





'''simple docstring'''




from __future__ import annotations

import pandas as pd






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	list[int]:
     __A		:					Tuple						=			[0] * no_of_processes
     __A		:					str						=			[0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(__snake_case     ):
          __A		:					int						=			burst_time[i]

     __A		:					str						=			0
     __A		:					Tuple						=			0
     __A		:					Optional[Any]						=			9_99_99_99_99
     __A		:					int						=			0
     __A		:					Optional[Any]						=			False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(__snake_case     ):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         __A		:					Dict						=			remaining_time[j]
                         __A		:					Optional[int]						=			j
                         __A		:					Union[str, Any]						=			True

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

          __A		:					str						=			remaining_time[short]
          if minm == 0:
               __A		:					Any						=			9_99_99_99_99

          if remaining_time[short] == 0:
               complete += 1
               __A		:					List[Any]						=			False

               # Find finish time of current process
               __A		:					Any						=			increment_time + 1

               # Calculate waiting time
               __A		:					Optional[int]						=			finish_time - arrival_time[short]
               __A		:					Dict						=			finar - burst_time[short]

               if waiting_time[short] < 0:
                    __A		:					List[str]						=			0

        # Increment time
          increment_time += 1
     return waiting_time






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					int					,				__snake_case    :					list[int]     )	->	list[int]:
     __A		:					Optional[Any]						=			[0] * no_of_processes
     for i in range(__snake_case     ):
          __A		:					Any						=			burst_time[i] + waiting_time[i]
     return turn_around_time






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	None:
     __A		:					Union[str, Any]						=			0
     __A		:					Optional[int]						=			0
     for i in range(__snake_case     ):
          __A		:					Optional[Any]						=			total_waiting_time + waiting_time[i]
          __A		:					List[str]						=			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''')
  lowercase__       : Dict         =       int(input())
  lowercase__       : str         =       [0] * no_of_processes
  lowercase__       : int         =       [0] * no_of_processes
  lowercase__       : Tuple         =       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))
    lowercase__      ,				lowercase__       : Dict         =       map(int, input().split())

  lowercase__       : int         =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  lowercase__       : Any         =       burst_time
  lowercase__       : Tuple         =       no_of_processes
  lowercase__       : List[str]         =       waiting_time
  lowercase__       : List[Any]         =       calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  lowercase__       : str         =       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)






'''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_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


lowercase__       : List[str]         =       logging.get_logger(__name__)

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              _UpperCAmelCase		,      tokenizer_file=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					List[str]						=			[self.cls_token_id]

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''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
          __A		:					List[Any]						=			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,)