





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Tuple:
					__A     :						List[str]          =    UNetaDModel(**a  )
					__A     :						int          =    checkpoint['all_modules.0.W'].data
					__A     :						str          =    checkpoint['all_modules.0.W'].data
					__A     :						Any          =    checkpoint['all_modules.1.weight'].data
					__A     :						Dict          =    checkpoint['all_modules.1.bias'].data

					__A     :						Optional[Any]          =    checkpoint['all_modules.2.weight'].data
					__A     :						Optional[Any]          =    checkpoint['all_modules.2.bias'].data

					__A     :						List[Any]          =    checkpoint['all_modules.3.weight'].data
					__A     :						Any          =    checkpoint['all_modules.3.bias'].data

					__A     :						Tuple          =    checkpoint[list(checkpoint.keys()  )[-4]].data
					__A     :						int          =    checkpoint[list(checkpoint.keys()  )[-3]].data
					__A     :						Tuple          =    checkpoint[list(checkpoint.keys()  )[-2]].data
					__A     :						str          =    checkpoint[list(checkpoint.keys()  )[-1]].data

					__A     :						Optional[Any]          =    4

					def set_attention_weights(a ,						a ,						a  ):
										__A     :						int          =    old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
										__A     :						int          =    old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
										__A     :						List[str]          =    old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

										__A     :						str          =    old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
										__A     :						List[str]          =    old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
										__A     :						str          =    old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

										__A     :						str          =    old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
										__A     :						str          =    old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

										__A     :						Optional[int]          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
										__A     :						str          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

					def set_resnet_weights(a ,						a ,						a  ):
										__A     :						int          =    old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
										__A     :						int          =    old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
										__A     :						List[Any]          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
										__A     :						Union[str, Any]          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

										__A     :						Optional[Any]          =    old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
										__A     :						Optional[Any]          =    old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
										__A     :						Union[str, Any]          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
										__A     :						Union[str, Any]          =    old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

										__A     :						List[Any]          =    old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
										__A     :						Optional[Any]          =    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     :						List[Any]          =    old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
															__A     :						Any          =    old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

					for i, block in enumerate(new_model_architecture.downsample_blocks  ):
										__A     :						Tuple          =    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     :						Optional[Any]          =    checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
															__A     :						Optional[int]          =    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     :						Dict          =    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     :						int          =    checkpoint[F"""all_modules.{module_index}.weight"""].data
															__A     :						str          =    checkpoint[F"""all_modules.{module_index}.bias"""].data
															module_index += 1
															__A     :						List[str]          =    checkpoint[F"""all_modules.{module_index}.weight"""].data
															__A     :						List[str]          =    checkpoint[F"""all_modules.{module_index}.bias"""].data
															module_index += 1
															set_resnet_weights(block.resnet_up ,						a ,						a  )
															module_index += 1

					__A     :						Dict          =    checkpoint[F"""all_modules.{module_index}.weight"""].data
					__A     :						Tuple          =    checkpoint[F"""all_modules.{module_index}.bias"""].data
					module_index += 1
					__A     :						Tuple          =    checkpoint[F"""all_modules.{module_index}.weight"""].data
					__A     :						Dict          =    checkpoint[F"""all_modules.{module_index}.bias"""].data

					return new_model_architecture.state_dict()


if __name__ == "__main__":
				UpperCAmelCase						:       Union[str, Any]        =					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.''',
				)

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

				UpperCAmelCase						:       str        =					torch.load(args.checkpoint_path, map_location='''cpu''')

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

				UpperCAmelCase						:       Tuple        =					convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

				UpperCAmelCase						:       Tuple        =					UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

								UpperCAmelCase						:       Optional[int]        =					ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)











from .hash_table import HashTable





class 						_A( snake_case__       ):


				"""simple docstring"""


				def __init__(      self					, *_A					, **_A					):
									super().__init__(*_A					, **_A					)





				def  UpperCAmelCase_		(      self					, _A					, _A=None					):
									__A     :						Optional[int]          =    1
									__A     :						Union[str, Any]          =    self.hash_function(key + i * i					)

									while self.values[new_key] is not None and self.values[new_key] != key:
														i += 1
														__A     :						Tuple          =    (
														    self.hash_function(key + i * i					)
														    if not self.balanced_factor() >= self.lim_charge
														    else None
														)

														if new_key is None:
																			break

									return new_key




