





'''simple docstring'''



from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
_SCREAMING_SNAKE_CASE    =			2_0

# Time between time steps in seconds
_SCREAMING_SNAKE_CASE    =			INTERVAL / 1_0_0_0






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = 1.0   ,_lowerCamelCase = 1.0   ,_lowerCamelCase = "blue"   ,)							->							None:







				'''simple docstring'''





				__lowercase   =							position_x
				__lowercase   =							position_y
				__lowercase   =							velocity_x
				__lowercase   =							velocity_y
				__lowercase   =							mass
				__lowercase   =							size
				__lowercase   =							color


	@property
	def   _UpperCAmelCase   (self       )							->							tuple[float, float]:







				'''simple docstring'''





				return self.position_x, self.position_y


	@property
	def   _UpperCAmelCase   (self       )							->							tuple[float, float]:







				'''simple docstring'''





				return self.velocity_x, self.velocity_y


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							None:







				'''simple docstring'''





				self.velocity_x += force_x * delta_time
				self.velocity_y += force_y * delta_time


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							None:







				'''simple docstring'''





				self.position_x += self.velocity_x * delta_time
				self.position_y += self.velocity_y * delta_time






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase = 1.0   ,_lowerCamelCase = 1.0   ,_lowerCamelCase = 0.0   ,)							->							None:







				'''simple docstring'''





				__lowercase   =							bodies
				__lowercase   =							gravitation_constant
				__lowercase   =							time_factor
				__lowercase   =							softening_factor


	def __len__(self       )							->							int:







				'''simple docstring'''





				return len(self.bodies       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							None:







				'''simple docstring'''





				for bodya in self.bodies:
							__lowercase   =							0.0
							__lowercase   =							0.0
							for bodya in self.bodies:
										if bodya != bodya:
													__lowercase   =							bodya.position_x - bodya.position_x
													__lowercase   =							bodya.position_y - bodya.position_y

													# Calculation of the distance using Pythagoras's theorem
													# Extra factor due to the softening technique
													__lowercase   =							(dif_x**2 + dif_y**2 + self.softening_factor) ** (
													    1 / 2
													)

													# Newton's law of universal gravitation.
													force_x += (
													    self.gravitation_constant * bodya.mass * dif_x / distance**3
													)
													force_y += (
													    self.gravitation_constant * bodya.mass * dif_y / distance**3
													)

            # Update the body's velocity once all the force components have been added
							bodya.update_velocity(_lowerCamelCase   ,_lowerCamelCase   ,delta_time * self.time_factor       )

				# Update the positions only after all the velocities have been updated
				for body in self.bodies:
							body.update_position(delta_time * self.time_factor       )





def       _lowerCAmelCase  ( lowerCamelCase_	:     BodySystem						,				lowerCamelCase_	:     float						,				lowerCamelCase_	:     list[plt.Circle] ):
			body_system.update_system(lowerCamelCase_ )

			# Update the positions of the patches
			for patch, body in zip(lowerCamelCase_						,				body_system.bodies ):
						__lowercase   =							(body.position_x, body.position_y)




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     BodySystem						,				lowerCamelCase_	:     float = -1						,				lowerCamelCase_	:     float = 1						,				lowerCamelCase_	:     float = -1						,				lowerCamelCase_	:     float = 1						,				):
			__lowercase   =							plt.figure()
			fig.canvas.set_window_title(lowerCamelCase_ )
			__lowercase   =							plt.axes(
			    xlim=(x_start, x_end)						,				ylim=(y_start, y_end) )  # Set section to be plotted
			plt.gca().set_aspect('''equal''' )  # Fix aspect ratio

			# Each body is drawn as a patch by the plt-function
			__lowercase   =							[
			    plt.Circle((body.position_x, body.position_y)						,				body.size						,				fc=body.color )
			    for body in body_system.bodies
			]

			for patch in patches:
						ax.add_patch(lowerCamelCase_ )

			# Function called at each step of the animation
			def update(lowerCamelCase_	:     int ) -> list[plt.Circle]:
						update_step(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )
						return patches

			__lowercase   =							animation.FuncAnimation(  # noqa: F841
			    lowerCamelCase_						,				lowerCamelCase_						,				interval=lowerCamelCase_						,				blit=lowerCamelCase_ )

			plt.show()




def       _lowerCAmelCase  ( ):
			__lowercase   =							0.9_70_04_36
			__lowercase   =							-0.24_30_87_53
			__lowercase   =							0.4_66_20_36_85
			__lowercase   =							0.43_23_65_73

			__lowercase   =							[
			    Body(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				size=0.2						,				color='''red''' ),
			    Body(-position_x						,				-position_y						,				lowerCamelCase_						,				lowerCamelCase_						,				size=0.2						,				color='''green''' ),
			    Body(0						,				0						,				-2 * velocity_x						,				-2 * velocity_y						,				size=0.2						,				color='''blue''' ),
			]
			return BodySystem(lowerCamelCase_						,				time_factor=3 )




def       _lowerCAmelCase  ( ):
			__lowercase   =							7.3_476E22
			__lowercase   =							5.972E24
			__lowercase   =							1_0_2_2
			__lowercase   =							3_8_4_3_9_9_0_0_0
			__lowercase   =							6.674E-11

			# Calculation of the respective velocities so that total impulse is zero,
			# i.e. the two bodies together don't move
			__lowercase   =							earth_mass * velocity_dif / (earth_mass + moon_mass)
			__lowercase   =							moon_velocity - velocity_dif

			__lowercase   =							Body(-earth_moon_distance						,				0						,				0						,				lowerCamelCase_						,				lowerCamelCase_						,				1_0_0_0_0_0_0_0						,				'''grey''' )
			__lowercase   =							Body(0						,				0						,				0						,				lowerCamelCase_						,				lowerCamelCase_						,				5_0_0_0_0_0_0_0						,				'''blue''' )
			return BodySystem([earth, moon]						,				lowerCamelCase_						,				time_factor=1_0_0_0_0_0_0 )




def       _lowerCAmelCase  ( ):
			__lowercase   =							[]
			for _ in range(1_0 ):
						__lowercase   =							random.uniform(-0.5						,				0.5 )
						__lowercase   =							random.uniform(-0.5						,				0.5 )

						# Bodies are created pairwise with opposite velocities so that the
						# total impulse remains zero
						bodies.append(
						    Body(
						        random.uniform(-0.5						,				0.5 )						,				random.uniform(-0.5						,				0.5 )						,				lowerCamelCase_						,				lowerCamelCase_						,				size=0.05						,				) )
						bodies.append(
						    Body(
						        random.uniform(-0.5						,				0.5 )						,				random.uniform(-0.5						,				0.5 )						,				-velocity_x						,				-velocity_y						,				size=0.05						,				) )
			return BodySystem(lowerCamelCase_						,				0.01						,				1_0						,				0.1 )


if __name__ == "__main__":
	plot('''Figure-8 solution to the 3-body-problem''', example_a(), -2, 2, -2, 2)
	plot(
	    '''Moon\'s orbit around the earth''',
	    example_a(),
	    -4_3_0_0_0_0_0_0_0,
	    4_3_0_0_0_0_0_0_0,
	    -4_3_0_0_0_0_0_0_0,
	    4_3_0_0_0_0_0_0_0,
	)
	plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)












'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

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

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

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

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

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

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

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

			def set_resnet_weights(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
						__lowercase   =							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:
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.''',
	)

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

	_SCREAMING_SNAKE_CASE    =			torch.load(args.checkpoint_path, map_location='''cpu''')

	with open(args.config_file) as f:
		_SCREAMING_SNAKE_CASE    =			json.loads(f.read())

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	_SCREAMING_SNAKE_CASE    =			UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

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

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





