"""simple docstring"""


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


lowerCAmelCase_:					Tuple     =					logging.get_logger(__name__)

lowerCAmelCase_:					str     =					{
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}






class    a__    (   _a	):
			snake_case_							=      "transfo-xl"
			snake_case_							=      ["mems"]
			snake_case_							=      {
			    "n_token": "vocab_size",
			    "hidden_size": "d_model",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}


			def __init__(     self,				_UpperCAmelCase=26_7735,				_UpperCAmelCase=[2_0000, 4_0000, 20_0000],				_UpperCAmelCase=1024,				_UpperCAmelCase=1024,				_UpperCAmelCase=16,				_UpperCAmelCase=64,				_UpperCAmelCase=4096,				_UpperCAmelCase=4,				_UpperCAmelCase=False,				_UpperCAmelCase=18,				_UpperCAmelCase=1600,				_UpperCAmelCase=1000,				_UpperCAmelCase=True,				_UpperCAmelCase=True,				_UpperCAmelCase=0,				_UpperCAmelCase=-1,				_UpperCAmelCase=True,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.0,				_UpperCAmelCase=True,				_UpperCAmelCase="normal",				_UpperCAmelCase=0.01,				_UpperCAmelCase=0.01,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-5,				_UpperCAmelCase=0,				**_UpperCAmelCase,				):
								'''simple docstring'''



								lowercase__				 =							vocab_size
								lowercase__				 =							[]
								self.cutoffs.extend(_UpperCAmelCase  )
								if proj_share_all_but_first:
													lowercase__				 =							[False] + [True] * len(self.cutoffs  )
								else:
													lowercase__				 =							[False] + [False] * len(self.cutoffs  )
								lowercase__				 =							d_model
								lowercase__				 =							d_embed
								lowercase__				 =							d_head
								lowercase__				 =							d_inner
								lowercase__				 =							div_val
								lowercase__				 =							pre_lnorm
								lowercase__				 =							n_layer
								lowercase__				 =							n_head
								lowercase__				 =							mem_len
								lowercase__				 =							same_length
								lowercase__				 =							attn_type
								lowercase__				 =							clamp_len
								lowercase__				 =							sample_softmax
								lowercase__				 =							adaptive
								lowercase__				 =							dropout
								lowercase__				 =							dropatt
								lowercase__				 =							untie_r
								lowercase__				 =							init
								lowercase__				 =							init_range
								lowercase__				 =							proj_init_std
								lowercase__				 =							init_std
								lowercase__				 =							layer_norm_epsilon
								super().__init__(eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase  )


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )
								return -1


			@max_position_embeddings.setter
			def 	snake_case__	(     self,				_UpperCAmelCase  ):
								'''simple docstring'''



								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )





"""simple docstring"""


from collections import deque






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							process_name  # process name
        lowercase__				 =							arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        lowercase__				 =							arrival_time
        lowercase__				 =							burst_time  # remaining burst time
        lowercase__				 =							0  # total time of the process wait in ready queue
        lowercase__				 =							0  # time from arrival time to completion time






class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							number_of_queues
        # time slice of queues that round robin algorithm applied
        lowercase__				 =							time_slices
        # unfinished process is in this ready_queue
        lowercase__				 =							queue
        # current time
        lowercase__				 =							current_time
        # finished process is in this sequence queue
        lowercase__				 =							deque()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(self.finish_queue  )  ):
             sequence.append(self.finish_queue[i].process_name  )
        return sequence


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             waiting_times.append(queue[i].waiting_time  )
        return waiting_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             turnaround_times.append(queue[i].turnaround_time  )
        return turnaround_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        for i in range(len(_UpperCAmelCase  )  ):
             completion_times.append(queue[i].stop_time  )
        return completion_times


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return [q.burst_time for q in queue]


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							deque()  # sequence deque of finished process
        while len(_UpperCAmelCase  ) != 0:
             lowercase__				 =							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(_UpperCAmelCase  )
             # update current time
             self.current_time += cp.burst_time
             # finish the process and set the process's burst-time 0
             lowercase__				 =							0
             # set the process's turnaround time because it is finished
             lowercase__				 =							self.current_time - cp.arrival_time
             # set the completion time
             lowercase__				 =							self.current_time
             # add the process to queue that has finished queue
             finished.append(_UpperCAmelCase  )

        self.finish_queue.extend(_UpperCAmelCase  )  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							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(_UpperCAmelCase  )
             # 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
                  lowercase__				 =							self.current_time
                  # locate the process behind the queue because it is not finished
                  ready_queue.append(_UpperCAmelCase  )
             else:
                  # use CPU for remaining burst time
                  self.current_time += cp.burst_time
                  # set burst time 0 because the process is finished
                  lowercase__				 =							0
                  # set the finish time
                  lowercase__				 =							self.current_time
                  # update the process' turnaround time because it is finished
                  lowercase__				 =							self.current_time - cp.arrival_time
                  # add the process to queue that has finished queue
                  finished.append(_UpperCAmelCase  )

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for i in range(self.number_of_queues - 1  ):
             lowercase__ ,						lowercase__				 =							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

 lowerCAmelCase_:					Optional[int]     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Union[str, Any]     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					str     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					int     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Dict     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					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])})

 lowerCAmelCase_:					Any     =					Process("P1", 0, 5_3)
 lowerCAmelCase_:					Tuple     =					Process("P2", 0, 1_7)
 lowerCAmelCase_:					Optional[int]     =					Process("P3", 0, 6_8)
 lowerCAmelCase_:					List[Any]     =					Process("P4", 0, 2_4)
 lowerCAmelCase_:					Union[str, Any]     =					3
 lowerCAmelCase_:					Any     =					[1_7, 2_5]
 lowerCAmelCase_:					Optional[Any]     =					deque([Pa, Pa, Pa, Pa])
 lowerCAmelCase_:					Union[str, Any]     =					MLFQ(number_of_queues, time_slices, queue, 0)
 lowerCAmelCase_:					Tuple     =					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()}'
 )




