

from statistics import mean

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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

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

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

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

							return turn_around_time





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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


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

					_UpperCAmelCase		:   int										=       calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					_UpperCAmelCase		:   int										=       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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					torch.tensor(
          [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


