






from collections import deque


class      __SCREAMING_SNAKE_CASE:
							def __init__(   self:					Any				,		UpperCamelCase:					str				,		UpperCamelCase:					int				,		UpperCamelCase:					int  )						->						str:
											snake_case__													=   process_name  # process name
											snake_case__													=   arrival_time  # arrival time of the process
											# completion time of finished process or last interrupted time
											snake_case__													=   arrival_time
											snake_case__													=   burst_time  # remaining burst time
											snake_case__													=   0  # total time of the process wait in ready queue
											snake_case__													=   0  # time from arrival time to completion time


class      __SCREAMING_SNAKE_CASE:
							def __init__(   self:					List[str]				,		UpperCamelCase:					int				,		UpperCamelCase:					list[int]				,		UpperCamelCase:					deque[Process]				,		UpperCamelCase:					int				,		)						->						List[Any]:
											snake_case__													=   number_of_queues
											# time slice of queues that round robin algorithm applied
											snake_case__													=   time_slices
											# unfinished process is in this ready_queue
											snake_case__													=   queue
											# current time
											snake_case__													=   current_time
											# finished process is in this sequence queue
											snake_case__													=   deque()
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						str:
											snake_case__													=   []
											for i in range(len(self.finish_queue  )  ):
															sequence.append(self.finish_queue[i].process_name  )
											return sequence
							def lowerCAmelCase_       (   self:					Tuple				,		UpperCamelCase:					list[Process]  )						->						int:
											snake_case__													=   []
											for i in range(len(a__  )  ):
															waiting_times.append(queue[i].waiting_time  )
											return waiting_times
							def lowerCAmelCase_       (   self:					Any				,		UpperCamelCase:					list[Process]  )						->						Union[str, Any]:
											snake_case__													=   []
											for i in range(len(a__  )  ):
															turnaround_times.append(queue[i].turnaround_time  )
											return turnaround_times
							def lowerCAmelCase_       (   self:					str				,		UpperCamelCase:					list[Process]  )						->						Union[str, Any]:
											snake_case__													=   []
											for i in range(len(a__  )  ):
															completion_times.append(queue[i].stop_time  )
											return completion_times
							def lowerCAmelCase_       (   self:					Optional[Any]				,		UpperCamelCase:					deque[Process]  )						->						Union[str, Any]:
											return [q.burst_time for q in queue]
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					Process  )						->						int:
											process.waiting_time += self.current_time - process.stop_time
											return process.waiting_time
							def lowerCAmelCase_       (   self:					Tuple				,		UpperCamelCase:					deque[Process]  )						->						Tuple:
											snake_case__													=   deque()  # sequence deque of finished process
											while len(a__  ) != 0:
															snake_case__													=   ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																			self.current_time += cp.arrival_time

															# update waiting time of current process
															self.update_waiting_time(a__  )
															# update current time
															self.current_time += cp.burst_time
															# finish the process and set the process's burst-time 0
															snake_case__													=   0
															# set the process's turnaround time because it is finished
															snake_case__													=   self.current_time - cp.arrival_time
															# set the completion time
															snake_case__													=   self.current_time
															# add the process to queue that has finished queue
															finished.append(a__  )

											self.finish_queue.extend(a__  )  # add finished process to finish queue
											# FCFS will finish all remaining processes
											return finished
							def lowerCAmelCase_       (   self:					Optional[int]				,		UpperCamelCase:					deque[Process]				,		UpperCamelCase:					int  )						->						Optional[Any]:
											snake_case__													=   deque()  # sequence deque of terminated process
											# just for 1 cycle and unfinished processes will go back to queue
											for _ in range(len(a__  )  ):
															snake_case__													=   ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																			self.current_time += cp.arrival_time

															# update waiting time of unfinished processes
															self.update_waiting_time(a__  )
															# if the burst time of process is bigger than time-slice
															if cp.burst_time > time_slice:
																			# use CPU for only time-slice
																			self.current_time += time_slice
																			# update remaining burst time
																			cp.burst_time -= time_slice
																			# update end point time
																			snake_case__													=   self.current_time
																			# locate the process behind the queue because it is not finished
																			ready_queue.append(a__  )
															else:
																			# use CPU for remaining burst time
																			self.current_time += cp.burst_time
																			# set burst time 0 because the process is finished
																			snake_case__													=   0
																			# set the finish time
																			snake_case__													=   self.current_time
																			# update the process' turnaround time because it is finished
																			snake_case__													=   self.current_time - cp.arrival_time
																			# add the process to queue that has finished queue
																			finished.append(a__  )

											self.finish_queue.extend(a__  )  # add finished process to finish queue
											# return finished processes queue and remaining processes queue
											return finished, ready_queue


							def lowerCAmelCase_       (   self:					List[Any]  )						->						int:



											for i in range(self.number_of_queues - 1  ):
															snake_case__							,  snake_case__													=   self.round_robin(
															    self.ready_queue				,		self.time_slices[i]  )
											#  the last queue has first_come_first_served algorithm
											self.first_come_first_served(self.ready_queue  )

											return self.finish_queue


if __name__ == "__main__":
							import doctest

							__UpperCamelCase    :						str		     =  Process("""P1""", 0, 53)
							__UpperCamelCase    :						Tuple		     =  Process("""P2""", 0, 17)
							__UpperCamelCase    :						int		     =  Process("""P3""", 0, 68)
							__UpperCamelCase    :						Tuple		     =  Process("""P4""", 0, 24)
							__UpperCamelCase    :						Any		     =  3
							__UpperCamelCase    :						Union[str, Any]		     =  [17, 25]
							__UpperCamelCase    :						Tuple		     =  deque([Pa, Pa, Pa, Pa])

							if len(time_slices) != number_of_queues - 1:
														raise SystemExit(0)

							doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})

							__UpperCamelCase    :						Tuple		     =  Process("""P1""", 0, 53)
							__UpperCamelCase    :						Union[str, Any]		     =  Process("""P2""", 0, 17)
							__UpperCamelCase    :						int		     =  Process("""P3""", 0, 68)
							__UpperCamelCase    :						Any		     =  Process("""P4""", 0, 24)
							__UpperCamelCase    :						Union[str, Any]		     =  3
							__UpperCamelCase    :						str		     =  [17, 25]
							__UpperCamelCase    :						str		     =  deque([Pa, Pa, Pa, Pa])
							__UpperCamelCase    :						Optional[int]		     =  MLFQ(number_of_queues, time_slices, queue, 0)
							__UpperCamelCase    :						Dict		     =  mlfq.multi_level_feedback_queue()

							# print total waiting times of processes(P1, P2, P3, P4)
							print(
							    f'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
							)
							# print completion times of processes(P1, P2, P3, P4)
							print(
							    f'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
							)
							# print total turnaround times of processes(P1, P2, P3, P4)
							print(
							    f'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
							)
							# print sequence of finished processes
							print(
							    f'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
							)











import warnings

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

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



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

         __snake_case							       =   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__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """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:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

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

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


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



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

