


from collections import deque






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   None:




           """simple docstring"""

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






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   None:




           """simple docstring"""

           _lowercase       :							List[str]      		=		number_of_queues
           # time slice of queues that round robin algorithm applied
           _lowercase       :							str      		=		time_slices
           # unfinished process is in this ready_queue
           _lowercase       :							Optional[Any]      		=		queue
           # current time
           _lowercase       :							Union[str, Any]      		=		current_time
           # finished process is in this sequence queue
           _lowercase       :							deque[Process]      		=		deque()
       def 			UpperCamelCase     (					self)       ->   list[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		[]
           for i in range(len(self.finish_queue)):
               sequence.append(self.finish_queue[i].process_name)
           return sequence
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase       :							Tuple      		=		[]
           for i in range(len(lowerCamelCase)):
               turnaround_times.append(queue[i].turnaround_time)
           return turnaround_times
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		[]
           for i in range(len(lowerCamelCase)):
               completion_times.append(queue[i].stop_time)
           return completion_times
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   list[int]:




           """simple docstring"""

           return [q.burst_time for q in queue]
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




           """simple docstring"""

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




           """simple docstring"""

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

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




           """simple docstring"""

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

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




       def 			UpperCamelCase     (					self)       ->   deque[Process]:




           """simple docstring"""

           for i in range(self.number_of_queues - 1):
               _lowercase		,				_lowercase       :							str      		=		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       :      Union[str, Any]												= Process("P1", 0, 53)
 SCREAMING_SNAKE_CASE       :      str												= Process("P2", 0, 17)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P3", 0, 68)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P4", 0, 24)
 SCREAMING_SNAKE_CASE       :      Optional[int]												= 3
 SCREAMING_SNAKE_CASE       :      List[str]												= [17, 25]
 SCREAMING_SNAKE_CASE       :      List[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])})

 SCREAMING_SNAKE_CASE       :      List[str]												= Process("P1", 0, 53)
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= Process("P2", 0, 17)
 SCREAMING_SNAKE_CASE       :      List[str]												= Process("P3", 0, 68)
 SCREAMING_SNAKE_CASE       :      Tuple												= Process("P4", 0, 24)
 SCREAMING_SNAKE_CASE       :      List[str]												= 3
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= [17, 25]
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= deque([Pa, Pa, Pa, Pa])
 SCREAMING_SNAKE_CASE       :      str												= MLFQ(number_of_queues, time_slices, queue, 0)
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





