code: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Tuple:
  						_a     =			UNetaDModel(**_UpperCAmelCase				)
  						_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(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				):
  												_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(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				):
  												_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(_UpperCAmelCase							,     'attentions'				)
  												for j in range(len(block.resnets				)				):
  																		set_resnet_weights(block.resnets[j]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  																		module_index += 1
  																		if has_attentions:
  																								set_attention_weights(block.attentions[j]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  																								module_index += 1
  
  												if hasattr(_UpperCAmelCase							,     'downsamplers'				) and block.downsamplers is not None:
  																		set_resnet_weights(block.resnet_down							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  																		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]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						module_index += 1
  						set_attention_weights(new_model_architecture.mid_block.attentions[0]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						module_index += 1
  						set_resnet_weights(new_model_architecture.mid_block.resnets[1]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						module_index += 1
  
  						for i, block in enumerate(new_model_architecture.up_blocks				):
  												_a     =			hasattr(_UpperCAmelCase							,     'attentions'				)
  												for j in range(len(block.resnets				)				):
  																		set_resnet_weights(block.resnets[j]							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  																		module_index += 1
  												if has_attentions:
  																		set_attention_weights(
  																		    block.attentions[0]							,     _UpperCAmelCase							,     _UpperCAmelCase				)  # why can there only be a single attention layer for up?
  																		module_index += 1
  
  												if hasattr(_UpperCAmelCase							,     '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							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  																		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__":
  		lowercase_								=    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.',
  		)
  
  		lowercase_								=    parser.parse_args()
  
  		lowercase_								=    torch.load(args.checkpoint_path, map_location='cpu')
  
  		with open(args.config_file) as f:
  				lowercase_								=    json.loads(f.read())
  
  		lowercase_								=    convert_ncsnpp_checkpoint(
  		    checkpoint,
  		    config,
  		)
  
  		if "sde" in config:
  				del config["sde"]
  
  		lowercase_								=    UNetaDModel(**config)
  		model.load_state_dict(converted_checkpoint)
  
  		try:
  				lowercase_								=    ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
  				lowercase_								=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  				pipe.save_pretrained(args.dump_path)
  		except:  # noqa: E722
  				model.save_pretrained(args.dump_path)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  