



'''simple docstring'''

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 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						Dict			)			->					Dict:
										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   :						Any      ,						a_   :						str=1			)			->					Tuple:
										# 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=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    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										TrainingJobAnalytics(a_			).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''			)




							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					int:
										# create estimator
										snake_case:    str      							=self.create_estimator()

										# run training
										estimator.fit()

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

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









'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




