





"""simple docstring"""





from collections import deque




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Any						,				__lowercase       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):


        '''simple docstring'''




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




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      List[str]						,				__lowercase       :      int						,				__lowercase       :      list[int]						,				__lowercase       :      deque[Process]						,				__lowercase       :      int						,				):


        '''simple docstring'''




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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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

      def       A_					(							self       :      Any						,				__lowercase       :      list[Process] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						[]
        for i in range(len(UpperCamelCase_ ) ):
          waiting_times.append(queue[i].waiting_time )
        return waiting_times

      def       A_					(							self       :      List[Any]						,				__lowercase       :      list[Process] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						[]
        for i in range(len(UpperCamelCase_ ) ):
          turnaround_times.append(queue[i].turnaround_time )
        return turnaround_times

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      list[Process] ):


        '''simple docstring'''




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

      def       A_					(							self       :      Tuple						,				__lowercase       :      deque[Process] ):


        '''simple docstring'''




        return [q.burst_time for q in queue]

      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Process ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[Any]						,				__lowercase       :      deque[Process] ):


        '''simple docstring'''




        __UpperCAmelCase    :						deque[Process]       =						deque()  # sequence deque of finished process
        while len(UpperCamelCase_ ) != 0:
          __UpperCAmelCase    :						Dict       =						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
          __UpperCAmelCase    :						Any       =						0
          # set the process's turnaround time because it is finished
          __UpperCAmelCase    :						Optional[Any]       =						self.current_time - cp.arrival_time
          # set the completion time
          __UpperCAmelCase    :						Union[str, Any]       =						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       A_					(							self       :      Optional[Any]						,				__lowercase       :      deque[Process]						,				__lowercase       :      int ):


        '''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(UpperCamelCase_ ) ):
          __UpperCAmelCase    :						Any       =						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
            __UpperCAmelCase    :						List[str]       =						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
            __UpperCAmelCase    :						Dict       =						0
            # set the finish time
            __UpperCAmelCase    :						Union[str, Any]       =						self.current_time
            # update the process' turnaround time because it is finished
            __UpperCAmelCase    :						str       =						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       A_					(							self       :      Optional[int] ):


        '''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

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

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





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


lowercase__				:Optional[int]				   =       logging.get_logger(__name__)




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Tuple         =       [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]

      def __init__(							self       :      Dict						,				**__lowercase       :      Dict ):


        '''simple docstring'''




        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            __UpperCAmelCase    :						Optional[Any]       =						deprecated_arg[3:]
            __UpperCAmelCase    :						List[str]       =						not kwargs.pop(__lowercase )
            logger.warning(
                f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                f''' {positive_arg}={kwargs[positive_arg]}''' )
        __UpperCAmelCase    :						int       =						kwargs.pop('''tpu_name'''						,				self.tpu_name )
        __UpperCAmelCase    :						Any       =						kwargs.pop('''device_idx'''						,				self.device_idx )
        __UpperCAmelCase    :						Tuple       =						kwargs.pop('''eager_mode'''						,				self.eager_mode )
        __UpperCAmelCase    :						Optional[int]       =						kwargs.pop('''use_xla'''						,				self.use_xla )
        super().__init__(**__lowercase )

      _A     :       str         =       field(
          default=__UpperCAmelCase      ,  metadata={'help': 'Name of TPU'}      ,  )
      _A     :       int         =       field(
          default=0      ,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}      ,  )
      _A     :       bool         =       field(default=__UpperCAmelCase      ,  metadata={'help': 'Benchmark models in eager model.'} )
      _A     :       bool         =       field(
          default=__UpperCAmelCase      ,  metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }      ,  )

      @cached_property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        __UpperCAmelCase    :						Any       =						None
        if self.tpu:
          try:
            if self.tpu_name:
              __UpperCAmelCase    :						List[str]       =						tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
            else:
              __UpperCAmelCase    :						Any       =						tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
            __UpperCAmelCase    :						int       =						None
        return tpu

      @cached_property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.is_tpu:
          tf.config.experimental_connect_to_cluster(self._setup_tpu )
          tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

          __UpperCAmelCase    :						Any       =						tf.distribute.TPUStrategy(self._setup_tpu )
        else:
          # currently no multi gpu is allowed
          if self.is_gpu:
            # TODO: Currently only single GPU is supported
            tf.config.set_visible_devices(self.gpu_list[self.device_idx]						,				'''GPU''' )
            __UpperCAmelCase    :						Any       =						tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
          else:
            tf.config.set_visible_devices([]						,				'''GPU''' )  # disable GPU
            __UpperCAmelCase    :						str       =						tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )

        return strategy

      @property
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_tpu is not None

      @property
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return self._setup_strategy

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        return tf.config.list_physical_devices('''GPU''' )

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        requires_backends(self						,				['''tf'''] )
        if self.cuda:
          return len(self.gpu_list )
        return 0







      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        return self.n_gpu > 0