import os

import pytest
from attr import dataclass


A_      =      "us-east-1"  # defaults region






@dataclass
class      __lowercase					:
      lowercase					 =     42
      lowercase					 =     'arn:aws:iam::558105141721:role/sagemaker_execution_role'
      lowercase					 =     {
          'task_name': 'mnli',
          'per_device_train_batch_size': 16,
          'per_device_eval_batch_size': 16,
          'do_train': True,
          'do_eval': True,
          'do_predict': True,
          'output_dir': '/opt/ml/model',
          'overwrite_output_dir': True,
          'max_steps': 500,
          'save_steps': 5500,
      }
      lowercase					 =     {**hyperparameters, 'max_steps': 1000}

      @property
      def        __a		(	self				:      Tuple   )							->		str:


         '''simple docstring'''

         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        __a		(	self				:      int   )							->		str:


         '''simple docstring'''

         return f'{self.framework}-transfromers-test'

      @property
      def        __a		(	self				:      Optional[Any]   )							->		str:


         '''simple docstring'''

         return f'./tests/sagemaker/scripts/{self.framework}'



      @property
      def        __a		(	self				:      Optional[int]   )							->		str:


         '''simple docstring'''

         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        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



   """simple docstring"""







   lowercase							=		SageMakerTestEnvironment(framework=request.cls.framework       )






from __future__ import annotations

import math






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:



			"""simple docstring"""







			if num <= 0:
						lowercase							=		f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(UpperCAmelCase       )

			lowercase							=		[True] * (num + 1)
			lowercase							=		[]
			lowercase							=		2
			lowercase							=		int(math.sqrt(UpperCAmelCase       )       )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(UpperCAmelCase       )

									# Set multiples of start be False
									for i in range(start * start,		num + 1,		UpperCAmelCase       ):
												if sieve[i] is True:
															lowercase							=		False

						start += 1

			for j in range(end + 1,		num + 1       ):
						if sieve[j] is True:
									prime.append(UpperCAmelCase       )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))





