



"""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


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    Dict      ,							**snake_case  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

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



		def 					A_		( self  :    str      ,							snake_case  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					[]
									A         =					[]
									A         =					element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
																A         =					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				)				)
																A         =					parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    List[str]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					BeautifulSoup(snake_case      ,							'html.parser'				)

									A         =					[]
									A         =					[]
									A         =					[]

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

																							A         =					html.unescape(snake_case				).strip()
																							if not text_in_this_tag:
																														continue

																							all_doc_strings.append(snake_case				)

																							A      ,  A         =					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 					A_		( self  :    Optional[int]      ,							snake_case  :    Any      ,							snake_case  :    List[str]				)			->						int:






									'''simple docstring'''

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



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






									'''simple docstring'''

									A         =					False

									# Check that strings has a valid type
									if isinstance(snake_case      ,							snake_case				):
																A         =					True
									elif isinstance(snake_case      ,							(list, tuple)				):
																if len(snake_case				) == 0 or isinstance(html_strings[0]      ,							snake_case				):
																							A         =					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				)}."""				)

									A         =					bool(isinstance(snake_case      ,							(list, tuple)				) and (isinstance(html_strings[0]      ,							snake_case				))				)

									if not is_batched:
																A         =					[html_strings]

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

									# return as Dict
									A         =					{'nodes': nodes, 'xpaths': xpaths}
									A         =					BatchFeature(data=snake_case      ,							tensor_type=snake_case				)

									return encoded_inputs








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



