



"""simple docstring"""

# Algorithm for the pigeonhole sorting
















def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					min(lowerCAmelCase_			)  # min() finds the minimum value
					snake_case_					:		str 					=					max(lowerCAmelCase_			)  # max() finds the maximum value

					snake_case_					:		Dict 					=					max_val - min_val + 1  # size is difference of max and min values plus one

					# list of pigeonholes of size equal to the variable size
					snake_case_					:		int 					=					[0] * size

					# Populate the pigeonholes.
					for x in a:
										assert isinstance(lowerCAmelCase_				,    lowerCAmelCase_			), "integers only please"
										holes[x - min_val] += 1

					# Putting the elements back into the array in an order.
					snake_case_					:		Optional[Any] 					=					0
					for count in range(lowerCAmelCase_			):
										while holes[count] > 0:
															holes[count] -= 1
															snake_case_					:		Optional[int] 					=					count + min_val
															i += 1







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					[8, 3, 2, 7, 4, 6, 8]
					pigeonhole_sort(lowerCAmelCase_			)
					print("""Sorted order is:"""				,    """ """.join(lowerCAmelCase_			)			)


if __name__ == "__main__":
			main()





"""simple docstring"""

import warnings

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







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					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__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					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__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







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

											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:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




