



import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Optional[Any]			,    SCREAMING_SNAKE_CASE__    :		List[str]=1			)					->       List[str]:
 if n_shave_prefix_segments >= 0:
  return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
 else:
  return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		List[Any]			,    SCREAMING_SNAKE_CASE__    :		Optional[int]=0			)					->       List[str]:
 _snake_case      :					Union[str, Any]					=   []
 for old_item in old_list:
  _snake_case      :					List[Any]					=   old_item
  _snake_case      :					Tuple					=   new_item.replace("""block."""			,    """resnets."""			)
  _snake_case      :					List[str]					=   new_item.replace("""conv_shorcut"""			,    """conv1"""			)
  _snake_case      :					Any					=   new_item.replace("""in_shortcut"""			,    """conv_shortcut"""			)
  _snake_case      :					Optional[Any]					=   new_item.replace("""temb_proj"""			,    """time_emb_proj"""			)

  _snake_case      :					str					=   shave_segments(SCREAMING_SNAKE_CASE__			,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE__			)

  mapping.append({"""old""": old_item, """new""": new_item}			)

 return mapping



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Optional[Any]			,    SCREAMING_SNAKE_CASE__    :		Union[str, Any]=0			,    SCREAMING_SNAKE_CASE__    :		Any=False			)					->       Optional[int]:
 _snake_case      :					List[Any]					=   []
 for old_item in old_list:
  _snake_case      :					Union[str, Any]					=   old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   _snake_case      :					Tuple					=   new_item.replace("""attn"""			,    """attentions"""			)
  _snake_case      :					int					=   new_item.replace(""".k."""			,    """.key."""			)
  _snake_case      :					int					=   new_item.replace(""".v."""			,    """.value."""			)
  _snake_case      :					Tuple					=   new_item.replace(""".q."""			,    """.query."""			)

  _snake_case      :					Dict					=   new_item.replace("""proj_out"""			,    """proj_attn"""			)
  _snake_case      :					Dict					=   new_item.replace("""norm"""			,    """group_norm"""			)

  _snake_case      :					List[str]					=   shave_segments(SCREAMING_SNAKE_CASE__			,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE__			)
  mapping.append({"""old""": old_item, """new""": new_item}			)

 return mapping



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Union[str, Any]			,    SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		List[str]			,    SCREAMING_SNAKE_CASE__    :		Union[str, Any]=None			,    SCREAMING_SNAKE_CASE__    :		List[Any]=None			,    SCREAMING_SNAKE_CASE__    :		Dict=None			)					->       Dict:
 assert isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			), "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():
   _snake_case      :					Optional[Any]					=   old_checkpoint[path]
   _snake_case      :					Any					=   old_tensor.shape[0] // 3

   _snake_case      :					Any					=   (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

   _snake_case      :					List[str]					=   old_tensor.shape[0] // config.get("""num_head_channels"""			,    1			) // 3

   _snake_case      :					List[Any]					=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
   _snake_case		,      _snake_case		,      _snake_case      :					Union[str, Any]					=   old_tensor.split(channels // num_heads			,    dim=1			)

   _snake_case      :					List[str]					=   query.reshape(SCREAMING_SNAKE_CASE__			).squeeze()
   _snake_case      :					str					=   key.reshape(SCREAMING_SNAKE_CASE__			).squeeze()
   _snake_case      :					Optional[Any]					=   value.reshape(SCREAMING_SNAKE_CASE__			).squeeze()

 for path in paths:
  _snake_case      :					int					=   path["""new"""]

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  _snake_case      :					List[str]					=   new_path.replace("""down."""			,    """down_blocks."""			)
  _snake_case      :					Union[str, Any]					=   new_path.replace("""up."""			,    """up_blocks."""			)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    _snake_case      :					Dict					=   new_path.replace(replacement["""old"""]			,    replacement["""new"""]			)

  if "attentions" in new_path:
   _snake_case      :					Optional[Any]					=   old_checkpoint[path["""old"""]].squeeze()
  else:
   _snake_case      :					Dict					=   old_checkpoint[path["""old"""]]



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Dict			,    SCREAMING_SNAKE_CASE__    :		int			)					->       List[Any]:
 _snake_case      :					Tuple					=   {}

 _snake_case      :					Optional[Any]					=   checkpoint["""temb.dense.0.weight"""]
 _snake_case      :					Dict					=   checkpoint["""temb.dense.0.bias"""]
 _snake_case      :					Union[str, Any]					=   checkpoint["""temb.dense.1.weight"""]
 _snake_case      :					List[str]					=   checkpoint["""temb.dense.1.bias"""]

 _snake_case      :					Tuple					=   checkpoint["""norm_out.weight"""]
 _snake_case      :					Union[str, Any]					=   checkpoint["""norm_out.bias"""]

 _snake_case      :					str					=   checkpoint["""conv_in.weight"""]
 _snake_case      :					Union[str, Any]					=   checkpoint["""conv_in.bias"""]
 _snake_case      :					str					=   checkpoint["""conv_out.weight"""]
 _snake_case      :					Optional[Any]					=   checkpoint["""conv_out.bias"""]

 _snake_case      :					str					=   len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
 _snake_case      :					List[str]					=   {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
 }

 _snake_case      :					List[Any]					=   len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
 _snake_case      :					Any					=   {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)}

 for i in range(SCREAMING_SNAKE_CASE__			):
  _snake_case      :					Dict					=   (i - 1) // (config["""layers_per_block"""] + 1)

  if any("""downsample""" in layer for layer in down_blocks[i]			):
   _snake_case      :					List[str]					=   checkpoint[
       F'''down.{i}.downsample.op.weight'''
   ]
   _snake_case      :					str					=   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]			):
   _snake_case      :					Optional[int]					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
   _snake_case      :					Optional[Any]					=   {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""]			):
     _snake_case      :					Optional[int]					=   renew_resnet_paths(blocks[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

  if any("""attn""" in layer for layer in down_blocks[i]			):
   _snake_case      :					Any					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """attn""" in layer}			)
   _snake_case      :					Optional[int]					=   {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""]			):
     _snake_case      :					Dict					=   renew_attention_paths(attns[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    config=SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Tuple					=   [key for key in checkpoint if """mid.block_1""" in key]
 _snake_case      :					Optional[int]					=   [key for key in checkpoint if """mid.block_2""" in key]
 _snake_case      :					Dict					=   [key for key in checkpoint if """mid.attn_1""" in key]

 # Mid new 2
 _snake_case      :					List[str]					=   renew_resnet_paths(SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]			,    )

 _snake_case      :					Tuple					=   renew_resnet_paths(SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]			,    )

 _snake_case      :					int					=   renew_attention_paths(SCREAMING_SNAKE_CASE__			,    in_mid=SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]			,    )

 for i in range(SCREAMING_SNAKE_CASE__			):
  _snake_case      :					Any					=   num_up_blocks - 1 - i

  if any("""upsample""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Tuple					=   checkpoint[
       F'''up.{i}.upsample.conv.weight'''
   ]
   _snake_case      :					Dict					=   checkpoint[F'''up.{i}.upsample.conv.bias''']

  if any("""block""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Optional[Any]					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """block""" in layer}			)
   _snake_case      :					str					=   {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""] + 1			):
     _snake_case      :					Any					=   {"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
     _snake_case      :					Dict					=   renew_resnet_paths(blocks[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[replace_indices]			)

  if any("""attn""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Union[str, Any]					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    2			).split("""."""			)[:2]			) for layer in up_blocks[i] if """attn""" in layer}			)
   _snake_case      :					str					=   {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""] + 1			):
     _snake_case      :					Tuple					=   {"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
     _snake_case      :					Optional[Any]					=   renew_attention_paths(attns[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[replace_indices]			)

 _snake_case      :					Tuple					=   {k.replace("""mid_new_2"""			,    """mid_block"""			): v for k, v in new_checkpoint.items()}
 return new_checkpoint



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Optional[Any]			,    SCREAMING_SNAKE_CASE__    :		Tuple			)					->       Tuple:
 _snake_case      :					Optional[int]					=   {}

 _snake_case      :					str					=   checkpoint["""encoder.norm_out.weight"""]
 _snake_case      :					List[str]					=   checkpoint["""encoder.norm_out.bias"""]

 _snake_case      :					List[str]					=   checkpoint["""encoder.conv_in.weight"""]
 _snake_case      :					List[str]					=   checkpoint["""encoder.conv_in.bias"""]
 _snake_case      :					Any					=   checkpoint["""encoder.conv_out.weight"""]
 _snake_case      :					Union[str, Any]					=   checkpoint["""encoder.conv_out.bias"""]

 _snake_case      :					str					=   checkpoint["""decoder.norm_out.weight"""]
 _snake_case      :					Dict					=   checkpoint["""decoder.norm_out.bias"""]

 _snake_case      :					Optional[int]					=   checkpoint["""decoder.conv_in.weight"""]
 _snake_case      :					Tuple					=   checkpoint["""decoder.conv_in.bias"""]
 _snake_case      :					Dict					=   checkpoint["""decoder.conv_out.weight"""]
 _snake_case      :					str					=   checkpoint["""decoder.conv_out.bias"""]

 _snake_case      :					Optional[Any]					=   len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
 _snake_case      :					Union[str, Any]					=   {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
 }

 _snake_case      :					List[str]					=   len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
 _snake_case      :					int					=   {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)}

 for i in range(SCREAMING_SNAKE_CASE__			):
  _snake_case      :					Optional[Any]					=   (i - 1) // (config["""layers_per_block"""] + 1)

  if any("""downsample""" in layer for layer in down_blocks[i]			):
   _snake_case      :					Optional[Any]					=   checkpoint[
       F'''encoder.down.{i}.downsample.conv.weight'''
   ]
   _snake_case      :					Dict					=   checkpoint[
       F'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any("""block""" in layer for layer in down_blocks[i]			):
   _snake_case      :					Tuple					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
   _snake_case      :					Any					=   {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""]			):
     _snake_case      :					Any					=   renew_resnet_paths(blocks[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

  if any("""attn""" in layer for layer in down_blocks[i]			):
   _snake_case      :					List[Any]					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
   _snake_case      :					int					=   {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""]			):
     _snake_case      :					Tuple					=   renew_attention_paths(attns[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    config=SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Optional[int]					=   [key for key in checkpoint if """mid.block_1""" in key]
 _snake_case      :					Any					=   [key for key in checkpoint if """mid.block_2""" in key]
 _snake_case      :					Optional[Any]					=   [key for key in checkpoint if """mid.attn_1""" in key]

 # Mid new 2
 _snake_case      :					Optional[int]					=   renew_resnet_paths(SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]			,    )

 _snake_case      :					Union[str, Any]					=   renew_resnet_paths(SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]			,    )

 _snake_case      :					Optional[Any]					=   renew_attention_paths(SCREAMING_SNAKE_CASE__			,    in_mid=SCREAMING_SNAKE_CASE__			)
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]			,    )

 for i in range(SCREAMING_SNAKE_CASE__			):
  _snake_case      :					Tuple					=   num_up_blocks - 1 - i

  if any("""upsample""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Any					=   checkpoint[
       F'''decoder.up.{i}.upsample.conv.weight'''
   ]
   _snake_case      :					int					=   checkpoint[
       F'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any("""block""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Optional[Any]					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
   _snake_case      :					Any					=   {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""] + 1			):
     _snake_case      :					Optional[int]					=   {"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
     _snake_case      :					List[Any]					=   renew_resnet_paths(blocks[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[replace_indices]			)

  if any("""attn""" in layer for layer in up_blocks[i]			):
   _snake_case      :					Dict					=   len(
       {""".""".join(shave_segments(SCREAMING_SNAKE_CASE__			,    3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
   _snake_case      :					List[Any]					=   {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE__			)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""] + 1			):
     _snake_case      :					List[str]					=   {"""old""": F'''up_blocks.{i}''', """new""": F'''up_blocks.{block_id}'''}
     _snake_case      :					Optional[Any]					=   renew_attention_paths(attns[j]			)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    additional_replacements=[replace_indices]			)

 _snake_case      :					List[str]					=   {k.replace("""mid_new_2"""			,    """mid_block"""			): v for k, v in new_checkpoint.items()}
 _snake_case      :					List[Any]					=   checkpoint["""quant_conv.weight"""]
 _snake_case      :					Optional[Any]					=   checkpoint["""quant_conv.bias"""]
 if "quantize.embedding.weight" in checkpoint:
  _snake_case      :					Any					=   checkpoint["""quantize.embedding.weight"""]
 _snake_case      :					Optional[Any]					=   checkpoint["""post_quant_conv.weight"""]
 _snake_case      :					int					=   checkpoint["""post_quant_conv.bias"""]

 return new_checkpoint


if __name__ == "__main__":
  a__      =		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.""")

  a__      =		parser.parse_args()
  a__      =		torch.load(args.checkpoint_path)

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

  # unet case
  a__      =		{key.split(""".""")[0] for key in checkpoint.keys()}
  if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    a__      =		convert_vq_autoenc_checkpoint(checkpoint, config)
  else:
    a__      =		convert_ddpm_checkpoint(checkpoint, config)

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

  if config["_class_name"] == "VQModel":
    a__      =		VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  elif config["_class_name"] == "AutoencoderKL":
    a__      =		AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
  else:
    a__      =		UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    a__      =		DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    a__      =		DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
