


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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def        _snake_case       (      self		)->Any:




         '''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=_SCREAMING_SNAKE_CASE				,       )
         assert hasattr(self				,       '''env'''		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Tuple		     =					{
             '''enabled''': True,
             '''processes_per_host''': 8,
         }
         A_      :				Tuple		     =					{
             '''enabled''': True,
             '''parameters''': {
                 '''microbatches''': 4,
                 '''placement_strategy''': '''spread''',
                 '''pipeline''': '''interleaved''',
                 '''optimize''': '''speed''',
                 '''partitions''': 4,
                 '''ddp''': True,
             },
         }

         A_      :				Tuple		     =					{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

         A_      :				Optional[Any]		     =					'''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
         # 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}-{instance_count}-smp-{name_extension}'''				,       instance_count=_SCREAMING_SNAKE_CASE				,       instance_type=self.instance_type				,       debugger_hook_config=_SCREAMING_SNAKE_CASE				,       hyperparameters={
                 **self.env.hyperparameters,
                 '''model_name_or_path''': self.model_name_or_path,
                 '''max_steps''': 500,
             }				,       metric_definitions=self.env.metric_definitions				,       distribution=_SCREAMING_SNAKE_CASE				,       py_version='''py36'''				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''		)







       @parameterized.expand([(1,)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Dict		     =					self.create_estimator(_SCREAMING_SNAKE_CASE		)

         # run training
         estimator.fit()

         # result dataframe
         A_      :				List[Any]		     =					TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

         # extract kpis
         A_      :				Tuple		     =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
         A_      :				Optional[int]		     =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         A_      :				Dict		     =					(
             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}				,       _SCREAMING_SNAKE_CASE		)








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




