
"""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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Tuple:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   Optional[Any]   )  -> Any:
							A     = F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
							# distributed data settings
							A     = {'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=A_	,instance_count=A_	,instance_type=self.instance_type	,debugger_hook_config=A_	,hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}	,metric_definitions=self.env.metric_definitions	,distribution=A_	,py_version='py36'	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							TrainingJobAnalytics(A_   ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'   )




						@parameterized.expand([(2,)]   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> str:
							# create estimator
							A     = self.create_estimator(A_   )

							# run training
							estimator.fit()

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

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






from __future__ import annotations

import numpy as np





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



							return np.maximum(0				,       lowercase				)


if __name__ == "__main__":
							print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]