




import numpy as np

from transformers import Pipeline







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any]      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    np.max(UpperCamelCase__ ,     axis=-1 ,     keepdims=UpperCamelCase__      )
				__lowerCamelCase		       =    np.exp(outputs - maxes      )
				return shifted_exp / shifted_exp.sum(axis=-1 ,     keepdims=UpperCamelCase__      )







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    {}
							if "second_text" in kwargs:
											__lowerCamelCase		       =    kwargs['second_text']
							return preprocess_kwargs, {}, {}


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=None							)    ->      Tuple:



							'''simple docstring'''
							return self.tokenizer(lowerCamelCase__			,						text_pair=lowerCamelCase__			,						return_tensors=self.framework							)


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							return self.model(**lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    model_outputs.logits[0].numpy()
							__lowerCamelCase		       =    softmax(lowerCamelCase__							)

							__lowerCamelCase		       =    np.argmax(lowerCamelCase__							)
							__lowerCamelCase		       =    self.model.config.idalabel[best_class]
							__lowerCamelCase		       =    probabilities[best_class].item()
							__lowerCamelCase		       =    logits.tolist()
							return {"label": label, "score": score, "logits": logits}






"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

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

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

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




