"""simple docstring"""




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"			,		"False"      )      ) is not True			,		reason="Skipping test because should only be run when releasing minor transformers version"			,		)
@pytest.mark.usefixtures("sm_env"      )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]      )
class    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							if self.framework == "pytorch":
									subprocess.run(
									    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()	,    encoding="""utf-8"""	,    check=snake_case__	,    )
							assert hasattr(self	,    """env"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Any=1  ):
							# creates estimator
							return HuggingFace(
							    entry_point=self.script	,    source_dir=self.env.test_path	,    role=self.env.role	,    image_uri=self.env.image_uri	,    base_job_name=F"""{self.env.base_job_name}-single"""	,    instance_count=snake_case__	,    instance_type=self.instance_type	,    debugger_hook_config=snake_case__	,    hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}	,    metric_definitions=self.env.metric_definitions	,    py_version="""py36"""	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Union[str, Any]  ):
							TrainingJobAnalytics(snake_case__  ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							# create estimator
							lowerCAmelCase__   = self.create_estimator()

							# run training
							estimator.fit()

							# result dataframe
							lowerCAmelCase__   = TrainingJobAnalytics(estimator.latest_training_job.name  ).dataframe()

							# extract kpis
							lowerCAmelCase__   = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
							lowerCAmelCase__   = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							lowerCAmelCase__   = (
							    Session().describe_training_job(estimator.latest_training_job.name  ).get("""TrainingTimeInSeconds"""	,    999999  )
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy  )
							assert all(t <= self.results["""eval_loss"""] for t in eval_loss  )

							# dump tests result into json file to share in PR
							with open(F"""{estimator.latest_training_job.name}.json"""	,    """w"""  ) as outfile:
									json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}	,    snake_case__  )





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return abs(lowerCamelCase__  ) if a == 0 else greatest_common_divisor(b % a      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase__ ,       lowerCAmelCase__   = y, x % y
		return abs(lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		try:
				lowerCAmelCase__   = input("""Enter two integers separated by comma (,): """  ).split(""","""  )
				lowerCAmelCase__   = int(nums[0]  )
				lowerCAmelCase__   = int(nums[1]  )
				print(
				    f"""greatest_common_divisor({num_a}, {num_a}) = """
				    f"""{greatest_common_divisor(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
				print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
		except (IndexError, UnboundLocalError, ValueError):
				print("""Wrong input"""  )


if __name__ == "__main__":
			main()




