
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def     a						(							snake_case__: List[Any]						,       snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = UNetaDModel(**snake_case__ )
				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(snake_case__: List[str]						,       snake_case__: Union[str, Any]						,       snake_case__: List[Any] ):
								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(snake_case__: Tuple						,       snake_case__: Union[str, Any]						,       snake_case__: str ):
								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(snake_case__						,       '''attentions''' )
								for j in range(len(block.resnets ) ):
												set_resnet_weights(block.resnets[j]						,       snake_case__						,       snake_case__ )
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]						,       snake_case__						,       snake_case__ )
																module_index += 1

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

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

								if hasattr(snake_case__						,       '''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						,       snake_case__						,       snake_case__ )
												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__":
						__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 __future__ import annotations

__a        = 'Alexander Joslin'




def     a						(							snake_case__: int ):


				'''simple docstring'''

				if number <= 0:
								lowercase_					  = F'''n must be greater than 0. Got n = {number}'''
								raise ValueError(snake_case__ )

				lowercase_					  = [number + 1] * (number + 1)

				# starting position
				lowercase_					  = 0
				for i in range(1						,       snake_case__ ):
								lowercase_					  = min(table[i + 1]						,       table[i] + 1 )
								# check if out of bounds
								if i * 2 <= number:
												lowercase_					  = min(table[i * 2]						,       table[i] + 1 )
								# check if out of bounds
								if i * 3 <= number:
												lowercase_					  = min(table[i * 3]						,       table[i] + 1 )
				return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




