


def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Union[str, Any]											= [701, 301, 132, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowercase_				,						len(lowercase_		)		):
                  a		:Optional[Any]											= collection[i]
                  a		:int											= i
                  while j >= gap and collection[j - gap] > insert_value:
                        a		:str											= collection[j - gap]
                        j -= gap
                  if j != i:
                        a		:int											= insert_value
      return collection


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    snake_case       :					Tuple        =  input('''Enter numbers separated by a comma:\n''').strip()
    snake_case       :					Optional[int]        =  [int(item) for item in user_input.split(''',''')]
    print(shell_sort(unsorted))









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						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = 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`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= 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					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= 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  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




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




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





