






import numpy as np

from transformers import Pipeline







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
							snake_case__			:				Any       =  np.max(snake_case_					,  axis=-1					,  keepdims=snake_case_       )
							snake_case__			:				int       =  np.exp(outputs - maxes       )
							return shifted_exp / shifted_exp.sum(axis=-1					,  keepdims=snake_case_       )





class 							SCREAMING_SNAKE_CASE__						(    __lowercase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  str							,		**__A					:  str	):
												snake_case__			:				int       =  {}
												if "second_text" in kwargs:
																			snake_case__			:				str       =  kwargs["second_text"]
												return preprocess_kwargs, {}, {}



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Dict							,		__A					:  str=None	):
												return self.tokenizer(UpperCAmelCase__							,		text_pair=UpperCAmelCase__							,		return_tensors=self.framework	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  Union[str, Any]	):
												return self.model(**UpperCAmelCase__	)






					def 	_lowercase					( self					:  str							,		__A					:  str	):
												snake_case__			:				Dict       =  model_outputs.logits[0].numpy()
												snake_case__			:				List[str]       =  softmax(UpperCAmelCase__	)

												snake_case__			:				Dict       =  np.argmax(UpperCAmelCase__	)
												snake_case__			:				Optional[Any]       =  self.model.config.idalabel[best_class]
												snake_case__			:				Optional[int]       =  probabilities[best_class].item()
												snake_case__			:				int       =  logits.tolist()
												return {"label": label, "score": score, "logits": logits}











import os

import pytest
from attr import dataclass


__lowerCamelCase :      Any										=				"""us-east-1"""  # defaults region



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
					a_     =		{
					    "task_name": "mnli",
					    "per_device_train_batch_size": 1_6,
					    "per_device_eval_batch_size": 1_6,
					    "do_train": True,
					    "do_eval": True,
					    "do_predict": True,
					    "output_dir": "/opt/ml/model",
					    "overwrite_output_dir": True,
					    "max_steps": 5_0_0,
					    "save_steps": 5_5_0_0,
					}
					a_     =		{**hyperparameters, "max_steps": 1_0_0_0}



					@property
					def 	_lowercase					( self					:  List[Any]	):
						if self.framework == "pytorch":
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
							    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
							]
						else:
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
							    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
							]



					@property
					def 	_lowercase					( self					:  Any	):
						return f'''{self.framework}-transfromers-test'''



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return f'''./tests/sagemaker/scripts/{self.framework}'''






					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self.framework == "pytorch":
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
						else:
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"       )
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[int]       =  SageMakerTestEnvironment(framework=request.cls.framework       )



