'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE__		   =						'''ChineseCLIPImageProcessor'''
     SCREAMING_SNAKE_CASE__		   =						('''BertTokenizer''', '''BertTokenizerFast''')







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'     ,			_lowerCamelCase     ,			)
             __UpperCamelCase	:	Any		    =  kwargs.pop('feature_extractor'    )

         __UpperCamelCase	:	int		    =  image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'    )
         if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'    )

         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  self.image_processor







     def __call__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase    ):

         if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'    )

         if text is not None:
             __UpperCamelCase	:	List[Any]		    =  self.tokenizer(_lowerCamelCase     ,			return_tensors=_lowerCamelCase     ,			**_lowerCamelCase    )

         if images is not None:
             __UpperCamelCase	:	Any		    =  self.image_processor(_lowerCamelCase     ,			return_tensors=_lowerCamelCase     ,			**_lowerCamelCase    )

         if text is not None and images is not None:
             __UpperCamelCase	:	str		    =  image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )     ,			tensor_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.tokenizer.model_input_names
         __UpperCamelCase	:	Tuple		    =  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     @property
     def lowerCAmelCase     (		self    ):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,			_lowerCamelCase     ,			)
         return self.image_processor_class
'''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  []
         __UpperCamelCase	:	Dict		    =  []
         __UpperCamelCase	:	Union[str, Any]		    =  element if element.name else element.parent
         for parent in child.parents:  # type: bs4.element.Tag
             __UpperCamelCase	:	str		    =  parent.find_all(child.name     ,			recursive=_lowerCamelCase    )
             xpath_tags.append(child.name    )
             xpath_subscripts.append(
                 0 if 1 == len(_lowerCamelCase    ) else next(i for i, s in enumerate(_lowerCamelCase     ,			1    ) if s is child    )    )
             __UpperCamelCase	:	Union[str, Any]		    =  parent
         xpath_tags.reverse()
         xpath_subscripts.reverse()
         return xpath_tags, xpath_subscripts







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  BeautifulSoup(_lowerCamelCase     ,			'html.parser'    )

         __UpperCamelCase	:	int		    =  []
         __UpperCamelCase	:	List[Any]		    =  []
         __UpperCamelCase	:	int		    =  []

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

                 __UpperCamelCase	:	Tuple		    =  html.unescape(_lowerCamelCase    ).strip()
                 if not text_in_this_tag:
                     continue

                 all_doc_strings.append(_lowerCamelCase    )

                 __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.xpath_soup(_lowerCamelCase    )
                 stringaxtag_seq.append(_lowerCamelCase    )
                 stringaxsubs_seq.append(_lowerCamelCase    )

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

         return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  ''
         for tagname, subs in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
             xpath += f"""/{tagname}"""
             if subs != 0:
                 xpath += f"""[{subs}]"""
         return xpath







     def __call__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  False

         # Check that strings has a valid type
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[str]		    =  True
         elif isinstance(_lowerCamelCase     ,			(list, tuple)    ):
             if len(_lowerCamelCase    ) == 0 or isinstance(html_strings[0]     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[int]		    =  True

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

         __UpperCamelCase	:	Dict		    =  bool(isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(html_strings[0]     ,			_lowerCamelCase    ))    )

         if not is_batched:
             __UpperCamelCase	:	Tuple		    =  [html_strings]

         # Get nodes + xpaths
         __UpperCamelCase	:	Tuple		    =  []
         __UpperCamelCase	:	Tuple		    =  []
         for html_string in html_strings:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.get_three_from_single(_lowerCamelCase    )
             nodes.append(_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  []
             for node, tag_list, sub_list in zip(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[Any]		    =  self.construct_xpath(_lowerCamelCase     ,			_lowerCamelCase    )
                 xpath_strings.append(_lowerCamelCase    )
             xpaths.append(_lowerCamelCase    )

         # return as Dict
         __UpperCamelCase	:	Optional[int]		    =  {'nodes': nodes, 'xpaths': xpaths}
         __UpperCamelCase	:	Union[str, Any]		    =  BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )

         return encoded_inputs



