

from __future__ import annotations
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   [0] * len(__UpperCamelCase )
 for i in range(1							,      len(__UpperCamelCase ) ):
  UpperCAmelCase__   :					Dict   				=   duration_times[i - 1] + waiting_times[i - 1]
 return waiting_times
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 return [
     duration_time + waiting_times[i]
     for i, duration_time in enumerate(__UpperCamelCase )
 ]
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 return sum(__UpperCamelCase ) / len(__UpperCamelCase )
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 return sum(__UpperCamelCase ) / len(__UpperCamelCase )


if __name__ == "__main__":
      # process id's
      __UpperCAmelCase	=			[1, 2, 3]

      # ensure that we actually have processes
      if len(processes) == 0:
            print('Zero amount of processes')
            raise SystemExit(0)

      # duration time of all processes
      __UpperCAmelCase	=			[19, 8, 9]

      # ensure we can match each id to a duration time
      if len(duration_times) != len(processes):
            print('Unable to match all id\'s with their duration time')
            raise SystemExit(0)

      # get the waiting times and the turnaround times
      __UpperCAmelCase	=			calculate_waiting_times(duration_times)
      __UpperCAmelCase	=			calculate_turnaround_times(duration_times, waiting_times)

      # get the average times
      __UpperCAmelCase	=			calculate_average_waiting_time(waiting_times)
      __UpperCAmelCase	=			calculate_average_turnaround_time(turnaround_times)

      # print all the results
      print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
      for i, process in enumerate(processes):
            print(
                F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
                F"{turnaround_times[i]}"
            )
      print(F"Average waiting time = {average_waiting_time}")
      print(F"Average turn around time = {average_turnaround_time}")








"""simple docstring"""




import os
from shutil import copyfile
from typing import Optional, Tuple

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


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      __UpperCAmelCase	=			None


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			'▁'

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

__UpperCAmelCase	=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__UpperCAmelCase	=			{
    'google/reformer-crime-and-punishment': 52_4288,
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask"""]
  snake_case_			    =    ReformerTokenizer

  def __init__(					self						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = 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(A      ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
    return
   UpperCAmelCase__   :					Tuple   				=   os.path.join(
       A   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

   return (out_vocab_file,)





