
from itertools import combinations, product

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->bool:
		if xa == ya == 0 or xa == ya == 0:
				return False
		A				=xa * xa + ya * ya
		A				=xa * xa + ya * ya
		A				=(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
		return (
		    a_square + b_square == c_square
		    or a_square + c_square == b_square
		    or b_square + c_square == a_square
		)

def    UpperCamelCase_ (	a_ = 50		)     ->int:
		return sum(
		    1
		    for pta, pta in combinations(product(range(limit + 1		)  ,				repeat=2		)  ,				2		)
		    if is_right(*a_  ,				*a_		)		)


if __name__ == "__main__":
	print(F'''{solution() = }''')






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

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

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

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

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=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
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=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		):
				A				=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:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						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.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

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

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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




