
'''simple docstring'''
import os

import pytest
from attr import dataclass


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


@dataclass
class       _lowerCAmelCase   :







       '''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": 5_00,
           "save_steps": 55_00,
       }
       lowerCAmelCase_ 						=						{**hyperparameters, "max_steps": 10_00}

       @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  )					->       Optional[int]:
              return f"""{self.framework}-transfromers-test"""

       @property
       def 	lowercase							(self  )					->       List[str]:
              return f"""./tests/sagemaker/scripts/{self.framework}"""





       @property
       def 	lowercase							(self  )					->       List[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				(  _SCREAMING_SNAKE_CASE				):
       _snake_case       =  SageMakerTestEnvironment(framework=request.cls.framework				)

'''simple docstring'''
from __future__ import annotations

import typing
from collections import Counter






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  Counter()
							for base in range(1							, max_perimeter + 1				):
														for perpendicular in range(_SCREAMING_SNAKE_CASE							, max_perimeter + 1				):
																					_snake_case       =  (base * base + perpendicular * perpendicular) ** 0.5
																					if hypotenuse == int(_SCREAMING_SNAKE_CASE				):
																												_snake_case       =  int(base + perpendicular + hypotenuse				)
																												if perimeter > max_perimeter:
																																			continue
																												triplets[perimeter] += 1
							return triplets






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = 1000				):
							_snake_case       =  pythagorean_triple(_SCREAMING_SNAKE_CASE				)
							return triplets.most_common(1				)[0][0]


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