

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      Any=1      )  ->						Tuple:
  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__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      List[Any]=0      )  ->						int:
  UpperCAmelCase	:							Dict						=			[]
  for old_item in old_list:
    UpperCAmelCase	:							Any						=			old_item.replace('''in_layers.0'''    ,			'''norm1'''      )
    UpperCAmelCase	:							List[str]						=			new_item.replace('''in_layers.2'''    ,			'''conv1'''      )

    UpperCAmelCase	:							int						=			new_item.replace('''out_layers.0'''    ,			'''norm2'''      )
    UpperCAmelCase	:							Union[str, Any]						=			new_item.replace('''out_layers.3'''    ,			'''conv2'''      )

    UpperCAmelCase	:							Dict						=			new_item.replace('''emb_layers.1'''    ,			'''time_emb_proj'''      )
    UpperCAmelCase	:							Optional[Any]						=			new_item.replace('''skip_connection'''    ,			'''conv_shortcut'''      )

    UpperCAmelCase	:							Dict						=			shave_segments(UpperCamelCase__    ,			n_shave_prefix_segments=UpperCamelCase__      )

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

  return mapping

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Union[str, Any]=0      )  ->						Optional[int]:
  UpperCAmelCase	:							Union[str, Any]						=			[]
  for old_item in old_list:
    UpperCAmelCase	:							str						=			old_item

    UpperCAmelCase	:							Dict						=			new_item.replace('''norm.weight'''    ,			'''group_norm.weight'''      )
    UpperCAmelCase	:							List[Any]						=			new_item.replace('''norm.bias'''    ,			'''group_norm.bias'''      )

    UpperCAmelCase	:							List[Any]						=			new_item.replace('''proj_out.weight'''    ,			'''proj_attn.weight'''      )
    UpperCAmelCase	:							Tuple						=			new_item.replace('''proj_out.bias'''    ,			'''proj_attn.bias'''      )

    UpperCAmelCase	:							Optional[int]						=			shave_segments(UpperCamelCase__    ,			n_shave_prefix_segments=UpperCamelCase__      )

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

  return mapping

def       a__					(		UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      List[Any]=None    ,			UpperCAmelCase							:      List[str]=None    ,			UpperCAmelCase							:      List[Any]=None      )  ->						Optional[Any]:
  assert isinstance(UpperCamelCase__    ,			UpperCamelCase__      ), "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():
      UpperCAmelCase	:							List[Any]						=			old_checkpoint[path]
      UpperCAmelCase	:							Optional[int]						=			old_tensor.shape[0] // 3

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

      UpperCAmelCase	:							int						=			old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

      UpperCAmelCase	:							Any						=			query.reshape(UpperCamelCase__      )
      UpperCAmelCase	:							Any						=			key.reshape(UpperCamelCase__      )
      UpperCAmelCase	:							Optional[int]						=			value.reshape(UpperCamelCase__      )

  for path in paths:
    UpperCAmelCase	:							Union[str, Any]						=			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
    UpperCAmelCase	:							List[Any]						=			new_path.replace('''middle_block.0'''    ,			'''mid_block.resnets.0'''      )
    UpperCAmelCase	:							List[Any]						=			new_path.replace('''middle_block.1'''    ,			'''mid_block.attentions.0'''      )
    UpperCAmelCase	:							Tuple						=			new_path.replace('''middle_block.2'''    ,			'''mid_block.resnets.1'''      )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        UpperCAmelCase	:							Dict						=			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:
      UpperCAmelCase	:							int						=			old_checkpoint[path['''old''']][:, :, 0]
    else:
      UpperCAmelCase	:							Any						=			old_checkpoint[path['''old''']]

def       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      Tuple      )  ->						List[str]:
  UpperCAmelCase	:							List[str]						=			{}

  UpperCAmelCase	:							Optional[int]						=			checkpoint['''time_embed.0.weight''']
  UpperCAmelCase	:							Dict						=			checkpoint['''time_embed.0.bias''']
  UpperCAmelCase	:							List[Any]						=			checkpoint['''time_embed.2.weight''']
  UpperCAmelCase	:							int						=			checkpoint['''time_embed.2.bias''']

  UpperCAmelCase	:							Optional[Any]						=			checkpoint['''input_blocks.0.0.weight''']
  UpperCAmelCase	:							Union[str, Any]						=			checkpoint['''input_blocks.0.0.bias''']

  UpperCAmelCase	:							Tuple						=			checkpoint['''out.0.weight''']
  UpperCAmelCase	:							Tuple						=			checkpoint['''out.0.bias''']
  UpperCAmelCase	:							Union[str, Any]						=			checkpoint['''out.2.weight''']
  UpperCAmelCase	:							Union[str, Any]						=			checkpoint['''out.2.bias''']

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

  # Retrieves the keys for the middle blocks only
  UpperCAmelCase	:							Optional[Any]						=			len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in checkpoint if '''middle_block''' in layer}      )
  UpperCAmelCase	:							int						=			{
      layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
      for layer_id in range(UpperCamelCase__      )
  }

  # Retrieves the keys for the output blocks only
  UpperCAmelCase	:							Optional[int]						=			len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in checkpoint if '''output_blocks''' in layer}      )
  UpperCAmelCase	:							List[str]						=			{
      layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
      for layer_id in range(UpperCamelCase__      )
  }

  for i in range(1    ,			UpperCamelCase__      ):
    UpperCAmelCase	:							Tuple						=			(i - 1) // (config['''num_res_blocks'''] + 1)
    UpperCAmelCase	:							Any						=			(i - 1) % (config['''num_res_blocks'''] + 1)

    UpperCAmelCase	:							Dict						=			[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
    UpperCAmelCase	:							str						=			[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:
      UpperCAmelCase	:							Optional[Any]						=			checkpoint[
          f'''input_blocks.{i}.0.op.weight'''
      ]
      UpperCAmelCase	:							Any						=			checkpoint[
          f'''input_blocks.{i}.0.op.bias'''
      ]
      continue

    UpperCAmelCase	:							List[str]						=			renew_resnet_paths(UpperCamelCase__      )
    UpperCAmelCase	:							Any						=			{'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    UpperCAmelCase	:							Optional[Any]						=			{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
    assign_to_checkpoint(
        UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			additional_replacements=[meta_path, resnet_op]    ,			config=UpperCamelCase__      )

    if len(UpperCamelCase__      ):
      UpperCAmelCase	:							Optional[Any]						=			renew_attention_paths(UpperCamelCase__      )
      UpperCAmelCase	:							List[str]						=			{
          '''old''': f'''input_blocks.{i}.1''',
          '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
      }
      UpperCAmelCase	:							Tuple						=			{
          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(
          UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			additional_replacements=[meta_path]    ,			attention_paths_to_split=UpperCamelCase__    ,			config=UpperCamelCase__    ,			)

  UpperCAmelCase	:							Dict						=			middle_blocks[0]
  UpperCAmelCase	:							Dict						=			middle_blocks[1]
  UpperCAmelCase	:							Dict						=			middle_blocks[2]

  UpperCAmelCase	:							Any						=			renew_resnet_paths(UpperCamelCase__      )
  assign_to_checkpoint(UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			config=UpperCamelCase__      )

  UpperCAmelCase	:							Dict						=			renew_resnet_paths(UpperCamelCase__      )
  assign_to_checkpoint(UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			config=UpperCamelCase__      )

  UpperCAmelCase	:							List[str]						=			renew_attention_paths(UpperCamelCase__      )
  UpperCAmelCase	:							List[str]						=			{
      '''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(
      UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			attention_paths_to_split=UpperCamelCase__    ,			config=UpperCamelCase__      )

  for i in range(UpperCamelCase__      ):
    UpperCAmelCase	:							Dict						=			i // (config['''num_res_blocks'''] + 1)
    UpperCAmelCase	:							str						=			i % (config['''num_res_blocks'''] + 1)
    UpperCAmelCase	:							Any						=			[shave_segments(UpperCamelCase__    ,			2      ) for name in output_blocks[i]]
    UpperCAmelCase	:							List[Any]						=			{}

    for layer in output_block_layers:
      UpperCAmelCase	:							Tuple						=			layer.split('''.'''      )[0], shave_segments(UpperCamelCase__    ,			1      )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(UpperCamelCase__      )
      else:
        UpperCAmelCase	:							str						=			[layer_name]

    if len(UpperCamelCase__      ) > 1:
      UpperCAmelCase	:							str						=			[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
      UpperCAmelCase	:							str						=			[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

      UpperCAmelCase	:							Optional[Any]						=			renew_resnet_paths(UpperCamelCase__      )
      UpperCAmelCase	:							int						=			renew_resnet_paths(UpperCamelCase__      )

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

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        UpperCAmelCase	:							Optional[int]						=			list(output_block_list.values()      ).index(['''conv.weight''', '''conv.bias''']      )
        UpperCAmelCase	:							Optional[Any]						=			checkpoint[
            f'''output_blocks.{i}.{index}.conv.weight'''
        ]
        UpperCAmelCase	:							Union[str, Any]						=			checkpoint[
            f'''output_blocks.{i}.{index}.conv.bias'''
        ]

        # Clear attentions as they have been attributed above.
        if len(UpperCamelCase__      ) == 2:
          UpperCAmelCase	:							Optional[Any]						=			[]

      if len(UpperCamelCase__      ):
        UpperCAmelCase	:							str						=			renew_attention_paths(UpperCamelCase__      )
        UpperCAmelCase	:							Optional[Any]						=			{
            '''old''': f'''output_blocks.{i}.1''',
            '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
        }
        UpperCAmelCase	:							str						=			{
            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(
            UpperCamelCase__    ,			UpperCamelCase__    ,			UpperCamelCase__    ,			additional_replacements=[meta_path]    ,			attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions      ) else None    ,			config=UpperCamelCase__    ,			)
    else:
      UpperCAmelCase	:							Dict						=			renew_resnet_paths(UpperCamelCase__    ,			n_shave_prefix_segments=1      )
      for path in resnet_0_paths:
        UpperCAmelCase	:							int						=			'''.'''.join(['''output_blocks''', str(UpperCamelCase__      ), path['''old''']]      )
        UpperCAmelCase	:							Dict						=			'''.'''.join(['''up_blocks''', str(UpperCamelCase__      ), '''resnets''', str(UpperCamelCase__      ), path['''new''']]      )

        UpperCAmelCase	:							Any						=			checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     _lowerCamelCase    :       List[Any]        =							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    :       Tuple        =							parser.parse_args()

     _lowerCamelCase    :       List[Any]        =							torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          _lowerCamelCase    :       Optional[Any]        =							json.loads(f.read())

     _lowerCamelCase    :       int        =							convert_ldm_checkpoint(checkpoint, config)

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

     _lowerCamelCase    :       Optional[Any]        =							UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          _lowerCamelCase    :       Tuple        =							DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
          _lowerCamelCase    :       Optional[Any]        =							VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

          _lowerCamelCase    :       List[Any]        =							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





