



'''simple docstring'''

import warnings

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

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[Any]           = ['image_processor', 'tokenizer']
							lowerCAmelCase	:   Optional[int]           = 'CLIPImageProcessor'
							lowerCAmelCase	:   Optional[int]           = ('CLIPTokenizer', 'CLIPTokenizerFast')







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	List[str]=None     ,**_UpperCAmelCase :	int		):
									_a					:				List[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.'     ,_UpperCAmelCase     ,)
											_a					:				Optional[int]				=							kwargs.pop('feature_extractor'		)

									_a					:				Optional[int]				=							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__(_UpperCAmelCase     ,_UpperCAmelCase		)







							def __call__(							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]=None     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	Any=None     ,**_UpperCAmelCase :	Tuple		):

									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					:				Optional[int]				=							self.tokenizer(_UpperCAmelCase     ,return_tensors=_UpperCAmelCase     ,**_UpperCAmelCase		)

									if images is not None:
											_a					:				List[str]				=							self.image_processor(_UpperCAmelCase     ,return_tensors=_UpperCAmelCase     ,**_UpperCAmelCase		)

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







							def 							__lowercase      (							self :	Any     ,*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Optional[Any]		):
									return self.tokenizer.batch_decode(*_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple     ,*_UpperCAmelCase :	Optional[Any]     ,**_UpperCAmelCase :	str		):
									return self.tokenizer.decode(*_UpperCAmelCase     ,**_UpperCAmelCase		)







							@property
							def 							__lowercase      (							self :	List[str]		):
									_a					:				Tuple				=							self.tokenizer.model_input_names
									_a					:				Dict				=							self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)







							@property
							def 							__lowercase      (							self :	Dict		):
									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,_UpperCAmelCase     ,)
									return self.image_processor_class







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









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




