
"""simple docstring"""
from __future__ import annotations

from statistics import mean




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    [0] * no_of_processes
	_UpperCAmelCase			=    [0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(lowerCAmelCase__   ):
		_UpperCAmelCase			=    burst_time[i]
	_UpperCAmelCase			=    []

	_UpperCAmelCase			=    0
	_UpperCAmelCase			=    0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		_UpperCAmelCase			=    []
		_UpperCAmelCase			=    -1

		for i in range(lowerCAmelCase__   ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(lowerCAmelCase__   )

		if len(lowerCAmelCase__   ) > 0:
			_UpperCAmelCase			=    ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					_UpperCAmelCase			=    i
			total_time += burst_time[target_process]
			completed += 1
			_UpperCAmelCase			=    0
			_UpperCAmelCase			=    (
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    [0] * no_of_processes
	for i in range(lowerCAmelCase__   ):
		_UpperCAmelCase			=    burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
			print('''[TEST CASE 01]''')

			__magic_name__									=					4
			__magic_name__									=					[2, 5, 3, 7]
			__magic_name__									=					[0, 0, 0, 0]
			__magic_name__									=					calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__magic_name__									=					calculate_turnaroundtime(
			    burst_time, no_of_processes, waiting_time
			)

			# Printing the Result
			print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
			for i, process_id in enumerate(list(range(1, 5))):
						print(
						    f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
						    f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
						)
			print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
			print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



