




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       :				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)












import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE__	:				List[str]  =       logging.get_logger(__name__)  # pylint: disable=invalid-name

def 					A				(				self			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase       :				Dict      			= image.to(device=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				str      			= image  # take image as image_embeddings
       lowerCamelCase       :				List[Any]      			= image_embeddings.unsqueeze(1   )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				str      			= image_embeddings.shape
       lowerCamelCase       :				List[str]      			= image_embeddings.repeat(1			,_SCREAMING_SNAKE_CASE			,1   )
       lowerCamelCase       :				Optional[int]      			= image_embeddings.view(bs_embed * num_images_per_prompt			,_SCREAMING_SNAKE_CASE			,-1   )

       if do_classifier_free_guidance:
              lowerCamelCase       :				Dict      			= torch.zeros_like(_SCREAMING_SNAKE_CASE   )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              lowerCamelCase       :				Optional[Any]      			= torch.cat([uncond_embeddings, image_embeddings]   )

       return image_embeddings




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  )					->				Tuple:
           super().__init__()

           lowerCamelCase       :				str      			= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

           lowerCamelCase       :				Dict      			= decoder_pipe_kwargs.get("torch_dtype"					,  UpperCamelCase__					) or prior.dtype

           lowerCamelCase       :				Dict      			= StableDiffusionImageVariationPipeline.from_pretrained(
               "lambdalabs/sd-image-variations-diffusers"					,  **UpperCamelCase__					)

           # replace `_encode_image` method
           lowerCamelCase       :				List[str]      			= types.MethodType(_encode_image					,  self.decoder_pipe					)

           self.register_modules(
               prior=UpperCamelCase__					,  tokenizer=UpperCamelCase__					,  text_encoder=UpperCamelCase__					,  prior_scheduler=UpperCamelCase__					,  )







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				Any:
           if text_model_output is None:
                  lowerCamelCase       :				List[str]      			= len(UpperCamelCase__					) if isinstance(UpperCamelCase__					,  UpperCamelCase__					) else 1
                  # get prompt text embeddings
                  lowerCamelCase       :				Any      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				int      			= text_inputs.input_ids
                  lowerCamelCase       :				Optional[Any]      			= text_inputs.attention_mask.bool().to(UpperCamelCase__					)

                  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                         lowerCamelCase       :				Optional[int]      			= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
                         logger.warning(
                             "The following part of your input was truncated because CLIP can only handle sequences up to"
                             F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''					)
                         lowerCamelCase       :				Optional[Any]      			= text_input_ids[:, : self.tokenizer.model_max_length]

                  lowerCamelCase       :				int      			= self.text_encoder(text_input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Optional[Any]      			= text_encoder_output.text_embeds
                  lowerCamelCase       :				Optional[int]      			= text_encoder_output.last_hidden_state

           else:
                  lowerCamelCase       :				Dict      			= text_model_output[0].shape[0]
                  lowerCamelCase			,				lowerCamelCase       :				int      			= text_model_output[0], text_model_output[1]
                  lowerCamelCase       :				int      			= text_attention_mask

           lowerCamelCase       :				Dict      			= text_embeddings.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				int      			= text_encoder_hidden_states.repeat_interleave(UpperCamelCase__					,  dim=0					)
           lowerCamelCase       :				List[str]      			= text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

           if do_classifier_free_guidance:
                  lowerCamelCase       :				Dict      			= [""] * batch_size

                  lowerCamelCase       :				List[Any]      			= self.tokenizer(
                      UpperCamelCase__					,  padding="max_length"					,  max_length=self.tokenizer.model_max_length					,  truncation=UpperCamelCase__					,  return_tensors="pt"					,  )
                  lowerCamelCase       :				str      			= uncond_input.attention_mask.bool().to(UpperCamelCase__					)
                  lowerCamelCase       :				int      			= self.text_encoder(uncond_input.input_ids.to(UpperCamelCase__					)					)

                  lowerCamelCase       :				Union[str, Any]      			= uncond_embeddings_text_encoder_output.text_embeds
                  lowerCamelCase       :				List[Any]      			= uncond_embeddings_text_encoder_output.last_hidden_state

                  # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.shape[1]
                  lowerCamelCase       :				str      			= uncond_embeddings.repeat(1					,  UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= uncond_embeddings.view(batch_size * num_images_per_prompt					,  UpperCamelCase__					)

                  lowerCamelCase       :				Any      			= uncond_text_encoder_hidden_states.shape[1]
                  lowerCamelCase       :				int      			= uncond_text_encoder_hidden_states.repeat(1					,  UpperCamelCase__					,  1					)
                  lowerCamelCase       :				Tuple      			= uncond_text_encoder_hidden_states.view(
                      batch_size * num_images_per_prompt					,  UpperCamelCase__					,  -1					)
                  lowerCamelCase       :				List[str]      			= uncond_text_mask.repeat_interleave(UpperCamelCase__					,  dim=0					)

                  # done duplicates

                  # For classifier free guidance, we need to do two forward passes.
                  # Here we concatenate the unconditional and text embeddings into a single batch
                  # to avoid doing two forward passes
                  lowerCamelCase       :				int      			= torch.cat([uncond_embeddings, text_embeddings]					)
                  lowerCamelCase       :				str      			= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

                  lowerCamelCase       :				Dict      			= torch.cat([uncond_text_mask, text_mask]					)

           return text_embeddings, text_encoder_hidden_states, text_mask







    @property
    def    _lowercase					(   self					)					->				Any:
           if self.device != torch.device("meta"					) or not hasattr(self.prior					,  "_hf_hook"					):
                  return self.device
           for module in self.prior.modules():
                  if (
                      hasattr(UpperCamelCase__					,  "_hf_hook"					)
                      and hasattr(module._hf_hook					,  "execution_device"					)
                      and module._hf_hook.execution_device is not None
                  ):
                         return torch.device(module._hf_hook.execution_device					)
           return self.device







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Any:
           if latents is None:
                  lowerCamelCase       :				Any      			= randn_tensor(UpperCamelCase__					,  generator=UpperCamelCase__					,  device=UpperCamelCase__					,  dtype=UpperCamelCase__					)
           else:
                  if latents.shape != shape:
                         raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
                  lowerCamelCase       :				Any      			= latents.to(UpperCamelCase__					)

           lowerCamelCase       :				List[str]      			= latents * scheduler.init_noise_sigma
           return latents







    def    _lowercase					(   self					,  UpperCamelCase__ = None					)					->				str:
           self.decoder_pipe.to(UpperCamelCase__					)
           super().to(UpperCamelCase__					)







    @torch.no_grad()
    def __call__(   self					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 25					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = 4.0					,  UpperCamelCase__ = 8.0					,  UpperCamelCase__ = 50					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 0.0					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = True					,  )					->				Union[str, Any]:
           if prompt is not None:
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Union[str, Any]      			= 1
                  elif isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				int      			= len(UpperCamelCase__					)
                  else:
                         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__					)}'''					)
           else:
                  lowerCamelCase       :				Any      			= text_model_output[0].shape[0]

           lowerCamelCase       :				str      			= self._execution_device

           lowerCamelCase       :				Optional[Any]      			= batch_size * num_images_per_prompt

           lowerCamelCase       :				int      			= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Tuple      			= self._encode_prompt(
               UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)

           # prior

           self.prior_scheduler.set_timesteps(UpperCamelCase__					,  device=UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.timesteps

           lowerCamelCase       :				List[Any]      			= self.prior.config.embedding_dim

           lowerCamelCase       :				Dict      			= self.prepare_latents(
               (batch_size, embedding_dim)					,  text_embeddings.dtype					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  self.prior_scheduler					,  )

           for i, t in enumerate(self.progress_bar(UpperCamelCase__					)					):
                  # expand the latents if we are doing classifier free guidance
                  lowerCamelCase       :				Tuple      			= torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

                  lowerCamelCase       :				List[Any]      			= self.prior(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  proj_embedding=UpperCamelCase__					,  encoder_hidden_states=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  ).predicted_image_embedding

                  if do_classifier_free_guidance:
                         lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= predicted_image_embedding.chunk(2					)
                         lowerCamelCase       :				Optional[Any]      			= predicted_image_embedding_uncond + prior_guidance_scale * (
                             predicted_image_embedding_text - predicted_image_embedding_uncond
                         )

                  if i + 1 == prior_timesteps_tensor.shape[0]:
                         lowerCamelCase       :				Union[str, Any]      			= None
                  else:
                         lowerCamelCase       :				Dict      			= prior_timesteps_tensor[i + 1]

                  lowerCamelCase       :				Optional[int]      			= self.prior_scheduler.step(
                      UpperCamelCase__					,  timestep=UpperCamelCase__					,  sample=UpperCamelCase__					,  generator=UpperCamelCase__					,  prev_timestep=UpperCamelCase__					,  ).prev_sample

           lowerCamelCase       :				Tuple      			= self.prior.post_process_latents(UpperCamelCase__					)

           lowerCamelCase       :				Any      			= prior_latents

           lowerCamelCase       :				Union[str, Any]      			= self.decoder_pipe(
               image=UpperCamelCase__					,  height=UpperCamelCase__					,  width=UpperCamelCase__					,  num_inference_steps=UpperCamelCase__					,  guidance_scale=UpperCamelCase__					,  generator=UpperCamelCase__					,  output_type=UpperCamelCase__					,  return_dict=UpperCamelCase__					,  num_images_per_prompt=UpperCamelCase__					,  eta=UpperCamelCase__					,  )
           return output






