code: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			List[str]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    UNetaDModel(**lowercase__			)
  		__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(lowercase__     ,				lowercase__     ,				lowercase__			):
  				__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(lowercase__     ,				lowercase__     ,				lowercase__			):
  				__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(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
  						__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]     ,				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			):
  				__lowercase=    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:
  						__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     ,				lowercase__     ,				lowercase__			)
  						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__":
  	lowerCAmelCase					 =	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.''',
  	)
  
  	lowerCAmelCase					 =	parser.parse_args()
  
  	lowerCAmelCase					 =	torch.load(args.checkpoint_path, map_location='''cpu''')
  
  	with open(args.config_file) as f:
  		lowerCAmelCase					 =	json.loads(f.read())
  
  	lowerCAmelCase					 =	convert_ncsnpp_checkpoint(
  	    checkpoint,
  	    config,
  	)
  
  	if "sde" in config:
  		del config["sde"]
  
  	lowerCAmelCase					 =	UNetaDModel(**config)
  	model.load_state_dict(converted_checkpoint)
  
  	try:
  		lowerCAmelCase					 =	ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
  		lowerCAmelCase					 =	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  		pipe.save_pretrained(args.dump_path)
  	except:  # noqa: E722
  		model.save_pretrained(args.dump_path)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  