code: import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
      assert isinstance(lowercase       ,							lowercase				), "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_:       List[Any]         =  old_checkpoint[path]
              lowerCAmelCase_:       List[Any]         =  old_tensor.shape[0] // 3
  
              lowerCAmelCase_:       Optional[Any]         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
              lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // config["num_head_channels"] // 3
  
              lowerCAmelCase_:       Union[str, Any]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
              lowerCAmelCase_:       List[str]         =  old_tensor.split(channels // num_heads       ,							dim=1				)
  
              lowerCAmelCase_:       Optional[Any]         =  query.reshape(lowercase				)
              lowerCAmelCase_:       int         =  key.reshape(lowercase				)
              lowerCAmelCase_:       Any         =  value.reshape(lowercase				)
  
      for path in paths:
          lowerCAmelCase_:       List[str]         =  path["new"]
  
          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
              continue
  
          if additional_replacements is not None:
              for replacement in additional_replacements:
                  lowerCAmelCase_:       Optional[Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)
  
          # proj_attn.weight has to be converted from conv 1D to linear
          lowerCAmelCase_:       str         =  old_checkpoint[path["old"]]
          lowerCAmelCase_:       Dict         =  ["proj_attn.weight"]
          lowerCAmelCase_:       Union[str, Any]         =  ["proj_out.weight", "proj_in.weight"]
          if any(k in new_path for k in names				):
              lowerCAmelCase_:       Tuple         =  weight[:, :, 0]
          elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
              lowerCAmelCase_:       Optional[int]         =  weight[:, :, 0]
          else:
              lowerCAmelCase_:       Union[str, Any]         =  weight
  def 		snake_case__  (		lowercase       ,							lowercase=0				):
      lowerCAmelCase_:       List[str]         =  []
      for old_item in old_list:
          lowerCAmelCase_:       Optional[Any]         =  old_item
  
          #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
          #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
          #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
          #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
          #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
          mapping.append({"old": old_item, "new": new_item}				)
  
      return mapping
  def 		snake_case__  (		lowercase       ,							lowercase=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 		snake_case__  (		lowercase       ,							lowercase=0				):
      lowerCAmelCase_:       Optional[int]         =  []
      for old_item in old_list:
          mapping.append({"old": old_item, "new": old_item}				)
  
      return mapping
  def 		snake_case__  (		lowercase       ,							lowercase=0				):
      lowerCAmelCase_:       Union[str, Any]         =  []
      for old_item in old_list:
          lowerCAmelCase_:       Tuple         =  old_item.replace("in_layers.0"       ,							"norm1"				)
          lowerCAmelCase_:       Dict         =  new_item.replace("in_layers.2"       ,							"conv1"				)
  
          lowerCAmelCase_:       Optional[int]         =  new_item.replace("out_layers.0"       ,							"norm2"				)
          lowerCAmelCase_:       List[str]         =  new_item.replace("out_layers.3"       ,							"conv2"				)
  
          lowerCAmelCase_:       List[Any]         =  new_item.replace("emb_layers.1"       ,							"time_emb_proj"				)
          lowerCAmelCase_:       Optional[int]         =  new_item.replace("skip_connection"       ,							"conv_shortcut"				)
  
          lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
  
          if "temopral_conv" not in old_item:
              mapping.append({"old": old_item, "new": new_item}				)
  
      return mapping
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=False				):
      lowerCAmelCase_:       Tuple         =  {}
      lowerCAmelCase_:       Any         =  list(checkpoint.keys()				)
  
      lowerCAmelCase_:       str         =  "model.diffusion_model."
  
      # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
      if sum(k.startswith("model_ema"				) for k in keys				) > 100 and extract_ema:
          print(F'''Checkpoint {path} has both EMA and non-EMA weights.'''				)
          print(
              "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
              " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."				)
          for key in keys:
              if key.startswith("model.diffusion_model"				):
                  lowerCAmelCase_:       List[str]         =  "model_ema." + "".join(key.split("."				)[1:]				)
                  lowerCAmelCase_:       Tuple         =  checkpoint.pop(lowercase				)
      else:
          if sum(k.startswith("model_ema"				) for k in keys				) > 100:
              print(
                  "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
                  " weights (usually better for inference), please make sure to add the `--extract_ema` flag."				)
  
          for key in keys:
              lowerCAmelCase_:       str         =  checkpoint.pop(lowercase				)
  
      lowerCAmelCase_:       Optional[Any]         =  {}
  
      lowerCAmelCase_:       str         =  unet_state_dict["time_embed.0.weight"]
      lowerCAmelCase_:       str         =  unet_state_dict["time_embed.0.bias"]
      lowerCAmelCase_:       Union[str, Any]         =  unet_state_dict["time_embed.2.weight"]
      lowerCAmelCase_:       Tuple         =  unet_state_dict["time_embed.2.bias"]
  
      if config["class_embed_type"] is None:
          # No parameters to port
          ...
      elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
          lowerCAmelCase_:       str         =  unet_state_dict["label_emb.0.0.weight"]
          lowerCAmelCase_:       int         =  unet_state_dict["label_emb.0.0.bias"]
          lowerCAmelCase_:       int         =  unet_state_dict["label_emb.0.2.weight"]
          lowerCAmelCase_:       List[str]         =  unet_state_dict["label_emb.0.2.bias"]
      else:
          raise NotImplementedError(F'''Not implemented `class_embed_type`: {config["class_embed_type"]}'''				)
  
      lowerCAmelCase_:       Optional[Any]         =  unet_state_dict["input_blocks.0.0.weight"]
      lowerCAmelCase_:       Tuple         =  unet_state_dict["input_blocks.0.0.bias"]
  
      lowerCAmelCase_:       List[Any]         =  [v for v in unet_state_dict if v.startswith("input_blocks.0.1"				)]
      lowerCAmelCase_:       int         =  renew_attention_paths(lowercase				)
      lowerCAmelCase_:       Optional[Any]         =  {"old": "input_blocks.0.1", "new": "transformer_in"}
      assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
      lowerCAmelCase_:       List[Any]         =  unet_state_dict["out.0.weight"]
      lowerCAmelCase_:       str         =  unet_state_dict["out.0.bias"]
      lowerCAmelCase_:       int         =  unet_state_dict["out.2.weight"]
      lowerCAmelCase_:       int         =  unet_state_dict["out.2.bias"]
  
      # Retrieves the keys for the input blocks only
      lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "input_blocks" in layer}				)
      lowerCAmelCase_:       Dict         =  {
          layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
          for layer_id in range(lowercase				)
      }
  
      # Retrieves the keys for the middle blocks only
      lowerCAmelCase_:       Union[str, Any]         =  len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "middle_block" in layer}				)
      lowerCAmelCase_:       Optional[int]         =  {
          layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
          for layer_id in range(lowercase				)
      }
  
      # Retrieves the keys for the output blocks only
      lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "output_blocks" in layer}				)
      lowerCAmelCase_:       int         =  {
          layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
          for layer_id in range(lowercase				)
      }
  
      for i in range(1       ,							lowercase				):
          lowerCAmelCase_:       Optional[int]         =  (i - 1) // (config["layers_per_block"] + 1)
          lowerCAmelCase_:       Tuple         =  (i - 1) % (config["layers_per_block"] + 1)
  
          lowerCAmelCase_:       Optional[int]         =  [
              key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
          ]
          lowerCAmelCase_:       Union[str, Any]         =  [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
          lowerCAmelCase_:       Dict         =  [key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]
  
          if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
              lowerCAmelCase_:       Any         =  unet_state_dict.pop(
                  F'''input_blocks.{i}.op.weight'''				)
              lowerCAmelCase_:       List[Any]         =  unet_state_dict.pop(
                  F'''input_blocks.{i}.op.bias'''				)
  
          lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
          lowerCAmelCase_:       int         =  {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          assign_to_checkpoint(
              lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
          lowerCAmelCase_:       Tuple         =  [key for key in resnets if "temopral_conv" in key]
          lowerCAmelCase_:       Dict         =  renew_temp_conv_paths(lowercase				)
          lowerCAmelCase_:       Optional[Any]         =  {
              "old": F'''input_blocks.{i}.0.temopral_conv''',
              "new": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
          }
          assign_to_checkpoint(
              lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
          if len(lowercase				):
              lowerCAmelCase_:       Union[str, Any]         =  renew_attention_paths(lowercase				)
              lowerCAmelCase_:       Tuple         =  {"old": F'''input_blocks.{i}.1''', "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
              assign_to_checkpoint(
                  lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
          if len(lowercase				):
              lowerCAmelCase_:       Tuple         =  renew_attention_paths(lowercase				)
              lowerCAmelCase_:       Union[str, Any]         =  {
                  "old": F'''input_blocks.{i}.2''',
                  "new": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
      lowerCAmelCase_:       List[Any]         =  middle_blocks[0]
      lowerCAmelCase_:       Any         =  [key for key in resnet_a if "temopral_conv" in key]
      lowerCAmelCase_:       int         =  middle_blocks[1]
      lowerCAmelCase_:       str         =  middle_blocks[2]
      lowerCAmelCase_:       List[Any]         =  middle_blocks[3]
      lowerCAmelCase_:       Dict         =  [key for key in resnet_a if "temopral_conv" in key]
  
      lowerCAmelCase_:       Tuple         =  renew_resnet_paths(lowercase				)
      lowerCAmelCase_:       Tuple         =  {"old": "middle_block.0", "new": "mid_block.resnets.0"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)
  
      lowerCAmelCase_:       Optional[int]         =  renew_temp_conv_paths(lowercase				)
      lowerCAmelCase_:       List[str]         =  {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)
  
      lowerCAmelCase_:       Optional[Any]         =  renew_resnet_paths(lowercase				)
      lowerCAmelCase_:       int         =  {"old": "middle_block.3", "new": "mid_block.resnets.1"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)
  
      lowerCAmelCase_:       Any         =  renew_temp_conv_paths(lowercase				)
      lowerCAmelCase_:       Optional[Any]         =  {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)
  
      lowerCAmelCase_:       Optional[int]         =  renew_attention_paths(lowercase				)
      lowerCAmelCase_:       Union[str, Any]         =  {"old": "middle_block.1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
      lowerCAmelCase_:       List[str]         =  renew_attention_paths(lowercase				)
      lowerCAmelCase_:       Union[str, Any]         =  {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
      assign_to_checkpoint(
          lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
      for i in range(lowercase				):
          lowerCAmelCase_:       int         =  i // (config["layers_per_block"] + 1)
          lowerCAmelCase_:       Dict         =  i % (config["layers_per_block"] + 1)
          lowerCAmelCase_:       Dict         =  [shave_segments(lowercase       ,							2				) for name in output_blocks[i]]
          lowerCAmelCase_:       Union[str, Any]         =  {}
  
          for layer in output_block_layers:
              lowerCAmelCase_:       str         =  layer.split("."				)[0], shave_segments(lowercase       ,							1				)
              if layer_id in output_block_list:
                  output_block_list[layer_id].append(lowercase				)
              else:
                  lowerCAmelCase_:       List[str]         =  [layer_name]
  
          if len(lowercase				) > 1:
              lowerCAmelCase_:       Tuple         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
              lowerCAmelCase_:       List[Any]         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
              lowerCAmelCase_:       Any         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]
  
              lowerCAmelCase_:       str         =  renew_resnet_paths(lowercase				)
              lowerCAmelCase_:       Tuple         =  renew_resnet_paths(lowercase				)
  
              lowerCAmelCase_:       str         =  {"old": F'''output_blocks.{i}.0''', "new": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
              assign_to_checkpoint(
                  lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
              lowerCAmelCase_:       List[str]         =  [key for key in resnets if "temopral_conv" in key]
              lowerCAmelCase_:       List[str]         =  renew_temp_conv_paths(lowercase				)
              lowerCAmelCase_:       Optional[Any]         =  {
                  "old": F'''output_blocks.{i}.0.temopral_conv''',
                  "new": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
              lowerCAmelCase_:       str         =  {k: sorted(lowercase				) for k, v in output_block_list.items()}
              if ["conv.bias", "conv.weight"] in output_block_list.values():
                  lowerCAmelCase_:       List[Any]         =  list(output_block_list.values()				).index(["conv.bias", "conv.weight"]				)
                  lowerCAmelCase_:       Dict         =  unet_state_dict[
                      F'''output_blocks.{i}.{index}.conv.weight'''
                  ]
                  lowerCAmelCase_:       List[str]         =  unet_state_dict[
                      F'''output_blocks.{i}.{index}.conv.bias'''
                  ]
  
                  # Clear attentions as they have been attributed above.
                  if len(lowercase				) == 2:
                      lowerCAmelCase_:       Dict         =  []
  
              if len(lowercase				):
                  lowerCAmelCase_:       int         =  renew_attention_paths(lowercase				)
                  lowerCAmelCase_:       Tuple         =  {
                      "old": F'''output_blocks.{i}.1''',
                      "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  assign_to_checkpoint(
                      lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
  
              if len(lowercase				):
                  lowerCAmelCase_:       Any         =  renew_attention_paths(lowercase				)
                  lowerCAmelCase_:       Dict         =  {
                      "old": F'''output_blocks.{i}.2''',
                      "new": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                  }
                  assign_to_checkpoint(
                      lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)
          else:
              lowerCAmelCase_:       Optional[int]         =  renew_resnet_paths(lowercase       ,							n_shave_prefix_segments=1				)
              for path in resnet_0_paths:
                  lowerCAmelCase_:       List[str]         =  ".".join(["output_blocks", str(lowercase				), path["old"]]				)
                  lowerCAmelCase_:       Dict         =  ".".join(["up_blocks", str(lowercase				), "resnets", str(lowercase				), path["new"]]				)
                  lowerCAmelCase_:       Union[str, Any]         =  unet_state_dict[old_path]
  
              lowerCAmelCase_:       str         =  [l for l in output_block_layers if "temopral_conv" in l]
              for path in temopral_conv_paths:
                  lowerCAmelCase_:       int         =  path.split("temopral_conv."				)[-1]
                  lowerCAmelCase_:       Union[str, Any]         =  ".".join(["output_blocks", str(lowercase				), str(lowercase				), "temopral_conv", pruned_path]				)
                  lowerCAmelCase_:       Any         =  ".".join(["up_blocks", str(lowercase				), "temp_convs", str(lowercase				), pruned_path]				)
                  lowerCAmelCase_:       Union[str, Any]         =  unet_state_dict[old_path]
  
      return new_checkpoint
  
  
  if __name__ == "__main__":
   a     :			str					    =						argparse.ArgumentParser()
  
   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
   )
   parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
   a     :			List[str]					    =						parser.parse_args()
  
   a     :			Union[str, Any]					    =						torch.load(args.checkpoint_path, map_location="""cpu""")
   a     :			int					    =						UNetaDConditionModel()
  
   a     :			Tuple					    =						convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
   a     :			Any					    =						set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   a     :			List[str]					    =						set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
   assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
   # load state_dict
   unet.load_state_dict(converted_ckpt)
  
   unet.save_pretrained(args.dump_path)
  
   # -- finish converting the unet --
style_context: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')