code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case=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 				UpperCamelCase	(  snake_case	,     snake_case=0   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Any       						= []
     for old_item in old_list:
        SCREAMING_SNAKE_CASE__			:				str       						= old_item
        SCREAMING_SNAKE_CASE__			:				List[str]       						= new_item.replace('''block.'''	,     '''resnets.'''   )
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= new_item.replace('''conv_shorcut'''	,     '''conv1'''   )
        SCREAMING_SNAKE_CASE__			:				List[str]       						= new_item.replace('''in_shortcut'''	,     '''conv_shortcut'''   )
        SCREAMING_SNAKE_CASE__			:				List[str]       						= new_item.replace('''temb_proj'''	,     '''time_emb_proj'''   )
  
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= shave_segments(_UpperCAmelCase	,     n_shave_prefix_segments=_UpperCAmelCase   )
  
        mapping.append({'''old''': old_item, '''new''': new_item}   )
  
     return mapping
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case=0	,     snake_case=False   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
     for old_item in old_list:
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= old_item
  
        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= new_item.replace('''attn'''	,     '''attentions'''   )
        SCREAMING_SNAKE_CASE__			:				List[str]       						= new_item.replace('''.k.'''	,     '''.key.'''   )
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= new_item.replace('''.v.'''	,     '''.value.'''   )
        SCREAMING_SNAKE_CASE__			:				Any       						= new_item.replace('''.q.'''	,     '''.query.'''   )
  
        SCREAMING_SNAKE_CASE__			:				List[str]       						= new_item.replace('''proj_out'''	,     '''proj_attn'''   )
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= new_item.replace('''norm'''	,     '''group_norm'''   )
  
        SCREAMING_SNAKE_CASE__			:				Any       						= shave_segments(_UpperCAmelCase	,     n_shave_prefix_segments=_UpperCAmelCase   )
        mapping.append({'''old''': old_item, '''new''': new_item}   )
  
     return mapping
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case	,     snake_case=None	,     snake_case=None	,     snake_case=None   ):
  
  
  
  
     '''simple docstring'''
  
     assert isinstance(_UpperCAmelCase	,     _UpperCAmelCase   ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
     if attention_paths_to_split is not None:
        if config is None:
           raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''   )
  
        for path, path_map in attention_paths_to_split.items():
           SCREAMING_SNAKE_CASE__			:				Dict       						= old_checkpoint[path]
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= old_tensor.shape[0] // 3
  
           SCREAMING_SNAKE_CASE__			:				str       						= (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= old_tensor.shape[0] // config.get('''num_head_channels'''	,     1   ) // 3
  
           SCREAMING_SNAKE_CASE__			:				Any       						= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]   )
           SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Any       						= old_tensor.split(channels // num_heads	,     dim=1   )
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= query.reshape(_UpperCAmelCase   ).squeeze()
           SCREAMING_SNAKE_CASE__			:				Dict       						= key.reshape(_UpperCAmelCase   ).squeeze()
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= value.reshape(_UpperCAmelCase   ).squeeze()
  
     for path in paths:
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= path['''new''']
  
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
           continue
  
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= new_path.replace('''down.'''	,     '''down_blocks.'''   )
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= new_path.replace('''up.'''	,     '''up_blocks.'''   )
  
        if additional_replacements is not None:
           for replacement in additional_replacements:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= new_path.replace(replacement['''old''']	,     replacement['''new''']   )
  
        if "attentions" in new_path:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= old_checkpoint[path['''old''']].squeeze()
        else:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= old_checkpoint[path['''old''']]
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= checkpoint['''temb.dense.0.weight''']
     SCREAMING_SNAKE_CASE__			:				Dict       						= checkpoint['''temb.dense.0.bias''']
     SCREAMING_SNAKE_CASE__			:				Dict       						= checkpoint['''temb.dense.1.weight''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= checkpoint['''temb.dense.1.bias''']
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint['''norm_out.weight''']
     SCREAMING_SNAKE_CASE__			:				List[str]       						= checkpoint['''norm_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= checkpoint['''conv_in.weight''']
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint['''conv_in.bias''']
     SCREAMING_SNAKE_CASE__			:				Dict       						= checkpoint['''conv_out.weight''']
     SCREAMING_SNAKE_CASE__			:				List[str]       						= checkpoint['''conv_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''down''' in layer}   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= {
         layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
     }
  
     SCREAMING_SNAKE_CASE__			:				List[str]       						= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''up''' in layer}   )
     SCREAMING_SNAKE_CASE__			:				Dict       						= {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )}
  
     for i in range(_UpperCAmelCase   ):
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= (i - 1) // (config['''layers_per_block'''] + 1)
  
        if any('''downsample''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint[
               f"""down.{i}.downsample.op.weight"""
           ]
           SCREAMING_SNAKE_CASE__			:				str       						= checkpoint[f"""down.{i}.downsample.op.bias"""]
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
        if any('''block''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     2   ).split('''.'''   )[:2]   ) for layer in down_blocks[i] if '''block''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= {
               layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
               for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_blocks > 0:
              for j in range(config['''layers_per_block''']   ):
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= renew_resnet_paths(blocks[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase   )
  
        if any('''attn''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     2   ).split('''.'''   )[:2]   ) for layer in down_blocks[i] if '''attn''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Tuple       						= {
               layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
               for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_attn > 0:
              for j in range(config['''layers_per_block''']   ):
                 SCREAMING_SNAKE_CASE__			:				Any       						= renew_attention_paths(attns[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     config=_UpperCAmelCase   )
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= [key for key in checkpoint if '''mid.block_1''' in key]
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [key for key in checkpoint if '''mid.block_2''' in key]
     SCREAMING_SNAKE_CASE__			:				str       						= [key for key in checkpoint if '''mid.attn_1''' in key]
  
     # Mid new 2
     SCREAMING_SNAKE_CASE__			:				str       						= renew_resnet_paths(_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]	,     )
  
     SCREAMING_SNAKE_CASE__			:				Any       						= renew_resnet_paths(_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]	,     )
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= renew_attention_paths(_UpperCAmelCase	,     in_mid=_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]	,     )
  
     for i in range(_UpperCAmelCase   ):
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= num_up_blocks - 1 - i
  
        if any('''upsample''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= checkpoint[
               f"""up.{i}.upsample.conv.weight"""
           ]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= checkpoint[f"""up.{i}.upsample.conv.bias"""]
  
        if any('''block''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     2   ).split('''.'''   )[:2]   ) for layer in up_blocks[i] if '''block''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= {
               layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_blocks > 0:
              for j in range(config['''layers_per_block'''] + 1   ):
                 SCREAMING_SNAKE_CASE__			:				str       						= {'''old''': f"""up_blocks.{i}""", '''new''': f"""up_blocks.{block_id}"""}
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= renew_resnet_paths(blocks[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[replace_indices]   )
  
        if any('''attn''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				List[str]       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     2   ).split('''.'''   )[:2]   ) for layer in up_blocks[i] if '''attn''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= {
               layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_attn > 0:
              for j in range(config['''layers_per_block'''] + 1   ):
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= {'''old''': f"""up_blocks.{i}""", '''new''': f"""up_blocks.{block_id}"""}
                 SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= renew_attention_paths(attns[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[replace_indices]   )
  
     SCREAMING_SNAKE_CASE__			:				Any       						= {k.replace('''mid_new_2'''	,     '''mid_block'''   ): v for k, v in new_checkpoint.items()}
     return new_checkpoint
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= {}
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= checkpoint['''encoder.norm_out.weight''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= checkpoint['''encoder.norm_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				Any       						= checkpoint['''encoder.conv_in.weight''']
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint['''encoder.conv_in.bias''']
     SCREAMING_SNAKE_CASE__			:				str       						= checkpoint['''encoder.conv_out.weight''']
     SCREAMING_SNAKE_CASE__			:				Any       						= checkpoint['''encoder.conv_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint['''decoder.norm_out.weight''']
     SCREAMING_SNAKE_CASE__			:				int       						= checkpoint['''decoder.norm_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= checkpoint['''decoder.conv_in.weight''']
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= checkpoint['''decoder.conv_in.bias''']
     SCREAMING_SNAKE_CASE__			:				Tuple       						= checkpoint['''decoder.conv_out.weight''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= checkpoint['''decoder.conv_out.bias''']
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= len({'''.'''.join(layer.split('''.'''   )[:3]   ) for layer in checkpoint if '''down''' in layer}   )
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
         layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
     }
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= len({'''.'''.join(layer.split('''.'''   )[:3]   ) for layer in checkpoint if '''up''' in layer}   )
     SCREAMING_SNAKE_CASE__			:				int       						= {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )}
  
     for i in range(_UpperCAmelCase   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= (i - 1) // (config['''layers_per_block'''] + 1)
  
        if any('''downsample''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= checkpoint[
               f"""encoder.down.{i}.downsample.conv.weight"""
           ]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= checkpoint[
               f"""encoder.down.{i}.downsample.conv.bias"""
           ]
  
        if any('''block''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Any       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     3   ).split('''.'''   )[:3]   ) for layer in down_blocks[i] if '''block''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= {
               layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
               for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_blocks > 0:
              for j in range(config['''layers_per_block''']   ):
                 SCREAMING_SNAKE_CASE__			:				Dict       						= renew_resnet_paths(blocks[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase   )
  
        if any('''attn''' in layer for layer in down_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Tuple       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     3   ).split('''.'''   )[:3]   ) for layer in down_blocks[i] if '''attn''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
               layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
               for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_attn > 0:
              for j in range(config['''layers_per_block''']   ):
                 SCREAMING_SNAKE_CASE__			:				int       						= renew_attention_paths(attns[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     config=_UpperCAmelCase   )
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [key for key in checkpoint if '''mid.block_1''' in key]
     SCREAMING_SNAKE_CASE__			:				str       						= [key for key in checkpoint if '''mid.block_2''' in key]
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= [key for key in checkpoint if '''mid.attn_1''' in key]
  
     # Mid new 2
     SCREAMING_SNAKE_CASE__			:				Tuple       						= renew_resnet_paths(_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]	,     )
  
     SCREAMING_SNAKE_CASE__			:				str       						= renew_resnet_paths(_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]	,     )
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= renew_attention_paths(_UpperCAmelCase	,     in_mid=_UpperCAmelCase   )
     assign_to_checkpoint(
         _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]	,     )
  
     for i in range(_UpperCAmelCase   ):
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= num_up_blocks - 1 - i
  
        if any('''upsample''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= checkpoint[
               f"""decoder.up.{i}.upsample.conv.weight"""
           ]
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint[
               f"""decoder.up.{i}.upsample.conv.bias"""
           ]
  
        if any('''block''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     3   ).split('''.'''   )[:3]   ) for layer in up_blocks[i] if '''block''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= {
               layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_blocks > 0:
              for j in range(config['''layers_per_block'''] + 1   ):
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= {'''old''': f"""up_blocks.{i}""", '''new''': f"""up_blocks.{block_id}"""}
                 SCREAMING_SNAKE_CASE__			:				int       						= renew_resnet_paths(blocks[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[replace_indices]   )
  
        if any('''attn''' in layer for layer in up_blocks[i]   ):
           SCREAMING_SNAKE_CASE__			:				Tuple       						= len(
               {'''.'''.join(shave_segments(_UpperCAmelCase	,     3   ).split('''.'''   )[:3]   ) for layer in up_blocks[i] if '''attn''' in layer}   )
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
               layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase   )
           }
  
           if num_attn > 0:
              for j in range(config['''layers_per_block'''] + 1   ):
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= {'''old''': f"""up_blocks.{i}""", '''new''': f"""up_blocks.{block_id}"""}
                 SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= renew_attention_paths(attns[j]   )
                 assign_to_checkpoint(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     additional_replacements=[replace_indices]   )
  
     SCREAMING_SNAKE_CASE__			:				str       						= {k.replace('''mid_new_2'''	,     '''mid_block'''   ): v for k, v in new_checkpoint.items()}
     SCREAMING_SNAKE_CASE__			:				str       						= checkpoint['''quant_conv.weight''']
     SCREAMING_SNAKE_CASE__			:				str       						= checkpoint['''quant_conv.bias''']
     if "quantize.embedding.weight" in checkpoint:
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= checkpoint['''quantize.embedding.weight''']
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= checkpoint['''post_quant_conv.weight''']
     SCREAMING_SNAKE_CASE__			:				Dict       						= checkpoint['''post_quant_conv.bias''']
  
     return new_checkpoint
  
  
  if __name__ == "__main__":
         lowercase__			=	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.''')
  
         lowercase__			=	parser.parse_args()
         lowercase__			=	torch.load(args.checkpoint_path)
  
         with open(args.config_file) as f:
                lowercase__			=	json.loads(f.read())
  
         # unet case
         lowercase__			=	{key.split('''.''')[0] for key in checkpoint.keys()}
         if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
                lowercase__			=	convert_vq_autoenc_checkpoint(checkpoint, config)
         else:
                lowercase__			=	convert_ddpm_checkpoint(checkpoint, config)
  
         if "ddpm" in config:
                del config["ddpm"]
  
         if config["_class_name"] == "VQModel":
                lowercase__			=	VQModel(**config)
                model.load_state_dict(converted_checkpoint)
                model.save_pretrained(args.dump_path)
         elif config["_class_name"] == "AutoencoderKL":
                lowercase__			=	AutoencoderKL(**config)
                model.load_state_dict(converted_checkpoint)
                model.save_pretrained(args.dump_path)
         else:
                lowercase__			=	UNetaDModel(**config)
                model.load_state_dict(converted_checkpoint)
  
                lowercase__			=	DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
                lowercase__			=	DDPMPipeline(unet=model, scheduler=scheduler)
                pipe.save_pretrained(args.dump_path)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_splinter import SplinterTokenizer
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{'''vocab_file''': '''vocab.txt'''}
  
  lowercase__			=	{
      '''vocab_file''': {
          '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
          '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
          '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
          '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
      }
  }
  
  lowercase__			=	{
      '''tau/splinter-base''': 5_1_2,
      '''tau/splinter-base-qass''': 5_1_2,
      '''tau/splinter-large''': 5_1_2,
      '''tau/splinter-large-qass''': 5_1_2,
  }
  
  lowercase__			=	{
      '''tau/splinter-base''': {'''do_lower_case''': False},
      '''tau/splinter-base-qass''': {'''do_lower_case''': False},
      '''tau/splinter-large''': {'''do_lower_case''': False},
      '''tau/splinter-large-qass''': {'''do_lower_case''': False},
  }
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  SplinterTokenizer
  
  
  
  
  
  
        def __init__(self ,  a__=None ,  a__=None ,  a__=True ,  a__="[UNK]" ,  a__="[SEP]" ,  a__="[PAD]" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__="[QUESTION]" ,  a__=True ,  a__=None ,  **a__ ,  )       ->   Any:
           super().__init__(
               a__ ,  tokenizer_file=a__ ,  do_lower_case=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  tokenize_chinese_chars=a__ ,  strip_accents=a__ ,  additional_special_tokens=(question_token,) ,  **a__ ,  )
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
           if (
               pre_tok_state.get('''lowercase''' ,  a__					) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,  a__					) != strip_accents
           ):
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(a__ ,  pre_tok_state.pop('''type'''					)					)
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= strip_accents
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pre_tok_class(**a__					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   List[str]:
           return self.convert_tokens_to_ids(self.question_token					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= [self.cls_token_id]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
           if self.padding_side == "right":
              # Input is question-then-context
              return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
           else:
              # Input is context-then-question
              return cls + token_ids_a + sep + token_ids_a + question_suffix + sep
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Any       						= [self.cls_token_id]
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
           if token_ids_a is None:
              return len(cls + token_ids_a + sep					) * [0]
  
           if self.padding_side == "right":
              # Input is question-then-context
              return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
           else:
              # Input is context-then-question
              return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
           SCREAMING_SNAKE_CASE__			:				int       						= self._tokenizer.model.save(a__ ,  name=a__					)
           return tuple(a__					)
  
  