"""simple docstring"""





import os

import pytest
from attr import dataclass


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




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						42
				a			=						"arn:aws:iam::558105141721:role/sagemaker_execution_role"
				a			=						{
				    "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,
				}
				a			=						{**hyperparameters, "max_steps": 10_00}





				@property
				def 		_lowerCAmelCase		(      self							:							List[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 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







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





				@property
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







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





				@property
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE			  =    SageMakerTestEnvironment(framework=request.cls.framework  )



"""simple docstring"""





from __future__ import annotations



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    tuple:
							if (electron_conc, hole_conc, intrinsic_conc).count(0  ) != 1:
														raise ValueError("""You cannot supply more or less than 2 values"""  )
							elif electron_conc < 0:
														raise ValueError("""Electron concentration cannot be negative in a semiconductor"""  )
							elif hole_conc < 0:
														raise ValueError("""Hole concentration cannot be negative in a semiconductor"""  )
							elif intrinsic_conc < 0:
														raise ValueError(
														    """Intrinsic concentration cannot be negative in a semiconductor"""  )
							elif electron_conc == 0:
														return (
														    "electron_conc",
														    intrinsic_conc**2 / hole_conc,
														)
							elif hole_conc == 0:
														return (
														    "hole_conc",
														    intrinsic_conc**2 / electron_conc,
														)
							elif intrinsic_conc == 0:
														return (
														    "intrinsic_conc",
														    (electron_conc * hole_conc) ** 0.5,
														)
							else:
														return (-1, -1)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


