



'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     for i in range(len(__A) - 1    ,      0    ,      -1):
          _a           =    False

          for j in range(__A    ,      0    ,      -1):
               if unsorted[j] < unsorted[j - 1]:
                    _a    ,       _a           =    unsorted[j - 1], unsorted[j]
                    _a           =    True

          for j in range(__A):
               if unsorted[j] > unsorted[j + 1]:
                    _a    ,       _a           =    unsorted[j + 1], unsorted[j]
                    _a           =    True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  lowercase_									=    input("Enter numbers separated by a comma:\n").strip()
  lowercase_									=    [int(item) for item in user_input.split(",")]
  print(F"""{cocktail_shaker_sort(unsorted) = }""")











from typing import List, Optional, Union

from ...image_utils import ImageInput
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"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





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



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





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



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

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





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

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


