code: 
  
  
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=1    )				->			List[Any]:
       if n_shave_prefix_segments >= 0:
            return ".".join(path.split("."    )[n_shave_prefix_segments:]    )
       else:
            return ".".join(path.split("."    )[:n_shave_prefix_segments]    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=0    )				->			List[Any]:
       UpperCamelCase__    :     Dict        =						[]
       for old_item in old_list:
            UpperCamelCase__    :     Optional[int]        =						old_item
            UpperCamelCase__    :     Dict        =						new_item.replace("block."      ,					"resnets."    )
            UpperCamelCase__    :     Union[str, Any]        =						new_item.replace("conv_shorcut"      ,					"conv1"    )
            UpperCamelCase__    :     List[str]        =						new_item.replace("in_shortcut"      ,					"conv_shortcut"    )
            UpperCamelCase__    :     Dict        =						new_item.replace("temb_proj"      ,					"time_emb_proj"    )
  
            UpperCamelCase__    :     Any        =						shave_segments(__lowerCAmelCase      ,					n_shave_prefix_segments=__lowerCAmelCase    )
  
            mapping.append({"old": old_item, "new": new_item}    )
  
       return mapping
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase=0      ,					__lowerCAmelCase=False    )				->			Dict:
       UpperCamelCase__    :     Optional[int]        =						[]
       for old_item in old_list:
            UpperCamelCase__    :     List[Any]        =						old_item
  
            # In `model.mid`, the layer is called `attn`.
            if not in_mid:
                 UpperCamelCase__    :     Dict        =						new_item.replace("attn"      ,					"attentions"    )
            UpperCamelCase__    :     List[Any]        =						new_item.replace(".k."      ,					".key."    )
            UpperCamelCase__    :     Union[str, Any]        =						new_item.replace(".v."      ,					".value."    )
            UpperCamelCase__    :     Dict        =						new_item.replace(".q."      ,					".query."    )
  
            UpperCamelCase__    :     Tuple        =						new_item.replace("proj_out"      ,					"proj_attn"    )
            UpperCamelCase__    :     List[Any]        =						new_item.replace("norm"      ,					"group_norm"    )
  
            UpperCamelCase__    :     int        =						shave_segments(__lowerCAmelCase      ,					n_shave_prefix_segments=__lowerCAmelCase    )
            mapping.append({"old": old_item, "new": new_item}    )
  
       return mapping
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None    )				->			Dict:
       assert isinstance(__lowerCAmelCase      ,					__lowerCAmelCase    ), "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__    :     Optional[int]        =						old_checkpoint[path]
                 UpperCamelCase__    :     Optional[Any]        =						old_tensor.shape[0] // 3
  
                 UpperCamelCase__    :     Tuple        =						(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)
  
                 UpperCamelCase__    :     Any        =						old_tensor.shape[0] // config.get("num_head_channels"      ,					1    ) // 3
  
                 UpperCamelCase__    :     List[str]        =						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
                 UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__    :     Union[str, Any]        =						old_tensor.split(channels // num_heads      ,					dim=1    )
  
                 UpperCamelCase__    :     int        =						query.reshape(__lowerCAmelCase    ).squeeze()
                 UpperCamelCase__    :     Dict        =						key.reshape(__lowerCAmelCase    ).squeeze()
                 UpperCamelCase__    :     Dict        =						value.reshape(__lowerCAmelCase    ).squeeze()
  
       for path in paths:
            UpperCamelCase__    :     Dict        =						path["new"]
  
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                 continue
  
            UpperCamelCase__    :     Optional[Any]        =						new_path.replace("down."      ,					"down_blocks."    )
            UpperCamelCase__    :     List[str]        =						new_path.replace("up."      ,					"up_blocks."    )
  
            if additional_replacements is not None:
                 for replacement in additional_replacements:
                      UpperCamelCase__    :     int        =						new_path.replace(replacement["old"]      ,					replacement["new"]    )
  
            if "attentions" in new_path:
                 UpperCamelCase__    :     Optional[int]        =						old_checkpoint[path["old"]].squeeze()
            else:
                 UpperCamelCase__    :     Optional[int]        =						old_checkpoint[path["old"]]
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[Any]:
       UpperCamelCase__    :     Optional[Any]        =						{}
  
       UpperCamelCase__    :     int        =						checkpoint["temb.dense.0.weight"]
       UpperCamelCase__    :     List[Any]        =						checkpoint["temb.dense.0.bias"]
       UpperCamelCase__    :     str        =						checkpoint["temb.dense.1.weight"]
       UpperCamelCase__    :     Optional[int]        =						checkpoint["temb.dense.1.bias"]
  
       UpperCamelCase__    :     List[Any]        =						checkpoint["norm_out.weight"]
       UpperCamelCase__    :     Optional[int]        =						checkpoint["norm_out.bias"]
  
       UpperCamelCase__    :     str        =						checkpoint["conv_in.weight"]
       UpperCamelCase__    :     List[str]        =						checkpoint["conv_in.bias"]
       UpperCamelCase__    :     Tuple        =						checkpoint["conv_out.weight"]
       UpperCamelCase__    :     Dict        =						checkpoint["conv_out.bias"]
  
       UpperCamelCase__    :     str        =						len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "down" in layer}    )
       UpperCamelCase__    :     Optional[Any]        =						{
           layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(__lowerCAmelCase    )
       }
  
       UpperCamelCase__    :     Dict        =						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(__lowerCAmelCase    )}
  
       for i in range(__lowerCAmelCase    ):
            UpperCamelCase__    :     List[str]        =						(i - 1) // (config["layers_per_block"] + 1)
  
            if any("downsample" in layer for layer in down_blocks[i]    ):
                 UpperCamelCase__    :     List[Any]        =						checkpoint[
                     f'down.{i}.downsample.op.weight'
                 ]
                 UpperCamelCase__    :     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[int]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					2    ).split("."    )[:2]    ) for layer in down_blocks[i] if "block" in layer}    )
                 UpperCamelCase__    :     List[Any]        =						{
                     layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                     for layer_id in range(__lowerCAmelCase    )
                 }
  
                 if num_blocks > 0:
                      for j in range(config["layers_per_block"]    ):
                           UpperCamelCase__    :     int        =						renew_resnet_paths(blocks[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
  
            if any("attn" in layer for layer in down_blocks[i]    ):
                 UpperCamelCase__    :     Any        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					2    ).split("."    )[:2]    ) for layer in down_blocks[i] if "attn" in layer}    )
                 UpperCamelCase__    :     int        =						{
                     layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                     for layer_id in range(__lowerCAmelCase    )
                 }
  
                 if num_attn > 0:
                      for j in range(config["layers_per_block"]    ):
                           UpperCamelCase__    :     Optional[Any]        =						renew_attention_paths(attns[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					config=__lowerCAmelCase    )
  
       UpperCamelCase__    :     List[str]        =						[key for key in checkpoint if "mid.block_1" in key]
       UpperCamelCase__    :     Optional[Any]        =						[key for key in checkpoint if "mid.block_2" in key]
       UpperCamelCase__    :     Tuple        =						[key for key in checkpoint if "mid.attn_1" in key]
  
       # Mid new 2
       UpperCamelCase__    :     int        =						renew_resnet_paths(__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]      ,					)
  
       UpperCamelCase__    :     str        =						renew_resnet_paths(__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]      ,					)
  
       UpperCamelCase__    :     Any        =						renew_attention_paths(__lowerCAmelCase      ,					in_mid=__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]      ,					)
  
       for i in range(__lowerCAmelCase    ):
            UpperCamelCase__    :     int        =						num_up_blocks - 1 - i
  
            if any("upsample" in layer for layer in up_blocks[i]    ):
                 UpperCamelCase__    :     List[Any]        =						checkpoint[
                     f'up.{i}.upsample.conv.weight'
                 ]
                 UpperCamelCase__    :     Optional[int]        =						checkpoint[f'up.{i}.upsample.conv.bias']
  
            if any("block" in layer for layer in up_blocks[i]    ):
                 UpperCamelCase__    :     Union[str, Any]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					2    ).split("."    )[:2]    ) for layer in up_blocks[i] if "block" in layer}    )
                 UpperCamelCase__    :     int        =						{
                     layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(__lowerCAmelCase    )
                 }
  
                 if num_blocks > 0:
                      for j in range(config["layers_per_block"] + 1    ):
                           UpperCamelCase__    :     Any        =						{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                           UpperCamelCase__    :     Optional[Any]        =						renew_resnet_paths(blocks[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[replace_indices]    )
  
            if any("attn" in layer for layer in up_blocks[i]    ):
                 UpperCamelCase__    :     List[str]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					2    ).split("."    )[:2]    ) for layer in up_blocks[i] if "attn" in layer}    )
                 UpperCamelCase__    :     List[str]        =						{
                     layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(__lowerCAmelCase    )
                 }
  
                 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__    :     Optional[Any]        =						renew_attention_paths(attns[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[replace_indices]    )
  
       UpperCamelCase__    :     int        =						{k.replace("mid_new_2"      ,					"mid_block"    ): v for k, v in new_checkpoint.items()}
       return new_checkpoint
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Union[str, Any]:
       UpperCamelCase__    :     Optional[Any]        =						{}
  
       UpperCamelCase__    :     str        =						checkpoint["encoder.norm_out.weight"]
       UpperCamelCase__    :     Union[str, Any]        =						checkpoint["encoder.norm_out.bias"]
  
       UpperCamelCase__    :     str        =						checkpoint["encoder.conv_in.weight"]
       UpperCamelCase__    :     Union[str, Any]        =						checkpoint["encoder.conv_in.bias"]
       UpperCamelCase__    :     Optional[int]        =						checkpoint["encoder.conv_out.weight"]
       UpperCamelCase__    :     int        =						checkpoint["encoder.conv_out.bias"]
  
       UpperCamelCase__    :     Union[str, Any]        =						checkpoint["decoder.norm_out.weight"]
       UpperCamelCase__    :     Optional[int]        =						checkpoint["decoder.norm_out.bias"]
  
       UpperCamelCase__    :     Any        =						checkpoint["decoder.conv_in.weight"]
       UpperCamelCase__    :     str        =						checkpoint["decoder.conv_in.bias"]
       UpperCamelCase__    :     int        =						checkpoint["decoder.conv_out.weight"]
       UpperCamelCase__    :     List[str]        =						checkpoint["decoder.conv_out.bias"]
  
       UpperCamelCase__    :     List[str]        =						len({".".join(layer.split("."    )[:3]    ) for layer in checkpoint if "down" in layer}    )
       UpperCamelCase__    :     Any        =						{
           layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(__lowerCAmelCase    )
       }
  
       UpperCamelCase__    :     Union[str, Any]        =						len({".".join(layer.split("."    )[:3]    ) 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(__lowerCAmelCase    )}
  
       for i in range(__lowerCAmelCase    ):
            UpperCamelCase__    :     Any        =						(i - 1) // (config["layers_per_block"] + 1)
  
            if any("downsample" in layer for layer in down_blocks[i]    ):
                 UpperCamelCase__    :     str        =						checkpoint[
                     f'encoder.down.{i}.downsample.conv.weight'
                 ]
                 UpperCamelCase__    :     int        =						checkpoint[
                     f'encoder.down.{i}.downsample.conv.bias'
                 ]
  
            if any("block" in layer for layer in down_blocks[i]    ):
                 UpperCamelCase__    :     Optional[int]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					3    ).split("."    )[:3]    ) 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(__lowerCAmelCase    )
                 }
  
                 if num_blocks > 0:
                      for j in range(config["layers_per_block"]    ):
                           UpperCamelCase__    :     Tuple        =						renew_resnet_paths(blocks[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
  
            if any("attn" in layer for layer in down_blocks[i]    ):
                 UpperCamelCase__    :     Any        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					3    ).split("."    )[:3]    ) for layer in down_blocks[i] if "attn" in layer}    )
                 UpperCamelCase__    :     int        =						{
                     layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                     for layer_id in range(__lowerCAmelCase    )
                 }
  
                 if num_attn > 0:
                      for j in range(config["layers_per_block"]    ):
                           UpperCamelCase__    :     Any        =						renew_attention_paths(attns[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					config=__lowerCAmelCase    )
  
       UpperCamelCase__    :     List[str]        =						[key for key in checkpoint if "mid.block_1" in key]
       UpperCamelCase__    :     List[Any]        =						[key for key in checkpoint if "mid.block_2" in key]
       UpperCamelCase__    :     List[str]        =						[key for key in checkpoint if "mid.attn_1" in key]
  
       # Mid new 2
       UpperCamelCase__    :     Any        =						renew_resnet_paths(__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]      ,					)
  
       UpperCamelCase__    :     str        =						renew_resnet_paths(__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]      ,					)
  
       UpperCamelCase__    :     List[str]        =						renew_attention_paths(__lowerCAmelCase      ,					in_mid=__lowerCAmelCase    )
       assign_to_checkpoint(
           __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]      ,					)
  
       for i in range(__lowerCAmelCase    ):
            UpperCamelCase__    :     List[str]        =						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__    :     List[Any]        =						checkpoint[
                     f'decoder.up.{i}.upsample.conv.bias'
                 ]
  
            if any("block" in layer for layer in up_blocks[i]    ):
                 UpperCamelCase__    :     Union[str, Any]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					3    ).split("."    )[:3]    ) 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(__lowerCAmelCase    )
                 }
  
                 if num_blocks > 0:
                      for j in range(config["layers_per_block"] + 1    ):
                           UpperCamelCase__    :     Union[str, Any]        =						{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                           UpperCamelCase__    :     List[Any]        =						renew_resnet_paths(blocks[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[replace_indices]    )
  
            if any("attn" in layer for layer in up_blocks[i]    ):
                 UpperCamelCase__    :     List[str]        =						len(
                     {".".join(shave_segments(__lowerCAmelCase      ,					3    ).split("."    )[:3]    ) for layer in up_blocks[i] if "attn" in layer}    )
                 UpperCamelCase__    :     Dict        =						{
                     layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(__lowerCAmelCase    )
                 }
  
                 if num_attn > 0:
                      for j in range(config["layers_per_block"] + 1    ):
                           UpperCamelCase__    :     Dict        =						{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                           UpperCamelCase__    :     List[str]        =						renew_attention_paths(attns[j]    )
                           assign_to_checkpoint(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					additional_replacements=[replace_indices]    )
  
       UpperCamelCase__    :     Union[str, Any]        =						{k.replace("mid_new_2"      ,					"mid_block"    ): v for k, v in new_checkpoint.items()}
       UpperCamelCase__    :     int        =						checkpoint["quant_conv.weight"]
       UpperCamelCase__    :     Any        =						checkpoint["quant_conv.bias"]
       if "quantize.embedding.weight" in checkpoint:
            UpperCamelCase__    :     Dict        =						checkpoint["quantize.embedding.weight"]
       UpperCamelCase__    :     Optional[Any]        =						checkpoint["post_quant_conv.weight"]
       UpperCamelCase__    :     Any        =						checkpoint["post_quant_conv.bias"]
  
       return new_checkpoint
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Optional[Any]       =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.''')
  
   lowerCamelCase			:							List[Any]       =parser.parse_args()
   lowerCamelCase			:							Any       =torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    lowerCamelCase			:							List[Any]       =json.loads(f.read())
  
   # unet case
   lowerCamelCase			:							Optional[Any]       ={key.split('''.''')[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    lowerCamelCase			:							List[str]       =convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    lowerCamelCase			:							Optional[Any]       =convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    lowerCamelCase			:							Dict       =VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    lowerCamelCase			:							List[Any]       =AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    lowerCamelCase			:							int       =UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    lowerCamelCase			:							Union[str, Any]       =DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
    lowerCamelCase			:							Optional[Any]       =DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
style_context: 
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
       return "".join(chr(ord(__lowerCAmelCase    ) + 32    ) if "A" <= char <= "Z" else char for char in word    )
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()