




import math


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							x
						lowerCamelCase_							:		str							       =							y
						lowerCamelCase_							:		str							       =							step_size
						lowerCamelCase_							:		Dict							       =							function_to_optimize

					def      lowercase_							(    self ):







						'''simple docstring'''




						return self.function(self.x			,	self.y )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.step_size
						return [
						    SearchProblem(__lowercase			,	__lowercase			,	__lowercase			,	self.function )
						    for x, y in (
						        (self.x - step_size, self.y - step_size),
						        (self.x - step_size, self.y),
						        (self.x - step_size, self.y + step_size),
						        (self.x, self.y - step_size),
						        (self.x, self.y + step_size),
						        (self.x + step_size, self.y - step_size),
						        (self.x + step_size, self.y),
						        (self.x + step_size, self.y + step_size),
						    )
						]

					def __hash__(    self ):







						'''simple docstring'''




						return hash(str(self ) )

					def __eq__(    self			,	__lowercase ):







						'''simple docstring'''




						if isinstance(__lowercase			,	__lowercase ):
							return hash(str(self ) ) == hash(str(__lowercase ) )
						return False





					def __str__(    self ):







						'''simple docstring'''




						return f"x: {self.x} y: {self.y}"






def  snake_case (							A						,       A = True						,       A = math.inf						,       A = -math.inf						,       A = math.inf						,       A = -math.inf						,       A = False						,       A = 10000						,       )							->							SearchProblem:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							       =							search_prob
	lowerCamelCase_							:		Optional[int]							       =							[]  # list to store the current score at each iteration
	lowerCamelCase_							:		Dict							       =							0
	lowerCamelCase_							:		Union[str, Any]							       =							False
	lowerCamelCase_							:		Any							       =							set()
	while not solution_found and iterations < max_iter:
		visited.add(A      )
		iterations += 1
		lowerCamelCase_							:		int							       =							current_state.score()
		scores.append(A      )
		lowerCamelCase_							:		Any							       =							current_state.get_neighbors()
		lowerCamelCase_							:		List[Any]							       =							-math.inf
		lowerCamelCase_							:		Union[str, Any]							       =							math.inf
		lowerCamelCase_							:		Tuple							       =							None  # to hold the next best neighbor
		for neighbor in neighbors:
			if neighbor in visited:
				continue  # do not want to visit the same state again
			if (
			    neighbor.x > max_x
			    or neighbor.x < min_x
			    or neighbor.y > max_y
			    or neighbor.y < min_y
			):
				continue  # neighbor outside our bounds
			lowerCamelCase_							:		Union[str, Any]							       =							neighbor.score() - current_score
			if find_max:  # finding max
				# going to direction with greatest ascent
				if change > max_change and change > 0:
					lowerCamelCase_							:		int							       =							change
					lowerCamelCase_							:		Tuple							       =							neighbor
			else:  # finding min
				# to direction with greatest descent
				if change < min_change and change < 0:
					lowerCamelCase_							:		List[Any]							       =							change
					lowerCamelCase_							:		int							       =							neighbor
		if next_state is not None:
			# we found at least one neighbor which improved the current state
			lowerCamelCase_							:		str							       =							next_state
		else:
			# since we have no neighbor that improves the solution we stop the search
			lowerCamelCase_							:		int							       =							True

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(A      )						,       A      )
		plt.xlabel('Iterations'      )
		plt.ylabel('Function values'      )
		plt.show()

	return current_state


if __name__ == "__main__":
							import doctest

							doctest.testmod()






							def  snake_case (							A						,       A      )							->							List[str]:




								'''simple docstring'''


								return (x**2) + (y**2)

							# starting the problem with initial coordinates (3, 4)
							UpperCamelCase__		=						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase__		=						hill_climbing(prob, find_max=False)
							print(
							    'The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '
							    F'{local_min.score()}'
							)

							# starting the problem with initial coordinates (12, 47)
							UpperCamelCase__		=						SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase__		=						hill_climbing(
							    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()}'
							)






							def  snake_case (							A						,       A      )							->							List[Any]:




								'''simple docstring'''


								return (3 * x**2) - (6 * y)

							UpperCamelCase__		=						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase__		=						hill_climbing(prob, find_max=True)



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






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			(HeunDiscreteScheduler,)
					_lowerCamelCase		  =			10

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02] ):
							self.check_over_configs(beta_start=__lowercase			,	beta_end=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for schedule in ["linear", "scaled_linear", "exp"]:
							self.check_over_configs(beta_schedule=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase			,	clip_sample=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for prediction_type in ["epsilon", "v_prediction", "sample"]:
							self.check_over_configs(prediction_type=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		List[str]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Union[str, Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		Dict							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		Any							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Tuple							       =							output.prev_sample

						lowerCamelCase_							:		str							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		str							       =							self.get_scheduler_config(prediction_type='v_prediction' )
						lowerCamelCase_							:		Optional[Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Any							       =							self.dummy_model()
						lowerCamelCase_							:		str							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		List[Any]							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Any							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		int							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		str							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
							lowerCamelCase_							:		Tuple							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		List[Any]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Union[str, Any]							       =							output.prev_sample

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Any							       =							torch.mean(torch.abs(__lowercase ) )

						if str(__lowercase ).startswith('cpu' ):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						elif str(__lowercase ).startswith('mps' ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02 ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase			,	use_karras_sigmas=__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		int							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
						lowerCamelCase_							:		Tuple							       =							sample.to(__lowercase )

						for t in scheduler.timesteps:
							lowerCamelCase_							:		str							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Dict							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Tuple							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2
