
def     __lowerCAmelCase			(			a__  )       ->  int:
				# if the collection is empty, returns empty
				if collection == []:
								return []

				# get some information about the collection
				__a										=    len(a__  )
				__a										=    max(a__  )
				__a										=    min(a__  )

				# create the counting array
				__a										=    coll_max + 1 - coll_min
				__a										=    [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						,    a__  ):
								__a										=    counting_arr[i] + counting_arr[i - 1]

				# create the output collection
				__a										=    [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						,    a__  )  ):
								__a										=    collection[i]
								counting_arr[collection[i] - coll_min] -= 1

				return ordered


def     __lowerCAmelCase			(			a__  )       ->  List[str]:
				return "".join([chr(a__  ) for i in counting_sort([ord(a__  ) for c in string]  )]  )


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

					A     :	List[Any]    = input('Enter numbers separated by a comma:\n').strip()
					A     :	Dict    = [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 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




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

								__a										=    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__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


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

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




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