






'''simple docstring'''



from statistics import mean

import numpy as np


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							list       ,   __A			:							int      )							->   list:



 """simple docstring"""




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

 # Sort by arrival time.
 lowercase       : int						=[burst_time[i] for i in np.argsort(__A      )]
 lowercase       : Optional[int]						=[process_name[i] for i in np.argsort(__A      )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  lowercase       : List[str]						=0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   lowercase       : Tuple						=arrival_time[i]

  lowercase       : Dict						=0
  # Index showing the location of the process being performed
  lowercase       : Any						=0
  # Saves the current response ratio.
  lowercase       : Union[str, Any]						=0
  for i in range(0       ,   __A      ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    lowercase       : int						=(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    lowercase       : Any						=temp
    lowercase       : Optional[int]						=i

        # Calculate the turn around time
  lowercase       : Union[str, Any]						=current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  lowercase       : str						=1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							list       ,   __A			:							int      )							->   list:



 """simple docstring"""




 lowercase       : Dict						=[0] * no_of_process
 for i in range(0       ,   __A      ):
  lowercase       : str						=turn_around_time[i] - burst_time[i]
 return waiting_time


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

      SCREAMING_SNAKE_CASE							=						calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      SCREAMING_SNAKE_CASE							=						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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE							=						None


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							=						{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE							=						{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

SCREAMING_SNAKE_CASE							=						'▁'

# Segments (not really needed)
SCREAMING_SNAKE_CASE							=						0
SCREAMING_SNAKE_CASE							=						1
SCREAMING_SNAKE_CASE							=						2
SCREAMING_SNAKE_CASE							=						3
SCREAMING_SNAKE_CASE							=						4
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      VOCAB_FILES_NAMES
      UpperCamelCase_             =      PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_             =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_             =      '''left'''
      UpperCamelCase_             =      XLNetTokenizer






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		str=False  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		int="<s>"  ,   UpperCAmelCase      :		Optional[int]="</s>"  ,   UpperCAmelCase      :		str="<unk>"  ,   UpperCAmelCase      :		Optional[Any]="<sep>"  ,   UpperCAmelCase      :		Optional[int]="<pad>"  ,   UpperCAmelCase      :		Optional[Any]="<cls>"  ,   UpperCAmelCase      :		Dict="<mask>"  ,   UpperCAmelCase      :		int=["<eop>", "<eod>"]  ,   **UpperCAmelCase      :		List[Any]  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=AddedToken(UpperCAmelCase  ,   lstrip=UpperCAmelCase  ,   rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) else mask_token

       super().__init__(
           vocab_file=UpperCAmelCase  ,   tokenizer_file=UpperCAmelCase  ,   do_lower_case=UpperCAmelCase  ,   remove_space=UpperCAmelCase  ,   keep_accents=UpperCAmelCase  ,   bos_token=UpperCAmelCase  ,   eos_token=UpperCAmelCase  ,   unk_token=UpperCAmelCase  ,   sep_token=UpperCAmelCase  ,   pad_token=UpperCAmelCase  ,   cls_token=UpperCAmelCase  ,   mask_token=UpperCAmelCase  ,   additional_special_tokens=UpperCAmelCase  ,   **UpperCAmelCase  ,   )

       lowercase       : Tuple						=3
       lowercase       : Union[str, Any]						=do_lower_case
       lowercase       : Any						=remove_space
       lowercase       : int						=keep_accents
       lowercase       : int						=vocab_file
       lowercase       : Union[str, Any]						=False if not self.vocab_file else True






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[self.sep_token_id]
       lowercase       : Optional[Any]						=[self.cls_token_id]
       if token_ids_a is None:
        return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=[self.sep_token_id]
       lowercase       : Union[str, Any]						=[2]

       if token_ids_a is None:
        return len(token_ids_a + sep		) * [0] + cls_segment_id
       return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[str] = None		)	->	Tuple[str]:


       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''		)

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase       : Dict						=os.path.join(
           UpperCAmelCase  ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase		):
        copyfile(self.vocab_file  ,   UpperCAmelCase		)

       return (out_vocab_file,)



