





"""simple docstring"""




import math
import random
from typing import Any

from .hill_climbing import SearchProblem






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    bool = True    ,       _lowercase						:    float = math.inf    ,       _lowercase						:    float = -math.inf    ,       _lowercase						:    float = math.inf    ,       _lowercase						:    float = -math.inf    ,       _lowercase						:    bool = False    ,       _lowercase						:    float = 100    ,       _lowercase						:    float = 0.01    ,       _lowercase						:    float = 1    ,       ) ->Any:






	'''simple docstring'''






	a      :		Union[str, Any]	=				False
	a      :		Any	=				search_prob
	a      :		Optional[int]	=				start_temperate
	a      :		Any	=				[]
	a      :		List[Any]	=				0
	a      :		Dict	=				None

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






			'''simple docstring'''






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

		# starting the problem with initial coordinates (12, 47)
		a						: Tuple     	=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		a						: Tuple     	=			simulated_annealing(
		    prob, find_max=False, max_x=100, 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)
		a						: str     	=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		a						: Dict     	=			simulated_annealing(
		    prob, find_max=True, max_x=100, 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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->Optional[Any]:






			'''simple docstring'''






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

		a						: Any     	=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		a						: Optional[int]     	=			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						: int     	=			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()}'''
		)









"""simple docstring"""




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		Any	=				torch.cat([classifier_free_guidance_embeddings, image_embeddings]      ,					dim=0      )

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								a      :		List[str]	=				prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


