


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''': 6_5_0, '''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''': 6_0_0, '''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''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]   )
class        lowerCamelCase      (     unittest.TestCase   ):


	def    A(						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=lowercase__							,    )
							assert hasattr(self							,    '''env''')


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


	def    A(						self							,    lowercase__):
							TrainingJobAnalytics(lowercase__).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


	@parameterized.expand([(2,)])
	def    A(						self							,    lowercase__):
							# create estimator
							__UpperCAmelCase :       int  						=  self.create_estimator(lowercase__)

							# run training
							estimator.fit()

							# result dataframe
							__UpperCAmelCase :       int  						=  TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

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





from __future__ import annotations




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						if not nums:
												return 0
						__UpperCAmelCase :       int  						=  nums[0]
						__UpperCAmelCase :       Optional[Any]  						=  0
						for num in nums[1:]:
												__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  (
												    max_excluding + num,
												    max(lowercase_			,					lowercase_			),
												)
						return max(lowercase_			,					lowercase_			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

