code: '''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					(       __SCREAMING_SNAKE_CASE						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					'MarkupLMFeatureExtractor'
  							lowerCAmelCase__       =					('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
  							lowerCAmelCase__       =					True
  
  
  
  							def __call__(    self     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None     ,   UpperCAmelCase = True     ,   UpperCAmelCase = False     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = 0     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = False     ,   UpperCAmelCase = False     ,   UpperCAmelCase = False     ,   UpperCAmelCase = False     ,   UpperCAmelCase = True     ,   UpperCAmelCase = None     ,   **UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														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`"""  )
  
  																					_lowercase				=	self.feature_extractor(UpperCamelCase__  )
  																					_lowercase				=	features['''nodes''']
  																					_lowercase				=	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__  ):
  																												_lowercase				=	[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
  														_lowercase				=	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     _UpperCAmelCase       (    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.tokenizer.batch_decode(*UpperCamelCase__     ,   **UpperCamelCase__  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.tokenizer.decode(*UpperCamelCase__     ,   **UpperCamelCase__  )
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.tokenizer.model_input_names
  														return tokenizer_input_names
  
  
  
style_context: import math
  from timeit import timeit
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	0
  							_lowercase				=	abs(_A					)
  							while True:
  														_lowercase				=	n // 10
  														digits += 1
  														if n == 0:
  																					break
  							return digits
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							return 1 if n == 0 else math.floor(math.log(abs(_A					) ,10					) + 1					)
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							return len(str(abs(_A					)					)					)
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							from collections.abc import Callable
  
  							def benchmark_a_function(_A ,_A					) -> None:
  														_lowercase				=	f'''{func.__name__}({value})'''
  														_lowercase				=	timeit(f'''__main__.{call}''' ,setup="""import __main__"""					)
  														print(f'''{call}: {func(_A					)} -- {timing} seconds'''					)
  
  							for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
  														for func in (num_digits, num_digits_fast, num_digits_faster):
  																					benchmark_a_function(_A ,_A					)
  														print()
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				benchmark()
  
  
  