code: import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]		,						lowercase_:	Dict=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 SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Optional[int]=0				):
         _UpperCamelCase     :       Any  						=  []
         for old_item in old_list:
                _UpperCamelCase     :       Optional[int]  						=  old_item
                _UpperCamelCase     :       Tuple  						=  new_item.replace('block.'		,						'resnets.'				)
                _UpperCamelCase     :       List[str]  						=  new_item.replace('conv_shorcut'		,						'conv1'				)
                _UpperCamelCase     :       Any  						=  new_item.replace('in_shortcut'		,						'conv_shortcut'				)
                _UpperCamelCase     :       List[str]  						=  new_item.replace('temb_proj'		,						'time_emb_proj'				)
  
                _UpperCamelCase     :       Any  						=  shave_segments(lowercase_		,						n_shave_prefix_segments=lowercase_				)
  
                mapping.append({'old': old_item, 'new': new_item}				)
  
         return mapping
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]		,						lowercase_:	Optional[int]=0		,						lowercase_:	int=False				):
         _UpperCamelCase     :       Dict  						=  []
         for old_item in old_list:
                _UpperCamelCase     :       str  						=  old_item
  
                # In `model.mid`, the layer is called `attn`.
                if not in_mid:
                       _UpperCamelCase     :       Optional[int]  						=  new_item.replace('attn'		,						'attentions'				)
                _UpperCamelCase     :       List[Any]  						=  new_item.replace('.k.'		,						'.key.'				)
                _UpperCamelCase     :       Union[str, Any]  						=  new_item.replace('.v.'		,						'.value.'				)
                _UpperCamelCase     :       List[str]  						=  new_item.replace('.q.'		,						'.query.'				)
  
                _UpperCamelCase     :       List[str]  						=  new_item.replace('proj_out'		,						'proj_attn'				)
                _UpperCamelCase     :       Dict  						=  new_item.replace('norm'		,						'group_norm'				)
  
                _UpperCamelCase     :       Dict  						=  shave_segments(lowercase_		,						n_shave_prefix_segments=lowercase_				)
                mapping.append({'old': old_item, 'new': new_item}				)
  
         return mapping
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[str]		,						lowercase_:	Any		,						lowercase_:	List[Any]=None		,						lowercase_:	str=None		,						lowercase_:	Optional[Any]=None				):
         assert isinstance(lowercase_		,						lowercase_				), "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():
                       _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[path]
                       _UpperCamelCase     :       Dict  						=  old_tensor.shape[0] // 3
  
                       _UpperCamelCase     :       List[Any]  						=  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
                       _UpperCamelCase     :       Optional[Any]  						=  old_tensor.shape[0] // config.get('num_head_channels'		,						1				) // 3
  
                       _UpperCamelCase     :       Dict  						=  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
                       _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[int]  						=  old_tensor.split(channels // num_heads		,						dim=1				)
  
                       _UpperCamelCase     :       int  						=  query.reshape(lowercase_				).squeeze()
                       _UpperCamelCase     :       int  						=  key.reshape(lowercase_				).squeeze()
                       _UpperCamelCase     :       List[str]  						=  value.reshape(lowercase_				).squeeze()
  
         for path in paths:
                _UpperCamelCase     :       str  						=  path['new']
  
                if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                       continue
  
                _UpperCamelCase     :       List[Any]  						=  new_path.replace('down.'		,						'down_blocks.'				)
                _UpperCamelCase     :       Optional[int]  						=  new_path.replace('up.'		,						'up_blocks.'				)
  
                if additional_replacements is not None:
                       for replacement in additional_replacements:
                              _UpperCamelCase     :       List[str]  						=  new_path.replace(replacement['old']		,						replacement['new']				)
  
                if "attentions" in new_path:
                       _UpperCamelCase     :       Tuple  						=  old_checkpoint[path['old']].squeeze()
                else:
                       _UpperCamelCase     :       List[Any]  						=  old_checkpoint[path['old']]
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Any				):
         _UpperCamelCase     :       List[Any]  						=  {}
  
         _UpperCamelCase     :       int  						=  checkpoint['temb.dense.0.weight']
         _UpperCamelCase     :       List[str]  						=  checkpoint['temb.dense.0.bias']
         _UpperCamelCase     :       List[Any]  						=  checkpoint['temb.dense.1.weight']
         _UpperCamelCase     :       Union[str, Any]  						=  checkpoint['temb.dense.1.bias']
  
         _UpperCamelCase     :       Tuple  						=  checkpoint['norm_out.weight']
         _UpperCamelCase     :       Dict  						=  checkpoint['norm_out.bias']
  
         _UpperCamelCase     :       str  						=  checkpoint['conv_in.weight']
         _UpperCamelCase     :       List[Any]  						=  checkpoint['conv_in.bias']
         _UpperCamelCase     :       List[str]  						=  checkpoint['conv_out.weight']
         _UpperCamelCase     :       Dict  						=  checkpoint['conv_out.bias']
  
         _UpperCamelCase     :       Dict  						=  len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'down' in layer}				)
         _UpperCamelCase     :       Optional[int]  						=  {
             layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(lowercase_				)
         }
  
         _UpperCamelCase     :       Optional[int]  						=  len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'up' in layer}				)
         _UpperCamelCase     :       Dict  						=  {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(lowercase_				)}
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       List[str]  						=  (i - 1) // (config['layers_per_block'] + 1)
  
                if any('downsample' in layer for layer in down_blocks[i]				):
                       _UpperCamelCase     :       str  						=  checkpoint[
                           F"down.{i}.downsample.op.weight"
                       ]
                       _UpperCamelCase     :       List[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]				):
                       _UpperCamelCase     :       Any  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'block' in layer}				)
                       _UpperCamelCase     :       Optional[Any]  						=  {
                           layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                           for layer_id in range(lowercase_				)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['layers_per_block']				):
                                     _UpperCamelCase     :       Tuple  						=  renew_resnet_paths(blocks[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_				)
  
                if any('attn' in layer for layer in down_blocks[i]				):
                       _UpperCamelCase     :       Tuple  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'attn' in layer}				)
                       _UpperCamelCase     :       Any  						=  {
                           layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                           for layer_id in range(lowercase_				)
                       }
  
                       if num_attn > 0:
                              for j in range(config['layers_per_block']				):
                                     _UpperCamelCase     :       List[str]  						=  renew_attention_paths(attns[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						config=lowercase_				)
  
         _UpperCamelCase     :       List[str]  						=  [key for key in checkpoint if 'mid.block_1' in key]
         _UpperCamelCase     :       List[str]  						=  [key for key in checkpoint if 'mid.block_2' in key]
         _UpperCamelCase     :       int  						=  [key for key in checkpoint if 'mid.attn_1' in key]
  
         # Mid new 2
         _UpperCamelCase     :       Optional[Any]  						=  renew_resnet_paths(lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]		,						)
  
         _UpperCamelCase     :       List[Any]  						=  renew_resnet_paths(lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]		,						)
  
         _UpperCamelCase     :       int  						=  renew_attention_paths(lowercase_		,						in_mid=lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]		,						)
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       Optional[Any]  						=  num_up_blocks - 1 - i
  
                if any('upsample' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       int  						=  checkpoint[
                           F"up.{i}.upsample.conv.weight"
                       ]
                       _UpperCamelCase     :       str  						=  checkpoint[F"up.{i}.upsample.conv.bias"]
  
                if any('block' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       Tuple  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'block' in layer}				)
                       _UpperCamelCase     :       List[Any]  						=  {
                           layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(lowercase_				)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['layers_per_block'] + 1				):
                                     _UpperCamelCase     :       int  						=  {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                                     _UpperCamelCase     :       Dict  						=  renew_resnet_paths(blocks[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[replace_indices]				)
  
                if any('attn' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       Union[str, Any]  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'attn' in layer}				)
                       _UpperCamelCase     :       int  						=  {
                           layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(lowercase_				)
                       }
  
                       if num_attn > 0:
                              for j in range(config['layers_per_block'] + 1				):
                                     _UpperCamelCase     :       Tuple  						=  {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                                     _UpperCamelCase     :       Union[str, Any]  						=  renew_attention_paths(attns[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[replace_indices]				)
  
         _UpperCamelCase     :       Dict  						=  {k.replace('mid_new_2'		,						'mid_block'				): v for k, v in new_checkpoint.items()}
         return new_checkpoint
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Dict				):
         _UpperCamelCase     :       Optional[int]  						=  {}
  
         _UpperCamelCase     :       List[str]  						=  checkpoint['encoder.norm_out.weight']
         _UpperCamelCase     :       Dict  						=  checkpoint['encoder.norm_out.bias']
  
         _UpperCamelCase     :       int  						=  checkpoint['encoder.conv_in.weight']
         _UpperCamelCase     :       Optional[Any]  						=  checkpoint['encoder.conv_in.bias']
         _UpperCamelCase     :       List[str]  						=  checkpoint['encoder.conv_out.weight']
         _UpperCamelCase     :       List[Any]  						=  checkpoint['encoder.conv_out.bias']
  
         _UpperCamelCase     :       Tuple  						=  checkpoint['decoder.norm_out.weight']
         _UpperCamelCase     :       Optional[int]  						=  checkpoint['decoder.norm_out.bias']
  
         _UpperCamelCase     :       Optional[Any]  						=  checkpoint['decoder.conv_in.weight']
         _UpperCamelCase     :       int  						=  checkpoint['decoder.conv_in.bias']
         _UpperCamelCase     :       int  						=  checkpoint['decoder.conv_out.weight']
         _UpperCamelCase     :       str  						=  checkpoint['decoder.conv_out.bias']
  
         _UpperCamelCase     :       Any  						=  len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'down' in layer}				)
         _UpperCamelCase     :       Union[str, Any]  						=  {
             layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(lowercase_				)
         }
  
         _UpperCamelCase     :       Tuple  						=  len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'up' in layer}				)
         _UpperCamelCase     :       Optional[Any]  						=  {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(lowercase_				)}
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       Any  						=  (i - 1) // (config['layers_per_block'] + 1)
  
                if any('downsample' in layer for layer in down_blocks[i]				):
                       _UpperCamelCase     :       Union[str, Any]  						=  checkpoint[
                           F"encoder.down.{i}.downsample.conv.weight"
                       ]
                       _UpperCamelCase     :       Tuple  						=  checkpoint[
                           F"encoder.down.{i}.downsample.conv.bias"
                       ]
  
                if any('block' in layer for layer in down_blocks[i]				):
                       _UpperCamelCase     :       int  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'block' in layer}				)
                       _UpperCamelCase     :       Dict  						=  {
                           layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                           for layer_id in range(lowercase_				)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['layers_per_block']				):
                                     _UpperCamelCase     :       List[Any]  						=  renew_resnet_paths(blocks[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_				)
  
                if any('attn' in layer for layer in down_blocks[i]				):
                       _UpperCamelCase     :       int  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'attn' in layer}				)
                       _UpperCamelCase     :       List[str]  						=  {
                           layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                           for layer_id in range(lowercase_				)
                       }
  
                       if num_attn > 0:
                              for j in range(config['layers_per_block']				):
                                     _UpperCamelCase     :       Optional[int]  						=  renew_attention_paths(attns[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						config=lowercase_				)
  
         _UpperCamelCase     :       Optional[Any]  						=  [key for key in checkpoint if 'mid.block_1' in key]
         _UpperCamelCase     :       int  						=  [key for key in checkpoint if 'mid.block_2' in key]
         _UpperCamelCase     :       Dict  						=  [key for key in checkpoint if 'mid.attn_1' in key]
  
         # Mid new 2
         _UpperCamelCase     :       List[Any]  						=  renew_resnet_paths(lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]		,						)
  
         _UpperCamelCase     :       Optional[Any]  						=  renew_resnet_paths(lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]		,						)
  
         _UpperCamelCase     :       Dict  						=  renew_attention_paths(lowercase_		,						in_mid=lowercase_				)
         assign_to_checkpoint(
             lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]		,						)
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       List[str]  						=  num_up_blocks - 1 - i
  
                if any('upsample' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       List[Any]  						=  checkpoint[
                           F"decoder.up.{i}.upsample.conv.weight"
                       ]
                       _UpperCamelCase     :       Any  						=  checkpoint[
                           F"decoder.up.{i}.upsample.conv.bias"
                       ]
  
                if any('block' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       str  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'block' in layer}				)
                       _UpperCamelCase     :       Tuple  						=  {
                           layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(lowercase_				)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['layers_per_block'] + 1				):
                                     _UpperCamelCase     :       List[Any]  						=  {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                                     _UpperCamelCase     :       Any  						=  renew_resnet_paths(blocks[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[replace_indices]				)
  
                if any('attn' in layer for layer in up_blocks[i]				):
                       _UpperCamelCase     :       List[str]  						=  len(
                           {'.'.join(shave_segments(lowercase_		,						3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'attn' in layer}				)
                       _UpperCamelCase     :       Tuple  						=  {
                           layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(lowercase_				)
                       }
  
                       if num_attn > 0:
                              for j in range(config['layers_per_block'] + 1				):
                                     _UpperCamelCase     :       List[str]  						=  {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                                     _UpperCamelCase     :       Optional[Any]  						=  renew_attention_paths(attns[j]				)
                                     assign_to_checkpoint(lowercase_		,						lowercase_		,						lowercase_		,						additional_replacements=[replace_indices]				)
  
         _UpperCamelCase     :       Dict  						=  {k.replace('mid_new_2'		,						'mid_block'				): v for k, v in new_checkpoint.items()}
         _UpperCamelCase     :       str  						=  checkpoint['quant_conv.weight']
         _UpperCamelCase     :       int  						=  checkpoint['quant_conv.bias']
         if "quantize.embedding.weight" in checkpoint:
                _UpperCamelCase     :       List[Any]  						=  checkpoint['quantize.embedding.weight']
         _UpperCamelCase     :       List[str]  						=  checkpoint['post_quant_conv.weight']
         _UpperCamelCase     :       Optional[int]  						=  checkpoint['post_quant_conv.bias']
  
         return new_checkpoint
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				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.''')
  
   UpperCAmelCase					=				parser.parse_args()
   UpperCAmelCase					=				torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    UpperCAmelCase					=				json.loads(f.read())
  
   # unet case
   UpperCAmelCase					=				{key.split('''.''')[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    UpperCAmelCase					=				convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    UpperCAmelCase					=				convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    UpperCAmelCase					=				VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    UpperCAmelCase					=				AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    UpperCAmelCase					=				UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    UpperCAmelCase					=				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
    UpperCAmelCase					=				DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  
  
  
  
style_context: from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  
  