



'''simple docstring'''





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

import pytest
from parameterized import parameterized, 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.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]		)
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self			)       ->	List[str]:


      """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    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
      # distributed data settings
      _a           =    {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

      # 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=A					,  instance_count=A					,  instance_type=self.instance_type					,  debugger_hook_config=A					,  hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,  metric_definitions=self.env.metric_definitions					,  distribution=A					,  py_version='''py36'''					,  )



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      TrainingJobAnalytics(A			).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''			)



 @parameterized.expand([(2,)]			)
 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.create_estimator(A			)

      # 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'''					,  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}					,  A			)








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     return len(set(__A)) == len(__A)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



