




from statistics import mean

import numpy as np







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       list:



       """simple docstring"""

       A__       =    0
       # Number of processes finished
       A__       =    0
       # Displays the finished process.
       # If it is 0, the performance is completed if it is 1, before the performance.
       A__       =    [0] * no_of_process
       # List to include calculation results
       A__       =    [0] * no_of_process

       # Sort by arrival time.
       A__       =    [burst_time[i] for i in np.argsort(lowercase_						)]
       A__       =    [process_name[i] for i in np.argsort(lowercase_						)]
       arrival_time.sort()

       while no_of_process > finished_process_count:
              A__       =    0
              while finished_process[i] == 1:
                     i += 1
              if current_time < arrival_time[i]:
                     A__       =    arrival_time[i]

              A__       =    0
              # Index showing the location of the process being performed
              A__       =    0
              # Saves the current response ratio.
              A__       =    0
              for i in range(0 ,				lowercase_						):
                     if finished_process[i] == 0 and arrival_time[i] <= current_time:
                            A__       =    (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                                i
                            ]
                     if response_ratio < temp:
                            A__       =    temp
                            A__       =    i

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

       return turn_around_time







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       list:



       """simple docstring"""

       A__       =    [0] * no_of_process
       for i in range(0 ,				lowercase_						):
              A__       =    turn_around_time[i] - burst_time[i]
       return waiting_time


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

  _lowerCamelCase	:		Union[str, Any]							       =   calculate_turn_around_time(
      process_name, arrival_time, burst_time, no_of_process
  )
  _lowerCamelCase	:		Tuple							       =   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}''')








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Any							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''encoder-decoder'''
    UpperCAmelCase__							  =      True


    def __init__(			self      :	List[str]					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           assert (
               "encoder" in kwargs and "decoder" in kwargs
           ), "Config has to be initialized with encoder and decoder config"
           A__       =    kwargs.pop('''encoder''')
           A__       =    encoder_config.pop('''model_type''')
           A__       =    kwargs.pop('''decoder''')
           A__       =    decoder_config.pop('''model_type''')

           from ..auto.configuration_auto import AutoConfig

           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    True


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	PretrainedConfig					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	Union[str, Any])						->PretrainedConfig:





           '''simple docstring'''

           logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
           A__       =    True
           A__       =    True

           return cls(encoder=encoder_config.to_dict()					, decoder=decoder_config.to_dict()					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.encoder.to_dict()
           A__       =    self.decoder.to_dict()
           A__       =    self.__class__.model_type
           return output


