



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


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):




     def __init__(					self			,    **a__):
         requires_backends(self			,    ['''bs4'''])
         super().__init__(**a__)




     def 		snake_case_		(					self			,    a__):
         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=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))
             A__					=       parent
         xpath_tags.reverse()
         xpath_subscripts.reverse()
         return xpath_tags, xpath_subscripts




     def 		snake_case_		(					self			,    a__):
         A__					=       BeautifulSoup(a__			,    '''html.parser''')

         A__					=       []
         A__					=       []
         A__					=       []

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

                 A__					=       html.unescape(a__).strip()
                 if not text_in_this_tag:
                     continue

                 all_doc_strings.append(a__)

                 A__			,						A__					=       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__):
         A__					=       ''''''
         for tagname, subs in zip(a__			,    a__):
             xpath += F"/{tagname}"
             if subs != 0:
                 xpath += F"[{subs}]"
         return xpath




     def __call__(					self			,    a__):
         A__					=       False

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

         A__					=       bool(isinstance(a__			,    (list, tuple)) and (isinstance(html_strings[0]			,    a__)))

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

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

         return encoded_inputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_upernet": ["UperNetConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "UperNetForSemanticSegmentation",
       "UperNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_upernet import UperNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
