


'''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_   :      Dict				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			**__magic_name__  )   ->							Tuple:
      requires_backends(self ,			['bs4']  )
      super().__init__(**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							int:
      _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=__magic_name__  )
           xpath_tags.append(child.name  )
           xpath_subscripts.append(
               0 if 1 == len(__magic_name__  ) else next(i for i, s in enumerate(__magic_name__ ,			1  ) if s is child  )  )
           _a      =							parent
      xpath_tags.reverse()
      xpath_subscripts.reverse()
      return xpath_tags, xpath_subscripts
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Tuple:
      _a      =							BeautifulSoup(__magic_name__ ,			'html.parser'  )

      _a      =							[]
      _a      =							[]
      _a      =							[]

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

                _a      =							html.unescape(__magic_name__  ).strip()
                if not text_in_this_tag:
                     continue

                all_doc_strings.append(__magic_name__  )

                _a ,				_a      =							self.xpath_soup(__magic_name__  )
                stringaxtag_seq.append(__magic_name__  )
                stringaxsubs_seq.append(__magic_name__  )

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

      return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							''
      for tagname, subs in zip(__magic_name__ ,			__magic_name__  ):
           xpath += f'/{tagname}'
           if subs != 0:
                xpath += f'[{subs}]'
      return xpath




 def __call__(	self ,			__magic_name__  )   ->							BatchFeature:
      _a      =							False

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

      _a      =							bool(isinstance(__magic_name__ ,			(list, tuple)  ) and (isinstance(html_strings[0] ,			__magic_name__  ))  )

      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(__magic_name__  )
           nodes.append(__magic_name__  )
           _a      =							[]
           for node, tag_list, sub_list in zip(__magic_name__ ,			__magic_name__ ,			__magic_name__  ):
                _a      =							self.construct_xpath(__magic_name__ ,			__magic_name__  )
                xpath_strings.append(__magic_name__  )
           xpaths.append(__magic_name__  )

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

      return encoded_inputs





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

