
'''simple docstring'''





def 							snake_case				(		UpperCAmelCase = "mytext.txt"   ,	UpperCAmelCase = False						)->      None:







      """simple docstring"""
      import socket

      __A													=      1_2_3_1_2  # Reserve a port for your service.
      __A													=      socket.socket()  # Create a socket object
      __A													=      socket.gethostname()  # Get local machine name
      sock.bind((host, port)						)  # Bind to the port
      sock.listen(5						)  # Now wait for client connection.

      print('Server listening....'						)

      while True:
            __A													=      sock.accept()  # Establish connection with client.
            print(f'Got connection from {addr}'						)
            __A													=      conn.recv(1_0_2_4						)
            print(f'Server received: {data = }'						)

            with open(UpperCAmelCase   ,	'rb'						) as in_file:
                  __A													=      in_file.read(1_0_2_4						)
                  while data:
                        conn.send(UpperCAmelCase						)
                        print(f'Sent {data!r}'						)
                        __A													=      in_file.read(1_0_2_4						)

            print('Done sending'						)
            conn.close()
            if testing:  # Allow the test to complete
                  break

      sock.shutdown(1						)
      sock.close()


if __name__ == "__main__":
 send_file()









import argparse
import json

import torch

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







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=1						)       ->       int:

 """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 _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     str=0						)       ->       Any:

 """simple docstring"""






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

  __lowerCAmelCase:  Any        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)

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

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Dict=0       ,   SCREAMING_SNAKE_CASE						:     str=False						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  str        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[int]        = old_item

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

  __lowerCAmelCase:  List[str]        = new_item.replace('proj_out'       ,   'proj_attn'						)
  __lowerCAmelCase:  Tuple        = new_item.replace('norm'       ,   'group_norm'						)

  __lowerCAmelCase:  int        = shave_segments(SCREAMING_SNAKE_CASE       ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE						)
  mapping.append({'old': old_item, 'new': new_item}						)

 return mapping







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=None						)       ->       Union[str, Any]:

 """simple docstring"""






 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():
   __lowerCAmelCase:  List[Any]        = old_checkpoint[path]
   __lowerCAmelCase:  Tuple        = old_tensor.shape[0] // 3

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

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

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

   __lowerCAmelCase:  Union[str, Any]        = query.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  Dict        = key.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  List[Any]        = value.reshape(SCREAMING_SNAKE_CASE						).squeeze()

 for path in paths:
  __lowerCAmelCase:  Optional[Any]        = path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

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

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = {}

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

 __lowerCAmelCase:  Tuple        = checkpoint['norm_out.weight']
 __lowerCAmelCase:  Dict        = checkpoint['norm_out.bias']

 __lowerCAmelCase:  Dict        = checkpoint['conv_in.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['conv_out.bias']

 __lowerCAmelCase:  List[Any]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  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						)
 }

 __lowerCAmelCase:  Optional[int]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  List[str]        = {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						):
  __lowerCAmelCase:  Any        = (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = checkpoint[
       f'''down.{i}.downsample.op.weight'''
   ]
   __lowerCAmelCase:  Tuple        = 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]						):
   __lowerCAmelCase:  Optional[Any]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Union[str, 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']						):
     __lowerCAmelCase:  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]						):
   __lowerCAmelCase:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Union[str, Any]        = {
       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']						):
     __lowerCAmelCase:  List[Any]        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  Union[str, Any]        = [key for key in checkpoint if 'mid.block_1' in key]
 __lowerCAmelCase:  List[Any]        = [key for key in checkpoint if 'mid.block_2' in key]
 __lowerCAmelCase:  Optional[Any]        = [key for key in checkpoint if 'mid.attn_1' in key]

 # Mid new 2
 __lowerCAmelCase:  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_1', 'new': 'resnets.0'}]       ,   )

 __lowerCAmelCase:  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_2', 'new': 'resnets.1'}]       ,   )

 __lowerCAmelCase:  Tuple        = 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						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

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

  if any('block' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Tuple        = {
       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						):
     __lowerCAmelCase:  List[str]        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  int        = 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]						):
   __lowerCAmelCase:  str        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[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						):
     __lowerCAmelCase:  str        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  Dict        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   additional_replacements=[replace_indices]						)

 __lowerCAmelCase:  Union[str, Any]        = {k.replace('mid_new_2'       ,   'mid_block'						): v for k, v in new_checkpoint.items()}
 return new_checkpoint







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  int        = {}

 __lowerCAmelCase:  List[Any]        = checkpoint['encoder.norm_out.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.norm_out.bias']

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

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

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

 __lowerCAmelCase:  Optional[int]        = len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  int        = {
     layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

 __lowerCAmelCase:  str        = len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
 __lowerCAmelCase:  Optional[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						):
  __lowerCAmelCase:  List[str]        = (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.weight'''
   ]
   __lowerCAmelCase:  Any        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any('block' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  List[str]        = {
       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']						):
     __lowerCAmelCase:  List[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]						):
   __lowerCAmelCase:  List[str]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  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']						):
     __lowerCAmelCase:  int        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

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

 # Mid new 2
 __lowerCAmelCase:  List[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_1', 'new': 'resnets.0'}]       ,   )

 __lowerCAmelCase:  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_2', 'new': 'resnets.1'}]       ,   )

 __lowerCAmelCase:  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						):
  __lowerCAmelCase:  Dict        = num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.weight'''
   ]
   __lowerCAmelCase:  Dict        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.bias'''
   ]

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

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





