


from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





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






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__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       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



