





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''': 1600, '''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''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]       )
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Dict  )      ->      Union[str, 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=__lowerCAmelCase				,      )
					assert hasattr(self				,      """env"""  )






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       List[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






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

					A__     =  {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

					A__     =  """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=__lowerCAmelCase				,      instance_type=self.instance_type				,      debugger_hook_config=__lowerCAmelCase				,      hyperparameters={
					        **self.env.hyperparameters,
					        """model_name_or_path""": self.model_name_or_path,
					        """max_steps""": 5_00,
					    }				,      metric_definitions=self.env.metric_definitions				,      distribution=__lowerCAmelCase				,      py_version="""py36"""				,      )






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       str  )      ->      int:







					"""simple docstring"""






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







			@parameterized.expand([(1,)]  )
			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.create_estimator(__lowerCAmelCase  )

					# 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"""				,      99_99_99  )
					)

					# 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}				,      __lowerCAmelCase  )










def   __lowerCamelCase   (				__a  :int = 4_0_0_0_0_0_0       )						->							int:






		"""simple docstring"""

		A__     =  []
		A__ ,     A__     =  0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(__a       )
				A__ ,     A__     =  b, a + b
		return sum(__a       )


if __name__ == "__main__":
					print(F'''{solution() = }''')



