


'''simple docstring'''







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class    lowercase_	(       lowerCamelCase__  ):
    __UpperCAmelCase       =      'MarkupLMFeatureExtractor'
    __UpperCAmelCase       =      ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
    __UpperCAmelCase       =      True


    def __call__(	self    ,   a=None    ,   a=None    ,   a=None    ,   a=None    ,   a=None    ,   a = True    ,   a = False    ,   a = None    ,   a = None    ,   a = 0    ,   a = None    ,   a = None    ,   a = None    ,   a = False    ,   a = False    ,   a = False    ,   a = False    ,   a = True    ,   a = None    ,   **a    ,   ):
          # 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__      =							self.feature_extractor(__UpperCamelCase       )
                UpperCamelCase__      =							features["nodes"]
                UpperCamelCase__      =							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(__UpperCamelCase    ,   __UpperCamelCase       ):
                      UpperCamelCase__      =							[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

          UpperCamelCase__      =							self.tokenizer(
              text=questions if questions is not None else nodes    ,   text_pair=nodes if questions is not None else None    ,   xpaths=__UpperCamelCase    ,   node_labels=__UpperCamelCase    ,   add_special_tokens=__UpperCamelCase    ,   padding=__UpperCamelCase    ,   truncation=__UpperCamelCase    ,   max_length=__UpperCamelCase    ,   stride=__UpperCamelCase    ,   pad_to_multiple_of=__UpperCamelCase    ,   return_token_type_ids=__UpperCamelCase    ,   return_attention_mask=__UpperCamelCase    ,   return_overflowing_tokens=__UpperCamelCase    ,   return_special_tokens_mask=__UpperCamelCase    ,   return_offsets_mapping=__UpperCamelCase    ,   return_length=__UpperCamelCase    ,   verbose=__UpperCamelCase    ,   return_tensors=__UpperCamelCase    ,   **__UpperCamelCase    ,   )

          return encoded_inputs


    def   __a				(	self    ,   *a    ,   **a       ):
          return self.tokenizer.batch_decode(*__UpperCamelCase    ,   **__UpperCamelCase       )


    def   __a				(	self    ,   *a    ,   **a       ):
          return self.tokenizer.decode(*__UpperCamelCase    ,   **__UpperCamelCase       )


    @property
    def   __a				(	self       ):
          UpperCamelCase__      =							self.tokenizer.model_input_names
          return tokenizer_input_names









'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase   ,	__lowerCamelCase					) or number < 0:
          raise ValueError("Input must be a non-negative integer"					)

     _a            =		number & ~(number - 1)
     _a            =		0
     while intermediate:
          intermediate >>= 1
          index += 1
     return index - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

