
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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Dict       )       ->     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=__snake_case       ,							)
					assert hasattr(self       ,							'''env'''       )
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=1       )       ->     int:
					# 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}-single'''       ,							instance_count=__snake_case       ,							instance_type=self.instance_type       ,							debugger_hook_config=__snake_case       ,							hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}       ,							metric_definitions=self.env.metric_definitions       ,							py_version='''py36'''       ,							)
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					TrainingJobAnalytics(__snake_case       ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''       )




	def 						_lowercase  (       self  :			Tuple       )       ->     Union[str, Any]:
					# create estimator
					lowercase_       =					self.create_estimator()

					# run training
					estimator.fit()

					# result dataframe
					lowercase_       =					TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

					# extract kpis
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowercase_       =					(
					    Session().describe_training_job(estimator.latest_training_job.name       ).get('''TrainingTimeInSeconds'''       ,							9_9_9_9_9_9       )
					)

					# 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}       ,							__snake_case       )








def 							__lowercase   ( __lowerCAmelCase							:				list[int]   ):
							a__             =     []
							if len(__lowerCAmelCase   ) == 1:
														return [nums.copy()]
							for _ in range(len(__lowerCAmelCase   )   ):
														a__             =     nums.pop(0   )
														a__             =     permute(__lowerCAmelCase   )
														for perm in permutations:
																					perm.append(__lowerCAmelCase   )
														result.extend(__lowerCAmelCase   )
														nums.append(__lowerCAmelCase   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):

							def backtrack(__lowerCAmelCase							:				Any   ):
														if start == len(__lowerCAmelCase   ) - 1:
																					output.append(nums[:]   )
														else:
																					for i in range(__lowerCAmelCase					,    len(__lowerCAmelCase   )   ):
																												a__      ,					a__             =     nums[i], nums[start]
																												backtrack(start + 1   )
																												a__      ,					a__             =     nums[i], nums[start]  # backtrack

							a__             =     []
							backtrack(0   )
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						snake_case							:       Optional[Any]     			=      permutea([1, 2, 3])
						print(res)
						doctest.testmod()




