





"""simple docstring"""





import argparse
import json

import torch

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




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							List[Any]=1				):

 '''simple docstring'''






 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('''.'''				)[n_shave_prefix_segments:]				)
 else:
  return ".".join(path.split('''.'''				)[:n_shave_prefix_segments]				)




def 		lowerCAmelCase_     (  lowercase_       :							Dict			,				lowercase_       :							str=0				):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
 for old_item in old_list:
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	old_item
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_item.replace('''block.'''			,				'''resnets.'''				)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	new_item.replace('''conv_shorcut'''			,				'''conv1'''				)
  __SCREAMING_SNAKE_CASE			:      str 							=	new_item.replace('''in_shortcut'''			,				'''conv_shortcut'''				)
  __SCREAMING_SNAKE_CASE			:      int 							=	new_item.replace('''temb_proj'''			,				'''time_emb_proj'''				)

  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	shave_segments(lowercase_			,				n_shave_prefix_segments=lowercase_				)

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

 return mapping




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							List[Any]=0			,				lowercase_       :							Optional[int]=False				):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[]
 for old_item in old_list:
  __SCREAMING_SNAKE_CASE			:      str 							=	old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	new_item.replace('''attn'''			,				'''attentions'''				)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	new_item.replace('''.k.'''			,				'''.key.'''				)
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	new_item.replace('''.v.'''			,				'''.value.'''				)
  __SCREAMING_SNAKE_CASE			:      Any 							=	new_item.replace('''.q.'''			,				'''.query.'''				)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_item.replace('''proj_out'''			,				'''proj_attn'''				)
  __SCREAMING_SNAKE_CASE			:      Any 							=	new_item.replace('''norm'''			,				'''group_norm'''				)

  __SCREAMING_SNAKE_CASE			:      Any 							=	shave_segments(lowercase_			,				n_shave_prefix_segments=lowercase_				)
  mapping.append({'''old''': old_item, '''new''': new_item}				)

 return mapping




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							int			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]=None			,				lowercase_       :							Any=None			,				lowercase_       :							List[Any]=None				):

 '''simple docstring'''






 assert isinstance(lowercase_			,				lowercase_				), "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():
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	old_checkpoint[path]
   __SCREAMING_SNAKE_CASE			:      int 							=	old_tensor.shape[0] // 3

   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

   __SCREAMING_SNAKE_CASE			:      Any 							=	old_tensor.shape[0] // config.get('''num_head_channels'''			,				1				) // 3

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

   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	query.reshape(lowercase_				).squeeze()
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	key.reshape(lowercase_				).squeeze()
   __SCREAMING_SNAKE_CASE			:      Any 							=	value.reshape(lowercase_				).squeeze()

 for path in paths:
  __SCREAMING_SNAKE_CASE			:      int 							=	path['''new''']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  __SCREAMING_SNAKE_CASE			:      List[str] 							=	new_path.replace('''down.'''			,				'''down_blocks.'''				)
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_path.replace('''up.'''			,				'''up_blocks.'''				)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	new_path.replace(replacement['''old''']			,				replacement['''new''']				)

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




def 		lowerCAmelCase_     (  lowercase_       :							int			,				lowercase_       :							Optional[Any]				):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	{}

 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.0.weight''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.0.bias''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.1.weight''']
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''temb.dense.1.bias''']

 __SCREAMING_SNAKE_CASE			:      Dict 							=	checkpoint['''norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	checkpoint['''norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	checkpoint['''conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	checkpoint['''conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	checkpoint['''conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''down''' in layer}				)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase_				)
 }

 __SCREAMING_SNAKE_CASE			:      str 							=	len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''up''' in layer}				)
 __SCREAMING_SNAKE_CASE			:      str 							=	{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase_				)}

 for i in range(lowercase_				):
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(i - 1) // (config['''layers_per_block'''] + 1)

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

   if num_blocks > 0:
    for j in range(config['''layers_per_block''']				):
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_resnet_paths(blocks[j]				)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_				)

  if any('''attn''' in layer for layer in down_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				2				).split('''.'''				)[:2]				) for layer in down_blocks[i] if '''attn''' in layer}				)
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	{
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(lowercase_				)
   }

   if num_attn > 0:
    for j in range(config['''layers_per_block''']				):
     __SCREAMING_SNAKE_CASE			:      str 							=	renew_attention_paths(attns[j]				)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				config=lowercase_				)

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.block_1''' in key]
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	[key for key in checkpoint if '''mid.block_2''' in key]
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.attn_1''' in key]

 # Mid new 2
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_resnet_paths(lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]			,				)

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	renew_resnet_paths(lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,				)

 __SCREAMING_SNAKE_CASE			:      Any 							=	renew_attention_paths(lowercase_			,				in_mid=lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]			,				)

 for i in range(lowercase_				):
  __SCREAMING_SNAKE_CASE			:      str 							=	num_up_blocks - 1 - i

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

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

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

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

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

 __SCREAMING_SNAKE_CASE			:      int 							=	{k.replace('''mid_new_2'''			,				'''mid_block'''				): v for k, v in new_checkpoint.items()}
 return new_checkpoint




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Dict				):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	{}

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''encoder.norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''encoder.norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''encoder.conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''encoder.conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''encoder.conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	checkpoint['''encoder.conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''decoder.norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''decoder.norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	checkpoint['''decoder.conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''decoder.conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''decoder.conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''decoder.conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	len({'''.'''.join(layer.split('''.'''				)[:3]				) for layer in checkpoint if '''down''' in layer}				)
 __SCREAMING_SNAKE_CASE			:      int 							=	{
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase_				)
 }

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	len({'''.'''.join(layer.split('''.'''				)[:3]				) for layer in checkpoint if '''up''' in layer}				)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase_				)}

 for i in range(lowercase_				):
  __SCREAMING_SNAKE_CASE			:      int 							=	(i - 1) // (config['''layers_per_block'''] + 1)

  if any('''downsample''' in layer for layer in down_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	checkpoint[
       F'''encoder.down.{i}.downsample.conv.weight'''
   ]
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	checkpoint[
       F'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any('''block''' in layer for layer in down_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3				).split('''.'''				)[:3]				) for layer in down_blocks[i] if '''block''' in layer}				)
   __SCREAMING_SNAKE_CASE			:      str 							=	{
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(lowercase_				)
   }

   if num_blocks > 0:
    for j in range(config['''layers_per_block''']				):
     __SCREAMING_SNAKE_CASE			:      Any 							=	renew_resnet_paths(blocks[j]				)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_				)

  if any('''attn''' in layer for layer in down_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3				).split('''.'''				)[:3]				) for layer in down_blocks[i] if '''attn''' in layer}				)
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	{
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(lowercase_				)
   }

   if num_attn > 0:
    for j in range(config['''layers_per_block''']				):
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_attention_paths(attns[j]				)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				config=lowercase_				)

 __SCREAMING_SNAKE_CASE			:      Any 							=	[key for key in checkpoint if '''mid.block_1''' in key]
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.block_2''' in key]
 __SCREAMING_SNAKE_CASE			:      Any 							=	[key for key in checkpoint if '''mid.attn_1''' in key]

 # Mid new 2
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	renew_resnet_paths(lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]			,				)

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	renew_resnet_paths(lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,				)

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	renew_attention_paths(lowercase_			,				in_mid=lowercase_				)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]			,				)

 for i in range(lowercase_				):
  __SCREAMING_SNAKE_CASE			:      str 							=	num_up_blocks - 1 - i

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

  if any('''block''' in layer for layer in up_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      Any 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3				).split('''.'''				)[:3]				) for layer in up_blocks[i] if '''block''' in layer}				)
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	{
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase_				)
   }

   if num_blocks > 0:
    for j in range(config['''layers_per_block'''] + 1				):
     __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_resnet_paths(blocks[j]				)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[replace_indices]				)

  if any('''attn''' in layer for layer in up_blocks[i]				):
   __SCREAMING_SNAKE_CASE			:      Any 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3				).split('''.'''				)[:3]				) for layer in up_blocks[i] if '''attn''' in layer}				)
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase_				)
   }

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

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	{k.replace('''mid_new_2'''			,				'''mid_block'''				): v for k, v in new_checkpoint.items()}
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''quant_conv.weight''']
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''quant_conv.bias''']
 if "quantize.embedding.weight" in checkpoint:
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	checkpoint['''quantize.embedding.weight''']
 __SCREAMING_SNAKE_CASE			:      Dict 							=	checkpoint['''post_quant_conv.weight''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''post_quant_conv.bias''']

 return new_checkpoint


if __name__ == "__main__":
    _lowerCamelCase  					=     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  					=     parser.parse_args()
    _lowerCamelCase  					=     torch.load(args.checkpoint_path)

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

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

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

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

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

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








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Any			,				lowercase_       :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	index
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 * index + 1
 __SCREAMING_SNAKE_CASE			:      Any 							=	2 * index + 2
 if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      str 							=	left_index

 if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	right_index

 if largest != index:
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[str] 							=	unsorted[index], unsorted[largest]
  heapify(lowercase_			,				lowercase_			,				lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	len(lowercase_		)
 for i in range(n // 2 - 1			,				-1			,				-1		):
  heapify(lowercase_			,				lowercase_			,				lowercase_		)
 for i in range(n - 1			,				0			,				-1		):
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	unsorted[i], unsorted[0]
  heapify(lowercase_			,				0			,				lowercase_		)
 return unsorted


if __name__ == "__main__":
    _lowerCamelCase  					=     input('''Enter numbers separated by a comma:\n''').strip()
    _lowerCamelCase  					=     [int(item) for item in user_input.split(''',''')]
    print(heap_sort(unsorted))

