



"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case_    					=	{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case_    					=	logging.get_logger(__name__)







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""maskformer"""
						__UpperCamelCase		    =							{"""hidden_size""": """mask_feature_size"""}
						__UpperCamelCase		    =							["""resnet""", """swin"""]
						__UpperCamelCase		    =							["""detr"""]






						def __init__( self			:Dict       ,  lowercase_			:int = 2_56       ,  lowercase_			:int = 2_56       ,  lowercase_			:float = 0.1       ,  lowercase_			:bool = False       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:float = 0.02       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 20.0       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:List[str]       ,  )    ->      Optional[int]:
												if backbone_config is None:
																		# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																		UpperCAmelCase					=			SwinConfig(
																		    image_size=3_84       ,  in_channels=3       ,  patch_size=4       ,  embed_dim=1_28       ,  depths=[2, 2, 18, 2]       ,  num_heads=[4, 8, 16, 32]       ,  window_size=12       ,  drop_path_rate=0.3       ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,  )

												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			backbone_config.pop('model_type' )
																		UpperCAmelCase					=			CONFIG_MAPPING[backbone_model_type]
																		UpperCAmelCase					=			config_class.from_dict(lowercase_ )

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																		logger.warning_once(
																		    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
																		    f"""Supported model types: {','.join(self.backbones_supported )}""" )

												if decoder_config is None:
																		# fall back to https://huggingface.co/facebook/detr-resnet-50
																		UpperCAmelCase					=			DetrConfig()
												else:
																		# verify that the decoder is supported
																		UpperCAmelCase					=			(
																		    decoder_config.pop('model_type' ) if isinstance(lowercase_       ,  lowercase_ ) else decoder_config.model_type
																		)
																		if decoder_type not in self.decoders_supported:
																								raise ValueError(
																								    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
																								    f""" {','.join(self.decoders_supported )}""" )
																		if isinstance(lowercase_       ,  lowercase_ ):
																								UpperCAmelCase					=			CONFIG_MAPPING[decoder_type]
																								UpperCAmelCase					=			config_class.from_dict(lowercase_ )

												UpperCAmelCase					=			backbone_config
												UpperCAmelCase					=			decoder_config
												# main feature dimension for the model
												UpperCAmelCase					=			fpn_feature_size
												UpperCAmelCase					=			mask_feature_size
												# initializer
												UpperCAmelCase					=			init_std
												UpperCAmelCase					=			init_xavier_std
												# Hungarian matcher && loss
												UpperCAmelCase					=			cross_entropy_weight
												UpperCAmelCase					=			dice_weight
												UpperCAmelCase					=			mask_weight
												UpperCAmelCase					=			use_auxiliary_loss
												UpperCAmelCase					=			no_object_weight
												UpperCAmelCase					=			output_auxiliary_logits

												UpperCAmelCase					=			self.decoder_config.encoder_attention_heads
												UpperCAmelCase					=			self.decoder_config.num_hidden_layers
												super().__init__(**lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:PretrainedConfig       ,  lowercase_			:PretrainedConfig       ,  **lowercase_			:int )    ->      List[Any]:
												return cls(
												    backbone_config=lowercase_       ,  decoder_config=lowercase_       ,  **lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Tuple )    ->      Dict[str, any]:
												UpperCAmelCase					=			copy.deepcopy(self.__dict__ )
												UpperCAmelCase					=			self.backbone_config.to_dict()
												UpperCAmelCase					=			self.decoder_config.to_dict()
												UpperCAmelCase					=			self.__class__.model_type
												return output










"""simple docstring"""


from collections import deque







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:str       ,  lowercase_			:int       ,  lowercase_			:int )    ->      None:
												UpperCAmelCase					=			process_name  # process name
												UpperCAmelCase					=			arrival_time  # arrival time of the process
												# completion time of finished process or last interrupted time
												UpperCAmelCase					=			arrival_time
												UpperCAmelCase					=			burst_time  # remaining burst time
												UpperCAmelCase					=			0  # total time of the process wait in ready queue
												UpperCAmelCase					=			0  # time from arrival time to completion time







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:int       ,  lowercase_			:list[int]       ,  lowercase_			:deque[Process]       ,  lowercase_			:int       ,  )    ->      None:
												# total number of mlfq's queues
												UpperCAmelCase					=			number_of_queues
												# time slice of queues that round robin algorithm applied
												UpperCAmelCase					=			time_slices
												# unfinished process is in this ready_queue
												UpperCAmelCase					=			queue
												# current time
												UpperCAmelCase					=			current_time
												# finished process is in this sequence queue
												UpperCAmelCase					=			deque()






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






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






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






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






						def      UpperCAmelCase__					( self			:str       ,  lowercase_			:deque[Process] )    ->      list[int]:
												return [q.burst_time for q in queue]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Process )    ->      int:
												process.waiting_time += self.current_time - process.stop_time
												return process.waiting_time






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

												self.finish_queue.extend(lowercase_ )  # add finished process to finish queue
												# FCFS will finish all remaining processes
												return finished






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

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






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

												#  all queues except last one have round_robin algorithm
												for i in range(self.number_of_queues - 1 ):
																		UpperCAmelCase				,   UpperCAmelCase					=			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_    					=	Process("""P1""", 0, 53)
				snake_case_    					=	Process("""P2""", 0, 17)
				snake_case_    					=	Process("""P3""", 0, 68)
				snake_case_    					=	Process("""P4""", 0, 24)
				snake_case_    					=	3
				snake_case_    					=	[17, 25]
				snake_case_    					=	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_    					=	Process("""P1""", 0, 53)
				snake_case_    					=	Process("""P2""", 0, 17)
				snake_case_    					=	Process("""P3""", 0, 68)
				snake_case_    					=	Process("""P4""", 0, 24)
				snake_case_    					=	3
				snake_case_    					=	[17, 25]
				snake_case_    					=	deque([Pa, Pa, Pa, Pa])
				snake_case_    					=	MLFQ(number_of_queues, time_slices, queue, 0)
				snake_case_    					=	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()}'''
				)





