






'''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.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]			)
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
								# distributed data settings
								lowerCamelCase__         =  {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

								# 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=_lowerCAmelCase   ,instance_count=_lowerCAmelCase   ,instance_type=self.instance_type   ,debugger_hook_config=_lowerCAmelCase   ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}   ,metric_definitions=self.env.metric_definitions   ,distribution=_lowerCAmelCase   ,py_version="""py36"""   ,)






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								TrainingJobAnalytics(_lowerCAmelCase       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )






		@parameterized.expand([(2,)]       )
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								# create estimator
								lowerCamelCase__         =  self.create_estimator(_lowerCAmelCase       )

								# run training
								estimator.fit()

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

								# extract kpis
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								lowerCamelCase__         =  (
								    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       )









'''simple docstring'''







def 						A__			(				):
						return [
						    a * b * (1000 - a - b)
						    for a in range(1   ,    999						)
						    for b in range(__lowerCAmelCase   ,    999						)
						    if (a * a + b * b == (1000 - a - b) ** 2)
						][0]


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

