


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A      :    Dict		=					False







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       pass







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


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"       )
             # remove text_unet
             pipe.remove_unused_weights()
             pipe.to(__lowerCamelCase       )
             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  "A painting of a squirrel eating a burger "
             lowerCamelCase__		:				List[Any]    =  torch.manual_seed(0       )
             lowerCamelCase__		:				Dict    =  pipe(
                 prompt=__lowerCamelCase					,      generator=__lowerCamelCase					,      guidance_scale=7.5					,      num_inference_steps=2					,      output_type="numpy"       ).images

             with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(__lowerCamelCase       )
                   lowerCamelCase__		:				int    =  VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase       )
             pipe.to(__lowerCamelCase       )
             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             lowerCamelCase__		:				int    =  generator.manual_seed(0       )
             lowerCamelCase__		:				Union[str, Any]    =  pipe(
                 prompt=__lowerCamelCase					,      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							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  VersatileDiffusionTextToImagePipeline.from_pretrained(
                 "shi-labs/versatile-diffusion"					,      torch_dtype=torch.floataa       )
             pipe.to(__lowerCamelCase       )
             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  "A painting of a squirrel eating a burger "
             lowerCamelCase__		:				List[str]    =  torch.manual_seed(0       )
             lowerCamelCase__		:				Union[str, Any]    =  pipe(
                 prompt=__lowerCamelCase					,      generator=__lowerCamelCase					,      guidance_scale=7.5					,      num_inference_steps=50					,      output_type="numpy"       ).images

             lowerCamelCase__		:				List[Any]    =  image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__		:				int    =  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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






import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

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

      if visualization:
            from matplotlib import pyplot as plt

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


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					simulated_annealing(
        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()}'
    )

    # starting the problem with initial coordinates (12, 47)
    A      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    int		=					simulated_annealing(
        prob, find_max=True, max_x=100, 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_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




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

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					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()}'
    )

    A      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					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()}'
    )


