

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def      UpperCAmelCase__							(     _A :					List[Any]				):





  '''simple docstring'''







  if "model" in orig_key:
    a__    =orig_key.replace('''model.'''      , ''''''				)
  if "norm1" in orig_key:
    a__    =orig_key.replace('''norm1'''      , '''attention.output.LayerNorm'''				)
  if "norm2" in orig_key:
    a__    =orig_key.replace('''norm2'''      , '''output.LayerNorm'''				)
  if "norm" in orig_key:
    a__    =orig_key.replace('''norm'''      , '''LayerNorm'''				)
  if "transformer" in orig_key:
    a__    =orig_key.split('''.'''				)[0].split('''_'''				)[-1]
    a__    =orig_key.replace(F"""transformer_{layer_num}"""      , F"""encoder.layer.{layer_num}"""				)
  if "mha.attn" in orig_key:
    a__    =orig_key.replace('''mha.attn'''      , '''attention.self'''				)
  if "mha" in orig_key:
    a__    =orig_key.replace('''mha'''      , '''attention'''				)
  if "W_q" in orig_key:
    a__    =orig_key.replace('''W_q'''      , '''self.query'''				)
  if "W_k" in orig_key:
    a__    =orig_key.replace('''W_k'''      , '''self.key'''				)
  if "W_v" in orig_key:
    a__    =orig_key.replace('''W_v'''      , '''self.value'''				)
  if "ff.0" in orig_key:
    a__    =orig_key.replace('''ff.0'''      , '''intermediate.dense'''				)
  if "ff.2" in orig_key:
    a__    =orig_key.replace('''ff.2'''      , '''output.dense'''				)
  if "ff" in orig_key:
    a__    =orig_key.replace('''ff'''      , '''output.dense'''				)
  if "mlm_class" in orig_key:
    a__    =orig_key.replace('''mlm.mlm_class'''      , '''cls.predictions.decoder'''				)
  if "mlm" in orig_key:
    a__    =orig_key.replace('''mlm'''      , '''cls.predictions.transform'''				)
  if "backbone.backbone.encoders" in orig_key:
    a__    =orig_key.replace('''backbone.backbone.encoders'''      , '''encoder.layer'''				)
  if "cls" not in orig_key:
    a__    ='mra.' + orig_key

  return orig_key




def      UpperCAmelCase__							(     _A :					Any      , _A :					int				):





  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a__    =orig_state_dict.pop(_A				)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      a__    =val

  a__    =orig_state_dict['cls.predictions.decoder.bias']
  a__    =torch.arange(_A				).expand((1, -1)				) + 2

  return orig_state_dict




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Optional[int]      , _A :					int				):





  '''simple docstring'''







  a__    =torch.load(_A      , map_location='''cpu'''				)['model_state_dict']
  a__    =MraConfig.from_json_file(_A				)
  a__    =MraForMaskedLM(_A				)

  a__    =convert_checkpoint_helper(config.max_position_embeddings      , _A				)

  print(model.load_state_dict(_A				)				)
  model.eval()
  model.save_pretrained(_A				)

  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""				)


if __name__ == "__main__":
       lowerCamelCase 	=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The json file for Mra model config.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       lowerCamelCase 	=   parser.parse_args()
       convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



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)





