


'''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": 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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''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 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  {
						    'enabled': True,
						    'processes_per_host': 8,
						}
						__a      :	str    =  {
						    'enabled': True,
						    'parameters': {
						        'microbatches': 4,
						        'placement_strategy': 'spread',
						        'pipeline': 'interleaved',
						        'optimize': 'speed',
						        'partitions': 4,
						        'ddp': True,
						    },
						}

						__a      :	str    =  {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

						__a      :	Tuple    =  '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 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						TrainingJobAnalytics(__a							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)





	@parameterized.expand([(1,)]							)
	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Tuple    =  self.create_estimator(__a							)

						# run training
						estimator.fit()

						# result dataframe
						__a      :	Optional[int]    =  TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

						# extract kpis
						__a      :	Any    =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']							)
						__a      :	str    =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']							)
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__a      :	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							)







'''simple docstring'''



__lowercase     :						Optional[Any]	=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase     :						List[str]	=     ['a', 'b', 'c', 'd', 'e']






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	Any    =  start
					# add current to visited
					visited.append(_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															__a      :	Dict    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # if all neighbors visited add current to sort
					sort.append(_SCREAMING_SNAKE_CASE       )
					# if all vertices haven't been visited select a new one to visit
					if len(_SCREAMING_SNAKE_CASE       ) != len(_SCREAMING_SNAKE_CASE       ):
										for vertice in vertices:
															if vertice not in visited:
																				__a      :	List[Any]    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # return sort
					return sort


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     topological_sort('a', [], [])
	print(sort)



