



'''simple docstring'''





import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=1							):
   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   (  lowerCamelCase_					,						lowerCamelCase_=0							):
   snake_case   :							Dict						  =[]
   for old_item in old_list:
      snake_case   :							Tuple						  =old_item
      snake_case   :							Optional[int]						  =new_item.replace('''block.'''					,						'''resnets.'''							)
      snake_case   :							List[Any]						  =new_item.replace('''conv_shorcut'''					,						'''conv1'''							)
      snake_case   :							Optional[Any]						  =new_item.replace('''in_shortcut'''					,						'''conv_shortcut'''							)
      snake_case   :							Any						  =new_item.replace('''temb_proj'''					,						'''time_emb_proj'''							)

      snake_case   :							str						  =shave_segments(lowerCamelCase_					,						n_shave_prefix_segments=lowerCamelCase_							)

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

   return mapping
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=0					,						lowerCamelCase_=False							):
   snake_case   :							List[str]						  =[]
   for old_item in old_list:
      snake_case   :							List[Any]						  =old_item

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

      snake_case   :							int						  =new_item.replace('''proj_out'''					,						'''proj_attn'''							)
      snake_case   :							Tuple						  =new_item.replace('''norm'''					,						'''group_norm'''							)

      snake_case   :							Optional[Any]						  =shave_segments(lowerCamelCase_					,						n_shave_prefix_segments=lowerCamelCase_							)
      mapping.append({'''old''': old_item, '''new''': new_item}							)

   return mapping
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_=None					,						lowerCamelCase_=None					,						lowerCamelCase_=None							):
   assert isinstance(lowerCamelCase_					,						lowerCamelCase_							), "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():
         snake_case   :							Optional[int]						  =old_checkpoint[path]
         snake_case   :							Optional[Any]						  =old_tensor.shape[0] // 3

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

         snake_case   :							List[str]						  =old_tensor.shape[0] // config.get('''num_head_channels'''					,						1							) // 3

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

         snake_case   :							str						  =query.reshape(lowerCamelCase_							).squeeze()
         snake_case   :							List[Any]						  =key.reshape(lowerCamelCase_							).squeeze()
         snake_case   :							Optional[int]						  =value.reshape(lowerCamelCase_							).squeeze()

   for path in paths:
      snake_case   :							Dict						  =path['''new''']

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      snake_case   :							str						  =new_path.replace('''down.'''					,						'''down_blocks.'''							)
      snake_case   :							Union[str, Any]						  =new_path.replace('''up.'''					,						'''up_blocks.'''							)

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

      if "attentions" in new_path:
         snake_case   :							Union[str, Any]						  =old_checkpoint[path['''old''']].squeeze()
      else:
         snake_case   :							str						  =old_checkpoint[path['''old''']]
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							List[Any]						  ={}

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

   snake_case   :							int						  =checkpoint['''norm_out.weight''']
   snake_case   :							Optional[int]						  =checkpoint['''norm_out.bias''']

   snake_case   :							Tuple						  =checkpoint['''conv_in.weight''']
   snake_case   :							int						  =checkpoint['''conv_in.bias''']
   snake_case   :							int						  =checkpoint['''conv_out.weight''']
   snake_case   :							Optional[int]						  =checkpoint['''conv_out.bias''']

   snake_case   :							str						  =len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''down''' in layer}							)
   snake_case   :							List[Any]						  ={
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
   }

   snake_case   :							List[str]						  =len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''up''' in layer}							)
   snake_case   :							Union[str, Any]						  ={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)}

   for i in range(lowerCamelCase_							):
      snake_case   :							str						  =(i - 1) // (config['''layers_per_block'''] + 1)

      if any('''downsample''' in layer for layer in down_blocks[i]							):
         snake_case   :							Optional[Any]						  =checkpoint[
             F'''down.{i}.downsample.op.weight'''
         ]
         snake_case   :							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]							):
         snake_case   :							Dict						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''block''' in layer}							)
         snake_case   :							Dict						  ={
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowerCamelCase_							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block''']							):
               snake_case   :							List[str]						  =renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)

      if any('''attn''' in layer for layer in down_blocks[i]							):
         snake_case   :							List[str]						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''attn''' in layer}							)
         snake_case   :							Tuple						  ={
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowerCamelCase_							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block''']							):
               snake_case   :							List[str]						  =renew_attention_paths(attns[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						config=lowerCamelCase_							)

   snake_case   :							int						  =[key for key in checkpoint if '''mid.block_1''' in key]
   snake_case   :							Union[str, Any]						  =[key for key in checkpoint if '''mid.block_2''' in key]
   snake_case   :							str						  =[key for key in checkpoint if '''mid.attn_1''' in key]

   # Mid new 2
   snake_case   :							Optional[int]						  =renew_resnet_paths(lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]					,						)

   snake_case   :							Any						  =renew_resnet_paths(lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]					,						)

   snake_case   :							str						  =renew_attention_paths(lowerCamelCase_					,						in_mid=lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]					,						)

   for i in range(lowerCamelCase_							):
      snake_case   :							Any						  =num_up_blocks - 1 - i

      if any('''upsample''' in layer for layer in up_blocks[i]							):
         snake_case   :							Union[str, Any]						  =checkpoint[
             F'''up.{i}.upsample.conv.weight'''
         ]
         snake_case   :							List[str]						  =checkpoint[F'''up.{i}.upsample.conv.bias''']

      if any('''block''' in layer for layer in up_blocks[i]							):
         snake_case   :							Tuple						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''block''' in layer}							)
         snake_case   :							Dict						  ={
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               snake_case   :							Dict						  ={'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
               snake_case   :							Optional[Any]						  =renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[replace_indices]							)

      if any('''attn''' in layer for layer in up_blocks[i]							):
         snake_case   :							str						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''attn''' in layer}							)
         snake_case   :							Dict						  ={
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               snake_case   :							List[str]						  ={'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
               snake_case   :							Optional[Any]						  =renew_attention_paths(attns[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[replace_indices]							)

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

   snake_case   :							Union[str, Any]						  =checkpoint['''encoder.norm_out.weight''']
   snake_case   :							List[Any]						  =checkpoint['''encoder.norm_out.bias''']

   snake_case   :							Optional[Any]						  =checkpoint['''encoder.conv_in.weight''']
   snake_case   :							str						  =checkpoint['''encoder.conv_in.bias''']
   snake_case   :							Dict						  =checkpoint['''encoder.conv_out.weight''']
   snake_case   :							Optional[int]						  =checkpoint['''encoder.conv_out.bias''']

   snake_case   :							Dict						  =checkpoint['''decoder.norm_out.weight''']
   snake_case   :							List[str]						  =checkpoint['''decoder.norm_out.bias''']

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

   snake_case   :							Tuple						  =len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''down''' in layer}							)
   snake_case   :							Optional[int]						  ={
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
   }

   snake_case   :							List[str]						  =len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''up''' in layer}							)
   snake_case   :							Any						  ={layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)}

   for i in range(lowerCamelCase_							):
      snake_case   :							Dict						  =(i - 1) // (config['''layers_per_block'''] + 1)

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

      if any('''block''' in layer for layer in down_blocks[i]							):
         snake_case   :							Union[str, Any]						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''block''' in layer}							)
         snake_case   :							List[Any]						  ={
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowerCamelCase_							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block''']							):
               snake_case   :							Union[str, Any]						  =renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)

      if any('''attn''' in layer for layer in down_blocks[i]							):
         snake_case   :							List[str]						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''attn''' in layer}							)
         snake_case   :							Union[str, Any]						  ={
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowerCamelCase_							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block''']							):
               snake_case   :							Any						  =renew_attention_paths(attns[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						config=lowerCamelCase_							)

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

   # Mid new 2
   snake_case   :							Union[str, Any]						  =renew_resnet_paths(lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]					,						)

   snake_case   :							Any						  =renew_resnet_paths(lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]					,						)

   snake_case   :							Optional[int]						  =renew_attention_paths(lowerCamelCase_					,						in_mid=lowerCamelCase_							)
   assign_to_checkpoint(
       lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]					,						)

   for i in range(lowerCamelCase_							):
      snake_case   :							Union[str, Any]						  =num_up_blocks - 1 - i

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

      if any('''block''' in layer for layer in up_blocks[i]							):
         snake_case   :							Any						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''block''' in layer}							)
         snake_case   :							Optional[Any]						  ={
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               snake_case   :							Dict						  ={'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
               snake_case   :							Optional[int]						  =renew_resnet_paths(blocks[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[replace_indices]							)

      if any('''attn''' in layer for layer in up_blocks[i]							):
         snake_case   :							Union[str, Any]						  =len(
             {'''.'''.join(shave_segments(lowerCamelCase_					,						3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''attn''' in layer}							)
         snake_case   :							List[Any]						  ={
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase_							)
         }

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] + 1							):
               snake_case   :							Tuple						  ={'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
               snake_case   :							Optional[int]						  =renew_attention_paths(attns[j]							)
               assign_to_checkpoint(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						additional_replacements=[replace_indices]							)

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

   return new_checkpoint


if __name__ == "__main__":
      A			:   Dict			=      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			:   List[Any]			=      parser.parse_args()
      A			:   Union[str, Any]			=      torch.load(args.checkpoint_path)

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

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

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

      if config["_class_name"] == "VQModel":
            A			:   Union[str, Any]			=      VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            A			:   int			=      AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            A			:   List[str]			=      UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

            A			:   Tuple			=      DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            A			:   Optional[int]			=      DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)










'''simple docstring'''





def 		_a   (  lowerCamelCase_							):
   return "".join(chr(ord(lowerCamelCase_							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





