
import warnings

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



class 						UpperCamelCase__    (			a_       ):
   _SCREAMING_SNAKE_CASE :				Tuple      			= ["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE :				Dict      			= "ViTImageProcessor"
   _SCREAMING_SNAKE_CASE :				Union[str, Any]      			= ("CLIPTokenizer", "CLIPTokenizerFast")
   def __init__(self					:   List[str]   ,							snake_case_					:   List[Any]=None   ,							snake_case_					:   Optional[int]=None   ,							**snake_case_					:   Tuple  ):
    __a					:    int													=    None
    if "feature_extractor" in kwargs:
     warnings.warn(
         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
         ''' instead.'''   ,							lowercase_   ,							)
     __a					:    Tuple													=    kwargs.pop('''feature_extractor'''  )

    __a					:    Union[str, Any]													=    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__(lowercase_   ,							lowercase_  )
   def __call__(self					:   str   ,							snake_case_					:   Dict=None   ,							snake_case_					:   int=None   ,							snake_case_					:   Union[str, Any]=None   ,							snake_case_					:   Any=None   ,							**snake_case_					:   Union[str, Any]  ):
    if text is None and visual_prompt is None and images is None:
     raise ValueError('''You have to specify either text, visual prompt or images.'''  )

    if text is not None and visual_prompt is not None:
     raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

    if text is not None:
     __a					:    str													=    self.tokenizer(lowercase_   ,							return_tensors=lowercase_   ,							**lowercase_  )

    if visual_prompt is not None:
     __a					:    Optional[Any]													=    self.image_processor(lowercase_   ,							return_tensors=lowercase_   ,							**lowercase_  )

    if images is not None:
     __a					:    Optional[Any]													=    self.image_processor(lowercase_   ,							return_tensors=lowercase_   ,							**lowercase_  )

    if visual_prompt is not None and images is not None:
     __a					:    Union[str, Any]													=    {
         '''pixel_values''': image_features.pixel_values,
         '''conditional_pixel_values''': prompt_features.pixel_values,
     }
     return encoding
    elif text is not None and images is not None:
     __a					:    Optional[Any]													=    image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    elif visual_prompt is not None:
     __a					:    Union[str, Any]													=    {
         '''conditional_pixel_values''': prompt_features.pixel_values,
     }
     return encoding
    else:
     return BatchEncoding(data=dict(**lowercase_  )   ,							tensor_type=lowercase_  )
   def  lowerCAmelCase     (self					:   List[str]   ,							*snake_case_					:   List[str]   ,							**snake_case_					:   Optional[int]  ):
    return self.tokenizer.batch_decode(*lowercase_   ,							**lowercase_  )
   def  lowerCAmelCase     (self					:   Optional[Any]   ,							*snake_case_					:   str   ,							**snake_case_					:   List[Any]  ):
    return self.tokenizer.decode(*lowercase_   ,							**lowercase_  )
   @property
   def  lowerCAmelCase     (self					:   List[str]  ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,							lowercase_   ,							)
    return self.image_processor_class







   @property
   def  lowerCAmelCase     (self					:   List[Any]  ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,							lowercase_   ,							)
    return self.image_processor



"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

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




