




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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]  )
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def       __snake_case		( 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=UpperCAmelCase_       , )
											assert hasattr(self       , '''env'''						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											# configuration for running training on smdistributed Model Parallel
											lowerCAmelCase   =		{
											    '''enabled''': True,
											    '''processes_per_host''': 8,
											}
											lowerCAmelCase   =		{
											    '''enabled''': True,
											    '''parameters''': {
											        '''microbatches''': 4,
											        '''placement_strategy''': '''spread''',
											        '''pipeline''': '''interleaved''',
											        '''optimize''': '''speed''',
											        '''partitions''': 4,
											        '''ddp''': True,
											    },
											}

											lowerCAmelCase   =		{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

											lowerCAmelCase   =		'''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
											# 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}-{instance_count}-smp-{name_extension}"""       , instance_count=UpperCAmelCase_       , instance_type=self.instance_type       , debugger_hook_config=UpperCAmelCase_       , hyperparameters={
											        **self.env.hyperparameters,
											        '''model_name_or_path''': self.model_name_or_path,
											        '''max_steps''': 5_00,
											    }       , metric_definitions=self.env.metric_definitions       , distribution=UpperCAmelCase_       , py_version='''py36'''       , )



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






				@parameterized.expand([(1,)]						)
				def       __snake_case		( self       , UpperCAmelCase_						):
											# create estimator
											lowerCAmelCase   =		self.create_estimator(UpperCAmelCase_						)

											# 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}       , UpperCAmelCase_						)









from collections.abc import Sequence



def       UpperCAmelCase       (     _snake_case						, _snake_case = False							):
							if not arr:
														return 0

							lowerCAmelCase   =		0 if allow_empty_subarrays else float('''-inf'''							)
							lowerCAmelCase   =		0.0
							for num in arr:
														lowerCAmelCase   =		max(0 if allow_empty_subarrays else num						, curr_sum + num							)
														lowerCAmelCase   =		max(_snake_case						, _snake_case							)

							return max_sum


if __name__ == "__main__":
		from doctest import testmod

		testmod()

		UpperCAmelCase_						=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
		print(F'''{max_subarray_sum(nums) = }''')



