code: 
  '''simple docstring'''
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=1	) ->      int:
  		if n_shave_prefix_segments >= 0:
  				return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
  		else:
  				return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0	) ->      Dict:
  		_UpperCamelCase       : Optional[int]    =      []
  		for old_item in old_list:
  				_UpperCamelCase       : str    =      old_item
  				_UpperCamelCase       : Union[str, Any]    =      new_item.replace('''block.'''			,'''resnets.'''	)
  				_UpperCamelCase       : Union[str, Any]    =      new_item.replace('''conv_shorcut'''			,'''conv1'''	)
  				_UpperCamelCase       : Optional[int]    =      new_item.replace('''in_shortcut'''			,'''conv_shortcut'''	)
  				_UpperCamelCase       : Tuple    =      new_item.replace('''temb_proj'''			,'''time_emb_proj'''	)
  
  				_UpperCamelCase       : Union[str, Any]    =      shave_segments(UpperCamelCase			,n_shave_prefix_segments=UpperCamelCase	)
  
  				mapping.append({'''old''': old_item, '''new''': new_item}	)
  
  		return mapping
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0			,UpperCamelCase=False	) ->      str:
  		_UpperCamelCase       : Union[str, Any]    =      []
  		for old_item in old_list:
  				_UpperCamelCase       : List[str]    =      old_item
  
  				# In `model.mid`, the layer is called `attn`.
  				if not in_mid:
  						_UpperCamelCase       : str    =      new_item.replace('''attn'''			,'''attentions'''	)
  				_UpperCamelCase       : int    =      new_item.replace('''.k.'''			,'''.key.'''	)
  				_UpperCamelCase       : Any    =      new_item.replace('''.v.'''			,'''.value.'''	)
  				_UpperCamelCase       : int    =      new_item.replace('''.q.'''			,'''.query.'''	)
  
  				_UpperCamelCase       : Union[str, Any]    =      new_item.replace('''proj_out'''			,'''proj_attn'''	)
  				_UpperCamelCase       : Any    =      new_item.replace('''norm'''			,'''group_norm'''	)
  
  				_UpperCamelCase       : List[Any]    =      shave_segments(UpperCamelCase			,n_shave_prefix_segments=UpperCamelCase	)
  				mapping.append({'''old''': old_item, '''new''': new_item}	)
  
  		return mapping
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase=None			,UpperCamelCase=None			,UpperCamelCase=None	) ->      List[str]:
  		assert isinstance(UpperCamelCase			,UpperCamelCase	), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  		if attention_paths_to_split is not None:
  				if config is None:
  						raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''	)
  
  				for path, path_map in attention_paths_to_split.items():
  						_UpperCamelCase       : int    =      old_checkpoint[path]
  						_UpperCamelCase       : Any    =      old_tensor.shape[0] // 3
  
  						_UpperCamelCase       : Optional[Any]    =      (-1, channels) if len(old_tensor.shape	) == 3 else (-1)
  
  						_UpperCamelCase       : List[Any]    =      old_tensor.shape[0] // config.get('''num_head_channels'''			,1	) // 3
  
  						_UpperCamelCase       : Union[str, Any]    =      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
  						_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Dict    =      old_tensor.split(channels // num_heads			,dim=1	)
  
  						_UpperCamelCase       : Union[str, Any]    =      query.reshape(UpperCamelCase	).squeeze()
  						_UpperCamelCase       : Any    =      key.reshape(UpperCamelCase	).squeeze()
  						_UpperCamelCase       : Union[str, Any]    =      value.reshape(UpperCamelCase	).squeeze()
  
  		for path in paths:
  				_UpperCamelCase       : Any    =      path['''new''']
  
  				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  						continue
  
  				_UpperCamelCase       : int    =      new_path.replace('''down.'''			,'''down_blocks.'''	)
  				_UpperCamelCase       : Dict    =      new_path.replace('''up.'''			,'''up_blocks.'''	)
  
  				if additional_replacements is not None:
  						for replacement in additional_replacements:
  								_UpperCamelCase       : List[str]    =      new_path.replace(replacement['''old''']			,replacement['''new''']	)
  
  				if "attentions" in new_path:
  						_UpperCamelCase       : Union[str, Any]    =      old_checkpoint[path['''old''']].squeeze()
  				else:
  						_UpperCamelCase       : Union[str, Any]    =      old_checkpoint[path['''old''']]
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      List[str]:
  		_UpperCamelCase       : Union[str, Any]    =      {}
  
  		_UpperCamelCase       : Any    =      checkpoint['''temb.dense.0.weight''']
  		_UpperCamelCase       : Any    =      checkpoint['''temb.dense.0.bias''']
  		_UpperCamelCase       : int    =      checkpoint['''temb.dense.1.weight''']
  		_UpperCamelCase       : Optional[int]    =      checkpoint['''temb.dense.1.bias''']
  
  		_UpperCamelCase       : List[Any]    =      checkpoint['''norm_out.weight''']
  		_UpperCamelCase       : Optional[Any]    =      checkpoint['''norm_out.bias''']
  
  		_UpperCamelCase       : List[str]    =      checkpoint['''conv_in.weight''']
  		_UpperCamelCase       : Optional[int]    =      checkpoint['''conv_in.bias''']
  		_UpperCamelCase       : int    =      checkpoint['''conv_out.weight''']
  		_UpperCamelCase       : Tuple    =      checkpoint['''conv_out.bias''']
  
  		_UpperCamelCase       : str    =      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''down''' in layer}	)
  		_UpperCamelCase       : List[str]    =      {
  		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  		}
  
  		_UpperCamelCase       : Optional[Any]    =      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''up''' in layer}	)
  		_UpperCamelCase       : str    =      {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)}
  
  		for i in range(UpperCamelCase	):
  				_UpperCamelCase       : Dict    =      (i - 1) // (config['''layers_per_block'''] + 1)
  
  				if any('''downsample''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : Tuple    =      checkpoint[
  						    f'''down.{i}.downsample.op.weight'''
  						]
  						_UpperCamelCase       : Union[str, Any]    =      checkpoint[f'''down.{i}.downsample.op.bias''']
  				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
  				if any('''block''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : Optional[Any]    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''block''' in layer}	)
  						_UpperCamelCase       : Any    =      {
  						    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  						    for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_blocks > 0:
  								for j in range(config['''layers_per_block''']	):
  										_UpperCamelCase       : Any    =      renew_resnet_paths(blocks[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)
  
  				if any('''attn''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : Any    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''attn''' in layer}	)
  						_UpperCamelCase       : Any    =      {
  						    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  						    for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_attn > 0:
  								for j in range(config['''layers_per_block''']	):
  										_UpperCamelCase       : str    =      renew_attention_paths(attns[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,config=UpperCamelCase	)
  
  		_UpperCamelCase       : Optional[int]    =      [key for key in checkpoint if '''mid.block_1''' in key]
  		_UpperCamelCase       : Dict    =      [key for key in checkpoint if '''mid.block_2''' in key]
  		_UpperCamelCase       : Any    =      [key for key in checkpoint if '''mid.attn_1''' in key]
  
  		# Mid new 2
  		_UpperCamelCase       : int    =      renew_resnet_paths(UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]			,)
  
  		_UpperCamelCase       : str    =      renew_resnet_paths(UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,)
  
  		_UpperCamelCase       : List[str]    =      renew_attention_paths(UpperCamelCase			,in_mid=UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]			,)
  
  		for i in range(UpperCamelCase	):
  				_UpperCamelCase       : str    =      num_up_blocks - 1 - i
  
  				if any('''upsample''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : Optional[int]    =      checkpoint[
  						    f'''up.{i}.upsample.conv.weight'''
  						]
  						_UpperCamelCase       : List[str]    =      checkpoint[f'''up.{i}.upsample.conv.bias''']
  
  				if any('''block''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : Dict    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''block''' in layer}	)
  						_UpperCamelCase       : Optional[Any]    =      {
  						    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_blocks > 0:
  								for j in range(config['''layers_per_block'''] + 1	):
  										_UpperCamelCase       : str    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
  										_UpperCamelCase       : List[Any]    =      renew_resnet_paths(blocks[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)
  
  				if any('''attn''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : Union[str, Any]    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''attn''' in layer}	)
  						_UpperCamelCase       : Tuple    =      {
  						    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_attn > 0:
  								for j in range(config['''layers_per_block'''] + 1	):
  										_UpperCamelCase       : int    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
  										_UpperCamelCase       : Optional[int]    =      renew_attention_paths(attns[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)
  
  		_UpperCamelCase       : List[Any]    =      {k.replace('''mid_new_2'''			,'''mid_block'''	): v for k, v in new_checkpoint.items()}
  		return new_checkpoint
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      Tuple:
  		_UpperCamelCase       : Tuple    =      {}
  
  		_UpperCamelCase       : Tuple    =      checkpoint['''encoder.norm_out.weight''']
  		_UpperCamelCase       : str    =      checkpoint['''encoder.norm_out.bias''']
  
  		_UpperCamelCase       : Optional[Any]    =      checkpoint['''encoder.conv_in.weight''']
  		_UpperCamelCase       : Any    =      checkpoint['''encoder.conv_in.bias''']
  		_UpperCamelCase       : Any    =      checkpoint['''encoder.conv_out.weight''']
  		_UpperCamelCase       : List[str]    =      checkpoint['''encoder.conv_out.bias''']
  
  		_UpperCamelCase       : str    =      checkpoint['''decoder.norm_out.weight''']
  		_UpperCamelCase       : Dict    =      checkpoint['''decoder.norm_out.bias''']
  
  		_UpperCamelCase       : Any    =      checkpoint['''decoder.conv_in.weight''']
  		_UpperCamelCase       : int    =      checkpoint['''decoder.conv_in.bias''']
  		_UpperCamelCase       : Dict    =      checkpoint['''decoder.conv_out.weight''']
  		_UpperCamelCase       : str    =      checkpoint['''decoder.conv_out.bias''']
  
  		_UpperCamelCase       : str    =      len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''down''' in layer}	)
  		_UpperCamelCase       : List[str]    =      {
  		    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  		}
  
  		_UpperCamelCase       : int    =      len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''up''' in layer}	)
  		_UpperCamelCase       : int    =      {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)}
  
  		for i in range(UpperCamelCase	):
  				_UpperCamelCase       : Union[str, Any]    =      (i - 1) // (config['''layers_per_block'''] + 1)
  
  				if any('''downsample''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : Tuple    =      checkpoint[
  						    f'''encoder.down.{i}.downsample.conv.weight'''
  						]
  						_UpperCamelCase       : str    =      checkpoint[
  						    f'''encoder.down.{i}.downsample.conv.bias'''
  						]
  
  				if any('''block''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : int    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''block''' in layer}	)
  						_UpperCamelCase       : str    =      {
  						    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  						    for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_blocks > 0:
  								for j in range(config['''layers_per_block''']	):
  										_UpperCamelCase       : Optional[int]    =      renew_resnet_paths(blocks[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase	)
  
  				if any('''attn''' in layer for layer in down_blocks[i]	):
  						_UpperCamelCase       : List[Any]    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''attn''' in layer}	)
  						_UpperCamelCase       : Any    =      {
  						    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  						    for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_attn > 0:
  								for j in range(config['''layers_per_block''']	):
  										_UpperCamelCase       : List[str]    =      renew_attention_paths(attns[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,config=UpperCamelCase	)
  
  		_UpperCamelCase       : List[Any]    =      [key for key in checkpoint if '''mid.block_1''' in key]
  		_UpperCamelCase       : Any    =      [key for key in checkpoint if '''mid.block_2''' in key]
  		_UpperCamelCase       : Any    =      [key for key in checkpoint if '''mid.attn_1''' in key]
  
  		# Mid new 2
  		_UpperCamelCase       : str    =      renew_resnet_paths(UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]			,)
  
  		_UpperCamelCase       : List[Any]    =      renew_resnet_paths(UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,)
  
  		_UpperCamelCase       : Union[str, Any]    =      renew_attention_paths(UpperCamelCase			,in_mid=UpperCamelCase	)
  		assign_to_checkpoint(
  		    UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]			,)
  
  		for i in range(UpperCamelCase	):
  				_UpperCamelCase       : Tuple    =      num_up_blocks - 1 - i
  
  				if any('''upsample''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : str    =      checkpoint[
  						    f'''decoder.up.{i}.upsample.conv.weight'''
  						]
  						_UpperCamelCase       : Optional[Any]    =      checkpoint[
  						    f'''decoder.up.{i}.upsample.conv.bias'''
  						]
  
  				if any('''block''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : Tuple    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''block''' in layer}	)
  						_UpperCamelCase       : Union[str, Any]    =      {
  						    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_blocks > 0:
  								for j in range(config['''layers_per_block'''] + 1	):
  										_UpperCamelCase       : List[Any]    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
  										_UpperCamelCase       : int    =      renew_resnet_paths(blocks[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)
  
  				if any('''attn''' in layer for layer in up_blocks[i]	):
  						_UpperCamelCase       : List[str]    =      len(
  						    {'''.'''.join(shave_segments(UpperCamelCase			,3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''attn''' in layer}	)
  						_UpperCamelCase       : int    =      {
  						    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase	)
  						}
  
  						if num_attn > 0:
  								for j in range(config['''layers_per_block'''] + 1	):
  										_UpperCamelCase       : Any    =      {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
  										_UpperCamelCase       : Any    =      renew_attention_paths(attns[j]	)
  										assign_to_checkpoint(UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,additional_replacements=[replace_indices]	)
  
  		_UpperCamelCase       : Tuple    =      {k.replace('''mid_new_2'''			,'''mid_block'''	): v for k, v in new_checkpoint.items()}
  		_UpperCamelCase       : int    =      checkpoint['''quant_conv.weight''']
  		_UpperCamelCase       : List[Any]    =      checkpoint['''quant_conv.bias''']
  		if "quantize.embedding.weight" in checkpoint:
  				_UpperCamelCase       : Optional[Any]    =      checkpoint['''quantize.embedding.weight''']
  		_UpperCamelCase       : Optional[Any]    =      checkpoint['''post_quant_conv.weight''']
  		_UpperCamelCase       : str    =      checkpoint['''post_quant_conv.bias''']
  
  		return new_checkpoint
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      List[str] 						=  argparse.ArgumentParser()
  
  	parser.add_argument(
  	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  	)
  
  	parser.add_argument(
  	    """--config_file""",
  	    default=None,
  	    type=str,
  	    required=True,
  	    help="""The config json file corresponding to the architecture.""",
  	)
  
  	parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  
  	_UpperCAmelCase   :      Optional[Any] 						=  parser.parse_args()
  	_UpperCAmelCase   :      Optional[int] 						=  torch.load(args.checkpoint_path)
  
  	with open(args.config_file) as f:
  		_UpperCAmelCase   :      Union[str, Any] 						=  json.loads(f.read())
  
  	# unet case
  	_UpperCAmelCase   :      Optional[int] 						=  {key.split(""".""")[0] for key in checkpoint.keys()}
  	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  		_UpperCAmelCase   :      Union[str, Any] 						=  convert_vq_autoenc_checkpoint(checkpoint, config)
  	else:
  		_UpperCAmelCase   :      Tuple 						=  convert_ddpm_checkpoint(checkpoint, config)
  
  	if "ddpm" in config:
  		del config["ddpm"]
  
  	if config["_class_name"] == "VQModel":
  		_UpperCAmelCase   :      int 						=  VQModel(**config)
  		model.load_state_dict(converted_checkpoint)
  		model.save_pretrained(args.dump_path)
  	elif config["_class_name"] == "AutoencoderKL":
  		_UpperCAmelCase   :      List[Any] 						=  AutoencoderKL(**config)
  		model.load_state_dict(converted_checkpoint)
  		model.save_pretrained(args.dump_path)
  	else:
  		_UpperCAmelCase   :      Tuple 						=  UNetaDModel(**config)
  		model.load_state_dict(converted_checkpoint)
  
  		_UpperCAmelCase   :      Union[str, Any] 						=  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
  		_UpperCAmelCase   :      Dict 						=  DDPMPipeline(unet=model, scheduler=scheduler)
  		pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _UpperCAmelCase   :      int 						=  {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      int 						=  [
  	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPTSanJapaneseForConditionalGeneration""",
  	    """GPTSanJapaneseModel""",
  	    """GPTSanJapanesePreTrainedModel""",
  	]
  	_UpperCAmelCase   :      str 						=  [
  	    """GPTSanJapaneseTokenizer""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptsan_japanese import (
  		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTSanJapaneseForConditionalGeneration,
  		    GPTSanJapaneseModel,
  		    GPTSanJapanesePreTrainedModel,
  		)
  		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  