




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}''')








'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''   ,				lowercase   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				lowercase   ,				)
           return self.image_processor_class




