


"""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     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          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=__UpperCAmelCase    ,				)
          assert hasattr(self    ,				'''env'''							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase=1							)       ->							str:
          # 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=__UpperCAmelCase    ,				instance_type=self.instance_type    ,				debugger_hook_config=__UpperCAmelCase    ,				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    ,				__UpperCAmelCase							)       ->							List[Any]:
          TrainingJobAnalytics(__UpperCAmelCase							).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'							)





     def 							_UpperCAmelCase      (      self							)       ->							str:
          # create estimator
          _a    	=				self.create_estimator()

          # run training
          estimator.fit()

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

          # extract kpis
          _a    	=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
          _a    	=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']							)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          _a    	=				(
              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}    ,				__UpperCAmelCase							)



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					Dict=2_81_23       ):






     """simple docstring"""

     _a    	=				[1] * (limit + 1)

     for i in range(2,     int(limit**0.5       ) + 1       ):
          sum_divs[i * i] += i
          for k in range(i + 1,     limit // i + 1       ):
               sum_divs[k * i] += k + i

     _a    	=				set()
     _a    	=				0

     for n in range(1,     limit + 1       ):
          if sum_divs[n] > n:
               abundants.add(_lowerCAmelCase       )

          if not any((n - a in abundants) for a in abundants       ):
               res += n

     return res


if __name__ == "__main__":
   print(solution())