





from __future__ import annotations




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       if (direction == 1 and array[indexa] > array[indexa]) or (
           direction == 0 and array[indexa] < array[indexa]
       ):
              _lowerCamelCase,	_lowerCamelCase				:  Optional[int]						=		array[indexa], array[indexa]




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       if length > 1:
              _lowerCamelCase				:  int						=		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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       if length > 1:
              _lowerCamelCase				:  Union[str, Any]						=		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__":
       _lowerCamelCase      							=   input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase      							=   [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=', ')








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class       __A							(   unittest.TestCase	):




      """simple docstring"""







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


      def 					__snake_case      (		self   ,				a__=1):



             """simple docstring"""





             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}-single"""   ,				instance_count=a__   ,				instance_type=self.instance_type   ,				debugger_hook_config=a__   ,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}   ,				metric_definitions=self.env.metric_definitions   ,				py_version='''py36'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             TrainingJobAnalytics(a__).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.create_estimator()

             # run training
             estimator.fit()

             # result dataframe
             _lowerCamelCase				:  Tuple						=		TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

             # extract kpis
             _lowerCamelCase				:  Tuple						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
             _lowerCamelCase				:  Dict						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             _lowerCamelCase				:  Optional[Any]						=		(
                 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__)

