



import math







class 							a_  :

						def __init__(    self	:Optional[int]   ,						_lowercase	:int   ,						_lowercase	:int   ,						_lowercase	:int   ,						_lowercase	:Dict)		->					Tuple:
												UpperCAmelCase_     							=     x
												UpperCAmelCase_     							=     y
												UpperCAmelCase_     							=     step_size
												UpperCAmelCase_     							=     function_to_optimize

						def 	__a       (    self	:Optional[int])		->					int:
												return self.function(self.x   ,						self.y)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.step_size
												return [
												    SearchProblem(_lowercase   ,						_lowercase   ,						_lowercase   ,						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	:Union[str, Any])		->					Tuple:
												return hash(str(self))

						def __eq__(    self	:Tuple   ,						_lowercase	:List[Any])		->					List[Any]:
												if isinstance(_lowercase   ,						_lowercase):
																		return hash(str(self)) == hash(str(_lowercase))
												return False




						def __str__(    self	:List[str])		->					Tuple:
												return f"x: {self.x} y: {self.y}"







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase = True							,					__UpperCAmelCase = math.inf							,					__UpperCAmelCase = -math.inf							,					__UpperCAmelCase = math.inf							,					__UpperCAmelCase = -math.inf							,					__UpperCAmelCase = False							,					__UpperCAmelCase = 1_0000							,					)       ->       SearchProblem:



						'''simple docstring'''






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



													'''simple docstring'''






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

							# starting the problem with initial coordinates (3, 4)
							UpperCamelCase_	       =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase_	       =   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)
							UpperCamelCase_	       =   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase_	       =   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 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[Any]:



													'''simple docstring'''






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

							UpperCamelCase_	       =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase_	       =   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 argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-4]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-3]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-2]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-1]].data

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

								if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

				for i, block in enumerate(new_model_architecture.downsample_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]		,a__		,a__)
																module_index += 1

								if hasattr(a__		,'''downsamplers''') and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down		,a__		,a__)
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
												module_index += 1

				set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,a__		,a__)
				module_index += 1
				set_attention_weights(new_model_architecture.mid_block.attentions[0]		,a__		,a__)
				module_index += 1
				set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,a__		,a__)
				module_index += 1

				for i, block in enumerate(new_model_architecture.up_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
								if has_attentions:
												set_attention_weights(
												    block.attentions[0]		,a__		,a__)  # why can there only be a single attention layer for up?
												module_index += 1

								if hasattr(a__		,'''resnet_up''') and block.resnet_up is not None:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
							    type=str,
							    required=False,
							    help='''Path to the checkpoint to convert.''',
							)

							parser.add_argument(
							    '''--config_file''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
							    type=str,
							    required=False,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument(
							    '''--dump_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
							    type=str,
							    required=False,
							    help='''Path to the output model.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

							if "sde" in config:
														del config["sde"]

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														snake_case_  :	List[Any]							  =							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														snake_case_  :	int							  =							ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)