




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a         =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a         =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a         =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a         =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       a         =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""

from collections import deque





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	int   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       process_name  # process name
       _A        =       arrival_time  # arrival time of the process
       # completion time of finished process or last interrupted time
       _A        =       arrival_time
       _A        =       burst_time  # remaining burst time
       _A        =       0  # total time of the process wait in ready queue
       _A        =       0  # time from arrival time to completion time





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	deque[Process]   ,							_UpperCAmelCase	:	int   ,							):
       # total number of mlfq's queues
       _A        =       number_of_queues
       # time slice of queues that round robin algorithm applied
       _A        =       time_slices
       # unfinished process is in this ready_queue
       _A        =       queue
       # current time
       _A        =       current_time
       # finished process is in this sequence queue
       _A        =       deque()

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       []
       for i in range(len(self.finish_queue		)		):
             sequence.append(self.finish_queue[i].process_name		)
       return sequence

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             waiting_times.append(queue[i].waiting_time		)
       return waiting_times

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             turnaround_times.append(queue[i].turnaround_time		)
       return turnaround_times

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	list[Process]		):
       _A        =       []
       for i in range(len(_UpperCAmelCase		)		):
             completion_times.append(queue[i].stop_time		)
       return completion_times

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	deque[Process]		):
       return [q.burst_time for q in queue]

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

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	deque[Process]		):
       _A        =       deque()  # sequence deque of finished process
       while len(_UpperCAmelCase		) != 0:
             _A        =       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
             _A        =       0
             # set the process's turnaround time because it is finished
             _A        =       self.current_time - cp.arrival_time
             # set the completion time
             _A        =       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       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	deque[Process]   ,							_UpperCAmelCase	:	int		):
       _A        =       deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(_UpperCAmelCase		)		):
             _A        =       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
                   _A        =       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
                   _A        =       0
                   # set the finish time
                   _A        =       self.current_time
                   # update the process' turnaround time because it is finished
                   _A        =       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       lowerCAmelCase_							(			self	:	str		):

       #  all queues except last one have round_robin algorithm
       for i in range(self.number_of_queues - 1		):
             _A       ,   _A        =       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

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

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

