code: 
  
  
  
  
  
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  def       a__						(		snake_case__				:       list[int]    ):
    _UpperCAmelCase		:       Dict       =    2
    _UpperCAmelCase		:       Optional[int]       =    list(snake_case__    )
    _UpperCAmelCase		:       Optional[Any]       =    [0] * len(snake_case__    )
    _UpperCAmelCase		:       Optional[int]       =    0
    while True:
      _UpperCAmelCase		:       Optional[int]       =    True
      for i, burst_time in enumerate(snake_case__    ):
        if rem_burst_times[i] > 0:
          _UpperCAmelCase		:       List[Any]       =    False
          if rem_burst_times[i] > quantum:
            t += quantum
            rem_burst_times[i] -= quantum
          else:
            t += rem_burst_times[i]
            _UpperCAmelCase		:       int       =    t - burst_time
            _UpperCAmelCase		:       List[Any]       =    0
      if done is True:
        return waiting_times
  
  
  
  
  def       a__						(		snake_case__				:       list[int]	,      snake_case__				:       list[int]    ):
    return [burst + waiting for burst, waiting in zip(snake_case__	,      snake_case__    )]
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__				:    List[str]         =    [3, 5, 7]
   SCREAMING_SNAKE_CASE__				:    int         =    calculate_waiting_times(burst_times)
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    calculate_turn_around_times(burst_times, waiting_times)
   print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
   for i, burst_time in enumerate(burst_times):
    print(
        F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
        F'{turn_around_times[i]}'
    )
   print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
   print(F'Average turn around time = {mean(turn_around_times):.5f}')
  
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    {
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    int         =    [
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    Dict         =    [
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )
  
  else:
   import sys
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  