


'''simple docstring'''







import os

import pytest
from attr import dataclass


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


@dataclass
class  lowerCAmelCase_						:





  """simple docstring"""



  a_       :str
  a_       :str												="""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
  a_       :Optional[Any]												={
      """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,
  }
  a_       :str												={**hyperparameters, """max_steps""": 1000}

  @property
  def __a						(					self     :							Any						):





       '''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     :							Tuple						):





       '''simple docstring'''

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

  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

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


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





       '''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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




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










'''simple docstring'''







from math import pi, sqrt




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




     if num <= 0:
          raise ValueError("""math domain error"""		)
     if num > 171.5:
          raise OverflowError("""math range error"""		)
     elif num - int(__SCREAMING_SNAKE_CASE		) not in (0, 0.5):
          raise NotImplementedError("""num must be an integer or a half-integer"""		)
     elif num == 0.5:
          return sqrt(__SCREAMING_SNAKE_CASE		)
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)




def        __lowercase						(      )				->					None:
     """simple docstring"""




     assert gamma(0.5		) == sqrt(__SCREAMING_SNAKE_CASE		)
     assert gamma(1		) == 1.0
     assert gamma(2		) == 1.0


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 SCREAMING_SNAKE_CASE_      =			1.0
 while num:
  SCREAMING_SNAKE_CASE_      =			float(input('Gamma of: '))
  print(f"""gamma({num}) = {gamma(num)}""")
  print('\nEnter 0 to exit...')






