""[{subs}]"""
				return xpath







		def __call__(	self						:  int			,_snake_case						:  Optional[int]						)					->		BatchFeature:






				"""simple docstring"""



				lowercase__   :    List[str]				=			False

				# Check that strings has a valid type
				if isinstance(_snake_case			,_snake_case						):
						lowercase__   :    List[Any]				=			True
				elif isinstance(_snake_case			,(list, tuple)						):
						if len(_snake_case						) == 0 or isinstance(html_strings[0]			,_snake_case						):
								lowercase__   :    Union[str, Any]				=			True

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

				lowercase__   :    Union[str, Any]				=			bool(isinstance(_snake_case			,(list, tuple)						) and (isinstance(html_strings[0]			,_snake_case						))						)

				if not is_batched:
						lowercase__   :    Optional[Any]				=			[html_strings]

				# Get nodes + xpaths
				lowercase__   :    int				=			[]
				lowercase__   :    Optional[int]				=			[]
				for html_string in html_strings:
						lowercase__		,			lowercase__		,			lowercase__   :    List[Any]				=			self.get_three_from_single(_snake_case						)
						nodes.append(_snake_case						)
						lowercase__   :    int				=			[]
						for node, tag_list, sub_list in zip(_snake_case			,_snake_case			,_snake_case						):
								lowercase__   :    Optional[Any]				=			self.construct_xpath(_snake_case			,_snake_case						)
								xpath_strings.append(_snake_case						)
						xpaths.append(_snake_case						)

				# return as Dict
				lowercase__   :    str				=			{'''nodes''': nodes, '''xpaths''': xpaths}
				lowercase__   :    List[Any]				=			BatchFeature(data=_snake_case			,tensor_type=_snake_case						)

				return encoded_inputs





"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
