






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( __lowerCamelCase      ):





       """simple docstring"""







       a       :	Any								='''MarkupLMFeatureExtractor'''
       a       :	Tuple								=('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
       a       :	Optional[int]								=True





       def __call__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase = True						,    _lowerCamelCase = False						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = 0						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = False						,    _lowerCamelCase = False						,    _lowerCamelCase = False						,    _lowerCamelCase = False						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    **_lowerCamelCase						,    ):
         # 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_:      Dict					   =       self.feature_extractor(_lowerCAmelCase							)
           UpperCamelCase_:      Tuple					   =       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_:      Tuple					   =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

         UpperCamelCase_:      Union[str, 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     _a					(      self						,    *_lowerCamelCase						,    **_lowerCamelCase							):
         return self.tokenizer.batch_decode(*_lowerCAmelCase						,    **_lowerCAmelCase							)





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





       @property
       def     _a					(      self							):
         UpperCamelCase_:      Union[str, Any]					   =       self.tokenizer.model_input_names
         return tokenizer_input_names







from math import factorial

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Dict:
  return factorial(UpperCAmelCase__						) / (factorial(UpperCAmelCase__						) * factorial(n - r						))

def 						snake_case      () ->       Optional[Any]:
  UpperCamelCase_:      int					   =       0

  for i in range(1			,					1_0_1						):
    for j in range(1			,					i + 1						):
      if combinations(UpperCAmelCase__			,					UpperCAmelCase__						) > 1E6:
        total += 1
  return total


if __name__ == "__main__":
   print(solution())