
"""simple docstring"""
import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = math.inf      ,  _lowerCAmelCase = -math.inf      ,  _lowerCAmelCase = math.inf      ,  _lowerCAmelCase = -math.inf      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 0.01      ,  _lowerCAmelCase = 1      ,  ):
						__lowerCAmelCase   					=   False
						__lowerCAmelCase   					=   search_prob
						__lowerCAmelCase   					=   start_temperate
						__lowerCAmelCase   					=   []
						__lowerCAmelCase   					=   0
						__lowerCAmelCase   					=   None

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

						if visualization:
												from matplotlib import pyplot as plt

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


if __name__ == "__main__":

			def       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase		):
									return (x**2) + (y**2)

			# starting the problem with initial coordinates (12, 47)
			SCREAMING_SNAKE_CASE_ 			=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			SCREAMING_SNAKE_CASE_ 			=     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)
			SCREAMING_SNAKE_CASE_ 			=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			SCREAMING_SNAKE_CASE_ 			=     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			(_lowerCAmelCase      ,  _lowerCAmelCase		):
									return (3 * x**2) - (6 * y)

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

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







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
