



'''simple docstring'''




import copy

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


a     :    Any      						=					logging.get_logger(__name__)

a     :    Dict      						=					{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'deta'
	lowercase    =				{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}





	def __init__(						self	,	A=None	,	A=900	,	A=2048	,	A=6	,	A=2048	,	A=8	,	A=6	,	A=1024	,	A=8	,	A=0.0	,	A=True	,	A="relu"	,	A=256	,	A=0.1	,	A=0.0	,	A=0.0	,	A=0.0_2	,	A=1.0	,	A=True	,	A=False	,	A="sine"	,	A=5	,	A=4	,	A=4	,	A=True	,	A=300	,	A=True	,	A=True	,	A=1	,	A=5	,	A=2	,	A=1	,	A=1	,	A=5	,	A=2	,	A=0.1	,	A=0.2_5	,	**A	,	)    ->    List[Any]:
								if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
															UpperCAmelCase  :	Optional[Any]							  =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]					)
								else:
															if isinstance(A	,	A					):
																						UpperCAmelCase  :	Dict							  =	backbone_config.pop("""model_type"""					)
																						UpperCAmelCase  :	Dict							  =	CONFIG_MAPPING[backbone_model_type]
																						UpperCAmelCase  :	int							  =	config_class.from_dict(A					)

								UpperCAmelCase  :	Optional[Any]							  =	backbone_config
								UpperCAmelCase  :	Union[str, Any]							  =	num_queries
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	List[Any]							  =	d_model
								UpperCAmelCase  :	List[str]							  =	encoder_ffn_dim
								UpperCAmelCase  :	Union[str, Any]							  =	encoder_layers
								UpperCAmelCase  :	int							  =	encoder_attention_heads
								UpperCAmelCase  :	Dict							  =	decoder_ffn_dim
								UpperCAmelCase  :	Tuple							  =	decoder_layers
								UpperCAmelCase  :	Optional[int]							  =	decoder_attention_heads
								UpperCAmelCase  :	str							  =	dropout
								UpperCAmelCase  :	Any							  =	attention_dropout
								UpperCAmelCase  :	Optional[int]							  =	activation_dropout
								UpperCAmelCase  :	List[Any]							  =	activation_function
								UpperCAmelCase  :	List[Any]							  =	init_std
								UpperCAmelCase  :	Optional[int]							  =	init_xavier_std
								UpperCAmelCase  :	str							  =	encoder_layerdrop
								UpperCAmelCase  :	Any							  =	auxiliary_loss
								UpperCAmelCase  :	Optional[int]							  =	position_embedding_type
								# deformable attributes
								UpperCAmelCase  :	Dict							  =	num_feature_levels
								UpperCAmelCase  :	List[Any]							  =	encoder_n_points
								UpperCAmelCase  :	Optional[Any]							  =	decoder_n_points
								UpperCAmelCase  :	Union[str, Any]							  =	two_stage
								UpperCAmelCase  :	str							  =	two_stage_num_proposals
								UpperCAmelCase  :	Optional[Any]							  =	with_box_refine
								UpperCAmelCase  :	int							  =	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
								UpperCAmelCase  :	int							  =	class_cost
								UpperCAmelCase  :	Optional[Any]							  =	bbox_cost
								UpperCAmelCase  :	int							  =	giou_cost
								# Loss coefficients
								UpperCAmelCase  :	Optional[int]							  =	mask_loss_coefficient
								UpperCAmelCase  :	Tuple							  =	dice_loss_coefficient
								UpperCAmelCase  :	Tuple							  =	bbox_loss_coefficient
								UpperCAmelCase  :	str							  =	giou_loss_coefficient
								UpperCAmelCase  :	List[Any]							  =	eos_coefficient
								UpperCAmelCase  :	Dict							  =	focal_alpha
								super().__init__(is_encoder_decoder=A	,	**A					)





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





	@property
	def 				_lowercase(						self					)    ->    int:
								return self.d_model






	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Dict							  =	copy.deepcopy(self.__dict__					)
								UpperCAmelCase  :	List[str]							  =	self.backbone_config.to_dict()
								UpperCAmelCase  :	Optional[Any]							  =	self.__class__.model_type
								return output






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

							while not search_end:
														UpperCAmelCase  :	List[str]							  =	current_state.score()
														if best_state is None or current_score > best_state.score():
																					UpperCAmelCase  :	List[Any]							  =	current_state
														scores.append(_lowercase	)
														iterations += 1
														UpperCAmelCase  :	Dict							  =	None
														UpperCAmelCase  :	Union[str, Any]							  =	current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					UpperCAmelCase  :	int							  =	random.randint(0			,       len(_lowercase	) - 1	)  # picking a random neighbor
																					UpperCAmelCase  :	int							  =	neighbors.pop(_lowercase	)
																					UpperCAmelCase  :	Tuple							  =	picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												UpperCAmelCase  :	Union[str, Any]							  =	change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												UpperCAmelCase  :	int							  =	picked_neighbor
																					else:
																												UpperCAmelCase  :	Optional[Any]							  =	(math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			UpperCAmelCase  :	Optional[int]							  =	picked_neighbor
														UpperCAmelCase  :	List[Any]							  =	current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					UpperCAmelCase  :	Optional[int]							  =	True
														else:
																					UpperCAmelCase  :	Optional[int]							  =	next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(_lowercase	)			,       _lowercase	)
														plt.xlabel("""Iterations"""	)
														plt.ylabel("""Function values"""	)
														plt.show()
							return best_state


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)

							# starting the problem with initial coordinates (12, 47)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

							a     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(prob, find_max=True, visualization=True)


							print(
							    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

