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_     ,  lowerCAmelCase_     ,  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_     ,  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: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor