






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       UNetaDModel(**_lowercase       )
					_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(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_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(_lowercase    ,	_lowercase    ,	_lowercase       ):
										_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(_lowercase    ,	'''attentions'''       )
										for j in range(len(block.resnets       )       ):
															set_resnet_weights(block.resnets[j]    ,	_lowercase    ,	_lowercase       )
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]    ,	_lowercase    ,	_lowercase       )
																				module_index += 1

										if hasattr(_lowercase    ,	'''downsamplers'''       ) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down    ,	_lowercase    ,	_lowercase       )
															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]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0]    ,	_lowercase    ,	_lowercase       )
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1]    ,	_lowercase    ,	_lowercase       )
					module_index += 1

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

										if hasattr(_lowercase    ,	'''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    ,	_lowercase    ,	_lowercase       )
															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)











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



