


'''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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """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""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]							)
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Union[str, Any]:
			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        __lowerCAmelCase		( self		:						int  ,					_A		:						Dict      )  ->			Dict:
			# configuration for running training on smdistributed Model Parallel
			__magic_name__	:   Optional[Any]					     =					{
			    'enabled': True,
			    'processes_per_host': 8,
			}
			__magic_name__	:   Tuple					     =					{
			    'enabled': True,
			    'parameters': {
			        'microbatches': 4,
			        'placement_strategy': 'spread',
			        'pipeline': 'interleaved',
			        'optimize': 'speed',
			        'partitions': 4,
			        'ddp': True,
			    },
			}

			__magic_name__	:   Optional[int]					     =					{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

			__magic_name__	:   List[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=_A  ,					instance_type=self.instance_type  ,					debugger_hook_config=_A  ,					hyperparameters={
			        **self.env.hyperparameters,
			        'model_name_or_path': self.model_name_or_path,
			        'max_steps': 500,
			    }  ,					metric_definitions=self.env.metric_definitions  ,					distribution=_A  ,					py_version='py36'  ,					)







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Union[str, Any]      )  ->			Union[str, Any]:
			TrainingJobAnalytics(_A      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )







		@parameterized.expand([(1,)]      )
		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Union[str, Any]      )  ->			List[Any]:
			# create estimator
			__magic_name__	:   List[str]					     =					self.create_estimator(_A      )

			# run training
			estimator.fit()

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

			# extract kpis
			__magic_name__	:   List[str]					     =					list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
			__magic_name__	:   Dict					     =					list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
			# get train time from SageMaker job, this includes starting, preprocessing, stopping
			__magic_name__	:   int					     =					(
			    Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'  ,					999999      )
			)

			# 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'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()