

'''simple docstring'''



from collections import deque







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       int			)  -> None:


      """simple docstring"""




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







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       list[int]					,							__SCREAMING_SNAKE_CASE					:       deque[Process]					,							__SCREAMING_SNAKE_CASE					:       int					,							)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      number_of_queues
      # time slice of queues that round robin algorithm applied
      __SCREAMING_SNAKE_CASE     =      time_slices
      # unfinished process is in this ready_queue
      __SCREAMING_SNAKE_CASE     =      queue
      # current time
      __SCREAMING_SNAKE_CASE     =      current_time
      # finished process is in this sequence queue
      __SCREAMING_SNAKE_CASE     =      deque()
 def       UpperCAmelCase__		(		self					:       Dict			)  -> list[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      []
      for i in range(len(self.finish_queue			)			):
           sequence.append(self.finish_queue[i].process_name			)
      return sequence
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       list[Process]			)  -> list[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      []
      for i in range(len(__SCREAMING_SNAKE_CASE			)			):
           waiting_times.append(queue[i].waiting_time			)
      return waiting_times
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       list[Process]			)  -> list[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      []
      for i in range(len(__SCREAMING_SNAKE_CASE			)			):
           turnaround_times.append(queue[i].turnaround_time			)
      return turnaround_times
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       list[Process]			)  -> list[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      []
      for i in range(len(__SCREAMING_SNAKE_CASE			)			):
           completion_times.append(queue[i].stop_time			)
      return completion_times
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       deque[Process]			)  -> list[int]:


      """simple docstring"""




      return [q.burst_time for q in queue]
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Process			)  -> int:


      """simple docstring"""




      process.waiting_time += self.current_time - process.stop_time
      return process.waiting_time
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       deque[Process]			)  -> deque[Process]:


      """simple docstring"""




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

      self.finish_queue.extend(__SCREAMING_SNAKE_CASE			)  # add finished process to finish queue
      # FCFS will finish all remaining processes
      return finished
 def       UpperCAmelCase__		(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       deque[Process]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> tuple[deque[Process], deque[Process]]:


      """simple docstring"""




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

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


 def       UpperCAmelCase__		(		self					:       Dict			)  -> deque[Process]:


      """simple docstring"""




      for i in range(self.number_of_queues - 1			):
           __SCREAMING_SNAKE_CASE   ,			__SCREAMING_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 :       Tuple			   =     Process('P1', 0, 5_3)
  UpperCAmelCase :       List[Any]			   =     Process('P2', 0, 1_7)
  UpperCAmelCase :       List[Any]			   =     Process('P3', 0, 6_8)
  UpperCAmelCase :       List[str]			   =     Process('P4', 0, 2_4)
  UpperCAmelCase :       Dict			   =     3
  UpperCAmelCase :       Any			   =     [1_7, 2_5]
  UpperCAmelCase :       int			   =     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 :       int			   =     Process('P1', 0, 5_3)
  UpperCAmelCase :       str			   =     Process('P2', 0, 1_7)
  UpperCAmelCase :       List[Any]			   =     Process('P3', 0, 6_8)
  UpperCAmelCase :       Dict			   =     Process('P4', 0, 2_4)
  UpperCAmelCase :       Any			   =     3
  UpperCAmelCase :       List[Any]			   =     [1_7, 2_5]
  UpperCAmelCase :       Union[str, Any]			   =     deque([Pa, Pa, Pa, Pa])
  UpperCAmelCase :       List[Any]			   =     MLFQ(number_of_queues, time_slices, queue, 0)
  UpperCAmelCase :       int			   =     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 math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


     return LambdaLR(a__      ,    lambda a__						: 1      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1.0      ,    a__						)						)
          return 1.0

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      sorted(rules_dict.keys()						)
               for i, sorted_step in enumerate(a__						):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          return max(
              0.0      ,    float(num_training_steps - current_step						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * float(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          if progress >= 1.0:
               return 0.0
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * ((float(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      optimizer.defaults["""lr"""]
     if not (lr_init > lr_end):
          raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'						)

     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(a__      ,    step_rules=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'						)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'						)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    power=a__      ,    last_epoch=a__      ,    )

     return schedule_func(
         a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    last_epoch=a__						)






