




import argparse
import json

import torch

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

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=1   )     ->			Dict:
       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			,_SCREAMING_SNAKE_CASE=0   )     ->			Optional[int]:
       lowerCamelCase       :				int      			= []
       for old_item in old_list:
              lowerCamelCase       :				Dict      			= old_item
              lowerCamelCase       :				List[Any]      			= new_item.replace("block."			,"resnets."   )
              lowerCamelCase       :				Optional[int]      			= new_item.replace("conv_shorcut"			,"conv1"   )
              lowerCamelCase       :				Optional[Any]      			= new_item.replace("in_shortcut"			,"conv_shortcut"   )
              lowerCamelCase       :				int      			= new_item.replace("temb_proj"			,"time_emb_proj"   )

              lowerCamelCase       :				Union[str, 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			,_SCREAMING_SNAKE_CASE=0			,_SCREAMING_SNAKE_CASE=False   )     ->			str:
       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       :				Tuple      			= new_item.replace("attn"			,"attentions"   )
              lowerCamelCase       :				Optional[Any]      			= new_item.replace(".k."			,".key."   )
              lowerCamelCase       :				Optional[Any]      			= new_item.replace(".v."			,".value."   )
              lowerCamelCase       :				str      			= new_item.replace(".q."			,".query."   )

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

              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			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=None			,_SCREAMING_SNAKE_CASE=None			,_SCREAMING_SNAKE_CASE=None   )     ->			Optional[int]:
       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       :				Optional[int]      			= old_checkpoint[path]
                     lowerCamelCase       :				Dict      			= old_tensor.shape[0] // 3

                     lowerCamelCase       :				int      			= (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

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

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

                     lowerCamelCase       :				Optional[int]      			= query.reshape(_SCREAMING_SNAKE_CASE   ).squeeze()
                     lowerCamelCase       :				int      			= key.reshape(_SCREAMING_SNAKE_CASE   ).squeeze()
                     lowerCamelCase       :				List[str]      			= 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       :				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       :				Dict      			= 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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase       :				List[Any]      			= {}

       lowerCamelCase       :				Union[str, Any]      			= checkpoint["temb.dense.0.weight"]
       lowerCamelCase       :				List[str]      			= checkpoint["temb.dense.0.bias"]
       lowerCamelCase       :				Tuple      			= checkpoint["temb.dense.1.weight"]
       lowerCamelCase       :				int      			= checkpoint["temb.dense.1.bias"]

       lowerCamelCase       :				int      			= checkpoint["norm_out.weight"]
       lowerCamelCase       :				Optional[int]      			= checkpoint["norm_out.bias"]

       lowerCamelCase       :				Tuple      			= checkpoint["conv_in.weight"]
       lowerCamelCase       :				Any      			= checkpoint["conv_in.bias"]
       lowerCamelCase       :				Dict      			= checkpoint["conv_out.weight"]
       lowerCamelCase       :				List[Any]      			= checkpoint["conv_out.bias"]

       lowerCamelCase       :				str      			= len({".".join(layer.split("."   )[:2]   ) for layer in checkpoint if "down" in layer}   )
       lowerCamelCase       :				Any      			= {
           layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE   )
       }

       lowerCamelCase       :				Union[str, Any]      			= 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(_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       :				int      			= checkpoint[
                         f'''down.{i}.downsample.op.weight'''
                     ]
                     lowerCamelCase       :				Dict      			= 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       :				Tuple      			= {
                         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       :				Tuple      			= 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       :				Dict      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,2   ).split("."   )[:2]   ) for layer in down_blocks[i] if "attn" in layer}   )
                     lowerCamelCase       :				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       :				str      			= renew_attention_paths(attns[j]   )
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,config=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				List[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       :				List[Any]      			= [key for key in checkpoint if "mid.attn_1" in key]

       # Mid new 2
       lowerCamelCase       :				List[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_1", "new": "resnets.0"}]			,)

       lowerCamelCase       :				Dict      			= 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       :				Dict      			= 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       :				Union[str, Any]      			= 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       :				Tuple      			= checkpoint[f'''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			,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(_SCREAMING_SNAKE_CASE   )
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"] + 1   ):
                                   lowerCamelCase       :				Dict      			= {"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       :				Tuple      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,2   ).split("."   )[:2]   ) for layer in up_blocks[i] if "attn" in layer}   )
                     lowerCamelCase       :				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       :				int      			= {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
                                   lowerCamelCase       :				Optional[int]      			= 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()}
       return new_checkpoint

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				int      			= {}

       lowerCamelCase       :				Tuple      			= checkpoint["encoder.norm_out.weight"]
       lowerCamelCase       :				Optional[Any]      			= checkpoint["encoder.norm_out.bias"]

       lowerCamelCase       :				Tuple      			= checkpoint["encoder.conv_in.weight"]
       lowerCamelCase       :				Dict      			= checkpoint["encoder.conv_in.bias"]
       lowerCamelCase       :				str      			= checkpoint["encoder.conv_out.weight"]
       lowerCamelCase       :				Any      			= checkpoint["encoder.conv_out.bias"]

       lowerCamelCase       :				str      			= checkpoint["decoder.norm_out.weight"]
       lowerCamelCase       :				Any      			= checkpoint["decoder.norm_out.bias"]

       lowerCamelCase       :				Tuple      			= checkpoint["decoder.conv_in.weight"]
       lowerCamelCase       :				Any      			= checkpoint["decoder.conv_in.bias"]
       lowerCamelCase       :				List[Any]      			= checkpoint["decoder.conv_out.weight"]
       lowerCamelCase       :				Dict      			= checkpoint["decoder.conv_out.bias"]

       lowerCamelCase       :				int      			= len({".".join(layer.split("."   )[:3]   ) for layer in checkpoint if "down" in layer}   )
       lowerCamelCase       :				List[str]      			= {
           layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE   )
       }

       lowerCamelCase       :				List[Any]      			= len({".".join(layer.split("."   )[:3]   ) 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'''encoder.down.{i}.downsample.conv.weight'''
                     ]
                     lowerCamelCase       :				int      			= checkpoint[
                         f'''encoder.down.{i}.downsample.conv.bias'''
                     ]

              if any("block" in layer for layer in down_blocks[i]   ):
                     lowerCamelCase       :				Union[str, Any]      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,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(_SCREAMING_SNAKE_CASE   )
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"]   ):
                                   lowerCamelCase       :				str      			= 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       :				Any      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,3   ).split("."   )[:3]   ) for layer in down_blocks[i] if "attn" in layer}   )
                     lowerCamelCase       :				Optional[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       :				Union[str, Any]      			= renew_attention_paths(attns[j]   )
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,config=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				Optional[int]      			= [key for key in checkpoint if "mid.block_1" in key]
       lowerCamelCase       :				int      			= [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       :				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_1", "new": "resnets.0"}]			,)

       lowerCamelCase       :				Optional[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_2", "new": "resnets.1"}]			,)

       lowerCamelCase       :				int      			= 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       :				Optional[Any]      			= 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       :				Any      			= checkpoint[
                         f'''decoder.up.{i}.upsample.conv.bias'''
                     ]

              if any("block" in layer for layer in up_blocks[i]   ):
                     lowerCamelCase       :				str      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,3   ).split("."   )[:3]   ) 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(_SCREAMING_SNAKE_CASE   )
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"] + 1   ):
                                   lowerCamelCase       :				Union[str, Any]      			= {"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       :				Tuple      			= len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE			,3   ).split("."   )[:3]   ) for layer in up_blocks[i] if "attn" in layer}   )
                     lowerCamelCase       :				List[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       :				List[str]      			= {"old": f'''up_blocks.{i}''', "new": f'''up_blocks.{block_id}'''}
                                   lowerCamelCase       :				Optional[Any]      			= renew_attention_paths(attns[j]   )
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,additional_replacements=[replace_indices]   )

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

       return new_checkpoint


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				Union[str, 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.')

       SCREAMING_SNAKE_CASE__	:				int  =       parser.parse_args()
       SCREAMING_SNAKE_CASE__	:				Any  =       torch.load(args.checkpoint_path)

       with open(args.config_file) as f:
              SCREAMING_SNAKE_CASE__	:				Union[str, Any]  =       json.loads(f.read())

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

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

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

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

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






