





'''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': 6_5_0, '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': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]    )
class       A__     ( unittest.TestCase    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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=A_ ,							)
       assert hasattr(self ,							"env"     )





      def  __UpperCamelCase(				self ,							A_=1     ):
       '''simple docstring'''




       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=A_ ,							instance_type=self.instance_type ,							debugger_hook_config=A_ ,							hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path} ,							metric_definitions=self.env.metric_definitions ,							py_version="py36" ,							)





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       TrainingJobAnalytics(A_     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCamelCase			:      Tuple 			=     TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

       # extract kpis
       UpperCamelCase			:      List[Any] 			=     list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
       UpperCamelCase			:      Union[str, Any] 			=     list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCamelCase			:      Optional[Any] 			=     (
           Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds" ,							99_9999     )
       )

       # 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} ,							A_     )







from math import loga



def 	A_       (  _lowerCAmelCase  )		->				int:
 if a < 0:
  raise ValueError("Input value must be a positive integer"  )
 elif isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 return 0 if (a == 0) else int(loga(a & -a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
