from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int]			      =	'MarkupLMFeatureExtractor'
 _lowerCamelCase  :		int			      =	('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 _lowerCamelCase  :		Tuple			      =	True

 def __call__(     self    :						int    ,     lowerCamelCase    :						Any=None    ,     lowerCamelCase    :						List[str]=None    ,     lowerCamelCase    :						Tuple=None    ,     lowerCamelCase    :						Optional[int]=None    ,     lowerCamelCase    :						int=None    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						Union[bool, str, PaddingStrategy] = False    ,     lowerCamelCase    :						Union[bool, str, TruncationStrategy] = None    ,     lowerCamelCase    :						Optional[int] = None    ,     lowerCamelCase    :						int = 0    ,     lowerCamelCase    :						Optional[int] = None    ,     lowerCamelCase    :						Optional[bool] = None    ,     lowerCamelCase    :						Optional[bool] = None    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						Optional[Union[str, TensorType]] = None    ,     **lowerCamelCase    :						Optional[Any]    ,     )	->							BatchEncoding:
        # first, create nodes and xpaths
        if self.parse_html:
               if html_strings is None:
                      raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'					)

               if nodes is not None or xpaths is not None or node_labels is not None:
                      raise ValueError(
                          'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'					)

               _UpperCamelCase       :    List[Any]					  =					self.feature_extractor(lowerCamelCase					)
               _UpperCamelCase       :    Optional[int]					  =					features['nodes']
               _UpperCamelCase       :    List[Any]					  =					features['xpaths']
        else:
               if html_strings is not None:
                      raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'					)
               if nodes is None or xpaths is None:
                      raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'					)

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
               if isinstance(lowerCamelCase    ,     lowerCamelCase					):
                      _UpperCamelCase       :    Union[str, Any]					  =					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        _UpperCamelCase       :    Optional[Any]					  =					self.tokenizer(
            text=questions if questions is not None else nodes    ,     text_pair=nodes if questions is not None else None    ,     xpaths=lowerCamelCase    ,     node_labels=lowerCamelCase    ,     add_special_tokens=lowerCamelCase    ,     padding=lowerCamelCase    ,     truncation=lowerCamelCase    ,     max_length=lowerCamelCase    ,     stride=lowerCamelCase    ,     pad_to_multiple_of=lowerCamelCase    ,     return_token_type_ids=lowerCamelCase    ,     return_attention_mask=lowerCamelCase    ,     return_overflowing_tokens=lowerCamelCase    ,     return_special_tokens_mask=lowerCamelCase    ,     return_offsets_mapping=lowerCamelCase    ,     return_length=lowerCamelCase    ,     verbose=lowerCamelCase    ,     return_tensors=lowerCamelCase    ,     **lowerCamelCase    ,     )

        return encoded_inputs

 def    __snake_case   (     self    :						List[str]    ,     *lowerCamelCase    :						str    ,     **lowerCamelCase    :						Dict					)	->							List[Any]:
        return self.tokenizer.batch_decode(*lowerCamelCase    ,     **lowerCamelCase					)

 def    __snake_case   (     self    :						Tuple    ,     *lowerCamelCase    :						Any    ,     **lowerCamelCase    :						List[Any]					)	->							Tuple:
        return self.tokenizer.decode(*lowerCamelCase    ,     **lowerCamelCase					)

 @property
 def    __snake_case   (     self    :						List[str]					)	->							int:
        _UpperCamelCase       :    Optional[int]					  =					self.tokenizer.model_input_names
        return tokenizer_input_names




def SCREAMING_SNAKE_CASE			(       lowercase_:		int = 4000000			):
       _UpperCamelCase       :    str					  =					1
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Tuple					  =					0
       while j <= n:
              if j % 2 == 0:
                     total += j
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					j, i + j

       return total


if __name__ == "__main__":
 print(f'''{solution() = }''')



