


'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_      =			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Optional[Any]:
					"""simple docstring"""




					if isinstance(__SCREAMING_SNAKE_CASE				,  torch.Tensor		):
										return image
					elif isinstance(__SCREAMING_SNAKE_CASE				,  PIL.Image.Image		):
										__a       =   [image]

					__a       =   [trans(img.convert("""RGB"""		)		) for img in image]
					__a       =   torch.stack(__SCREAMING_SNAKE_CASE		)
					return image



class  lowerCAmelCase_						(       snake_case__      ):





		"""simple docstring"""



		def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





							'''simple docstring'''

							super().__init__()

							# make sure scheduler can always be converted to DDIM
							__a       =   DDIMScheduler.from_config(scheduler.config						)

							self.register_modules(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)

		def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





							'''simple docstring'''

							if strength < 0 or strength > 1:
												raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''						)

		def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





							'''simple docstring'''

							__a       =   min(int(num_inference_steps * strength						)				,							SCREAMING_SNAKE_CASE__						)

							__a       =   max(num_inference_steps - init_timestep				,							0						)
							__a       =   self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start

		def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None						):





							'''simple docstring'''

							if not isinstance(SCREAMING_SNAKE_CASE__				,							(torch.Tensor, PIL.Image.Image, list)						):
												raise ValueError(
												    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE__						)}'''						)

							__a       =   image.to(device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)

							if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						) and len(SCREAMING_SNAKE_CASE__						) != batch_size:
												raise ValueError(
												    f'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__						)}, but requested an effective batch'''
												    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

							__a       =   init_latents.shape
							__a       =   randn_tensor(SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)

							# get latents
							print("""add noise to latents at timestep"""				,							SCREAMING_SNAKE_CASE__						)
							__a       =   self.scheduler.add_noise(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
							__a       =   init_latents

							return latents


		@torch.no_grad()
		def __call__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[torch.FloatTensor, PIL.Image.Image] = None				,							SCREAMING_SNAKE_CASE__     :							float = 0.8				,							SCREAMING_SNAKE_CASE__     :							int = 1				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE__     :							float = 0.0				,							SCREAMING_SNAKE_CASE__     :							int = 5_0				,							SCREAMING_SNAKE_CASE__     :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





							'''simple docstring'''

							self.check_inputs(SCREAMING_SNAKE_CASE__						)

							# 2. Preprocess image
							__a       =   preprocess(SCREAMING_SNAKE_CASE__						)

							# 3. set timesteps
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				,							device=self.device						)
							__a ,						__a       =   self.get_timesteps(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							self.device						)
							__a       =   timesteps[:1].repeat(SCREAMING_SNAKE_CASE__						)

							# 4. Prepare latent variables
							__a       =   self.prepare_latents(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							self.unet.dtype				,							self.device				,							SCREAMING_SNAKE_CASE__						)
							__a       =   latents

							# 5. Denoising loop
							for t in self.progress_bar(SCREAMING_SNAKE_CASE__						):
												# 1. predict noise model_output
												__a       =   self.unet(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												__a       =   self.scheduler.step(
												    SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							eta=SCREAMING_SNAKE_CASE__				,							use_clipped_model_output=SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							).prev_sample

							__a       =   (image / 2 + 0.5).clamp(0				,							1						)
							__a       =   image.cpu().permute(0				,							2				,							3				,							1						).numpy()
							if output_type == "pil":
												__a       =   self.numpy_to_pil(SCREAMING_SNAKE_CASE__						)

							if not return_dict:
												return (image, latent_timestep.item())

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def        __lowercase						(      __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 = 100				,  __SCREAMING_SNAKE_CASE = 0.01				,  __SCREAMING_SNAKE_CASE = 1				,  )				->					Any:
     """simple docstring"""




     __a       =   False
     __a       =   search_prob
     __a       =   start_temperate
     __a       =   []
     __a       =   0
     __a       =   None

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

     if visualization:
          from matplotlib import pyplot as plt

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


if __name__ == "__main__":




 def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




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

 # starting the problem with initial coordinates (12, 47)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			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)
 SCREAMING_SNAKE_CASE_      =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 SCREAMING_SNAKE_CASE_      =			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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
      """simple docstring"""




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

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

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






