



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__		:					List[str]							=logging.get_logger(__name__)

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= element if element.name else element.parent
													for parent in child.parents:  # type: bs4.element.Tag
																				__SCREAMING_SNAKE_CASE									= parent.find_all(child.name				,							recursive=_A		)
																				xpath_tags.append(child.name		)
																				xpath_subscripts.append(
																				    0 if 1 == len(_A		) else next(i for i, s in enumerate(_A				,							1		) if s is child		)		)
																				__SCREAMING_SNAKE_CASE									= parent
													xpath_tags.reverse()
													xpath_subscripts.reverse()
													return xpath_tags, xpath_subscripts






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= BeautifulSoup(_A				,							'html.parser'		)

													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []

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

																											__SCREAMING_SNAKE_CASE									= html.unescape(_A		).strip()
																											if not text_in_this_tag:
																																		continue

																											all_doc_strings.append(_A		)

																											__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.xpath_soup(_A		)
																											stringaxtag_seq.append(_A		)
																											stringaxsubs_seq.append(_A		)

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

													return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						def __call__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= False

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

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

													__SCREAMING_SNAKE_CASE									= bool(isinstance(_A				,							(list, tuple)		) and (isinstance(html_strings[0]				,							_A		))		)

													if not is_batched:
																				__SCREAMING_SNAKE_CASE									= [html_strings]

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

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

													return encoded_inputs











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

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






