



"""simple docstring"""
import math
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				Optional[int]		,	snake_case:				int		,	snake_case:				int		,	snake_case:				int		,	snake_case:				Union[str, Any]		)  ->      str:
											snake_case_					:Tuple		=     x
											snake_case_					:List[Any]		=     y
											snake_case_					:Any		=     step_size
											snake_case_					:Tuple		=     function_to_optimize






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      int:
											return self.function(self.x		,	self.y		)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Optional[int]:
											snake_case_					:Tuple		=     self.step_size
											return [
											    SearchProblem(snake_case		,	snake_case		,	snake_case		,	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:				str		)  ->      Tuple:
											return hash(str(self		)		)






					def __eq__(			self:				Tuple		,	snake_case:				List[str]		)  ->      Optional[int]:
											if isinstance(snake_case		,	snake_case		):
																	return hash(str(self		)		) == hash(str(snake_case		)		)
											return False






					def __str__(			self:				Tuple		)  ->      str:
											return f"""x: {self.x} y: {self.y}"""

def   A_    (     _lowercase,   _lowercase = True,   _lowercase = math.inf,   _lowercase = -math.inf,   _lowercase = math.inf,   _lowercase = -math.inf,   _lowercase = False,   _lowercase = 10000,   ):







						'''simple docstring'''


						snake_case_					:Tuple		=     search_prob
						snake_case_					:int		=     []  # list to store the current score at each iteration
						snake_case_					:Union[str, Any]		=     0
						snake_case_					:Optional[Any]		=     False
						snake_case_					:str		=     set()
						while not solution_found and iterations < max_iter:
												visited.add(_lowercase      )
												iterations += 1
												snake_case_					:Dict		=     current_state.score()
												scores.append(_lowercase      )
												snake_case_					:Any		=     current_state.get_neighbors()
												snake_case_					:Optional[int]		=     -math.inf
												snake_case_					:List[str]		=     math.inf
												snake_case_					:Any		=     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
																		snake_case_					:List[str]		=     neighbor.score() - current_score
																		if find_max:  # finding max
																								# going to direction with greatest ascent
																								if change > max_change and change > 0:
																														snake_case_					:int		=     change
																														snake_case_					:Optional[int]		=     neighbor
																		else:  # finding min
																								# to direction with greatest descent
																								if change < min_change and change < 0:
																														snake_case_					:Any		=     change
																														snake_case_					:Optional[int]		=     neighbor
												if next_state is not None:
																		# we found at least one neighbor which improved the current state
																		snake_case_					:Tuple		=     next_state
												else:
																		# since we have no neighbor that improves the solution we stop the search
																		snake_case_					:Union[str, Any]		=     True

						if visualization:
												from matplotlib import pyplot as plt

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

						return current_state


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						def   A_    (     _lowercase,   _lowercase      ):







												'''simple docstring'''


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

						# starting the problem with initial coordinates (3, 4)
						__a           =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__a           =							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)
						__a           =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						__a           =							hill_climbing(
						    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()}"""
						)
						def   A_    (     _lowercase,   _lowercase      ):







												'''simple docstring'''


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

						__a           =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__a           =							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()}"""
						)








'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             super().__init__()

             self.register_modules(vae=_UpperCAmelCase  ,		text_encoder=_UpperCAmelCase  ,		tokenizer=_UpperCAmelCase  ,		unet=_UpperCAmelCase  ,		scheduler=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with torch.no_grad():
                    UpperCAmelCase__	:   Dict												=	self.vae.encode(tfms.ToTensor()(_UpperCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                    UpperCAmelCase__	:   int												=	0.1_82_15 * latent.latent_dist.sample()
             return latent




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	(1 / 0.1_82_15) * latent
             with torch.no_grad():
                    UpperCAmelCase__	:   Optional[Any]												=	self.vae.decode(_UpperCAmelCase						).sample
             UpperCAmelCase__	:   List[str]												=	(img / 2 + 0.5).clamp(0  ,		1						)
             UpperCAmelCase__	:   List[Any]												=	img.detach().cpu().permute(0  ,		2  ,		3  ,		1						).numpy()
             UpperCAmelCase__	:   Union[str, Any]												=	(img * 255).round().astype('''uint8'''						)
             return Image.fromarray(img[0]						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 _UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   Tuple												=	self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 ''''''  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   List[Any]												=	self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 0.3  ,		_UpperCAmelCase = 0.6  ,		_UpperCAmelCase = 0.5  ,		_UpperCAmelCase = 42  ,		_UpperCAmelCase = 50  ,		_UpperCAmelCase = 7.5  ,		):
             UpperCAmelCase__	:   Any												=	steps - int(kmin * steps						)
             UpperCAmelCase__	:   Any												=	steps - int(kmax * steps						)

             UpperCAmelCase__	:   List[str]												=	self.prep_text(_UpperCAmelCase						)

             self.scheduler.set_timesteps(_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[str]												=	img.size
             UpperCAmelCase__	:   str												=	self.encode(_UpperCAmelCase						)

             torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)  ,		).to(self.device						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler.add_noise(
                 _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=self.scheduler.timesteps[tmax]  ,		)

             UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

             UpperCAmelCase__	:   Dict												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]						)

             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	self.unet(
                        _UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	pred.chunk(2						)
             UpperCAmelCase__	:   List[str]												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		_UpperCAmelCase						).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  UpperCAmelCase__	:   Dict												=	self.scheduler.add_noise(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=_UpperCAmelCase  ,		)

                                  UpperCAmelCase__	:   Union[str, Any]												=	(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  UpperCAmelCase__	:   Tuple												=	torch.cat([input] * 2						)

                           else:  # content generation phase
                                  UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

                           UpperCAmelCase__	:   str												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		_UpperCAmelCase						)

                           with torch.no_grad():
                                  UpperCAmelCase__	:   Any												=	self.unet(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

                           UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	pred.chunk(2						)
                           UpperCAmelCase__	:   str												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return self.decode(_UpperCAmelCase						)