
from statistics import mean

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   list:
       lowercase							:					Any	     =				0
       # Number of processes finished
       lowercase							:					Dict	     =				0
       # Displays the finished process.
       # If it is 0, the performance is completed if it is 1, before the performance.
       lowercase							:					Dict	     =				[0] * no_of_process
       # List to include calculation results
       lowercase							:					Dict	     =				[0] * no_of_process

       # Sort by arrival time.
       lowercase							:					Optional[int]	     =				[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__    )]
       lowercase							:					List[Any]	     =				[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__    )]
       arrival_time.sort()

       while no_of_process > finished_process_count:
              lowercase							:					Union[str, Any]	     =				0
              while finished_process[i] == 1:
                     i += 1
              if current_time < arrival_time[i]:
                     lowercase							:					Any	     =				arrival_time[i]

              lowercase							:					int	     =				0
              # Index showing the location of the process being performed
              lowercase							:					str	     =				0
              # Saves the current response ratio.
              lowercase							:					Dict	     =				0
              for i in range(0 ,     SCREAMING_SNAKE_CASE__    ):
                     if finished_process[i] == 0 and arrival_time[i] <= current_time:
                            lowercase							:					Optional[int]	     =				(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                                i
                            ]
                     if response_ratio < temp:
                            lowercase							:					List[Any]	     =				temp
                            lowercase							:					Union[str, Any]	     =				i

        # Calculate the turn around time
              lowercase							:					int	     =				current_time + burst_time[loc] - arrival_time[loc]
              current_time += burst_time[loc]
              # Indicates that the process has been performed.
              lowercase							:					int	     =				1
              # Increase finished_process_count by 1
              finished_process_count += 1

       return turn_around_time







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   list:
       lowercase							:					List[str]	     =				[0] * no_of_process
       for i in range(0 ,     SCREAMING_SNAKE_CASE__    ):
              lowercase							:					List[Any]	     =				turn_around_time[i] - burst_time[i]
       return waiting_time


if __name__ == "__main__":
  lowercase  : Optional[Any]						   =							5
  lowercase  : List[str]						   =							["""A""", """B""", """C""", """D""", """E"""]
  lowercase  : Union[str, Any]						   =							[1, 2, 3, 4, 5]
  lowercase  : List[str]						   =							[1, 2, 3, 4, 5]

  lowercase  : Any						   =							calculate_turn_around_time(
      process_name, arrival_time, burst_time, no_of_process
  )
  lowercase  : Any						   =							calculate_waiting_time(
      process_name, turn_around_time, burst_time, no_of_process
  )

  print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
  for i in range(0, no_of_process):
    print(
        F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
        F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
    )

  print(F'''average waiting time : {mean(waiting_time):.5f}''')
  print(F'''average turn around time : {mean(turn_around_time):.5f}''')


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
