



def     __lowercase(	UpperCAmelCase__ ):


						"""simple docstring"""



						if collection == []:
												return []

						# get some information about the collection
						lowerCamelCase								=		len(UpperCAmelCase__ )
						lowerCamelCase								=		max(UpperCAmelCase__ )
						lowerCamelCase								=		min(UpperCAmelCase__ )

						# create the counting array
						lowerCamelCase								=		coll_max + 1 - coll_min
						lowerCamelCase								=		[0] * counting_arr_length

						# count how much a number appears in the collection
						for number in collection:
												counting_arr[number - coll_min] += 1

						# sum each position with it's predecessors. now, counting_arr[i] tells
						# us how many elements <= i has in the collection
						for i in range(1   ,   UpperCAmelCase__ ):
												lowerCamelCase								=		counting_arr[i] + counting_arr[i - 1]

						# create the output collection
						lowerCamelCase								=		[0] * coll_len

						# place the elements in the output, respecting the original order (stable
						# sort) from end to begin, updating counting_arr
						for i in reversed(range(0   ,   UpperCAmelCase__ ) ):
												lowerCamelCase								=		collection[i]
												counting_arr[collection[i] - coll_min] -= 1

						return ordered

def     __lowercase(	UpperCAmelCase__ ):


						"""simple docstring"""



						return "".join([chr(UpperCAmelCase__ ) for i in counting_sort([ord(UpperCAmelCase__ ) for c in string] )] )


if __name__ == "__main__":
					# Test string sort
					assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

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




import warnings

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





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'CLIPImageProcessor'
     _A       =			('CLIPTokenizer', 'CLIPTokenizerFast')







     def __init__(self				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



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

           lowerCamelCase								=		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__(__a				,			__a    )







     def __call__(self				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''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:
                 lowerCamelCase								=		self.tokenizer(__a				,			return_tensors=__a				,			**__a    )

           if images is not None:
                 lowerCamelCase								=		self.image_processor(__a				,			return_tensors=__a				,			**__a    )

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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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