"""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_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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			# configuration for running training on smdistributed Model Parallel
			__lowerCAmelCase      :						Dict						      =				{
			    'enabled': True,
			    'processes_per_host': 8,
			}
			__lowerCAmelCase      :						Union[str, Any]						      =				{
			    'enabled': True,
			    'parameters': {
			        'microbatches': 4,
			        'placement_strategy': 'spread',
			        'pipeline': 'interleaved',
			        'optimize': 'speed',
			        'partitions': 4,
			        'ddp': True,
			    },
			}

			__lowerCAmelCase      :						Optional[int]						      =				{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

			__lowerCAmelCase      :						Union[str, Any]						      =				'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=__lowerCAmelCase	,		instance_type=self.instance_type	,		debugger_hook_config=__lowerCAmelCase	,		hyperparameters={
			        **self.env.hyperparameters,
			        'model_name_or_path': self.model_name_or_path,
			        'max_steps': 5_00,
			    }	,		metric_definitions=self.env.metric_definitions	,		distribution=__lowerCAmelCase	,		py_version='py36'	,		)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			TrainingJobAnalytics(__lowerCAmelCase			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)







		@parameterized.expand([(1,)]			)
		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			# create estimator
			__lowerCAmelCase      :						List[Any]						      =				self.create_estimator(__lowerCAmelCase			)

			# run training
			estimator.fit()

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

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







class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				size
			__lowerCAmelCase      :						str						      =				[0] * size
			__lowerCAmelCase      :						Any						      =				[0] * size

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return index | (index + 1)

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return (index & (index + 1)) - 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				value
			while index < self.size:
				__lowerCAmelCase      :						Dict						      =				self.get_prev(_SCREAMING_SNAKE_CASE			) + 1
				if current_left_border == index:
					__lowerCAmelCase      :						Any						      =				value
				else:
					__lowerCAmelCase      :						Any						      =				max(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				self.get_next(_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			right -= 1  # Because of right is exclusive
			__lowerCAmelCase      :						Optional[int]						      =				0
			while left <= right:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_prev(_SCREAMING_SNAKE_CASE			)
				if left <= current_left:
					__lowerCAmelCase      :						Optional[Any]						      =				max(_SCREAMING_SNAKE_CASE	,		self.tree[right]			)
					__lowerCAmelCase      :						Optional[Any]						      =				current_left
				else:
					__lowerCAmelCase      :						List[str]						      =				max(_SCREAMING_SNAKE_CASE	,		self.arr[right]			)
					right -= 1
			return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()