






"""simple docstring"""




from statistics import mean

import numpy as np






def        lowerCAmelCase (      UpperCamelCase_:							list     , UpperCamelCase_:							list     , UpperCamelCase_:							list     , UpperCamelCase_:							int					)       ->							list:







  '''simple docstring'''







  _a							=	0
  # Number of processes finished
  _a							=	0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  _a							=	[0] * no_of_process
  # List to include calculation results
  _a							=	[0] * no_of_process

  # Sort by arrival time.
  _a							=	[burst_time[i] for i in np.argsort(__snake_case					)]
  _a							=	[process_name[i] for i in np.argsort(__snake_case					)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    _a							=	0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      _a							=	arrival_time[i]

    _a							=	0
    # Index showing the location of the process being performed
    _a							=	0
    # Saves the current response ratio.
    _a							=	0
    for i in range(0     , __snake_case					):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        _a							=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        _a							=	temp
        _a							=	i

        # Calculate the turn around time
    _a							=	current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    _a							=	1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time






def        lowerCAmelCase (      UpperCamelCase_:							list     , UpperCamelCase_:							list     , UpperCamelCase_:							list     , UpperCamelCase_:							int					)       ->							list:







  '''simple docstring'''







  _a							=	[0] * no_of_process
  for i in range(0     , __snake_case					):
    _a							=	turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
     UpperCamelCase							       =    5
     UpperCamelCase							       =    ["A", "B", "C", "D", "E"]
     UpperCamelCase							       =    [1, 2, 3, 4, 5]
     UpperCamelCase							       =    [1, 2, 3, 4, 5]

     UpperCamelCase							       =    calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     UpperCamelCase							       =    calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
     for i in range(0, no_of_process):
          print(
              f"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
              f"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
          )

     print(f"average waiting time : {mean(waiting_time):.5f}")
     print(f"average turn around time : {mean(turn_around_time):.5f}")










"""simple docstring"""




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    lowercase_    (unittest.TestCase     ):






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''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 			lowerCamelCase__    (    self     ,     a_=1	)	->int:







        '''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 			lowerCamelCase__    (    self     ,     a_	)	->str:







        '''simple docstring'''


        TrainingJobAnalytics(a_	).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.create_estimator()

        # run training
        estimator.fit()

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

        # extract kpis
        _a							=	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
        _a							=	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        _a							=	(
            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}     ,     a_	)


