



"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1	)						->				List[str]:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
  else:
    return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=0	)						->				Tuple:
  _snake_case   	=      []
  for old_item in old_list:
    _snake_case   	=      old_item.replace('''in_layers.0'''  ,						'''norm1'''	)
    _snake_case   	=      new_item.replace('''in_layers.2'''  ,						'''conv1'''	)

    _snake_case   	=      new_item.replace('''out_layers.0'''  ,						'''norm2'''	)
    _snake_case   	=      new_item.replace('''out_layers.3'''  ,						'''conv2'''	)

    _snake_case   	=      new_item.replace('''emb_layers.1'''  ,						'''time_emb_proj'''	)
    _snake_case   	=      new_item.replace('''skip_connection'''  ,						'''conv_shortcut'''	)

    _snake_case   	=      shave_segments(lowerCAmelCase_  ,						n_shave_prefix_segments=lowerCAmelCase_	)

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

  return mapping




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=0	)						->				Dict:
  _snake_case   	=      []
  for old_item in old_list:
    _snake_case   	=      old_item

    _snake_case   	=      new_item.replace('''norm.weight'''  ,						'''group_norm.weight'''	)
    _snake_case   	=      new_item.replace('''norm.bias'''  ,						'''group_norm.bias'''	)

    _snake_case   	=      new_item.replace('''proj_out.weight'''  ,						'''proj_attn.weight'''	)
    _snake_case   	=      new_item.replace('''proj_out.bias'''  ,						'''proj_attn.bias'''	)

    _snake_case   	=      shave_segments(lowerCAmelCase_  ,						n_shave_prefix_segments=lowerCAmelCase_	)

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

  return mapping




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[Any]:
  assert isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _snake_case   	=      old_checkpoint[path]
      _snake_case   	=      old_tensor.shape[0] // 3

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

      _snake_case   	=      old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

      _snake_case   	=      query.reshape(lowerCAmelCase_	)
      _snake_case   	=      key.reshape(lowerCAmelCase_	)
      _snake_case   	=      value.reshape(lowerCAmelCase_	)

  for path in paths:
    _snake_case   	=      path['''new''']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    _snake_case   	=      new_path.replace('''middle_block.0'''  ,						'''mid_block.resnets.0'''	)
    _snake_case   	=      new_path.replace('''middle_block.1'''  ,						'''mid_block.attentions.0'''	)
    _snake_case   	=      new_path.replace('''middle_block.2'''  ,						'''mid_block.resnets.1'''	)

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

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      _snake_case   	=      old_checkpoint[path['''old''']][:, :, 0]
    else:
      _snake_case   	=      old_checkpoint[path['''old''']]




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Dict:
  _snake_case   	=      {}

  _snake_case   	=      checkpoint['''time_embed.0.weight''']
  _snake_case   	=      checkpoint['''time_embed.0.bias''']
  _snake_case   	=      checkpoint['''time_embed.2.weight''']
  _snake_case   	=      checkpoint['''time_embed.2.bias''']

  _snake_case   	=      checkpoint['''input_blocks.0.0.weight''']
  _snake_case   	=      checkpoint['''input_blocks.0.0.bias''']

  _snake_case   	=      checkpoint['''out.0.weight''']
  _snake_case   	=      checkpoint['''out.0.bias''']
  _snake_case   	=      checkpoint['''out.2.weight''']
  _snake_case   	=      checkpoint['''out.2.bias''']

  # Retrieves the keys for the input blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''input_blocks''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  # Retrieves the keys for the middle blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''middle_block''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  # Retrieves the keys for the output blocks only
  _snake_case   	=      len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''output_blocks''' in layer}	)
  _snake_case   	=      {
      layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase_	)
  }

  for i in range(1  ,						lowerCAmelCase_	):
    _snake_case   	=      (i - 1) // (config['''num_res_blocks'''] + 1)
    _snake_case   	=      (i - 1) % (config['''num_res_blocks'''] + 1)

    _snake_case   	=      [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
    _snake_case   	=      [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

    if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
      _snake_case   	=      checkpoint[
          f"""input_blocks.{i}.0.op.weight"""
      ]
      _snake_case   	=      checkpoint[
          f"""input_blocks.{i}.0.op.bias"""
      ]
      continue

    _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
    _snake_case   	=      {'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    _snake_case   	=      {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
    assign_to_checkpoint(
        lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path, resnet_op]  ,						config=lowerCAmelCase_	)

    if len(lowerCAmelCase_	):
      _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
      _snake_case   	=      {
          '''old''': f"""input_blocks.{i}.1""",
          '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
      }
      _snake_case   	=      {
          f"""input_blocks.{i}.1.qkv.bias""": {
              '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
              '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
              '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
          },
          f"""input_blocks.{i}.1.qkv.weight""": {
              '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
              '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
              '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
          },
      }
      assign_to_checkpoint(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						attention_paths_to_split=lowerCAmelCase_  ,						config=lowerCAmelCase_  ,						)

  _snake_case   	=      middle_blocks[0]
  _snake_case   	=      middle_blocks[1]
  _snake_case   	=      middle_blocks[2]

  _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
  assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
  assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
  _snake_case   	=      {
      '''middle_block.1.qkv.bias''': {
          '''key''': '''mid_block.attentions.0.key.bias''',
          '''query''': '''mid_block.attentions.0.query.bias''',
          '''value''': '''mid_block.attentions.0.value.bias''',
      },
      '''middle_block.1.qkv.weight''': {
          '''key''': '''mid_block.attentions.0.key.weight''',
          '''query''': '''mid_block.attentions.0.query.weight''',
          '''value''': '''mid_block.attentions.0.value.weight''',
      },
  }
  assign_to_checkpoint(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						attention_paths_to_split=lowerCAmelCase_  ,						config=lowerCAmelCase_	)

  for i in range(lowerCAmelCase_	):
    _snake_case   	=      i // (config['''num_res_blocks'''] + 1)
    _snake_case   	=      i % (config['''num_res_blocks'''] + 1)
    _snake_case   	=      [shave_segments(lowerCAmelCase_  ,						2	) for name in output_blocks[i]]
    _snake_case   	=      {}

    for layer in output_block_layers:
      _snake_case	,     _snake_case   	=      layer.split('''.'''	)[0], shave_segments(lowerCAmelCase_  ,						1	)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase_	)
      else:
        _snake_case   	=      [layer_name]

    if len(lowerCAmelCase_	) > 1:
      _snake_case   	=      [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      _snake_case   	=      [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)
      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_	)

      _snake_case   	=      {'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						config=lowerCAmelCase_	)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _snake_case   	=      list(output_block_list.values()	).index(['''conv.weight''', '''conv.bias''']	)
        _snake_case   	=      checkpoint[
            f"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case   	=      checkpoint[
            f"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase_	) == 2:
          _snake_case   	=      []

      if len(lowerCAmelCase_	):
        _snake_case   	=      renew_attention_paths(lowerCAmelCase_	)
        _snake_case   	=      {
            '''old''': f"""output_blocks.{i}.1""",
            '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        _snake_case   	=      {
            f"""output_blocks.{i}.1.qkv.bias""": {
                '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
            },
            f"""output_blocks.{i}.1.qkv.weight""": {
                '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
            },
        }
        assign_to_checkpoint(
            lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						additional_replacements=[meta_path]  ,						attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions	) else None  ,						config=lowerCAmelCase_  ,						)
    else:
      _snake_case   	=      renew_resnet_paths(lowerCAmelCase_  ,						n_shave_prefix_segments=1	)
      for path in resnet_0_paths:
        _snake_case   	=      '''.'''.join(['''output_blocks''', str(lowerCAmelCase_	), path['''old''']]	)
        _snake_case   	=      '''.'''.join(['''up_blocks''', str(lowerCAmelCase_	), '''resnets''', str(lowerCAmelCase_	), path['''new''']]	)

        _snake_case   	=      checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     snake_case   					=				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   					=				parser.parse_args()

     snake_case   					=				torch.load(args.checkpoint_path)

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

     snake_case   					=				convert_ldm_checkpoint(checkpoint, config)

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

     snake_case   					=				UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          snake_case   					=				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
          snake_case   					=				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

          snake_case   					=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)










"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     UpperCAmelCase    (	metaclass=__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[int]           =  ['''torch''', '''scipy''']



   def __init__(       self    :	Any							,       *__lowerCamelCase    :	List[Any]							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     requires_backends(self							,       ['''torch''', '''scipy''']				)



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Dict							,       *__lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





   @classmethod
   def    __UpperCAmelCase    (       cls    :	int							,       *__lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     requires_backends(cls							,       ['''torch''', '''scipy''']				)





