

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 SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
				# distributed data settings
				snake_case_   		=			{'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=__UpperCamelCase			,					instance_count=__UpperCamelCase			,					instance_type=self.instance_type			,					debugger_hook_config=__UpperCamelCase			,					hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}			,					metric_definitions=self.env.metric_definitions			,					distribution=__UpperCamelCase			,					py_version='py36'			,					)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				TrainingJobAnalytics(__UpperCamelCase				).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""				)







			@parameterized.expand([(2,)]				)
			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.create_estimator(__UpperCamelCase				)

				# run training
				estimator.fit()

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

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





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if (direction == 1 and array[indexa] > array[indexa]) or (
	    direction == 0 and array[indexa] < array[indexa]
	):
		snake_case_		,							snake_case_   		=			array[indexa], array[indexa]





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		for i in range(lowercase__      ,			low + middle						):
			comp_and_swap(lowercase__      ,			lowercase__      ,			i + middle      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			low + middle      ,			lowercase__      ,			lowercase__						)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		bitonic_sort(lowercase__      ,			lowercase__      ,			lowercase__      ,			1						)
		bitonic_sort(lowercase__      ,			low + middle      ,			lowercase__      ,			0						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)


if __name__ == "__main__":
		A									=  input('Enter numbers separated by a comma:\n').strip()
		A									=  [int(item.strip()) for item in user_input.split(',')]

		bitonic_sort(unsorted, 0, len(unsorted), 1)
		print('\nSorted array in ascending order is: ', end='')
		print(*unsorted, sep=', ')

		bitonic_merge(unsorted, 0, len(unsorted), 0)
		print('Sorted array in descending order is: ', end='')
		print(*unsorted, sep=', ')
