

from __future__ import annotations

import pandas as pd





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     [0] * no_of_processes
       UpperCAmelCase__										=     [0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(__A			):
              UpperCAmelCase__										=     burst_time[i]

       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     9_9_9_9_9_9_9_9_9
       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(__A			):
                     if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                            if remaining_time[j] < minm:
                                   UpperCAmelCase__										=     remaining_time[j]
                                   UpperCAmelCase__										=     j
                                   UpperCAmelCase__										=     True

              if not check:
                     increment_time += 1
                     continue
              remaining_time[short] -= 1

              UpperCAmelCase__										=     remaining_time[short]
              if minm == 0:
                     UpperCAmelCase__										=     9_9_9_9_9_9_9_9_9

              if remaining_time[short] == 0:
                     complete += 1
                     UpperCAmelCase__										=     False

                     # Find finish time of current process
                     UpperCAmelCase__										=     increment_time + 1

                     # Calculate waiting time
                     UpperCAmelCase__										=     finish_time - arrival_time[short]
                     UpperCAmelCase__										=     finar - burst_time[short]

                     if waiting_time[short] < 0:
                            UpperCAmelCase__										=     0

        # Increment time
              increment_time += 1
       return waiting_time





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					list[int]:



       '''simple docstring'''





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





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     0
       UpperCAmelCase__										=     0
       for i in range(__A			):
              UpperCAmelCase__										=     total_waiting_time + waiting_time[i]
              UpperCAmelCase__										=     total_turn_around_time + turn_around_time[i]
       print(F"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""			)
       print("Average turn around time =" ,						total_turn_around_time / no_of_processes			)


if __name__ == "__main__":
    print("Enter how many process you want to analyze")
    A      = int(input())
    A      = [0] * no_of_processes
    A      = [0] * no_of_processes
    A      = list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("Enter the arrival time and burst time for process:--" + str(i + 1))
        A, A      = map(int, input().split())

    A      = calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    A      = burst_time
    A      = no_of_processes
    A      = waiting_time
    A      = calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    A      = pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            "Process",
            "BurstTime",
            "ArrivalTime",
            "WaitingTime",
            "TurnAroundTime",
        ],
    )

    # Printing the dataFrame
    pd.set_option("display.max_rows", fcfs.shape[0] + 1)
    print(fcfs)









import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.checkpoint		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer.get_vocab() )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)
         processor.save_pretrained(
             self.tmpdirname		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						speaker_embeddings_directory=self.speaker_embeddings_directory		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             self.tmpdirname		,						self.speaker_embeddings_dict_path		,						bos_token="(BOS)"		,						eos_token="(EOS)"		,						)

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer_add_kwargs.get_vocab() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "semantic_prompt": np.ones(_lowercase ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

         UpperCAmelCase__										=     inputs["history_prompt"]
         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from npz file
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						"file.npz" )
         np.savez(_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )
         UpperCAmelCase__										=     inputs["history_prompt"]

         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from the hub
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=self.voice_preset )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						add_special_tokens=_lowercase		,						return_attention_mask=_lowercase		,						return_token_type_ids=_lowercase		,						)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]		,						encoded_processor[key].squeeze().tolist() )





