






'''simple docstring'''







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


_A			:    Union[str, Any]					  =logging.get_logger(__name__)

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

class _lowercase			(       __lowercase	):
							a          = """conditional_detr"""
							a          = ["""past_key_values"""]
							a          = {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       Optional[int]=3    ,							UpperCamelCase__:       Dict=300    ,							UpperCamelCase__:       Union[str, Any]=6    ,							UpperCamelCase__:       Any=2_048    ,							UpperCamelCase__:       Dict=8    ,							UpperCamelCase__:       List[str]=6    ,							UpperCamelCase__:       List[Any]=2_048    ,							UpperCamelCase__:       Tuple=8    ,							UpperCamelCase__:       int=0.0    ,							UpperCamelCase__:       List[str]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Any="relu"    ,							UpperCamelCase__:       Dict=256    ,							UpperCamelCase__:       List[str]=0.1    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       List[str]=0.0    ,							UpperCamelCase__:       str=0.02    ,							UpperCamelCase__:       Tuple=1.0    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       int="sine"    ,							UpperCamelCase__:       Optional[Any]="resnet50"    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       str=False    ,							UpperCamelCase__:       Optional[int]=2    ,							UpperCamelCase__:       Union[str, Any]=5    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       Optional[Any]=1    ,							UpperCamelCase__:       str=1    ,							UpperCamelCase__:       List[str]=2    ,							UpperCamelCase__:       List[str]=5    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       Dict=0.25    ,							**UpperCamelCase__:       str    ,							):
														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."""  )
																												lowerCamelCase__      :			Tuple 						= CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
																					elif isinstance(_A    ,							_A  ):
																												lowerCamelCase__      :			str 						= backbone_config.get("""model_type"""  )
																												lowerCamelCase__      :			Optional[Any] 						= CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__      :			Tuple 						= config_class.from_dict(_A  )

														lowerCamelCase__      :			Any 						= use_timm_backbone
														lowerCamelCase__      :			Any 						= backbone_config
														lowerCamelCase__      :			List[Any] 						= num_channels
														lowerCamelCase__      :			Dict 						= num_queries
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			str 						= encoder_ffn_dim
														lowerCamelCase__      :			str 						= encoder_layers
														lowerCamelCase__      :			List[str] 						= encoder_attention_heads
														lowerCamelCase__      :			List[str] 						= decoder_ffn_dim
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			str 						= decoder_attention_heads
														lowerCamelCase__      :			Tuple 						= dropout
														lowerCamelCase__      :			List[str] 						= attention_dropout
														lowerCamelCase__      :			int 						= activation_dropout
														lowerCamelCase__      :			List[Any] 						= activation_function
														lowerCamelCase__      :			int 						= init_std
														lowerCamelCase__      :			Any 						= init_xavier_std
														lowerCamelCase__      :			str 						= encoder_layerdrop
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			List[str] 						= encoder_layers
														lowerCamelCase__      :			Optional[Any] 						= auxiliary_loss
														lowerCamelCase__      :			List[Any] 						= position_embedding_type
														lowerCamelCase__      :			List[str] 						= backbone
														lowerCamelCase__      :			str 						= use_pretrained_backbone
														lowerCamelCase__      :			Optional[int] 						= dilation
														# Hungarian matcher
														lowerCamelCase__      :			str 						= class_cost
														lowerCamelCase__      :			int 						= bbox_cost
														lowerCamelCase__      :			Dict 						= giou_cost
														# Loss coefficients
														lowerCamelCase__      :			str 						= mask_loss_coefficient
														lowerCamelCase__      :			Optional[Any] 						= dice_loss_coefficient
														lowerCamelCase__      :			str 						= cls_loss_coefficient
														lowerCamelCase__      :			Optional[Any] 						= bbox_loss_coefficient
														lowerCamelCase__      :			Any 						= giou_loss_coefficient
														lowerCamelCase__      :			List[Any] 						= focal_alpha
														super().__init__(is_encoder_decoder=_A    ,							**_A  )




							@property
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														return self.encoder_attention_heads




							@property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return self.d_model




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[Any] 						= copy.deepcopy(self.__dict__  )
														if self.backbone_config is not None:
																					lowerCamelCase__      :			Any 						= self.backbone_config.to_dict()
														lowerCamelCase__      :			Tuple 						= self.__class__.model_type
														return output



class _lowercase			(       __lowercase	):
							a          = version.parse("""1.11"""	)




							@property
							def 	lowerCamelCase_       (							self:       str  ):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														        ("""pixel_mask""", {0: """batch"""}),
														    ]  )




							@property
							def 	lowerCamelCase_       (							self:       int  ):
														return 1e-5




							@property
							def 	lowerCamelCase_       (							self:       Any  ):
														return 12









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

														lowerCamelCase__      :			Any 						= 0
														# Index showing the location of the process being performed
														lowerCamelCase__      :			Union[str, Any] 						= 0
														# Saves the current response ratio.
														lowerCamelCase__      :			Any 						= 0
														for i in range(0       ,    UpperCamelCase   ):
																					if finished_process[i] == 0 and arrival_time[i] <= current_time:
																												lowerCamelCase__      :			Optional[int] 						= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																												    i
																												]
																					if response_ratio < temp:
																												lowerCamelCase__      :			int 						= temp
																												lowerCamelCase__      :			str 						= i

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
			_A			:    List[str]					  =5
			_A			:    Optional[Any]					  =['''A''', '''B''', '''C''', '''D''', '''E''']
			_A			:    Optional[int]					  =[1, 2, 3, 4, 5]
			_A			:    Dict					  =[1, 2, 3, 4, 5]

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[int]					  =calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
			for i in range(0, no_of_process):
						print(
						    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(F'average waiting time : {mean(waiting_time):.5f}')
			print(F'average turn around time : {mean(turn_around_time):.5f}')

