



'''simple docstring'''


import warnings
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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""image_processor""", """tokenizer"""]
    _SCREAMING_SNAKE_CASE	  =  """ViltImageProcessor"""
    _SCREAMING_SNAKE_CASE	  =  ("""BertTokenizer""", """BertTokenizerFast""")






    def __init__(  self      :					Any  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					List[Any]=None  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  None
       if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'  ,						UpperCamelCase__  ,						)
          UpperCamelCase					=  kwargs.pop('feature_extractor' )

       UpperCamelCase					=  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.' )
       if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.' )

       super().__init__(UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  self.image_processor






    def __call__(  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Union[bool, str, PaddingStrategy] = False  ,						UpperCamelCase__      :					Union[bool, str, TruncationStrategy] = None  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					int = 0  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[Union[str, TensorType]] = None  ,						**UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(
           text=UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						stride=UpperCamelCase__  ,						pad_to_multiple_of=UpperCamelCase__  ,						return_token_type_ids=UpperCamelCase__  ,						return_attention_mask=UpperCamelCase__  ,						return_overflowing_tokens=UpperCamelCase__  ,						return_special_tokens_mask=UpperCamelCase__  ,						return_offsets_mapping=UpperCamelCase__  ,						return_length=UpperCamelCase__  ,						verbose=UpperCamelCase__  ,						return_tensors=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
       # add pixel_values + pixel_mask
       UpperCamelCase					=  self.image_processor(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ )
       encoding.update(UpperCamelCase__ )

       return encoding






    def  A       (  self      :					int  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







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






    def  A       (  self      :					Tuple  ,						*UpperCamelCase__      :					Optional[int]  ,						**UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







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






    @property
    def  A       (  self      :					str ):


       """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 ) )






    @property
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,						UpperCamelCase__  ,						)
       return self.image_processor_class






    @property
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,						UpperCamelCase__  ,						)
       return self.image_processor






'''simple docstring'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


if __name__ == "__main__":
   _lowerCamelCase	:       int      	=	input("Enter numbers separated by a comma:\n").strip()
   _lowerCamelCase	:       Union[str, Any]      	=	[int(item) for item in user_input.split(",")]
   print(binary_insertion_sort(unsorted))

