'''simple docstring'''




import os

import pytest
from attr import dataclass


__lowercase          =  '''us-east-1'''  # defaults region






@dataclass
class 				a__:


			'''simple docstring'''






			UpperCAmelCase_			:     str
			UpperCAmelCase_			:     Optional[Any]      =       '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
			UpperCAmelCase_			:     Union[str, Any]      =       {
			    '''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_			:     Optional[int]      =       {**hyperparameters, '''max_steps''': 1_0_0_0}



			@property
			def 							a_       (   self):




					"""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):




					"""simple docstring"""


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



			@property
			def 							a_       (   self):




					"""simple docstring"""


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







			@property
			def 							a_       (   self):




					"""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 				snake_case__							(			_A:					List[str] )    ->	List[Any]:

		'''simple docstring'''


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





'''simple docstring'''




from math import sqrt






def 				snake_case__							(			_A:					int = 1000000 )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  42

		while num_cuboids <= limit:
				max_cuboid_size += 1
				for sum_shortest_sides in range(2      ,   2 * max_cuboid_size + 1 ):
						if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
								num_cuboids += (
								    min(_A      ,   sum_shortest_sides // 2 )
								    - max(1      ,   sum_shortest_sides - max_cuboid_size )
								    + 1
								)

		return max_cuboid_size


if __name__ == "__main__":
						print(f'{solution() = }')




