


import os

import pytest
from attr import dataclass


snake_case							:	Optional[Any]					      =     '''us-east-1'''  # defaults region







@dataclass
class 	snake_case_       :
					UpperCAmelCase__ :   Tuple   				=      4_2
					UpperCAmelCase__ :   int   				=      '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
					UpperCAmelCase__ :   Tuple   				=      {
					    '''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,
					}
					UpperCAmelCase__ :   Tuple   				=      {**hyperparameters, '''max_steps''': 1_0_0_0}
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   str:
												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 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return F'{self.framework}-transfromers-test'
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   str:
												return F'./tests/sagemaker/scripts/{self.framework}'



					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												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 							__lowercase       (			__lowerCAmelCase							:    str       ):
							a__					=					SageMakerTestEnvironment(framework=request.cls.framework       )








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




