


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


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

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






'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             if text is None and images is None:
                    raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

             if text is not None:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if images is not None:
                    _a   :			Any					 =self.image_processor(SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if text is not None and images is not None:
                    _a   :			Dict					 =image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE     )					,						tensor_type=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


