import math



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Dict      ,snake_case					:							List[str]      ,snake_case					:							Any      ,snake_case					:							Dict      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=x
           SCREAMING_SNAKE_CASE										=y
           SCREAMING_SNAKE_CASE										=step_size
           SCREAMING_SNAKE_CASE										=function_to_optimize
      def 	_lowerCAmelCase					(				self					:							str    ):
           return self.function(self.x      ,self.y    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.step_size
           return [
               SearchProblem(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_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					:							Any    ):
           return hash(str(self    )    )
      def __eq__(				self					:							List[str]      ,snake_case					:							Dict    ):






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


      def __str__(				self					:							Tuple    ):
           return f'x: {self.x} y: {self.y}'







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_ = True,   lowerCAmelCase_ = math.inf,   lowerCAmelCase_ = -math.inf,   lowerCAmelCase_ = math.inf,   lowerCAmelCase_ = -math.inf,   lowerCAmelCase_ = False,   lowerCAmelCase_ = 10000,   ):





     """simple docstring"""







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

     if visualization:
          from matplotlib import pyplot as plt

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

     return current_state


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





      """simple docstring"""







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

 # starting the problem with initial coordinates (3, 4)
 _lowerCamelCase								=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCamelCase								=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)
 _lowerCamelCase								=SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCamelCase								=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       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





      """simple docstring"""







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

 _lowerCamelCase								=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCamelCase								=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 gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										=self.get_dummy_inputs(snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe(**snake_case    ).images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

