


"""simple docstring"""







import copy

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


a__				:							Optional[Any]						=     logging.get_logger(__name__)

a__				:							Union[str, Any]						=     {
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Tuple    					=			"deta"
	snake_case__				:  List[Any]    					=			{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}




	def __init__( self			:				Optional[int]			,			UpperCAmelCase__			:				List[Any]=None			,			UpperCAmelCase__			:				List[Any]=9_0_0			,			UpperCAmelCase__			:				Optional[int]=2_0_4_8			,			UpperCAmelCase__			:				Optional[int]=6			,			UpperCAmelCase__			:				Optional[Any]=2_0_4_8			,			UpperCAmelCase__			:				int=8			,			UpperCAmelCase__			:				Optional[int]=6			,			UpperCAmelCase__			:				str=1_0_2_4			,			UpperCAmelCase__			:				Optional[Any]=8			,			UpperCAmelCase__			:				Union[str, Any]=0.0			,			UpperCAmelCase__			:				int=True			,			UpperCAmelCase__			:				Tuple="relu"			,			UpperCAmelCase__			:				int=2_5_6			,			UpperCAmelCase__			:				List[Any]=0.1			,			UpperCAmelCase__			:				Any=0.0			,			UpperCAmelCase__			:				int=0.0			,			UpperCAmelCase__			:				int=0.02			,			UpperCAmelCase__			:				Optional[int]=1.0			,			UpperCAmelCase__			:				Tuple=True			,			UpperCAmelCase__			:				Union[str, Any]=False			,			UpperCAmelCase__			:				Any="sine"			,			UpperCAmelCase__			:				Optional[int]=5			,			UpperCAmelCase__			:				Union[str, Any]=4			,			UpperCAmelCase__			:				Union[str, Any]=4			,			UpperCAmelCase__			:				int=True			,			UpperCAmelCase__			:				Any=3_0_0			,			UpperCAmelCase__			:				List[str]=True			,			UpperCAmelCase__			:				Optional[Any]=True			,			UpperCAmelCase__			:				Optional[Any]=1			,			UpperCAmelCase__			:				List[str]=5			,			UpperCAmelCase__			:				int=2			,			UpperCAmelCase__			:				Union[str, Any]=1			,			UpperCAmelCase__			:				Optional[Any]=1			,			UpperCAmelCase__			:				Optional[Any]=5			,			UpperCAmelCase__			:				str=2			,			UpperCAmelCase__			:				Union[str, Any]=0.1			,			UpperCAmelCase__			:				Union[str, Any]=0.25			,			**UpperCAmelCase__			:				Dict			,			)       ->	Union[str, Any]:
						if backbone_config is None:
											logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."   )
											__SCREAMING_SNAKE_CASE						   =				CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]   )
						else:
											if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				backbone_config.pop("model_type"   )
																__SCREAMING_SNAKE_CASE						   =				CONFIG_MAPPING[backbone_model_type]
																__SCREAMING_SNAKE_CASE						   =				config_class.from_dict(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				backbone_config
						__SCREAMING_SNAKE_CASE						   =				num_queries
						__SCREAMING_SNAKE_CASE						   =				max_position_embeddings
						__SCREAMING_SNAKE_CASE						   =				d_model
						__SCREAMING_SNAKE_CASE						   =				encoder_ffn_dim
						__SCREAMING_SNAKE_CASE						   =				encoder_layers
						__SCREAMING_SNAKE_CASE						   =				encoder_attention_heads
						__SCREAMING_SNAKE_CASE						   =				decoder_ffn_dim
						__SCREAMING_SNAKE_CASE						   =				decoder_layers
						__SCREAMING_SNAKE_CASE						   =				decoder_attention_heads
						__SCREAMING_SNAKE_CASE						   =				dropout
						__SCREAMING_SNAKE_CASE						   =				attention_dropout
						__SCREAMING_SNAKE_CASE						   =				activation_dropout
						__SCREAMING_SNAKE_CASE						   =				activation_function
						__SCREAMING_SNAKE_CASE						   =				init_std
						__SCREAMING_SNAKE_CASE						   =				init_xavier_std
						__SCREAMING_SNAKE_CASE						   =				encoder_layerdrop
						__SCREAMING_SNAKE_CASE						   =				auxiliary_loss
						__SCREAMING_SNAKE_CASE						   =				position_embedding_type
						# deformable attributes
						__SCREAMING_SNAKE_CASE						   =				num_feature_levels
						__SCREAMING_SNAKE_CASE						   =				encoder_n_points
						__SCREAMING_SNAKE_CASE						   =				decoder_n_points
						__SCREAMING_SNAKE_CASE						   =				two_stage
						__SCREAMING_SNAKE_CASE						   =				two_stage_num_proposals
						__SCREAMING_SNAKE_CASE						   =				with_box_refine
						__SCREAMING_SNAKE_CASE						   =				assign_first_stage
						if two_stage is True and with_box_refine is False:
											raise ValueError("If two_stage is True, with_box_refine must be True."   )
						# Hungarian matcher
						__SCREAMING_SNAKE_CASE						   =				class_cost
						__SCREAMING_SNAKE_CASE						   =				bbox_cost
						__SCREAMING_SNAKE_CASE						   =				giou_cost
						# Loss coefficients
						__SCREAMING_SNAKE_CASE						   =				mask_loss_coefficient
						__SCREAMING_SNAKE_CASE						   =				dice_loss_coefficient
						__SCREAMING_SNAKE_CASE						   =				bbox_loss_coefficient
						__SCREAMING_SNAKE_CASE						   =				giou_loss_coefficient
						__SCREAMING_SNAKE_CASE						   =				eos_coefficient
						__SCREAMING_SNAKE_CASE						   =				focal_alpha
						super().__init__(is_encoder_decoder=UpperCAmelCase__			,			**UpperCAmelCase__   )




	@property
	def   UpperCAmelCase_      ( self			:				str   )       ->	int:
						return self.encoder_attention_heads




	@property
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	int:
						return self.d_model




	def   UpperCAmelCase_      ( self			:				str   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				copy.deepcopy(self.__dict__   )
						__SCREAMING_SNAKE_CASE						   =				self.backbone_config.to_dict()
						__SCREAMING_SNAKE_CASE						   =				self.__class__.model_type
						return output




"""simple docstring"""







from __future__ import annotations

import pandas as pd




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					# Copy the burst time into remaining_time[]
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i]

					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				9_9999_9999
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				False

					# Process until all processes are completed
					while complete != no_of_processes:
										for j in range(lowerCAmelCase_   ):
															if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																				if remaining_time[j] < minm:
																									__SCREAMING_SNAKE_CASE						   =				remaining_time[j]
																									__SCREAMING_SNAKE_CASE						   =				j
																									__SCREAMING_SNAKE_CASE						   =				True

										if not check:
															increment_time += 1
															continue
										remaining_time[short] -= 1

										__SCREAMING_SNAKE_CASE						   =				remaining_time[short]
										if minm == 0:
															__SCREAMING_SNAKE_CASE						   =				9_9999_9999

										if remaining_time[short] == 0:
															complete += 1
															__SCREAMING_SNAKE_CASE						   =				False

															# Find finish time of current process
															__SCREAMING_SNAKE_CASE						   =				increment_time + 1

															# Calculate waiting time
															__SCREAMING_SNAKE_CASE						   =				finish_time - arrival_time[short]
															__SCREAMING_SNAKE_CASE						   =				finar - burst_time[short]

															if waiting_time[short] < 0:
																				__SCREAMING_SNAKE_CASE						   =				0

        # Increment time
										increment_time += 1
					return waiting_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i] + waiting_time[i]
					return turn_around_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				total_waiting_time + waiting_time[i]
										__SCREAMING_SNAKE_CASE						   =				total_turn_around_time + turn_around_time[i]
					print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""   )
					print("Average turn around time ="  ,						total_turn_around_time / no_of_processes   )


if __name__ == "__main__":
		print('''Enter how many process you want to analyze''')
		a__				:							Optional[Any]						=     int(input())
		a__				:							Optional[int]						=     [0] * no_of_processes
		a__				:							int						=     [0] * no_of_processes
		a__				:							List[Any]						=     list(range(1, no_of_processes + 1))

		for i in range(no_of_processes):
				print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
				a__			,     a__				:							Tuple						=     map(int, input().split())

		a__				:							int						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		a__				:							Dict						=     burst_time
		a__				:							Any						=     no_of_processes
		a__				:							Optional[int]						=     waiting_time
		a__				:							Union[str, Any]						=     calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		a__				:							str						=     pd.DataFrame(
		    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
		    columns=[
		        '''Process''',
		        '''BurstTime''',
		        '''ArrivalTime''',
		        '''WaitingTime''',
		        '''TurnAroundTime''',
		    ],
		)

		# Printing the dataFrame
		pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
		print(fcfs)
