


'''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:
     a_      			=				None


a_      			=				logging.get_logger(__name__)

a_      			=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_      			=				{
    "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",
    },
}

a_      			=				{
    "camembert-base": 512,
}

a_      			=				"▁"





class   snake_case					(					_A    ):





  """simple docstring"""




  _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"""
   # Mask token behave like a normal word, i.e. include the space before it
   lowerCamelCase_  				=						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    ,						)

   lowerCamelCase_  				=						vocab_file
   lowerCamelCase_  				=						False if not self.vocab_file else True






  def 						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]
   lowerCamelCase_  				=						[self.cls_token_id]
   lowerCamelCase_  				=						[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						[self.sep_token_id]
   lowerCamelCase_  				=						[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 						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
   lowerCamelCase_  				=						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,)







'''simple docstring'''

from __future__ import annotations

import pandas as pd





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						[0] * no_of_processes
 lowerCamelCase_  				=						[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i]

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						999999999
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						False

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

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

  lowerCamelCase_  				=						remaining_time[short]
  if minm == 0:
   lowerCamelCase_  				=						999999999

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

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

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

   if waiting_time[short] < 0:
    lowerCamelCase_  				=						0

        # Increment time
  increment_time += 1
 return waiting_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		list[int]	):
 lowerCamelCase_  				=						[0] * no_of_processes
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i] + waiting_time[i]
 return turn_around_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						total_waiting_time + waiting_time[i]
  lowerCamelCase_  				=						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_						:       Dict      			=				int(input())
     a_						:       Any      			=				[0] * no_of_processes
     a_						:       Optional[int]      			=				[0] * no_of_processes
     a_						:       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))
          a_			,     a_						:       str      			=				map(int, input().split())

     a_						:       List[Any]      			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     a_						:       int      			=				burst_time
     a_						:       Union[str, Any]      			=				no_of_processes
     a_						:       Optional[int]      			=				waiting_time
     a_						:       Any      			=				calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     a_						:       Optional[int]      			=				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)



