



"""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    __snake_case(					__A       ):
    _A       = '''MarkupLMFeatureExtractor'''
    _A       = ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    _A       = 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_    ,							):





      '''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`'''			)

        _SCREAMING_SNAKE_CASE        =			self.feature_extractor(A_			)
        _SCREAMING_SNAKE_CASE        =			features['''nodes''']
        _SCREAMING_SNAKE_CASE        =			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(A_    ,							A_			):
          _SCREAMING_SNAKE_CASE        =			[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

      return encoded_inputs




    def   A   (   self    ,							*A_    ,							**A_			):





      '''simple docstring'''



      return self.tokenizer.batch_decode(*A_    ,							**A_			)




    def   A   (   self    ,							*A_    ,							**A_			):





      '''simple docstring'''



      return self.tokenizer.decode(*A_    ,							**A_			)




    @property
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.tokenizer.model_input_names
      return tokenizer_input_names





"""simple docstring"""






import math


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if not isinstance(UpperCamelCase__       ,							UpperCamelCase__    ):
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCamelCase__    )
  if number < 1:
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be a positive integer'''
    raise ValueError(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			[number]
  while number != 1:
    if number % 2 == 0:
      _SCREAMING_SNAKE_CASE        =			math.floor(math.sqrt(UpperCamelCase__    )    )
    else:
      _SCREAMING_SNAKE_CASE        =			math.floor(
          math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    )    )
    sequence.append(UpperCamelCase__    )
  return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()
