



from collections import deque

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



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

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Any							,      lowerCamelCase	:							int							,      lowerCamelCase	:							list[int]							,      lowerCamelCase	:							deque[Process]							,      lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							__lowercase		       =			number_of_queues
							# time slice of queues that round robin algorithm applied
							__lowercase		       =			time_slices
							# unfinished process is in this ready_queue
							__lowercase		       =			queue
							# current time
							__lowercase		       =			current_time
							# finished process is in this sequence queue
							__lowercase		       =			deque()



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							list[Process]	):






							'''simple docstring'''



							__lowercase		       =			[]
							for i in range(len(lowerCamelCase	)	):
								waiting_times.append(queue[i].waiting_time	)
							return waiting_times



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							list[Process]	):






							'''simple docstring'''



							__lowercase		       =			[]
							for i in range(len(lowerCamelCase	)	):
								turnaround_times.append(queue[i].turnaround_time	)
							return turnaround_times



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							list[Process]	):






							'''simple docstring'''



							__lowercase		       =			[]
							for i in range(len(lowerCamelCase	)	):
								completion_times.append(queue[i].stop_time	)
							return completion_times



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							deque[Process]	):






							'''simple docstring'''



							return [q.burst_time for q in queue]



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Process	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							deque[Process]	):






							'''simple docstring'''



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






							'''simple docstring'''



							__lowercase		       =			deque()  # sequence deque of terminated process
							# just for 1 cycle and unfinished processes will go back to queue
							for _ in range(len(lowerCamelCase	)	):
								__lowercase		       =			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		       =			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		       =			0
									# set the finish time
									__lowercase		       =			self.current_time
									# update the process' turnaround time because it is finished
									__lowercase		       =			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     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							for i in range(self.number_of_queues - 1	):
								__lowercase       ,    __lowercase		       =			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

	snake_case__					:     List[Any]    		=   Process("""P1""", 0, 53)
	snake_case__					:     List[str]    		=   Process("""P2""", 0, 17)
	snake_case__					:     List[Any]    		=   Process("""P3""", 0, 68)
	snake_case__					:     Union[str, Any]    		=   Process("""P4""", 0, 24)
	snake_case__					:     Union[str, Any]    		=   3
	snake_case__					:     Union[str, Any]    		=   [17, 25]
	snake_case__					:     Dict    		=   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])})

	snake_case__					:     Optional[Any]    		=   Process("""P1""", 0, 53)
	snake_case__					:     Dict    		=   Process("""P2""", 0, 17)
	snake_case__					:     Dict    		=   Process("""P3""", 0, 68)
	snake_case__					:     int    		=   Process("""P4""", 0, 24)
	snake_case__					:     Union[str, Any]    		=   3
	snake_case__					:     Optional[Any]    		=   [17, 25]
	snake_case__					:     Union[str, Any]    		=   deque([Pa, Pa, Pa, Pa])
	snake_case__					:     List[str]    		=   MLFQ(number_of_queues, time_slices, queue, 0)
	snake_case__					:     List[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()}'''
	)








import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case__					:     int    		=   logging.get_logger(__name__)

snake_case__					:     Optional[int]    		=   {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """conditional_detr"""
						_snake_case			:     Union[str, Any]      							=      ["""past_key_values"""]
						_snake_case			:     Optional[int]      							=      {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}



						def __init__(	self	:							Optional[Any]							,      lowerCamelCase	:							int=True							,      lowerCamelCase	:							Tuple=None							,      lowerCamelCase	:							Optional[int]=3							,      lowerCamelCase	:							Optional[int]=300							,      lowerCamelCase	:							List[Any]=6							,      lowerCamelCase	:							str=2_048							,      lowerCamelCase	:							Any=8							,      lowerCamelCase	:							List[str]=6							,      lowerCamelCase	:							Any=2_048							,      lowerCamelCase	:							List[Any]=8							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str="relu"							,      lowerCamelCase	:							int=256							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Optional[Any]=0.0							,      lowerCamelCase	:							Dict=0.0							,      lowerCamelCase	:							Tuple=0.02							,      lowerCamelCase	:							int=1.0							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							List[str]="sine"							,      lowerCamelCase	:							List[Any]="resnet50"							,      lowerCamelCase	:							Any=True							,      lowerCamelCase	:							Any=False							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							List[Any]=5							,      lowerCamelCase	:							str=2							,      lowerCamelCase	:							Dict=1							,      lowerCamelCase	:							List[str]=1							,      lowerCamelCase	:							Union[str, Any]=2							,      lowerCamelCase	:							Dict=5							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							Tuple=0.25							,      **lowerCamelCase	:							List[str]							,      ):






							'''simple docstring'''



							if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

							if not use_timm_backbone:
								if backbone_config is None:
									logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
									__lowercase		       =			CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			backbone_config.get("model_type"	)
									__lowercase		       =			CONFIG_MAPPING[backbone_model_type]
									__lowercase		       =			config_class.from_dict(lowerCamelCase	)

							__lowercase		       =			use_timm_backbone
							__lowercase		       =			backbone_config
							__lowercase		       =			num_channels
							__lowercase		       =			num_queries
							__lowercase		       =			d_model
							__lowercase		       =			encoder_ffn_dim
							__lowercase		       =			encoder_layers
							__lowercase		       =			encoder_attention_heads
							__lowercase		       =			decoder_ffn_dim
							__lowercase		       =			decoder_layers
							__lowercase		       =			decoder_attention_heads
							__lowercase		       =			dropout
							__lowercase		       =			attention_dropout
							__lowercase		       =			activation_dropout
							__lowercase		       =			activation_function
							__lowercase		       =			init_std
							__lowercase		       =			init_xavier_std
							__lowercase		       =			encoder_layerdrop
							__lowercase		       =			decoder_layerdrop
							__lowercase		       =			encoder_layers
							__lowercase		       =			auxiliary_loss
							__lowercase		       =			position_embedding_type
							__lowercase		       =			backbone
							__lowercase		       =			use_pretrained_backbone
							__lowercase		       =			dilation
							# Hungarian matcher
							__lowercase		       =			class_cost
							__lowercase		       =			bbox_cost
							__lowercase		       =			giou_cost
							# Loss coefficients
							__lowercase		       =			mask_loss_coefficient
							__lowercase		       =			dice_loss_coefficient
							__lowercase		       =			cls_loss_coefficient
							__lowercase		       =			bbox_loss_coefficient
							__lowercase		       =			giou_loss_coefficient
							__lowercase		       =			focal_alpha
							super().__init__(is_encoder_decoder=lowerCamelCase							,      **lowerCamelCase	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self.encoder_attention_heads



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return self.d_model



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			copy.deepcopy(self.__dict__	)
							if self.backbone_config is not None:
								__lowercase		       =			self.backbone_config.to_dict()
							__lowercase		       =			self.__class__.model_type
							return output

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      version.parse("""1.11"""	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							        ("pixel_mask", {0: "batch"}),
							    ]	)



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							return 1e-5



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






							'''simple docstring'''



							return 12



