


'''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     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def 			snake_case__ (   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=__lowercase   ,   )
  assert hasattr(self   ,   'env'    )


 def 			snake_case__ (   self   ,   __lowercase=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=__lowercase   ,   instance_type=self.instance_type   ,   debugger_hook_config=__lowercase   ,   hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}   ,   metric_definitions=self.env.metric_definitions   ,   py_version='py36'   ,   )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.create_estimator()

  # run training
  estimator.fit()

  # result dataframe
  __A	:		List[str]	  =      TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

  # extract kpis
  __A	:		Dict	  =      list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']    )
  __A	:		Optional[Any]	  =      list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']    )
  # get train time from SageMaker job, this includes starting, preprocessing, stopping
  __A	:		str	  =      (
      Session().describe_training_job(estimator.latest_training_job.name    ).get('TrainingTimeInSeconds'   ,   999_999    )
  )

  # 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}   ,   __lowercase    )













def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:
   if num < 0:
      return False

   A_       :					int        =      num
   A_       :					int        =      0
   while num > 0:
      A_       :					str        =      rev_num * 10 + (num % 10)
      num //= 10

   return num_copy == rev_num


if __name__ == "__main__":
 import doctest

 doctest.testmod()
