

import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = True	,SCREAMING_SNAKE_CASE = math.inf	,SCREAMING_SNAKE_CASE = -math.inf	,SCREAMING_SNAKE_CASE = math.inf	,SCREAMING_SNAKE_CASE = -math.inf	,SCREAMING_SNAKE_CASE = False	,SCREAMING_SNAKE_CASE = 1_0_0	,SCREAMING_SNAKE_CASE = 0.01	,SCREAMING_SNAKE_CASE = 1	,):
	UpperCAmelCase__:	Optional[int]									= False
	UpperCAmelCase__:	Optional[int]									= search_prob
	UpperCAmelCase__:	Optional[Any]									= start_temperate
	UpperCAmelCase__:	Union[str, Any]									= []
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[Any]									= None

	while not search_end:
		UpperCAmelCase__:	Dict									= current_state.score()
		if best_state is None or current_score > best_state.score():
			UpperCAmelCase__:	Tuple									= current_state
		scores.append(_lowerCAmelCase      )
		iterations += 1
		UpperCAmelCase__:	Dict									= None
		UpperCAmelCase__:	List[str]									= current_state.get_neighbors()
		while (
		    next_state is None and neighbors
		):  # till we do not find a neighbor that we can move to
			UpperCAmelCase__:	Tuple									= random.randint(0	,len(_lowerCAmelCase      ) - 1      )  # picking a random neighbor
			UpperCAmelCase__:	str									= neighbors.pop(_lowerCAmelCase      )
			UpperCAmelCase__:	Optional[Any]									= 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__:	Optional[Any]									= change * -1  # in case we are finding minimum
			if change > 0:  # improves the solution
				UpperCAmelCase__:	List[str]									= picked_neighbor
			else:
				UpperCAmelCase__:	Dict									= (math.e) ** (
				    change / current_temp
				)  # probability generation function
				if random.random() < probability:  # random number within probability
					UpperCAmelCase__:	Dict									= 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__:	int									= True
		else:
			UpperCAmelCase__:	int									= next_state

	if visualization:
		from matplotlib import pyplot as plt

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


if __name__ == "__main__":



					def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
						return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					_lowerCAmelCase  :      int			 =SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					_lowerCAmelCase  :      Optional[int]			 =simulated_annealing(
					    prob, find_max=False, max_x=1_00, min_x=5, max_y=50, 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)
					_lowerCAmelCase  :      Tuple			 =SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					_lowerCAmelCase  :      Optional[Any]			 =simulated_annealing(
					    prob, find_max=True, max_x=1_00, min_x=5, max_y=50, 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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
						return (3 * x**2) - (6 * y)

					_lowerCAmelCase  :      List[Any]			 =SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					_lowerCAmelCase  :      List[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()}"
					)

					_lowerCAmelCase  :      Dict			 =SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					_lowerCAmelCase  :      Optional[int]			 =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()}"
					)

"""simple docstring"""
from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
