'''simple docstring'''






from collections import deque




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= process_name  # process name
    __UpperCAmelCase:					Optional[int]		= arrival_time  # arrival time of the process
    # completion time of finished process or last interrupted time
    __UpperCAmelCase:					Tuple		= arrival_time
    __UpperCAmelCase:					List[str]		= burst_time  # remaining burst time
    __UpperCAmelCase:					List[str]		= 0  # total time of the process wait in ready queue
    __UpperCAmelCase:					Union[str, Any]		= 0  # time from arrival time to completion time




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= number_of_queues
    # time slice of queues that round robin algorithm applied
    __UpperCAmelCase:					Union[str, Any]		= time_slices
    # unfinished process is in this ready_queue
    __UpperCAmelCase:					Dict		= queue
    # current time
    __UpperCAmelCase:					int		= current_time
    # finished process is in this sequence queue
    __UpperCAmelCase:					deque[Process]		= deque()







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= []
    for i in range(len(snake_case_					)					):
      waiting_times.append(queue[i].waiting_time					)
    return waiting_times







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= []
    for i in range(len(snake_case_					)					):
      turnaround_times.append(queue[i].turnaround_time					)
    return turnaround_times







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= []
    for i in range(len(snake_case_					)					):
      completion_times.append(queue[i].stop_time					)
    return completion_times







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return [q.burst_time for q in queue]







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					deque[Process]		= deque()  # sequence deque of finished process
    while len(snake_case_					) != 0:
      __UpperCAmelCase:					Optional[int]		= 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(snake_case_					)
      # update current time
      self.current_time += cp.burst_time
      # finish the process and set the process's burst-time 0
      __UpperCAmelCase:					Any		= 0
      # set the process's turnaround time because it is finished
      __UpperCAmelCase:					Tuple		= self.current_time - cp.arrival_time
      # set the completion time
      __UpperCAmelCase:					Optional[Any]		= self.current_time
      # add the process to queue that has finished queue
      finished.append(snake_case_					)

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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					deque[Process]		= deque()  # sequence deque of terminated process
    # just for 1 cycle and unfinished processes will go back to queue
    for _ in range(len(snake_case_					)					):
      __UpperCAmelCase:					Optional[int]		= 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(snake_case_					)
      # 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
        __UpperCAmelCase:					Dict		= self.current_time
        # locate the process behind the queue because it is not finished
        ready_queue.append(snake_case_					)
      else:
        # use CPU for remaining burst time
        self.current_time += cp.burst_time
        # set burst time 0 because the process is finished
        __UpperCAmelCase:					Union[str, Any]		= 0
        # set the finish time
        __UpperCAmelCase:					Optional[Any]		= self.current_time
        # update the process' turnaround time because it is finished
        __UpperCAmelCase:					List[str]		= self.current_time - cp.arrival_time
        # add the process to queue that has finished queue
        finished.append(snake_case_					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    for i in range(self.number_of_queues - 1					):
      __UpperCAmelCase:					List[Any]		= 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

    SCREAMING_SNAKE_CASE_	   =			Process('P1', 0, 53)
    SCREAMING_SNAKE_CASE_	   =			Process('P2', 0, 17)
    SCREAMING_SNAKE_CASE_	   =			Process('P3', 0, 68)
    SCREAMING_SNAKE_CASE_	   =			Process('P4', 0, 24)
    SCREAMING_SNAKE_CASE_	   =			3
    SCREAMING_SNAKE_CASE_	   =			[17, 25]
    SCREAMING_SNAKE_CASE_	   =			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])})

    SCREAMING_SNAKE_CASE_	   =			Process('P1', 0, 53)
    SCREAMING_SNAKE_CASE_	   =			Process('P2', 0, 17)
    SCREAMING_SNAKE_CASE_	   =			Process('P3', 0, 68)
    SCREAMING_SNAKE_CASE_	   =			Process('P4', 0, 24)
    SCREAMING_SNAKE_CASE_	   =			3
    SCREAMING_SNAKE_CASE_	   =			[17, 25]
    SCREAMING_SNAKE_CASE_	   =			deque([Pa, Pa, Pa, Pa])
    SCREAMING_SNAKE_CASE_	   =			MLFQ(number_of_queues, time_slices, queue, 0)
    SCREAMING_SNAKE_CASE_	   =			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()}"""
    )
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )