





"""simple docstring"""






import os

import pytest
from attr import dataclass


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




@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  42
					__lowerCAmelCase  =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
					__lowerCAmelCase  =  {
					    '''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,
					}
					__lowerCAmelCase  =  {**hyperparameters, '''max_steps''': 1000}







					@property
					def  _lowerCamelCase     (						self     ):
									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     ):
									return f"""{self.framework}-transfromers-test"""







					@property
					def  _lowerCamelCase     (						self     ):
									return f"""./tests/sagemaker/scripts/{self.framework}"""







					@property
					def  _lowerCamelCase     (						self     ):
									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     __A      (			a_						:Dict)		->		str:
				__a :						Optional[Any]			       =  SageMakerTestEnvironment(framework=request.cls.framework)






"""simple docstring"""






from __future__ import annotations

import typing
from collections import Counter

def     __A      (			a_						:int)		->		typing.Counter[int]:
				__a :						typing.Counter[int]			       =  Counter()
				for base in range(1   ,       max_perimeter + 1):
								for perpendicular in range(a_   ,       max_perimeter + 1):
												__a :						Any			       =  (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a_):
																__a :						List[Any]			       =  int(base + perpendicular + hypotenuse)
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets

def     __A      (			a_						:int = 10_00)		->		int:
				__a :						Dict			       =  pythagorean_triple(a_)
				return triplets.most_common(1)[0][0]


if __name__ == "__main__":
		print(F'Perimeter {solution()} has maximum solutions')