"""simple docstring"""




from __future__ import annotations

from statistics import mean





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]			,_lowerCamelCase						:       list[int]			,_lowerCamelCase						:       int	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     [0] * no_of_processes
  _lowerCAmelCase						:       Optional[Any]  			=     [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[Any]  			=     burst_time[i]
  _lowerCAmelCase						:       list[int]  			=     []

  _lowerCAmelCase						:       List[Any]  			=     0
  _lowerCAmelCase						:       int  			=     0

  # When processes are not completed,
  # A process whose arrival time has passed \
  # and has remaining execution time is put into the ready_process.
  # The shortest process in the ready_process, target_process is executed.

  while completed != no_of_processes:
    _lowerCAmelCase						:       str  			=     []
    _lowerCAmelCase						:       List[str]  			=     -1

    for i in range(_lowerCamelCase	):
      if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
        ready_process.append(_lowerCamelCase	)

    if len(_lowerCamelCase	) > 0:
      _lowerCAmelCase						:       Tuple  			=     ready_process[0]
      for i in ready_process:
        if remaining_time[i] < remaining_time[target_process]:
          _lowerCAmelCase						:       Tuple  			=     i
      total_time += burst_time[target_process]
      completed += 1
      _lowerCAmelCase						:       Union[str, Any]  			=     0
      _lowerCAmelCase						:       Optional[int]  			=     (
          total_time - arrival_time[target_process] - burst_time[target_process]
      )
    else:
      total_time += 1

  return waiting_time





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


if __name__ == "__main__":
     print('[TEST CASE 01]')

     _a      :		Dict           =   4
     _a      :		Optional[Any]           =   [2, 5, 3, 7]
     _a      :		List[str]           =   [0, 0, 0, 0]
     _a      :		Optional[Any]           =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
     _a      :		str           =   calculate_turnaroundtime(
         burst_time, no_of_processes, waiting_time
     )

     # Printing the Result
     print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
     for i, process_id in enumerate(list(range(1, 5))):
          print(
              F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
              F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
     print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_a      :		List[Any]           =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a      :		Union[str, Any]           =   {
    'vocab_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
        ),
        'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-generator': (
            'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
        ),
    },
}

_a      :		Optional[Any]           =   {
    'google/electra-small-generator': 512,
    'google/electra-base-generator': 512,
    'google/electra-large-generator': 512,
    'google/electra-small-discriminator': 512,
    'google/electra-base-discriminator': 512,
    'google/electra-large-discriminator': 512,
}

_a      :		Any           =   {
    'google/electra-small-generator': {'do_lower_case': True},
    'google/electra-base-generator': {'do_lower_case': True},
    'google/electra-large-generator': {'do_lower_case': True},
    'google/electra-small-discriminator': {'do_lower_case': True},
    'google/electra-base-discriminator': {'do_lower_case': True},
    'google/electra-large-discriminator': {'do_lower_case': True},
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Union[str, Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[Any]							=		ElectraTokenizer






     def __init__(		self				,     a__=None				,     a__=None				,     a__=True				,     a__="[UNK]"				,     a__="[SEP]"				,     a__="[PAD]"				,     a__="[CLS]"				,     a__="[MASK]"				,     a__=True				,     a__=None				,     **a__				,     ):
       super().__init__(
           a__				,     tokenizer_file=a__				,     do_lower_case=a__				,     unk_token=a__				,     sep_token=a__				,     pad_token=a__				,     cls_token=a__				,     mask_token=a__				,     tokenize_chinese_chars=a__				,     strip_accents=a__				,     **a__				,     )

       _lowerCAmelCase						:       int  			=     json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get("""lowercase"""				,     a__  ) != do_lower_case
           or normalizer_state.get("""strip_accents"""				,     a__  ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""				,     a__  ) != tokenize_chinese_chars
       ):
         _lowerCAmelCase						:       Dict  			=     getattr(a__				,     normalizer_state.pop("""type"""  )  )
         _lowerCAmelCase						:       int  			=     do_lower_case
         _lowerCAmelCase						:       str  			=     strip_accents
         _lowerCAmelCase						:       Dict  			=     tokenize_chinese_chars
         _lowerCAmelCase						:       str  			=     normalizer_class(**a__  )

       _lowerCAmelCase						:       List[str]  			=     do_lower_case






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       int  			=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       List[str]  			=     [self.sep_token_id]
       _lowerCAmelCase						:       Any  			=     [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  ) * [0] + len(token_ids_a + sep  ) * [1]






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._tokenizer.model.save(a__				,     name=a__  )
       return tuple(a__  )




