





from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings


@maybe_allow_in_graph
class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:List[Any]      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:Any=0.0      ,   snake_case							:Optional[int] = None      ,   snake_case							:str = "geglu"      ,   snake_case							:Optional[int] = None      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = False      ,   snake_case							:bool = True      ,   snake_case							:str = "layer_norm"      ,   snake_case							:bool = False      ,   ):





        '''simple docstring'''




        super().__init__()
        A_       :					int        =      only_cross_attention

        A_       :					Tuple        =      (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
        A_       :					Union[str, Any]        =      (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
           raise ValueError(
               f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
               f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."	)

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
           A_       :					List[Any]        =      AdaLayerNorm(snake_case      ,   snake_case	)
        elif self.use_ada_layer_norm_zero:
           A_       :					List[Any]        =      AdaLayerNormZero(snake_case      ,   snake_case	)
        else:
           A_       :					Tuple        =      nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case	)
        A_       :					List[str]        =      Attention(
            query_dim=snake_case      ,   heads=snake_case      ,   dim_head=snake_case      ,   dropout=snake_case      ,   bias=snake_case      ,   cross_attention_dim=cross_attention_dim if only_cross_attention else None      ,   upcast_attention=snake_case      ,   )

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
           # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
           # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
           # the second cross attention block.
           A_       :					Optional[int]        =      (
               AdaLayerNorm(snake_case      ,   snake_case	)
               if self.use_ada_layer_norm
               else nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case	)
           )
           A_       :					str        =      Attention(
               query_dim=snake_case      ,   cross_attention_dim=cross_attention_dim if not double_self_attention else None      ,   heads=snake_case      ,   dim_head=snake_case      ,   dropout=snake_case      ,   bias=snake_case      ,   upcast_attention=snake_case      ,   )  # is self-attn if encoder_hidden_states is none
        else:
           A_       :					Optional[Any]        =      None
           A_       :					Tuple        =      None

        # 3. Feed-forward
        A_       :					Dict        =      nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case	)
        A_       :					Optional[int]        =      FeedForward(snake_case      ,   dropout=snake_case      ,   activation_fn=snake_case      ,   final_dropout=snake_case	)

        # let chunk size default to None
        A_       :					Tuple        =      None
        A_       :					List[Any]        =      0



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Optional[int]      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Tuple        =      chunk_size
        A_       :					Any        =      dim





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:torch.FloatTensor      ,   snake_case							:Optional[torch.FloatTensor] = None      ,   snake_case							:Optional[torch.FloatTensor] = None      ,   snake_case							:Optional[torch.FloatTensor] = None      ,   snake_case							:Optional[torch.LongTensor] = None      ,   snake_case							:Dict[str, Any] = None      ,   snake_case							:Optional[torch.LongTensor] = None      ,   ):





        '''simple docstring'''




        if self.use_ada_layer_norm:
           A_       :					List[str]        =      self.norma(snake_case      ,   snake_case	)
        elif self.use_ada_layer_norm_zero:
           A_   ,    A_   ,    A_   ,    A_   ,    A_       :					Optional[int]        =      self.norma(
               snake_case      ,   snake_case      ,   snake_case      ,   hidden_dtype=hidden_states.dtype	)
        else:
           A_       :					Optional[int]        =      self.norma(snake_case	)

        A_       :					Union[str, Any]        =      cross_attention_kwargs if cross_attention_kwargs is not None else {}

        A_       :					Dict        =      self.attna(
            snake_case      ,   encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None      ,   attention_mask=snake_case      ,   **snake_case      ,   )
        if self.use_ada_layer_norm_zero:
           A_       :					Optional[int]        =      gate_msa.unsqueeze(1	) * attn_output
        A_       :					Any        =      attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
           A_       :					str        =      (
               self.norma(snake_case      ,   snake_case	) if self.use_ada_layer_norm else self.norma(snake_case	)
           )

           A_       :					Dict        =      self.attna(
               snake_case      ,   encoder_hidden_states=snake_case      ,   attention_mask=snake_case      ,   **snake_case      ,   )
           A_       :					List[Any]        =      attn_output + hidden_states

        # 3. Feed-forward
        A_       :					List[str]        =      self.norma(snake_case	)

        if self.use_ada_layer_norm_zero:
           A_       :					Optional[Any]        =      norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
           # "feed_forward_chunk_size" can be used to save memory
           if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
              raise ValueError(
                  f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."	)

           A_       :					str        =      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
           A_       :					Union[str, Any]        =      torch.cat(
               [self.ff(snake_case	) for hid_slice in norm_hidden_states.chunk(snake_case      ,   dim=self._chunk_dim	)]      ,   dim=self._chunk_dim      ,   )
        else:
           A_       :					Optional[int]        =      self.ff(snake_case	)

        if self.use_ada_layer_norm_zero:
           A_       :					Optional[int]        =      gate_mlp.unsqueeze(1	) * ff_output

        A_       :					List[Any]        =      ff_output + hidden_states

        return hidden_states




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:Optional[Any]      ,   snake_case							:int      ,   snake_case							:Optional[int] = None      ,   snake_case							:int = 4      ,   snake_case							:float = 0.0      ,   snake_case							:str = "geglu"      ,   snake_case							:bool = False      ,   ):





        '''simple docstring'''




        super().__init__()
        A_       :					Optional[Any]        =      int(dim * mult	)
        A_       :					Any        =      dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
           A_       :					Any        =      GELU(snake_case      ,   snake_case	)
        if activation_fn == "gelu-approximate":
           A_       :					int        =      GELU(snake_case      ,   snake_case      ,   approximate="tanh"	)
        elif activation_fn == "geglu":
           A_       :					Any        =      GEGLU(snake_case      ,   snake_case	)
        elif activation_fn == "geglu-approximate":
           A_       :					Tuple        =      ApproximateGELU(snake_case      ,   snake_case	)

        A_       :					Union[str, Any]        =      nn.ModuleList([]	)
        # project in
        self.net.append(snake_case	)
        # project dropout
        self.net.append(nn.Dropout(snake_case	)	)
        # project out
        self.net.append(nn.Linear(snake_case      ,   snake_case	)	)
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
           self.net.append(nn.Dropout(snake_case	)	)





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        for module in self.net:
           A_       :					Optional[int]        =      module(snake_case	)
        return hidden_states




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:str      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:str = "none"	):





        '''simple docstring'''




        super().__init__()
        A_       :					Any        =      nn.Linear(snake_case      ,   snake_case	)
        A_       :					Union[str, Any]        =      approximate



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        if gate.device.type != "mps":
           return F.gelu(snake_case      ,   approximate=self.approximate	)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa	)      ,   approximate=self.approximate	).to(dtype=gate.dtype	)





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]	):





        '''simple docstring'''




        A_       :					List[str]        =      self.proj(snake_case	)
        A_       :					List[Any]        =      self.gelu(snake_case	)
        return hidden_states




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:Optional[Any]      ,   snake_case							:int      ,   snake_case							:int	):





        '''simple docstring'''




        super().__init__()
        A_       :					int        =      nn.Linear(snake_case      ,   dim_out * 2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:Any	):





        '''simple docstring'''




        if gate.device.type != "mps":
           return F.gelu(snake_case	)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa	)	).to(dtype=gate.dtype	)





     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Tuple	):





        '''simple docstring'''




        A_   ,    A_       :					Dict        =      self.proj(snake_case	).chunk(2      ,   dim=-1	)
        return hidden_states * self.gelu(snake_case	)




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:Tuple      ,   snake_case							:int      ,   snake_case							:int	):





        '''simple docstring'''




        super().__init__()
        A_       :					Any        =      nn.Linear(snake_case      ,   snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.proj(snake_case	)
        return x * torch.sigmoid(1.702 * x	)




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:List[str]      ,   snake_case							:List[Any]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        super().__init__()
        A_       :					Any        =      nn.Embedding(snake_case      ,   snake_case	)
        A_       :					Union[str, Any]        =      nn.SiLU()
        A_       :					List[Any]        =      nn.Linear(snake_case      ,   embedding_dim * 2	)
        A_       :					Dict        =      nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.linear(self.silu(self.emb(snake_case	)	)	)
        A_   ,    A_       :					List[Any]        =      torch.chunk(snake_case      ,   2	)
        A_       :					Tuple        =      self.norm(snake_case	) * (1 + scale) + shift
        return x




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        super().__init__()

        A_       :					Dict        =      CombinedTimestepLabelEmbeddings(snake_case      ,   snake_case	)

        A_       :					Union[str, Any]        =      nn.SiLU()
        A_       :					int        =      nn.Linear(snake_case      ,   6 * embedding_dim      ,   bias=snake_case	)
        A_       :					Optional[Any]        =      nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case      ,   eps=1e-6	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:int      ,   snake_case							:str      ,   snake_case							:Tuple      ,   snake_case							:Optional[Any]=None	):





        '''simple docstring'''




        A_       :					Any        =      self.linear(self.silu(self.emb(snake_case      ,   snake_case      ,   hidden_dtype=snake_case	)	)	)
        A_   ,    A_   ,    A_   ,    A_   ,    A_   ,    A_       :					int        =      emb.chunk(6      ,   dim=1	)
        A_       :					List[Any]        =      self.norm(snake_case	) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp




class        __magic_name__	(				nn.Module				):





     """simple docstring"""



     def __init__(				self							:Tuple      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:int      ,   snake_case							:Optional[str] = None      ,   snake_case							:float = 1e-5	):





        '''simple docstring'''




        super().__init__()
        A_       :					Any        =      num_groups
        A_       :					Any        =      eps

        if act_fn is None:
           A_       :					str        =      None
        else:
           A_       :					Union[str, Any]        =      get_activation(snake_case	)

        A_       :					int        =      nn.Linear(snake_case      ,   out_dim * 2	)





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:Any      ,   snake_case							:Dict	):





        '''simple docstring'''




        if self.act:
           A_       :					Optional[int]        =      self.act(snake_case	)
        A_       :					Optional[Any]        =      self.linear(snake_case	)
        A_       :					Optional[Any]        =      emb[:, :, None, None]
        A_   ,    A_       :					Any        =      emb.chunk(2      ,   dim=1	)

        A_       :					List[str]        =      F.group_norm(snake_case      ,   self.num_groups      ,   eps=self.eps	)
        A_       :					str        =      x * (1 + scale) + shift
        return x







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Union[str, Any]=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   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]=0						)			->    int:
   A_       :					Union[str, Any]        =      []
   for old_item in old_list:
      A_       :					Tuple        =      old_item.replace("in_layers.0"					,					"norm1"						)
      A_       :					str        =      new_item.replace("in_layers.2"					,					"conv1"						)

      A_       :					List[str]        =      new_item.replace("out_layers.0"					,					"norm2"						)
      A_       :					Optional[int]        =      new_item.replace("out_layers.3"					,					"conv2"						)

      A_       :					Union[str, Any]        =      new_item.replace("emb_layers.1"					,					"time_emb_proj"						)
      A_       :					Tuple        =      new_item.replace("skip_connection"					,					"conv_shortcut"						)

      A_       :					Any        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

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

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			List[str]=0						)			->    Tuple:
   A_       :					Any        =      []
   for old_item in old_list:
      A_       :					int        =      old_item

      A_       :					Dict        =      new_item.replace("norm.weight"					,					"group_norm.weight"						)
      A_       :					Any        =      new_item.replace("norm.bias"					,					"group_norm.bias"						)

      A_       :					Any        =      new_item.replace("proj_out.weight"					,					"proj_attn.weight"						)
      A_       :					List[str]        =      new_item.replace("proj_out.bias"					,					"proj_attn.bias"						)

      A_       :					List[Any]        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

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

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Dict=None					,					_lowerCAmelCase			:			Union[str, Any]=None					,					_lowerCAmelCase			:			List[str]=None						)			->    Optional[Any]:
   assert isinstance(_lowerCAmelCase					,					_lowerCAmelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         A_       :					Any        =      old_checkpoint[path]
         A_       :					Tuple        =      old_tensor.shape[0] // 3

         A_       :					Union[str, Any]        =      (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

         A_       :					Optional[Any]        =      old_tensor.shape[0] // config["num_head_channels"] // 3

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

         A_       :					Tuple        =      query.reshape(_lowerCAmelCase						)
         A_       :					List[Any]        =      key.reshape(_lowerCAmelCase						)
         A_       :					Union[str, Any]        =      value.reshape(_lowerCAmelCase						)

   for path in paths:
      A_       :					Any        =      path["new"]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      # Global renaming happens here
      A_       :					List[str]        =      new_path.replace("middle_block.0"					,					"mid_block.resnets.0"						)
      A_       :					int        =      new_path.replace("middle_block.1"					,					"mid_block.attentions.0"						)
      A_       :					Tuple        =      new_path.replace("middle_block.2"					,					"mid_block.resnets.1"						)

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

        # proj_attn.weight has to be converted from conv 1D to linear
      if "proj_attn.weight" in new_path:
         A_       :					Tuple        =      old_checkpoint[path["old"]][:, :, 0]
      else:
         A_       :					str        =      old_checkpoint[path["old"]]

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

   A_       :					Union[str, Any]        =      checkpoint["time_embed.0.weight"]
   A_       :					List[Any]        =      checkpoint["time_embed.0.bias"]
   A_       :					Any        =      checkpoint["time_embed.2.weight"]
   A_       :					Optional[Any]        =      checkpoint["time_embed.2.bias"]

   A_       :					List[str]        =      checkpoint["input_blocks.0.0.weight"]
   A_       :					Union[str, Any]        =      checkpoint["input_blocks.0.0.bias"]

   A_       :					str        =      checkpoint["out.0.weight"]
   A_       :					Optional[int]        =      checkpoint["out.0.bias"]
   A_       :					Optional[int]        =      checkpoint["out.2.weight"]
   A_       :					Optional[int]        =      checkpoint["out.2.bias"]

   # Retrieves the keys for the input blocks only
   A_       :					List[str]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
   A_       :					Union[str, Any]        =      {
       layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   # Retrieves the keys for the middle blocks only
   A_       :					Union[str, Any]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
   A_       :					Tuple        =      {
       layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   # Retrieves the keys for the output blocks only
   A_       :					Dict        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
   A_       :					Optional[Any]        =      {
       layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   for i in range(1					,					_lowerCAmelCase						):
      A_       :					Union[str, Any]        =      (i - 1) // (config["num_res_blocks"] + 1)
      A_       :					Optional[int]        =      (i - 1) % (config["num_res_blocks"] + 1)

      A_       :					str        =      [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
      A_       :					List[Any]        =      [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

      if f"input_blocks.{i}.0.op.weight" in checkpoint:
         A_       :					Tuple        =      checkpoint[
             f"input_blocks.{i}.0.op.weight"
         ]
         A_       :					List[Any]        =      checkpoint[
             f"input_blocks.{i}.0.op.bias"
         ]
         continue

      A_       :					List[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
      A_       :					Any        =      {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
      A_       :					Any        =      {"old": "resnets.2.op", "new": "downsamplers.0.op"}
      assign_to_checkpoint(
          _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path, resnet_op]					,					config=_lowerCAmelCase						)

      if len(_lowerCAmelCase						):
         A_       :					Tuple        =      renew_attention_paths(_lowerCAmelCase						)
         A_       :					List[str]        =      {
             "old": f"input_blocks.{i}.1",
             "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
         }
         A_       :					Any        =      {
             f"input_blocks.{i}.1.qkv.bias": {
                 "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                 "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                 "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
             },
             f"input_blocks.{i}.1.qkv.weight": {
                 "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                 "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                 "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
             },
         }
         assign_to_checkpoint(
             _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					attention_paths_to_split=_lowerCAmelCase					,					config=_lowerCAmelCase					,					)

   A_       :					List[Any]        =      middle_blocks[0]
   A_       :					Tuple        =      middle_blocks[1]
   A_       :					Dict        =      middle_blocks[2]

   A_       :					str        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Tuple        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Any        =      renew_attention_paths(_lowerCAmelCase						)
   A_       :					Optional[int]        =      {
       "middle_block.1.qkv.bias": {
           "key": "mid_block.attentions.0.key.bias",
           "query": "mid_block.attentions.0.query.bias",
           "value": "mid_block.attentions.0.value.bias",
       },
       "middle_block.1.qkv.weight": {
           "key": "mid_block.attentions.0.key.weight",
           "query": "mid_block.attentions.0.query.weight",
           "value": "mid_block.attentions.0.value.weight",
       },
   }
   assign_to_checkpoint(
       _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					attention_paths_to_split=_lowerCAmelCase					,					config=_lowerCAmelCase						)

   for i in range(_lowerCAmelCase						):
      A_       :					Optional[int]        =      i // (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      i % (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      [shave_segments(_lowerCAmelCase					,					2						) for name in output_blocks[i]]
      A_       :					Union[str, Any]        =      {}

      for layer in output_block_layers:
         A_   ,    A_       :					str        =      layer.split("."						)[0], shave_segments(_lowerCAmelCase					,					1						)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_lowerCAmelCase						)
         else:
            A_       :					List[str]        =      [layer_name]

      if len(_lowerCAmelCase						) > 1:
         A_       :					Dict        =      [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
         A_       :					List[Any]        =      [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

         A_       :					Optional[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
         A_       :					Any        =      renew_resnet_paths(_lowerCAmelCase						)

         A_       :					Optional[Any]        =      {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
         assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					config=_lowerCAmelCase						)

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            A_       :					int        =      list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
            A_       :					Union[str, Any]        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.weight"
            ]
            A_       :					int        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.bias"
            ]

            # Clear attentions as they have been attributed above.
            if len(_lowerCAmelCase						) == 2:
               A_       :					Union[str, Any]        =      []

         if len(_lowerCAmelCase						):
            A_       :					Dict        =      renew_attention_paths(_lowerCAmelCase						)
            A_       :					Union[str, Any]        =      {
                "old": f"output_blocks.{i}.1",
                "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            A_       :					Any        =      {
                f"output_blocks.{i}.1.qkv.bias": {
                    "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                    "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                    "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                },
                f"output_blocks.{i}.1.qkv.weight": {
                    "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                    "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                    "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                },
            }
            assign_to_checkpoint(
                _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,					config=_lowerCAmelCase					,					)
      else:
         A_       :					Optional[int]        =      renew_resnet_paths(_lowerCAmelCase					,					n_shave_prefix_segments=1						)
         for path in resnet_0_paths:
            A_       :					List[Any]        =      ".".join(["output_blocks", str(_lowerCAmelCase						), path["old"]]						)
            A_       :					Optional[Any]        =      ".".join(["up_blocks", str(_lowerCAmelCase						), "resnets", str(_lowerCAmelCase						), path["new"]]						)

            A_       :					Optional[int]        =      checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase				:   List[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.''')

 _lowerCAmelCase				:   Optional[Any]						       =						parser.parse_args()

 _lowerCAmelCase				:   Optional[Any]						       =						torch.load(args.checkpoint_path)

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

 _lowerCAmelCase				:   Tuple						       =						convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase				:   Optional[Any]						       =						UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase				:   List[str]						       =						DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase				:   List[str]						       =						VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase				:   Optional[Any]						       =						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)
