from __future__ import annotations

from statistics import mean


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Any					  =					list(lowercase_			)
       _UpperCamelCase       :    str					  =					[0] * len(lowercase_			)
       _UpperCamelCase       :    Dict					  =					0
       while True:
              _UpperCamelCase       :    Optional[int]					  =					True
              for i, burst_time in enumerate(lowercase_			):
                     if rem_burst_times[i] > 0:
                            _UpperCamelCase       :    Dict					  =					False
                            if rem_burst_times[i] > quantum:
                                   t += quantum
                                   rem_burst_times[i] -= quantum
                            else:
                                   t += rem_burst_times[i]
                                   _UpperCamelCase       :    List[str]					  =					t - burst_time
                                   _UpperCamelCase       :    Union[str, Any]					  =					0
              if done is True:
                     return waiting_times


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]						,  lowercase_:		list[int]			):
       return [burst + waiting for burst, waiting in zip(lowercase_						,  lowercase_			)]


if __name__ == "__main__":
 UpperCAmelCase     						=			[3, 5, 7]
 UpperCAmelCase     						=			calculate_waiting_times(burst_times)
 UpperCAmelCase     						=			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}''')




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



