





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class        snake_case_		(			_A						,					_A						,					unittest.TestCase):
		lowerCamelCase    :List[str]						=     IFInpaintingSuperResolutionPipeline
		lowerCamelCase    :Dict						=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		lowerCamelCase    :List[str]						=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
		lowerCamelCase    :List[Any]						=     PipelineTesterMixin.required_optional_params - {"latents"}







		def 			__lowercase   (		self   )				->   Optional[Any]:
				return self._get_superresolution_dummy_components()







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase=0   )				->   Dict:
				if str(__A   ).startswith('''mps'''   ):
						lowerCamelCase   : List[str]								=torch.manual_seed(__A   )
				else:
						lowerCamelCase   : str								=torch.Generator(device=__A   ).manual_seed(__A   )

				lowerCamelCase   : List[Any]								=floats_tensor((1, 3, 1_6, 1_6)			,     rng=random.Random(__A   )   ).to(__A   )
				lowerCamelCase   : int								=floats_tensor((1, 3, 3_2, 3_2)			,     rng=random.Random(__A   )   ).to(__A   )
				lowerCamelCase   : Optional[int]								=floats_tensor((1, 3, 3_2, 3_2)			,     rng=random.Random(__A   )   ).to(__A   )

				lowerCamelCase   : List[Any]								={
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "original_image": original_image,
				    "mask_image": mask_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "numpy",
				}

				return inputs







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()			,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,     )
		def 			__lowercase   (		self   )				->   Tuple:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )







		def 			__lowercase   (		self   )				->   List[Any]:
				self._test_save_load_optional_components()







		@unittest.skipIf(torch_device != '''cuda'''			,     reason='''float16 requires CUDA'''   )
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1   )







		def 			__lowercase   (		self   )				->   str:
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )







		def 			__lowercase   (		self   )				->   List[str]:
				self._test_save_load_local()







		def 			__lowercase   (		self   )				->   List[str]:
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2			,     )








import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 					A__	(       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_ = 1_0_0							,       SCREAMING_SNAKE_CASE_ = 0.0_1							,       SCREAMING_SNAKE_CASE_ = 1							,       )   ->     Any:
		lowerCamelCase   : Union[str, Any]								=False
		lowerCamelCase   : int								=search_prob
		lowerCamelCase   : int								=start_temperate
		lowerCamelCase   : Any								=[]
		lowerCamelCase   : Any								=0
		lowerCamelCase   : Union[str, Any]								=None

		while not search_end:
				lowerCamelCase   : Dict								=current_state.score()
				if best_state is None or current_score > best_state.score():
						lowerCamelCase   : Tuple								=current_state
				scores.append(SCREAMING_SNAKE_CASE_		)
				iterations += 1
				lowerCamelCase   : Union[str, Any]								=None
				lowerCamelCase   : int								=current_state.get_neighbors()
				while (
				    next_state is None and neighbors
				):  # till we do not find a neighbor that we can move to
						lowerCamelCase   : Union[str, Any]								=random.randint(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		)  # picking a random neighbor
						lowerCamelCase   : List[str]								=neighbors.pop(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Any								=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[int]								=picked_neighbor
						else:
								lowerCamelCase   : Any								=(math.e) ** (
								    change / current_temp
								)  # probability generation function
								if random.random() < probability:  # random number within probability
										lowerCamelCase   : Union[str, Any]								=picked_neighbor
				lowerCamelCase   : Union[str, 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   : Optional[int]								=True
				else:
						lowerCamelCase   : List[str]								=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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
						return (x**2) + (y**2)

				# starting the problem with initial coordinates (12, 47)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
						return (3 * x**2) - (6 * y)

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

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

