


'''simple docstring'''

import numpy as np

from transformers import Pipeline

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				np.max(lowerCamelCase,				axis=-1,				keepdims=lowerCamelCase)
						__lowerCAmelCase							       =				np.exp(outputs - maxes)
						return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=lowerCamelCase)







class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "second_text" in kwargs:
																__lowerCAmelCase							       =				kwargs['''second_text''']
										return preprocess_kwargs, {}, {}




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										return self.tokenizer(__lowercase       ,			text_pair=__lowercase       ,			return_tensors=self.framework		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.model(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_outputs.logits[0].numpy()
										__lowerCAmelCase							       =				softmax(__lowercase		)

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





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

