




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	):

      def 	__magic_name__(   self		):

        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 	__magic_name__(   self      ,				__UpperCAmelCase=1		):
        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 	__magic_name__(   self      ,				__UpperCAmelCase		):
        TrainingJobAnalytics(_A		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.create_estimator()

        # run training
        estimator.fit()

        # result dataframe
        lowerCAmelCase__				:  Union[str, Any]		       =     TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

        # extract kpis
        lowerCAmelCase__				:  Optional[int]		       =     list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
        lowerCAmelCase__				:  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
        lowerCAmelCase__				:  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 __future__ import annotations



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[int]:  # This function is recursive
  lowerCAmelCase__				:  Tuple		       =     len(UpperCamelCase      )
  # If the array contains only one element, we return it (it's the stop condition of
  # recursion)
  if array_length <= 1:
    return array
    # Else
  lowerCAmelCase__				:  Optional[int]		       =     array[0]
  lowerCAmelCase__				:  Union[str, Any]		       =     False
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  list[int]		       =     []
  while not is_found and i < array_length:
    if array[i] < pivot:
      lowerCAmelCase__				:  List[Any]		       =     True
      lowerCAmelCase__				:  Optional[int]		       =     [element for element in array[i:] if element >= array[i]]
      lowerCAmelCase__				:  Any		       =     longest_subsequence(UpperCamelCase      )
      if len(UpperCamelCase      ) > len(UpperCamelCase      ):
        lowerCAmelCase__				:  Optional[int]		       =     temp_array
    else:
      i += 1

  lowerCAmelCase__				:  Optional[Any]		       =     [element for element in array[1:] if element >= pivot]
  lowerCAmelCase__				:  List[Any]		       =     [pivot, *longest_subsequence(UpperCamelCase      )]
  if len(UpperCamelCase      ) > len(UpperCamelCase      ):
    return temp_array
  else:
    return longest_subseq


if __name__ == "__main__":
     import doctest

     doctest.testmod()

