





import math







class     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       x
         _A    =       y
         _A    =       step_size
         _A    =       function_to_optimize




     def 					lowerCAmelCase__		(						self       ):
         return self.function(self.x							,    self.y       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       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       ):
         return hash(str(self       )       )




     def __eq__(						self							,    snake_case_       ):
         if isinstance(snake_case_							,    snake_case_       ):
             return hash(str(self       )       ) == hash(str(snake_case_       )       )
         return False




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





def 							__lowerCAmelCase(					_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 = 10_000       ,    )       ->   SearchProblem:




    """simple docstring"""




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

    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 current_state


if __name__ == "__main__":
 import doctest

 doctest.testmod()





 def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Union[str, Any]:




     """simple docstring"""




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

 # starting the problem with initial coordinates (3, 4)
 __A       : List[Any]						    = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __A       : Union[str, Any]						    = 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       : List[Any]						    = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 __A       : Union[str, Any]						    = 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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




     """simple docstring"""




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

 __A       : str						    = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 __A       : List[Any]						    = 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 unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     @property
     def 					lowerCAmelCase__		(						self       ):
         _A    =       1
         _A    =       3
         _A    =       8
         _A    =       8

         _A    =       torch.rand((batch_size, num_channels, height, width)       )

         return sample




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         if isinstance(image[0]							,    PIL.Image.Image       ):
             return np.stack([np.array(snake_case_       ) for i in image]							,    axis=0       )
         elif isinstance(snake_case_							,    torch.Tensor       ):
             return image.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         return image




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       self.to_np(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(
                 image_processor.preprocess(snake_case_       )							,    output_type=snake_case_							,    )
             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )
         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )

             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         _A    =       image_processor.numpy_to_pil(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )
             for i, o in zip(snake_case_							,    snake_case_       ):
                 _A    =       np.array(snake_case_       )
                 _A    =       self.to_np(snake_case_       ) if output_type == 'pil' else (self.to_np(snake_case_       ) * 255).round()
                 assert (
                     np.abs(in_np - out_np       ).max() < 1E-6
                 ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       input_pt_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         _A    =       input_np_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_ad       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_ad       ).max() < 1E-6




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_list       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_list       ).max() < 1E-6





