



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[int]      						=					['''image_processor''', '''tokenizer''']
 _lowercase   :    List[str]      						=					'''CLIPImageProcessor'''
 _lowercase   :    Any      						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= None
  if "feature_extractor" in kwargs:
   warnings.warn(
       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
       ''' instead.'''		,				SCREAMING_SNAKE_CASE		,				)
   a__												= kwargs.pop('''feature_extractor'''      )

  a__												= 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__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:

  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:
   a__												= self.tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if text is not None and images is not None:
   a__												= image_features.pixel_values
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE      )		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Any:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= self.tokenizer.model_input_names
  a__												= self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  warnings.warn(
      '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,				SCREAMING_SNAKE_CASE		,				)
  return self.image_processor_class



 @property
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  warnings.warn(
      '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,				SCREAMING_SNAKE_CASE		,				)
  return self.image_processor










from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 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(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 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							,  __UpperCAmelCase       ):
   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     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


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

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     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}')





