'''simple docstring'''


import argparse
import json

import torch

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


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				str=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       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				List[Any]=0  ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = []
     for old_item in old_list:
          UpperCAmelCase_				:  str  = old_item
          UpperCAmelCase_				:  str  = new_item.replace('block.'      ,	'resnets.'  )
          UpperCAmelCase_				:  List[Any]  = new_item.replace('conv_shorcut'      ,	'conv1'  )
          UpperCAmelCase_				:  Any  = new_item.replace('in_shortcut'      ,	'conv_shortcut'  )
          UpperCAmelCase_				:  Any  = new_item.replace('temb_proj'      ,	'time_emb_proj'  )

          UpperCAmelCase_				:  List[str]  = shave_segments(_UpperCamelCase      ,	n_shave_prefix_segments=_UpperCamelCase  )

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

     return mapping


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Optional[Any]=0      ,	lowerCamelCase_   :				str=False  ):



     """simple docstring"""





     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_				:  List[Any]  = new_item.replace('attn'      ,	'attentions'  )
          UpperCAmelCase_				:  Any  = new_item.replace('.k.'      ,	'.key.'  )
          UpperCAmelCase_				:  Optional[int]  = new_item.replace('.v.'      ,	'.value.'  )
          UpperCAmelCase_				:  Union[str, Any]  = new_item.replace('.q.'      ,	'.query.'  )

          UpperCAmelCase_				:  Union[str, Any]  = new_item.replace('proj_out'      ,	'proj_attn'  )
          UpperCAmelCase_				:  str  = new_item.replace('norm'      ,	'group_norm'  )

          UpperCAmelCase_				:  Tuple  = shave_segments(_UpperCamelCase      ,	n_shave_prefix_segments=_UpperCamelCase  )
          mapping.append({'old': old_item, 'new': new_item}  )

     return mapping


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Optional[int]      ,	lowerCamelCase_   :				int=None      ,	lowerCamelCase_   :				List[Any]=None      ,	lowerCamelCase_   :				List[Any]=None  ):



     """simple docstring"""





     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_				:  Optional[int]  = old_checkpoint[path]
               UpperCAmelCase_				:  List[Any]  = old_tensor.shape[0] // 3

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

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

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

               UpperCAmelCase_				:  Union[str, Any]  = query.reshape(_UpperCamelCase  ).squeeze()
               UpperCAmelCase_				:  Union[str, Any]  = key.reshape(_UpperCamelCase  ).squeeze()
               UpperCAmelCase_				:  str  = value.reshape(_UpperCamelCase  ).squeeze()

     for path in paths:
          UpperCAmelCase_				:  str  = path['new']

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          UpperCAmelCase_				:  Optional[int]  = new_path.replace('down.'      ,	'down_blocks.'  )
          UpperCAmelCase_				:  Tuple  = new_path.replace('up.'      ,	'up_blocks.'  )

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

          if "attentions" in new_path:
               UpperCAmelCase_				:  Union[str, Any]  = old_checkpoint[path['old']].squeeze()
          else:
               UpperCAmelCase_				:  str  = old_checkpoint[path['old']]


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Any  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = {}

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

     UpperCAmelCase_				:  int  = checkpoint['norm_out.weight']
     UpperCAmelCase_				:  Tuple  = checkpoint['norm_out.bias']

     UpperCAmelCase_				:  Any  = checkpoint['conv_in.weight']
     UpperCAmelCase_				:  Optional[int]  = checkpoint['conv_in.bias']
     UpperCAmelCase_				:  int  = checkpoint['conv_out.weight']
     UpperCAmelCase_				:  Optional[Any]  = checkpoint['conv_out.bias']

     UpperCAmelCase_				:  Any  = len({'.'.join(layer.split('.'  )[:2]  ) 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(_UpperCamelCase  )
     }

     UpperCAmelCase_				:  Union[str, Any]  = len({'.'.join(layer.split('.'  )[:2]  ) 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_				:  List[Any]  = (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_				:  List[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_				:  Dict  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	2  ).split('.'  )[:2]  ) for layer in down_blocks[i] if 'block' in layer}  )
               UpperCAmelCase_				:  Optional[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_				:  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_				:  Tuple  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	2  ).split('.'  )[:2]  ) for layer in down_blocks[i] if 'attn' in layer}  )
               UpperCAmelCase_				:  Optional[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_				:  Optional[int]  = 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_				:  str  = [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_				:  Tuple  = 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_				:  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_				:  Dict  = num_up_blocks - 1 - i

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

          if any('block' in layer for layer in up_blocks[i]  ):
               UpperCAmelCase_				:  List[Any]  = 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_				:  Any  = {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                         UpperCAmelCase_				:  Optional[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[Any]  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	2  ).split('.'  )[:2]  ) for layer in up_blocks[i] if 'attn' in layer}  )
               UpperCAmelCase_				:  List[Any]  = {
                   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_				:  Dict  = {k.replace('mid_new_2'      ,	'mid_block'  ): v for k, v in new_checkpoint.items()}
     return new_checkpoint


def    _lowerCamelCase       ( lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = {}

     UpperCAmelCase_				:  List[str]  = checkpoint['encoder.norm_out.weight']
     UpperCAmelCase_				:  Tuple  = checkpoint['encoder.norm_out.bias']

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

     UpperCAmelCase_				:  str  = checkpoint['decoder.norm_out.weight']
     UpperCAmelCase_				:  int  = checkpoint['decoder.norm_out.bias']

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

     UpperCAmelCase_				:  Any  = len({'.'.join(layer.split('.'  )[:3]  ) for layer in checkpoint if 'down' in layer}  )
     UpperCAmelCase_				:  List[Any]  = {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(_UpperCamelCase  )
     }

     UpperCAmelCase_				:  List[Any]  = len({'.'.join(layer.split('.'  )[:3]  ) for layer in checkpoint if 'up' in layer}  )
     UpperCAmelCase_				:  Optional[Any]  = {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_				:  Tuple  = (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_				:  Union[str, Any]  = checkpoint[
                   F'''encoder.down.{i}.downsample.conv.bias'''
               ]

          if any('block' in layer for layer in down_blocks[i]  ):
               UpperCAmelCase_				:  Dict  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	3  ).split('.'  )[:3]  ) for layer in down_blocks[i] if 'block' in layer}  )
               UpperCAmelCase_				:  Optional[int]  = {
                   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_				:  Tuple  = 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      ,	3  ).split('.'  )[:3]  ) for layer in down_blocks[i] if 'attn' in layer}  )
               UpperCAmelCase_				:  Tuple  = {
                   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_				:  Optional[int]  = renew_attention_paths(attns[j]  )
                         assign_to_checkpoint(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      ,	config=_UpperCamelCase  )

     UpperCAmelCase_				:  int  = [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_				:  Union[str, 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_				:  Optional[int]  = 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_				:  Tuple  = 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_				:  Dict  = num_up_blocks - 1 - i

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

          if any('block' in layer for layer in up_blocks[i]  ):
               UpperCAmelCase_				:  Dict  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	3  ).split('.'  )[:3]  ) for layer in up_blocks[i] if 'block' in layer}  )
               UpperCAmelCase_				:  Optional[int]  = {
                   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_				:  int  = {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                         UpperCAmelCase_				:  List[str]  = 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_				:  Any  = len(
                   {'.'.join(shave_segments(_UpperCamelCase      ,	3  ).split('.'  )[:3]  ) for layer in up_blocks[i] if 'attn' in layer}  )
               UpperCAmelCase_				:  Optional[Any]  = {
                   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_				:  Tuple  = {'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
                         UpperCAmelCase_				:  List[Any]  = renew_attention_paths(attns[j]  )
                         assign_to_checkpoint(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      ,	additional_replacements=[replace_indices]  )

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

     return new_checkpoint


if __name__ == "__main__":
 snake_case__	:     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.''')

 snake_case__	:     Dict      =    parser.parse_args()
 snake_case__	:     Dict      =    torch.load(args.checkpoint_path)

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

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

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

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

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

  snake_case__	:     Any      =    DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)









def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				index
  _snake_case		=				2 * index + 1
  _snake_case		=				2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    _snake_case		=				left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    _snake_case		=				right_index

  if largest != index:
    _snake_case,	_snake_case		=				unsorted[index], unsorted[largest]
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)



def  snake_case_(_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  for i in range(n // 2 - 1					,			-1					,			-1	):
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)
  for i in range(n - 1					,			0					,			-1	):
    _snake_case,	_snake_case		=				unsorted[i], unsorted[0]
    heapify(_UpperCamelCase					,			0					,			_UpperCamelCase	)
  return unsorted


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

