




import math





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	Union[str, Any]       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	Any		):
       SCREAMING_SNAKE_CASE_					=    x
       SCREAMING_SNAKE_CASE_					=    y
       SCREAMING_SNAKE_CASE_					=    step_size
       SCREAMING_SNAKE_CASE_					=    function_to_optimize



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       return self.function(self.x       ,       self.y		)



 def    lowerCAmelCase_						(							self						:	List[str]		):
       SCREAMING_SNAKE_CASE_					=    self.step_size
       return [
           SearchProblem(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase       ,       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						:	Optional[Any]		):
       return hash(str(self		)		)



 def __eq__(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Union[str, Any]		):
       if isinstance(_lowerCAmelCase       ,       _lowerCAmelCase		):
             return hash(str(self		)		) == hash(str(_lowerCAmelCase		)		)
       return False




 def __str__(							self						:	Tuple		):
       return F"x: {self.x} y: {self.y}"


def       UpperCAmelCase_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			bool = True    ,    __UpperCAmelCase		:			float = math.inf    ,    __UpperCAmelCase		:			float = -math.inf    ,    __UpperCAmelCase		:			float = math.inf    ,    __UpperCAmelCase		:			float = -math.inf    ,    __UpperCAmelCase		:			bool = False    ,    __UpperCAmelCase		:			int = 1_00_00    ,    )      ->				SearchProblem:
      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(__UpperCAmelCase							)
            iterations += 1
            SCREAMING_SNAKE_CASE_					=    current_state.score()
            scores.append(__UpperCAmelCase							)
            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(__UpperCAmelCase							)    ,    __UpperCAmelCase							)
            plt.xlabel('Iterations'							)
            plt.ylabel('Function values'							)
            plt.show()

      return current_state


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			Union[str, Any]							)      ->				Dict:
         return (x**2) + (y**2)

   # starting the problem with initial coordinates (3, 4)
   lowerCamelCase__				:     str			       =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   lowerCamelCase__				:     Optional[int]			       =							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__				:     str			       =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   lowerCamelCase__				:     str			       =							hill_climbing(
       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()}'''
   )

   def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Any							)      ->				List[Any]:
         return (3 * x**2) - (6 * y)

   lowerCamelCase__				:     Any			       =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   lowerCamelCase__				:     str			       =							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 tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__				:     int			       =							False





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


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCAmelCase		)

       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    generator.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    'cyberpunk 2077'
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt=_lowerCAmelCase       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='numpy'       ,       ).images

       SCREAMING_SNAKE_CASE_					=    image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2