

import warnings

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

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["image_processor", "tokenizer"]
						_lowercase    						=			"CLIPImageProcessor"
						_lowercase    						=			("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


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







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, 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__						,					)
														_UpperCAmelCase       :		int 		=    kwargs.pop("feature_extractor"	)

										_UpperCAmelCase       :		List[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__(UpperCAmelCase__						,					UpperCAmelCase__	)


						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:
														_UpperCAmelCase       :		Dict 		=    self.tokenizer(UpperCAmelCase__						,					return_tensors=UpperCAmelCase__						,					**UpperCAmelCase__	)

										if images is not None:
														_UpperCAmelCase       :		Dict 		=    self.image_processor(UpperCAmelCase__						,					return_tensors=UpperCAmelCase__						,					**UpperCAmelCase__	)

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


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.tokenizer.model_input_names
										_UpperCAmelCase       :		str 		=    self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)







from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


