


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Optional[int]=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 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  List[Any]=0						):
       _A				:	List[str]       =			[]
       for old_item in old_list:
              _A				:	Union[str, Any]       =			old_item
              _A				:	List[str]       =			new_item.replace("block."    ,							"resnets."						)
              _A				:	Optional[int]       =			new_item.replace("conv_shorcut"    ,							"conv1"						)
              _A				:	int       =			new_item.replace("in_shortcut"    ,							"conv_shortcut"						)
              _A				:	Dict       =			new_item.replace("temb_proj"    ,							"time_emb_proj"						)

              _A				:	List[str]       =			shave_segments(UpperCamelCase__    ,							n_shave_prefix_segments=UpperCamelCase__						)

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

       return mapping
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  Union[str, Any]=0    ,							UpperCamelCase__					:  Dict=False						):
       _A				:	str       =			[]
       for old_item in old_list:
              _A				:	int       =			old_item

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

              _A				:	List[Any]       =			new_item.replace("proj_out"    ,							"proj_attn"						)
              _A				:	str       =			new_item.replace("norm"    ,							"group_norm"						)

              _A				:	Union[str, Any]       =			shave_segments(UpperCamelCase__    ,							n_shave_prefix_segments=UpperCamelCase__						)
              mapping.append({"old": old_item, "new": new_item}						)

       return mapping
def 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  str=None    ,							UpperCamelCase__					:  Dict=None    ,							UpperCamelCase__					:  Tuple=None						):
       assert isinstance(UpperCamelCase__    ,							UpperCamelCase__						), "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():
                     _A				:	List[Any]       =			old_checkpoint[path]
                     _A				:	str       =			old_tensor.shape[0] // 3

                     _A				:	Dict       =			(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                     _A				:	List[Any]       =			old_tensor.shape[0] // config.get("num_head_channels"    ,							1						) // 3

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

                     _A				:	int       =			query.reshape(UpperCamelCase__						).squeeze()
                     _A				:	str       =			key.reshape(UpperCamelCase__						).squeeze()
                     _A				:	Optional[Any]       =			value.reshape(UpperCamelCase__						).squeeze()

       for path in paths:
              _A				:	List[str]       =			path["new"]

              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              _A				:	Union[str, Any]       =			new_path.replace("down."    ,							"down_blocks."						)
              _A				:	Dict       =			new_path.replace("up."    ,							"up_blocks."						)

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

              if "attentions" in new_path:
                     _A				:	List[str]       =			old_checkpoint[path["old"]].squeeze()
              else:
                     _A				:	Optional[Any]       =			old_checkpoint[path["old"]]
def 							_UpperCAmelCase    (UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  Any						):
       _A				:	Dict       =			{}

       _A				:	int       =			checkpoint["temb.dense.0.weight"]
       _A				:	Optional[Any]       =			checkpoint["temb.dense.0.bias"]
       _A				:	str       =			checkpoint["temb.dense.1.weight"]
       _A				:	Any       =			checkpoint["temb.dense.1.bias"]

       _A				:	Union[str, Any]       =			checkpoint["norm_out.weight"]
       _A				:	str       =			checkpoint["norm_out.bias"]

       _A				:	List[Any]       =			checkpoint["conv_in.weight"]
       _A				:	Optional[Any]       =			checkpoint["conv_in.bias"]
       _A				:	Dict       =			checkpoint["conv_out.weight"]
       _A				:	Optional[Any]       =			checkpoint["conv_out.bias"]

       _A				:	int       =			len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
       _A				:	Any       =			{
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
       }

       _A				:	List[Any]       =			len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
       _A				:	Union[str, Any]       =			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)}

       for i in range(UpperCamelCase__						):
              _A				:	int       =			(i - 1) // (config["layers_per_block"] + 1)

              if any("downsample" in layer for layer in down_blocks[i]						):
                     _A				:	List[Any]       =			checkpoint[
                         f"down.{i}.downsample.op.weight"
                     ]
                     _A				:	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]						):
                     _A				:	Optional[int]       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
                     _A				:	Dict       =			{
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(UpperCamelCase__						)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"]						):
                                   _A				:	Union[str, Any]       =			renew_resnet_paths(blocks[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

              if any("attn" in layer for layer in down_blocks[i]						):
                     _A				:	List[Any]       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
                     _A				:	Tuple       =			{
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(UpperCamelCase__						)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"]						):
                                   _A				:	List[Any]       =			renew_attention_paths(attns[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							config=UpperCamelCase__						)

       _A				:	Dict       =			[key for key in checkpoint if "mid.block_1" in key]
       _A				:	Optional[Any]       =			[key for key in checkpoint if "mid.block_2" in key]
       _A				:	Union[str, Any]       =			[key for key in checkpoint if "mid.attn_1" in key]

       # Mid new 2
       _A				:	Any       =			renew_resnet_paths(UpperCamelCase__						)
       assign_to_checkpoint(
           UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]    ,							)

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

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

       for i in range(UpperCamelCase__						):
              _A				:	List[Any]       =			num_up_blocks - 1 - i

              if any("upsample" in layer for layer in up_blocks[i]						):
                     _A				:	Any       =			checkpoint[
                         f"up.{i}.upsample.conv.weight"
                     ]
                     _A				:	List[Any]       =			checkpoint[f"up.{i}.upsample.conv.bias"]

              if any("block" in layer for layer in up_blocks[i]						):
                     _A				:	int       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
                     _A				:	Optional[int]       =			{
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"] + 1						):
                                   _A				:	Optional[Any]       =			{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                                   _A				:	Tuple       =			renew_resnet_paths(blocks[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[replace_indices]						)

              if any("attn" in layer for layer in up_blocks[i]						):
                     _A				:	List[str]       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
                     _A				:	int       =			{
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"] + 1						):
                                   _A				:	Tuple       =			{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                                   _A				:	List[str]       =			renew_attention_paths(attns[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[replace_indices]						)

       _A				:	str       =			{k.replace("mid_new_2"    ,							"mid_block"						): v for k, v in new_checkpoint.items()}
       return new_checkpoint
def 							_UpperCAmelCase    (UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  Union[str, Any]						):
       _A				:	List[Any]       =			{}

       _A				:	int       =			checkpoint["encoder.norm_out.weight"]
       _A				:	Optional[Any]       =			checkpoint["encoder.norm_out.bias"]

       _A				:	Tuple       =			checkpoint["encoder.conv_in.weight"]
       _A				:	Optional[Any]       =			checkpoint["encoder.conv_in.bias"]
       _A				:	Optional[int]       =			checkpoint["encoder.conv_out.weight"]
       _A				:	Optional[Any]       =			checkpoint["encoder.conv_out.bias"]

       _A				:	List[str]       =			checkpoint["decoder.norm_out.weight"]
       _A				:	Optional[Any]       =			checkpoint["decoder.norm_out.bias"]

       _A				:	int       =			checkpoint["decoder.conv_in.weight"]
       _A				:	Optional[int]       =			checkpoint["decoder.conv_in.bias"]
       _A				:	Optional[Any]       =			checkpoint["decoder.conv_out.weight"]
       _A				:	Union[str, Any]       =			checkpoint["decoder.conv_out.bias"]

       _A				:	Tuple       =			len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
       _A				:	List[str]       =			{
           layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
       }

       _A				:	str       =			len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
       _A				:	Dict       =			{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)}

       for i in range(UpperCamelCase__						):
              _A				:	Optional[int]       =			(i - 1) // (config["layers_per_block"] + 1)

              if any("downsample" in layer for layer in down_blocks[i]						):
                     _A				:	str       =			checkpoint[
                         f"encoder.down.{i}.downsample.conv.weight"
                     ]
                     _A				:	Any       =			checkpoint[
                         f"encoder.down.{i}.downsample.conv.bias"
                     ]

              if any("block" in layer for layer in down_blocks[i]						):
                     _A				:	Optional[int]       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
                     _A				:	Any       =			{
                         layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                         for layer_id in range(UpperCamelCase__						)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"]						):
                                   _A				:	Optional[int]       =			renew_resnet_paths(blocks[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

              if any("attn" in layer for layer in down_blocks[i]						):
                     _A				:	Dict       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
                     _A				:	Optional[int]       =			{
                         layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                         for layer_id in range(UpperCamelCase__						)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"]						):
                                   _A				:	Dict       =			renew_attention_paths(attns[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							config=UpperCamelCase__						)

       _A				:	str       =			[key for key in checkpoint if "mid.block_1" in key]
       _A				:	Union[str, Any]       =			[key for key in checkpoint if "mid.block_2" in key]
       _A				:	Any       =			[key for key in checkpoint if "mid.attn_1" in key]

       # Mid new 2
       _A				:	Dict       =			renew_resnet_paths(UpperCamelCase__						)
       assign_to_checkpoint(
           UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]    ,							)

       _A				:	Optional[Any]       =			renew_resnet_paths(UpperCamelCase__						)
       assign_to_checkpoint(
           UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]    ,							)

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

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

              if any("upsample" in layer for layer in up_blocks[i]						):
                     _A				:	Any       =			checkpoint[
                         f"decoder.up.{i}.upsample.conv.weight"
                     ]
                     _A				:	Dict       =			checkpoint[
                         f"decoder.up.{i}.upsample.conv.bias"
                     ]

              if any("block" in layer for layer in up_blocks[i]						):
                     _A				:	Dict       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
                     _A				:	Optional[Any]       =			{
                         layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
                     }

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

              if any("attn" in layer for layer in up_blocks[i]						):
                     _A				:	Any       =			len(
                         {".".join(shave_segments(UpperCamelCase__    ,							3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
                     _A				:	Any       =			{
                         layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase__						)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"] + 1						):
                                   _A				:	List[str]       =			{"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"}
                                   _A				:	Dict       =			renew_attention_paths(attns[j]						)
                                   assign_to_checkpoint(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							additional_replacements=[replace_indices]						)

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

       return new_checkpoint


if __name__ == "__main__":
    lowerCAmelCase__						=			argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
    )

    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

    lowerCAmelCase__						=			parser.parse_args()
    lowerCAmelCase__						=			torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        lowerCAmelCase__						=			json.loads(f.read())

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

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

    if config["_class_name"] == "VQModel":
        lowerCAmelCase__						=			VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        lowerCAmelCase__						=			AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        lowerCAmelCase__						=			UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

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

        lowerCAmelCase__						=			DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)




from __future__ import annotations

from typing import Any







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    None:  # noqa: A002
            _A				:	List[str]       =			item
            _A				:	List[Any]       =			next







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self)      ->    None:
            _A				:	Node | None       =			None
            _A				:	List[Any]       =			0


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	Union[str, Any]       =			Node(__lowerCamelCase			,						self.head)
            self.size += 1


     def 			_lowerCamelCase			(						self)      ->    Any:
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   _A				:	str       =			self.head.item
                   _A				:	List[Any]       =			self.head.next
                   self.size -= 1
                   return item


     def 			_lowerCamelCase			(						self)      ->    bool:
            return self.head is None


     def __str__(						self)      ->    str:
            if self.is_empty():
                   return ""
            else:
                   _A				:	List[str]       =			self.head
                   _A				:	Optional[int]       =			""
                   _A				:	list[str]       =			[]
                   while iterate:
                          item_list.append(str(iterate.item))
                          _A				:	Optional[Any]       =			iterate.next

                   _A				:	List[str]       =			" --> ".join(__lowerCamelCase)

                   return item_str


     def __len__(						self)      ->    int:
            return self.size
