from statistics import mean

import numpy as np


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  0
      # Number of processes finished
      SCREAMING_SNAKE_CASE_          =  0
      # Displays the finished process.
      # If it is 0, the performance is completed if it is 1, before the performance.
      SCREAMING_SNAKE_CASE_          =  [0] * no_of_process
      # List to include calculation results
      SCREAMING_SNAKE_CASE_          =  [0] * no_of_process

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

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

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

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

      return turn_around_time


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  [0] * no_of_process
      for i in range(0     ,      _UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  turn_around_time[i] - burst_time[i]
      return waiting_time


if __name__ == "__main__":
      A     :  Optional[int]					     =       5
      A     :  str					     =       ["A", "B", "C", "D", "E"]
      A     :  List[str]					     =       [1, 2, 3, 4, 5]
      A     :  Tuple					     =       [1, 2, 3, 4, 5]

      A     :  Optional[int]					     =       calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      A     :  Optional[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}")



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    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  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():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  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():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


