



'''simple docstring'''





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      torch.FloatTensor







class        lowerCAmelCase_(				nn.Module     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=3  ,__UpperCAmelCase=("DownEncoderBlock2D",)  ,__UpperCAmelCase=(64,)  ,__UpperCAmelCase=2  ,__UpperCAmelCase=32  ,__UpperCAmelCase="silu"  ,__UpperCAmelCase=True  ,)    ->		Optional[int]:
      super().__init__()
      lowerCAmelCase__ :  Union[str, Any]							      =					layers_per_block

      lowerCAmelCase__ :  Dict							      =					torch.nn.Convad(
          __UpperCAmelCase  ,block_out_channels[0]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

      lowerCAmelCase__ :  Optional[Any]							      =					None
      lowerCAmelCase__ :  Optional[Any]							      =					nn.ModuleList([]      )

      # down
      lowerCAmelCase__ :  str							      =					block_out_channels[0]
      for i, down_block_type in enumerate(__UpperCAmelCase      ):
          lowerCAmelCase__ :  Any							      =					output_channel
          lowerCAmelCase__ :  str							      =					block_out_channels[i]
          lowerCAmelCase__ :  Union[str, Any]							      =					i == len(__UpperCAmelCase      ) - 1

          lowerCAmelCase__ :  Tuple							      =					get_down_block(
              __UpperCAmelCase  ,num_layers=self.layers_per_block  ,in_channels=__UpperCAmelCase  ,out_channels=__UpperCAmelCase  ,add_downsample=not is_final_block  ,resnet_eps=1E-6  ,downsample_padding=0  ,resnet_act_fn=__UpperCAmelCase  ,resnet_groups=__UpperCAmelCase  ,attention_head_dim=__UpperCAmelCase  ,temb_channels=__UpperCAmelCase  ,)
          self.down_blocks.append(__UpperCAmelCase      )

      # mid
      lowerCAmelCase__ :  Any							      =					UNetMidBlockaD(
          in_channels=block_out_channels[-1]  ,resnet_eps=1E-6  ,resnet_act_fn=__UpperCAmelCase  ,output_scale_factor=1  ,resnet_time_scale_shift="""default"""  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=__UpperCAmelCase  ,temb_channels=__UpperCAmelCase  ,)

      # out
      lowerCAmelCase__ :  List[str]							      =					nn.GroupNorm(num_channels=block_out_channels[-1]  ,num_groups=__UpperCAmelCase  ,eps=1E-6      )
      lowerCAmelCase__ :  List[str]							      =					nn.SiLU()

      lowerCAmelCase__ :  int							      =					2 * out_channels if double_z else out_channels
      lowerCAmelCase__ :  Dict							      =					nn.Convad(block_out_channels[-1]  ,__UpperCAmelCase  ,3  ,padding=1      )

      lowerCAmelCase__ :  Optional[int]							      =					False






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Any							      =					x
      lowerCAmelCase__ :  Optional[Any]							      =					self.conv_in(__UpperCAmelCase      )

      if self.training and self.gradient_checkpointing:

          def create_custom_forward(__UpperCAmelCase      ):
              def custom_forward(*__UpperCAmelCase      ):
                  return module(*__UpperCAmelCase      )

              return custom_forward

          # down
          if is_torch_version(""">="""  ,"""1.11.0"""      ):
              for down_block in self.down_blocks:
                  lowerCAmelCase__ :  Tuple							      =					torch.utils.checkpoint.checkpoint(
                      create_custom_forward(__UpperCAmelCase      )  ,__UpperCAmelCase  ,use_reentrant=__UpperCAmelCase      )
              # middle
              lowerCAmelCase__ :  List[Any]							      =					torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.mid_block      )  ,__UpperCAmelCase  ,use_reentrant=__UpperCAmelCase      )
          else:
              for down_block in self.down_blocks:
                  lowerCAmelCase__ :  Union[str, Any]							      =					torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase      )  ,__UpperCAmelCase      )
              # middle
              lowerCAmelCase__ :  List[str]							      =					torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )  ,__UpperCAmelCase      )

      else:
          # down
          for down_block in self.down_blocks:
              lowerCAmelCase__ :  Union[str, Any]							      =					down_block(__UpperCAmelCase      )

          # middle
          lowerCAmelCase__ :  Optional[int]							      =					self.mid_block(__UpperCAmelCase      )

      # post-process
      lowerCAmelCase__ :  str							      =					self.conv_norm_out(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					self.conv_act(__UpperCAmelCase      )
      lowerCAmelCase__ :  int							      =					self.conv_out(__UpperCAmelCase      )

      return sample







class        lowerCAmelCase_(				nn.Module     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=3  ,__UpperCAmelCase=("UpDecoderBlock2D",)  ,__UpperCAmelCase=(64,)  ,__UpperCAmelCase=2  ,__UpperCAmelCase=32  ,__UpperCAmelCase="silu"  ,__UpperCAmelCase="group"  ,)    ->		List[Any]:
      super().__init__()
      lowerCAmelCase__ :  Dict							      =					layers_per_block

      lowerCAmelCase__ :  Any							      =					nn.Convad(
          __UpperCAmelCase  ,block_out_channels[-1]  ,kernel_size=3  ,stride=1  ,padding=1  ,)

      lowerCAmelCase__ :  Union[str, Any]							      =					None
      lowerCAmelCase__ :  Dict							      =					nn.ModuleList([]      )

      lowerCAmelCase__ :  Tuple							      =					in_channels if norm_type == """spatial""" else None

      # mid
      lowerCAmelCase__ :  Optional[Any]							      =					UNetMidBlockaD(
          in_channels=block_out_channels[-1]  ,resnet_eps=1E-6  ,resnet_act_fn=__UpperCAmelCase  ,output_scale_factor=1  ,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=__UpperCAmelCase  ,temb_channels=__UpperCAmelCase  ,)

      # up
      lowerCAmelCase__ :  Optional[int]							      =					list(reversed(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  Tuple							      =					reversed_block_out_channels[0]
      for i, up_block_type in enumerate(__UpperCAmelCase      ):
          lowerCAmelCase__ :  Optional[Any]							      =					output_channel
          lowerCAmelCase__ :  int							      =					reversed_block_out_channels[i]

          lowerCAmelCase__ :  Dict							      =					i == len(__UpperCAmelCase      ) - 1

          lowerCAmelCase__ :  Union[str, Any]							      =					get_up_block(
              __UpperCAmelCase  ,num_layers=self.layers_per_block + 1  ,in_channels=__UpperCAmelCase  ,out_channels=__UpperCAmelCase  ,prev_output_channel=__UpperCAmelCase  ,add_upsample=not is_final_block  ,resnet_eps=1E-6  ,resnet_act_fn=__UpperCAmelCase  ,resnet_groups=__UpperCAmelCase  ,attention_head_dim=__UpperCAmelCase  ,temb_channels=__UpperCAmelCase  ,resnet_time_scale_shift=__UpperCAmelCase  ,)
          self.up_blocks.append(__UpperCAmelCase      )
          lowerCAmelCase__ :  Union[str, Any]							      =					output_channel

      # out
      if norm_type == "spatial":
          lowerCAmelCase__ :  Optional[Any]							      =					SpatialNorm(block_out_channels[0]  ,__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  List[Any]							      =					nn.GroupNorm(num_channels=block_out_channels[0]  ,num_groups=__UpperCAmelCase  ,eps=1E-6      )
      lowerCAmelCase__ :  int							      =					nn.SiLU()
      lowerCAmelCase__ :  Dict							      =					nn.Convad(block_out_channels[0]  ,__UpperCAmelCase  ,3  ,padding=1      )

      lowerCAmelCase__ :  Optional[Any]							      =					False






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[Any]							      =					z
      lowerCAmelCase__ :  List[Any]							      =					self.conv_in(__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					next(iter(self.up_blocks.parameters()      )      ).dtype
      if self.training and self.gradient_checkpointing:

          def create_custom_forward(__UpperCAmelCase      ):
              def custom_forward(*__UpperCAmelCase      ):
                  return module(*__UpperCAmelCase      )

              return custom_forward

          if is_torch_version(""">="""  ,"""1.11.0"""      ):
              # middle
              lowerCAmelCase__ :  Union[str, Any]							      =					torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.mid_block      )  ,__UpperCAmelCase  ,__UpperCAmelCase  ,use_reentrant=__UpperCAmelCase      )
              lowerCAmelCase__ :  List[Any]							      =					sample.to(__UpperCAmelCase      )

              # up
              for up_block in self.up_blocks:
                  lowerCAmelCase__ :  Optional[Any]							      =					torch.utils.checkpoint.checkpoint(
                      create_custom_forward(__UpperCAmelCase      )  ,__UpperCAmelCase  ,__UpperCAmelCase  ,use_reentrant=__UpperCAmelCase      )
          else:
              # middle
              lowerCAmelCase__ :  int							      =					torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.mid_block      )  ,__UpperCAmelCase  ,__UpperCAmelCase      )
              lowerCAmelCase__ :  str							      =					sample.to(__UpperCAmelCase      )

              # up
              for up_block in self.up_blocks:
                  lowerCAmelCase__ :  List[Any]							      =					torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase      )  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      else:
          # middle
          lowerCAmelCase__ :  Any							      =					self.mid_block(__UpperCAmelCase  ,__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					sample.to(__UpperCAmelCase      )

          # up
          for up_block in self.up_blocks:
              lowerCAmelCase__ :  Dict							      =					up_block(__UpperCAmelCase  ,__UpperCAmelCase      )

        # post-process
      if latent_embeds is None:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.conv_norm_out(__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  List[Any]							      =					self.conv_norm_out(__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					self.conv_act(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					self.conv_out(__UpperCAmelCase      )

      return sample







class        lowerCAmelCase_(				nn.Module     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase="random"  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True      )    ->		List[Any]:
      super().__init__()
      lowerCAmelCase__ :  List[str]							      =					n_e
      lowerCAmelCase__ :  Dict							      =					vq_embed_dim
      lowerCAmelCase__ :  List[Any]							      =					beta
      lowerCAmelCase__ :  Union[str, Any]							      =					legacy

      lowerCAmelCase__ :  Dict							      =					nn.Embedding(self.n_e  ,self.vq_embed_dim      )
      self.embedding.weight.data.uniform_(-1.0 / self.n_e  ,1.0 / self.n_e      )

      lowerCAmelCase__ :  int							      =					remap
      if self.remap is not None:
          self.register_buffer("""used"""  ,torch.tensor(np.load(self.remap      )      )      )
          lowerCAmelCase__ :  List[Any]							      =					self.used.shape[0]
          lowerCAmelCase__ :  Optional[Any]							      =					unknown_index  # "random" or "extra" or integer
          if self.unknown_index == "extra":
              lowerCAmelCase__ :  Optional[int]							      =					self.re_embed
              lowerCAmelCase__ :  Dict							      =					self.re_embed + 1
          print(
              F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
              F"""Using {self.unknown_index} for unknown indices."""      )
      else:
          lowerCAmelCase__ :  List[Any]							      =					n_e

      lowerCAmelCase__ :  Tuple							      =					sane_index_shape




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  Union[str, Any]							      =					inds.shape
      assert len(__UpperCAmelCase      ) > 1
      lowerCAmelCase__ :  Optional[int]							      =					inds.reshape(ishape[0]  ,-1      )
      lowerCAmelCase__ :  Any							      =					self.used.to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					(inds[:, :, None] == used[None, None, ...]).long()
      lowerCAmelCase__ :  List[Any]							      =					match.argmax(-1      )
      lowerCAmelCase__ :  Optional[int]							      =					match.sum(2      ) < 1
      if self.unknown_index == "random":
          lowerCAmelCase__ :  int							      =					torch.randint(0  ,self.re_embed  ,size=new[unknown].shape      ).to(device=new.device      )
      else:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.unknown_index
      return new.reshape(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  Dict							      =					inds.shape
      assert len(__UpperCAmelCase      ) > 1
      lowerCAmelCase__ :  List[str]							      =					inds.reshape(ishape[0]  ,-1      )
      lowerCAmelCase__ :  int							      =					self.used.to(__UpperCAmelCase      )
      if self.re_embed > self.used.shape[0]:  # extra token
          lowerCAmelCase__ :  Any							      =					0  # simply set to zero
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.gather(used[None, :][inds.shape[0] * [0], :]  ,1  ,__UpperCAmelCase      )
      return back.reshape(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      # reshape z -> (batch, height, width, channel) and flatten
      lowerCAmelCase__ :  Optional[int]							      =					z.permute(0  ,2  ,3  ,1      ).contiguous()
      lowerCAmelCase__ :  Tuple							      =					z.view(-1  ,self.vq_embed_dim      )

      # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
      lowerCAmelCase__ :  Optional[Any]							      =					torch.argmin(torch.cdist(__UpperCAmelCase  ,self.embedding.weight      )  ,dim=1      )

      lowerCAmelCase__ :  Dict							      =					self.embedding(__UpperCAmelCase      ).view(z.shape      )
      lowerCAmelCase__ :  Tuple							      =					None
      lowerCAmelCase__ :  int							      =					None

      # compute loss for embedding
      if not self.legacy:
          lowerCAmelCase__ :  List[Any]							      =					self.beta * torch.mean((z_q.detach() - z) ** 2      ) + torch.mean((z_q - z.detach()) ** 2      )
      else:
          lowerCAmelCase__ :  int							      =					torch.mean((z_q.detach() - z) ** 2      ) + self.beta * torch.mean((z_q - z.detach()) ** 2      )

      # preserve gradients
      lowerCAmelCase__ :  Optional[Any]							      =					z + (z_q - z).detach()

      # reshape back to match original input shape
      lowerCAmelCase__ :  List[Any]							      =					z_q.permute(0  ,3  ,1  ,2      ).contiguous()

      if self.remap is not None:
          lowerCAmelCase__ :  Tuple							      =					min_encoding_indices.reshape(z.shape[0]  ,-1      )  # add batch axis
          lowerCAmelCase__ :  int							      =					self.remap_to_used(__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					min_encoding_indices.reshape(-1  ,1      )  # flatten

      if self.sane_index_shape:
          lowerCAmelCase__ :  Union[str, Any]							      =					min_encoding_indices.reshape(z_q.shape[0]  ,z_q.shape[2]  ,z_q.shape[3]      )

      return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      # shape specifying (batch, height, width, channel)
      if self.remap is not None:
          lowerCAmelCase__ :  str							      =					indices.reshape(shape[0]  ,-1      )  # add batch axis
          lowerCAmelCase__ :  Dict							      =					self.unmap_to_all(__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					indices.reshape(-1      )  # flatten again

      # get quantized latent vectors
      lowerCAmelCase__ :  Optional[int]							      =					self.embedding(__UpperCAmelCase      )

      if shape is not None:
          lowerCAmelCase__ :  str							      =					z_q.view(__UpperCAmelCase      )
          # reshape back to match original input shape
          lowerCAmelCase__ :  Dict							      =					z_q.permute(0  ,3  ,1  ,2      ).contiguous()

      return z_q







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=False      )    ->		Dict:
      lowerCAmelCase__ :  int							      =					parameters
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					torch.chunk(__UpperCAmelCase  ,2  ,dim=1      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.clamp(self.logvar  ,-3_0.0  ,2_0.0      )
      lowerCAmelCase__ :  List[Any]							      =					deterministic
      lowerCAmelCase__ :  str							      =					torch.exp(0.5 * self.logvar      )
      lowerCAmelCase__ :  str							      =					torch.exp(self.logvar      )
      if self.deterministic:
          lowerCAmelCase__ :  Optional[int]							      =					torch.zeros_like(
              self.mean  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase = None      )    ->		torch.FloatTensor:
      # make sure sample is on the same device as the parameters and has same dtype
      lowerCAmelCase__ :  str							      =					randn_tensor(
          self.mean.shape  ,generator=__UpperCAmelCase  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )
      lowerCAmelCase__ :  List[str]							      =					self.mean + self.std * sample
      return x




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=None      )    ->		str:
      if self.deterministic:
          return torch.Tensor([0.0]      )
      else:
          if other is None:
              return 0.5 * torch.sum(torch.pow(self.mean  ,2      ) + self.var - 1.0 - self.logvar  ,dim=[1, 2, 3]      )
          else:
              return 0.5 * torch.sum(
                  torch.pow(self.mean - other.mean  ,2      ) / other.var
                  + self.var / other.var
                  - 1.0
                  - self.logvar
                  + other.logvar  ,dim=[1, 2, 3]  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=[1, 2, 3]      )    ->		Optional[Any]:
      if self.deterministic:
          return torch.Tensor([0.0]      )
      lowerCAmelCase__ :  str							      =					np.log(2.0 * np.pi      )
      return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean  ,2      ) / self.var  ,dim=__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      return self.mean







'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=1			):



    """simple docstring"""





    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

        lowerCAmelCase__ :  str							      =					new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""skip_connection"""							,    """conv_shortcut"""			)

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "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():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

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

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  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
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                lowerCAmelCase__ :  Any							      =					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:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

    # Retrieves the keys for the input blocks only
    lowerCAmelCase__ :  Optional[Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """input_blocks""" in layer}			)
    lowerCAmelCase__ :  Optional[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

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

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

    for i in range(1							,    UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[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:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

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

        if len(UpperCamelCase			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                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(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  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(
        UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase			)

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

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

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

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

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

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase			) == 2:
                    lowerCAmelCase__ :  Tuple							      =					[]

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    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(
                    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions			) else None							,    config=UpperCamelCase							,    )
        else:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint[old_path]

    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())

 _lowerCAmelCase      						=	convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase      						=	UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

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

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


