

'''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					:			str							=    logging.get_logger(__name__)



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			**a_  )				->					Dict:


    """simple docstring"""
    requires_backends(self ,			['bs4']  )
    super().__init__(**a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      UpperCAmelCase    				=						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  )  )
      UpperCAmelCase    				=						parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def     snake_case_      (  self ,			a_  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						BeautifulSoup(a_ ,			'html.parser'  )

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						[]

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

        UpperCAmelCase    				=						html.unescape(a_  ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(a_  )

        UpperCAmelCase				,   UpperCAmelCase    				=						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     snake_case_      (  self ,			a_ ,			a_  )				->					Dict:


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





  def __call__(  self ,			a_  )				->					BatchFeature:


    """simple docstring"""
    UpperCAmelCase    				=						False

    # Check that strings has a valid type
    if isinstance(a_ ,			a_  ):
      UpperCAmelCase    				=						True
    elif isinstance(a_ ,			(list, tuple)  ):
      if len(a_  ) == 0 or isinstance(html_strings[0] ,			a_  ):
        UpperCAmelCase    				=						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_  )}.'''  )

    UpperCAmelCase    				=						bool(isinstance(a_ ,			(list, tuple)  ) and (isinstance(html_strings[0] ,			a_  ))  )

    if not is_batched:
      UpperCAmelCase    				=						[html_strings]

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

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

    return encoded_inputs





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

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


