





import argparse
import json

import torch

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






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[str]=1      )						->      Any:
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split("""."""      )[n_shave_prefix_segments:]      )
     else:
          return ".".join(path.split("""."""      )[:n_shave_prefix_segments]      )






def 				_lowerCAmelCase			(	_a :       List[Any]       , _a :       Union[str, Any]=0      )						->      List[str]:
     lowerCAmelCase_       :							List[str]			 =		[]
     for old_item in old_list:
          lowerCAmelCase_       :							List[str]			 =		old_item
          lowerCAmelCase_       :							Any			 =		new_item.replace("""block."""       , """resnets."""      )
          lowerCAmelCase_       :							Dict			 =		new_item.replace("""conv_shorcut"""       , """conv1"""      )
          lowerCAmelCase_       :							Tuple			 =		new_item.replace("""in_shortcut"""       , """conv_shortcut"""      )
          lowerCAmelCase_       :							Optional[int]			 =		new_item.replace("""temb_proj"""       , """time_emb_proj"""      )

          lowerCAmelCase_       :							Union[str, Any]			 =		shave_segments(_a       , n_shave_prefix_segments=_a      )

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

     return mapping






def 				_lowerCAmelCase			(	_a :       Tuple       , _a :       Tuple=0       , _a :       Tuple=False      )						->      Union[str, Any]:
     lowerCAmelCase_       :							str			 =		[]
     for old_item in old_list:
          lowerCAmelCase_       :							Optional[Any]			 =		old_item

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

          lowerCAmelCase_       :							Optional[int]			 =		new_item.replace("""proj_out"""       , """proj_attn"""      )
          lowerCAmelCase_       :							Tuple			 =		new_item.replace("""norm"""       , """group_norm"""      )

          lowerCAmelCase_       :							Dict			 =		shave_segments(_a       , n_shave_prefix_segments=_a      )
          mapping.append({"""old""": old_item, """new""": new_item}      )

     return mapping






def 				_lowerCAmelCase			(	_a :       List[str]       , _a :       int       , _a :       str       , _a :       Tuple=None       , _a :       List[Any]=None       , _a :       str=None      )						->      Optional[Any]:
     assert isinstance(_a       , _a      ), "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_       :							Union[str, Any]			 =		old_checkpoint[path]
               lowerCAmelCase_       :							Optional[Any]			 =		old_tensor.shape[0] // 3

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

               lowerCAmelCase_       :							Tuple			 =		old_tensor.shape[0] // config.get("""num_head_channels"""       , 1      ) // 3

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

               lowerCAmelCase_       :							Any			 =		query.reshape(_a      ).squeeze()
               lowerCAmelCase_       :							Any			 =		key.reshape(_a      ).squeeze()
               lowerCAmelCase_       :							List[str]			 =		value.reshape(_a      ).squeeze()

     for path in paths:
          lowerCAmelCase_       :							str			 =		path["""new"""]

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          lowerCAmelCase_       :							List[str]			 =		new_path.replace("""down."""       , """down_blocks."""      )
          lowerCAmelCase_       :							Optional[Any]			 =		new_path.replace("""up."""       , """up_blocks."""      )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    lowerCAmelCase_       :							Optional[Any]			 =		new_path.replace(replacement["""old"""]       , replacement["""new"""]      )

          if "attentions" in new_path:
               lowerCAmelCase_       :							List[str]			 =		old_checkpoint[path["""old"""]].squeeze()
          else:
               lowerCAmelCase_       :							List[Any]			 =		old_checkpoint[path["""old"""]]






def 				_lowerCAmelCase			(	_a :       List[str]       , _a :       int      )						->      int:
     lowerCAmelCase_       :							Optional[Any]			 =		{}

     lowerCAmelCase_       :							Optional[Any]			 =		checkpoint["""temb.dense.0.weight"""]
     lowerCAmelCase_       :							List[Any]			 =		checkpoint["""temb.dense.0.bias"""]
     lowerCAmelCase_       :							Optional[Any]			 =		checkpoint["""temb.dense.1.weight"""]
     lowerCAmelCase_       :							List[Any]			 =		checkpoint["""temb.dense.1.bias"""]

     lowerCAmelCase_       :							Any			 =		checkpoint["""norm_out.weight"""]
     lowerCAmelCase_       :							Tuple			 =		checkpoint["""norm_out.bias"""]

     lowerCAmelCase_       :							List[Any]			 =		checkpoint["""conv_in.weight"""]
     lowerCAmelCase_       :							Optional[int]			 =		checkpoint["""conv_in.bias"""]
     lowerCAmelCase_       :							str			 =		checkpoint["""conv_out.weight"""]
     lowerCAmelCase_       :							Dict			 =		checkpoint["""conv_out.bias"""]

     lowerCAmelCase_       :							Optional[int]			 =		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(_a      )
     }

     lowerCAmelCase_       :							List[str]			 =		len({""".""".join(layer.split("""."""      )[:2]      ) for layer in checkpoint if """up""" in layer}      )
     lowerCAmelCase_       :							Tuple			 =		{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_a      )}

     for i in range(_a      ):
          lowerCAmelCase_       :							str			 =		(i - 1) // (config["""layers_per_block"""] + 1)

          if any("""downsample""" in layer for layer in down_blocks[i]      ):
               lowerCAmelCase_       :							Dict			 =		checkpoint[
                   F'down.{i}.downsample.op.weight'
               ]
               lowerCAmelCase_       :							List[str]			 =		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_       :							Any			 =		len(
                   {""".""".join(shave_segments(_a       , 2      ).split("""."""      )[:2]      ) for layer in down_blocks[i] if """block""" in layer}      )
               lowerCAmelCase_       :							Dict			 =		{
                   layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                   for layer_id in range(_a      )
               }

               if num_blocks > 0:
                    for j in range(config["""layers_per_block"""]      ):
                         lowerCAmelCase_       :							Dict			 =		renew_resnet_paths(blocks[j]      )
                         assign_to_checkpoint(_a       , _a       , _a      )

          if any("""attn""" in layer for layer in down_blocks[i]      ):
               lowerCAmelCase_       :							int			 =		len(
                   {""".""".join(shave_segments(_a       , 2      ).split("""."""      )[:2]      ) 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(_a      )
               }

               if num_attn > 0:
                    for j in range(config["""layers_per_block"""]      ):
                         lowerCAmelCase_       :							Tuple			 =		renew_attention_paths(attns[j]      )
                         assign_to_checkpoint(_a       , _a       , _a       , config=_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		[key for key in checkpoint if """mid.block_1""" in key]
     lowerCAmelCase_       :							Dict			 =		[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_       :							List[str]			 =		renew_resnet_paths(_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]       , )

     lowerCAmelCase_       :							str			 =		renew_resnet_paths(_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]       , )

     lowerCAmelCase_       :							List[Any]			 =		renew_attention_paths(_a       , in_mid=_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]       , )

     for i in range(_a      ):
          lowerCAmelCase_       :							List[Any]			 =		num_up_blocks - 1 - i

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

          if any("""block""" in layer for layer in up_blocks[i]      ):
               lowerCAmelCase_       :							List[Any]			 =		len(
                   {""".""".join(shave_segments(_a       , 2      ).split("""."""      )[:2]      ) for layer in up_blocks[i] if """block""" in layer}      )
               lowerCAmelCase_       :							Any			 =		{
                   layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_a      )
               }

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

          if any("""attn""" in layer for layer in up_blocks[i]      ):
               lowerCAmelCase_       :							Tuple			 =		len(
                   {""".""".join(shave_segments(_a       , 2      ).split("""."""      )[:2]      ) for layer in up_blocks[i] if """attn""" in layer}      )
               lowerCAmelCase_       :							Tuple			 =		{
                   layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_a      )
               }

               if num_attn > 0:
                    for j in range(config["""layers_per_block"""] + 1      ):
                         lowerCAmelCase_       :							Dict			 =		{"""old""": F'up_blocks.{i}', """new""": F'up_blocks.{block_id}'}
                         lowerCAmelCase_       :							Any			 =		renew_attention_paths(attns[j]      )
                         assign_to_checkpoint(_a       , _a       , _a       , additional_replacements=[replace_indices]      )

     lowerCAmelCase_       :							Optional[int]			 =		{k.replace("""mid_new_2"""       , """mid_block"""      ): v for k, v in new_checkpoint.items()}
     return new_checkpoint






def 				_lowerCAmelCase			(	_a :       Dict       , _a :       Optional[int]      )						->      Optional[int]:
     lowerCAmelCase_       :							List[str]			 =		{}

     lowerCAmelCase_       :							Any			 =		checkpoint["""encoder.norm_out.weight"""]
     lowerCAmelCase_       :							Dict			 =		checkpoint["""encoder.norm_out.bias"""]

     lowerCAmelCase_       :							Optional[int]			 =		checkpoint["""encoder.conv_in.weight"""]
     lowerCAmelCase_       :							Dict			 =		checkpoint["""encoder.conv_in.bias"""]
     lowerCAmelCase_       :							Tuple			 =		checkpoint["""encoder.conv_out.weight"""]
     lowerCAmelCase_       :							Tuple			 =		checkpoint["""encoder.conv_out.bias"""]

     lowerCAmelCase_       :							Any			 =		checkpoint["""decoder.norm_out.weight"""]
     lowerCAmelCase_       :							int			 =		checkpoint["""decoder.norm_out.bias"""]

     lowerCAmelCase_       :							int			 =		checkpoint["""decoder.conv_in.weight"""]
     lowerCAmelCase_       :							Optional[int]			 =		checkpoint["""decoder.conv_in.bias"""]
     lowerCAmelCase_       :							str			 =		checkpoint["""decoder.conv_out.weight"""]
     lowerCAmelCase_       :							str			 =		checkpoint["""decoder.conv_out.bias"""]

     lowerCAmelCase_       :							Dict			 =		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(_a      )
     }

     lowerCAmelCase_       :							Union[str, Any]			 =		len({""".""".join(layer.split("""."""      )[:3]      ) for layer in checkpoint if """up""" in layer}      )
     lowerCAmelCase_       :							str			 =		{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_a      )}

     for i in range(_a      ):
          lowerCAmelCase_       :							List[str]			 =		(i - 1) // (config["""layers_per_block"""] + 1)

          if any("""downsample""" in layer for layer in down_blocks[i]      ):
               lowerCAmelCase_       :							Optional[Any]			 =		checkpoint[
                   F'encoder.down.{i}.downsample.conv.weight'
               ]
               lowerCAmelCase_       :							Dict			 =		checkpoint[
                   F'encoder.down.{i}.downsample.conv.bias'
               ]

          if any("""block""" in layer for layer in down_blocks[i]      ):
               lowerCAmelCase_       :							str			 =		len(
                   {""".""".join(shave_segments(_a       , 3      ).split("""."""      )[:3]      ) for layer in down_blocks[i] if """block""" in layer}      )
               lowerCAmelCase_       :							str			 =		{
                   layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                   for layer_id in range(_a      )
               }

               if num_blocks > 0:
                    for j in range(config["""layers_per_block"""]      ):
                         lowerCAmelCase_       :							Dict			 =		renew_resnet_paths(blocks[j]      )
                         assign_to_checkpoint(_a       , _a       , _a      )

          if any("""attn""" in layer for layer in down_blocks[i]      ):
               lowerCAmelCase_       :							Any			 =		len(
                   {""".""".join(shave_segments(_a       , 3      ).split("""."""      )[:3]      ) 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(_a      )
               }

               if num_attn > 0:
                    for j in range(config["""layers_per_block"""]      ):
                         lowerCAmelCase_       :							Optional[int]			 =		renew_attention_paths(attns[j]      )
                         assign_to_checkpoint(_a       , _a       , _a       , config=_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		[key for key in checkpoint if """mid.block_1""" in key]
     lowerCAmelCase_       :							str			 =		[key for key in checkpoint if """mid.block_2""" in key]
     lowerCAmelCase_       :							int			 =		[key for key in checkpoint if """mid.attn_1""" in key]

     # Mid new 2
     lowerCAmelCase_       :							List[str]			 =		renew_resnet_paths(_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]       , )

     lowerCAmelCase_       :							Tuple			 =		renew_resnet_paths(_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]       , )

     lowerCAmelCase_       :							List[str]			 =		renew_attention_paths(_a       , in_mid=_a      )
     assign_to_checkpoint(
         _a       , _a       , _a       , additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]       , )

     for i in range(_a      ):
          lowerCAmelCase_       :							int			 =		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_       :							List[str]			 =		checkpoint[
                   F'decoder.up.{i}.upsample.conv.bias'
               ]

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

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

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

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

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

     return new_checkpoint


if __name__ == "__main__":
      UpperCAmelCase_       : 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.""")

      UpperCAmelCase_       : Union[str, Any]							=    parser.parse_args()
      UpperCAmelCase_       : Dict							=    torch.load(args.checkpoint_path)

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

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

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

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

            UpperCAmelCase_       : Optional[Any]							=    DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            UpperCAmelCase_       : Union[str, Any]							=    DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)







def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
