


from collections import deque







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       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 lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       # total number of mlfq's queues
       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 		UpperCamelCase__  (			self      ):
       snake_case_ =   []
       for i in range(len(self.finish_queue      )      ):
             sequence.append(self.finish_queue[i].process_name      )
       return sequence



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   []
       for i in range(len(_UpperCAmelCase      )      ):
             waiting_times.append(queue[i].waiting_time      )
       return waiting_times



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   []
       for i in range(len(_UpperCAmelCase      )      ):
             turnaround_times.append(queue[i].turnaround_time      )
       return turnaround_times



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   []
       for i in range(len(_UpperCAmelCase      )      ):
             completion_times.append(queue[i].stop_time      )
       return completion_times



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return [q.burst_time for q in queue]



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   deque()  # sequence deque of finished process
       while len(_UpperCAmelCase      ) != 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(_UpperCAmelCase      )
             # 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(_UpperCAmelCase      )

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(_UpperCAmelCase      )      ):
             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(_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
                   snake_case_ =   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
                   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(_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 		UpperCamelCase__  (			self      ):

       #  all queues except last one have round_robin algorithm
       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    =					Process("""P1""", 0, 53)
   UpperCAmelCase    =					Process("""P2""", 0, 17)
   UpperCAmelCase    =					Process("""P3""", 0, 68)
   UpperCAmelCase    =					Process("""P4""", 0, 24)
   UpperCAmelCase    =					3
   UpperCAmelCase    =					[17, 25]
   UpperCAmelCase    =					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    =					Process("""P1""", 0, 53)
   UpperCAmelCase    =					Process("""P2""", 0, 17)
   UpperCAmelCase    =					Process("""P3""", 0, 68)
   UpperCAmelCase    =					Process("""P4""", 0, 24)
   UpperCAmelCase    =					3
   UpperCAmelCase    =					[17, 25]
   UpperCAmelCase    =					deque([Pa, Pa, Pa, Pa])
   UpperCAmelCase    =					MLFQ(number_of_queues, time_slices, queue, 0)
   UpperCAmelCase    =					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()}'''
   )



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase="cls"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   **_UpperCAmelCase	,   ):
       super().__init__(pad_token_id=_UpperCAmelCase	,   bos_token_id=_UpperCAmelCase	,   eos_token_id=_UpperCAmelCase	,   **_UpperCAmelCase      )
       snake_case_ =   project_dim
       snake_case_ =   pooler_fn
       snake_case_ =   learn_encoder
       snake_case_ =   use_attention_mask







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[R"pooler", R"logit_scale"]
 __snake_case				=						[R"position_ids", R"predictions.decoder.bias"]
 __snake_case				=						"roberta"
 __snake_case				=						RobertaSeriesConfig



 def __init__(			self	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase      )
       snake_case_ =   XLMRobertaModel(_UpperCAmelCase      )
       snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
       snake_case_ =   getattr(_UpperCAmelCase	,   '''has_pre_transformation'''	,   _UpperCAmelCase      )
       if self.has_pre_transformation:
             snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
             snake_case_ =   nn.LayerNorm(config.hidden_size	,   eps=config.layer_norm_eps      )
       self.post_init()





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case_ =   self.base_model(
           input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   position_ids=_UpperCAmelCase	,   head_mask=_UpperCAmelCase	,   inputs_embeds=_UpperCAmelCase	,   encoder_hidden_states=_UpperCAmelCase	,   encoder_attention_mask=_UpperCAmelCase	,   output_attentions=_UpperCAmelCase	,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,   return_dict=_UpperCAmelCase	,   )

       if self.has_pre_transformation:
             snake_case_ =   outputs['''hidden_states'''][-2]
             snake_case_ =   self.pre_LN(_UpperCAmelCase      )
             snake_case_ =   self.transformation_pre(_UpperCAmelCase      )

             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )
       else:
             snake_case_ =   self.transformation(outputs.last_hidden_state      )
             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )