code: 
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase=1				)					-> Optional[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'				)[n_shave_prefix_segments:]				)
      else:
          return ".".join(path.split('.'				)[:n_shave_prefix_segments]				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase=0				)					-> Union[str, Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      []
      for old_item in old_list:
          lowerCAmelCase				:			Dict    			=      old_item
          lowerCAmelCase				:			Optional[Any]    			=      new_item.replace('block.',      'resnets.'				)
          lowerCAmelCase				:			str    			=      new_item.replace('conv_shorcut',      'conv1'				)
          lowerCAmelCase				:			List[str]    			=      new_item.replace('in_shortcut',      'conv_shortcut'				)
          lowerCAmelCase				:			str    			=      new_item.replace('temb_proj',      'time_emb_proj'				)
  
          lowerCAmelCase				:			Dict    			=      shave_segments(_SCREAMING_SNAKE_CASE,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE				)
  
          mapping.append({'old': old_item, 'new': new_item}				)
  
      return mapping
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase=0,      _UpperCAmelCase=False				)					-> List[Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      []
      for old_item in old_list:
          lowerCAmelCase				:			Optional[int]    			=      old_item
  
          # In `model.mid`, the layer is called `attn`.
          if not in_mid:
              lowerCAmelCase				:			Union[str, Any]    			=      new_item.replace('attn',      'attentions'				)
          lowerCAmelCase				:			int    			=      new_item.replace('.k.',      '.key.'				)
          lowerCAmelCase				:			Optional[Any]    			=      new_item.replace('.v.',      '.value.'				)
          lowerCAmelCase				:			int    			=      new_item.replace('.q.',      '.query.'				)
  
          lowerCAmelCase				:			Optional[Any]    			=      new_item.replace('proj_out',      'proj_attn'				)
          lowerCAmelCase				:			List[Any]    			=      new_item.replace('norm',      'group_norm'				)
  
          lowerCAmelCase				:			str    			=      shave_segments(_SCREAMING_SNAKE_CASE,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE				)
          mapping.append({'old': old_item, 'new': new_item}				)
  
      return mapping
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=None,      _UpperCAmelCase=None,      _UpperCAmelCase=None				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      assert isinstance(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE				), "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():
              lowerCAmelCase				:			Optional[int]    			=      old_checkpoint[path]
              lowerCAmelCase				:			str    			=      old_tensor.shape[0] // 3
  
              lowerCAmelCase				:			Tuple    			=      (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
              lowerCAmelCase				:			Optional[Any]    			=      old_tensor.shape[0] // config.get('num_head_channels',      1				) // 3
  
              lowerCAmelCase				:			Optional[Any]    			=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
              lowerCAmelCase				:			Dict    			=      old_tensor.split(channels // num_heads,      dim=1				)
  
              lowerCAmelCase				:			List[str]    			=      query.reshape(_SCREAMING_SNAKE_CASE				).squeeze()
              lowerCAmelCase				:			List[Any]    			=      key.reshape(_SCREAMING_SNAKE_CASE				).squeeze()
              lowerCAmelCase				:			Optional[Any]    			=      value.reshape(_SCREAMING_SNAKE_CASE				).squeeze()
  
      for path in paths:
          lowerCAmelCase				:			Union[str, Any]    			=      path['new']
  
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
              continue
  
          lowerCAmelCase				:			Optional[Any]    			=      new_path.replace('down.',      'down_blocks.'				)
          lowerCAmelCase				:			Tuple    			=      new_path.replace('up.',      'up_blocks.'				)
  
          if additional_replacements is not None:
              for replacement in additional_replacements:
                  lowerCAmelCase				:			Tuple    			=      new_path.replace(replacement['old'],      replacement['new']				)
  
          if "attentions" in new_path:
              lowerCAmelCase				:			List[Any]    			=      old_checkpoint[path['old']].squeeze()
          else:
              lowerCAmelCase				:			Tuple    			=      old_checkpoint[path['old']]
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      {}
  
      lowerCAmelCase				:			List[str]    			=      checkpoint['temb.dense.0.weight']
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['temb.dense.0.bias']
      lowerCAmelCase				:			List[Any]    			=      checkpoint['temb.dense.1.weight']
      lowerCAmelCase				:			Any    			=      checkpoint['temb.dense.1.bias']
  
      lowerCAmelCase				:			Optional[int]    			=      checkpoint['norm_out.weight']
      lowerCAmelCase				:			List[str]    			=      checkpoint['norm_out.bias']
  
      lowerCAmelCase				:			List[str]    			=      checkpoint['conv_in.weight']
      lowerCAmelCase				:			Dict    			=      checkpoint['conv_in.bias']
      lowerCAmelCase				:			Tuple    			=      checkpoint['conv_out.weight']
      lowerCAmelCase				:			List[Any]    			=      checkpoint['conv_out.bias']
  
      lowerCAmelCase				:			Dict    			=      len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'down' in layer}				)
      lowerCAmelCase				:			Union[str, Any]    			=      {
          layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
      }
  
      lowerCAmelCase				:			List[Any]    			=      len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'up' in layer}				)
      lowerCAmelCase				:			Optional[Any]    			=      {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)}
  
      for i in range(_SCREAMING_SNAKE_CASE				):
          lowerCAmelCase				:			int    			=      (i - 1) // (config['layers_per_block'] + 1)
  
          if any('downsample' in layer for layer in down_blocks[i]				):
              lowerCAmelCase				:			Any    			=      checkpoint[
                  f"down.{i}.downsample.op.weight"
              ]
              lowerCAmelCase				:			Optional[int]    			=      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]				):
              lowerCAmelCase				:			List[str]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'block' in layer}				)
              lowerCAmelCase				:			Dict    			=      {
                  layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                  for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_blocks > 0:
                  for j in range(config['layers_per_block']				):
                      lowerCAmelCase				:			Optional[int]    			=      renew_resnet_paths(blocks[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE				)
  
          if any('attn' in layer for layer in down_blocks[i]				):
              lowerCAmelCase				:			Tuple    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'attn' in layer}				)
              lowerCAmelCase				:			List[Any]    			=      {
                  layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                  for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_attn > 0:
                  for j in range(config['layers_per_block']				):
                      lowerCAmelCase				:			Tuple    			=      renew_attention_paths(attns[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      config=_SCREAMING_SNAKE_CASE				)
  
      lowerCAmelCase				:			str    			=      [key for key in checkpoint if 'mid.block_1' in key]
      lowerCAmelCase				:			Optional[int]    			=      [key for key in checkpoint if 'mid.block_2' in key]
      lowerCAmelCase				:			List[Any]    			=      [key for key in checkpoint if 'mid.attn_1' in key]
  
      # Mid new 2
      lowerCAmelCase				:			str    			=      renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}],      )
  
      lowerCAmelCase				:			str    			=      renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}],      )
  
      lowerCAmelCase				:			Optional[int]    			=      renew_attention_paths(_SCREAMING_SNAKE_CASE,      in_mid=_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}],      )
  
      for i in range(_SCREAMING_SNAKE_CASE				):
          lowerCAmelCase				:			Dict    			=      num_up_blocks - 1 - i
  
          if any('upsample' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			str    			=      checkpoint[
                  f"up.{i}.upsample.conv.weight"
              ]
              lowerCAmelCase				:			Optional[int]    			=      checkpoint[f"up.{i}.upsample.conv.bias"]
  
          if any('block' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			Optional[Any]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'block' in layer}				)
              lowerCAmelCase				:			str    			=      {
                  layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_blocks > 0:
                  for j in range(config['layers_per_block'] + 1				):
                      lowerCAmelCase				:			List[str]    			=      {'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                      lowerCAmelCase				:			List[Any]    			=      renew_resnet_paths(blocks[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[replace_indices]				)
  
          if any('attn' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			Union[str, Any]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'attn' in layer}				)
              lowerCAmelCase				:			int    			=      {
                  layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_attn > 0:
                  for j in range(config['layers_per_block'] + 1				):
                      lowerCAmelCase				:			int    			=      {'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                      lowerCAmelCase				:			Optional[int]    			=      renew_attention_paths(attns[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[replace_indices]				)
  
      lowerCAmelCase				:			str    			=      {k.replace('mid_new_2',      'mid_block'				): v for k, v in new_checkpoint.items()}
      return new_checkpoint
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Tuple    			=      {}
  
      lowerCAmelCase				:			str    			=      checkpoint['encoder.norm_out.weight']
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['encoder.norm_out.bias']
  
      lowerCAmelCase				:			Tuple    			=      checkpoint['encoder.conv_in.weight']
      lowerCAmelCase				:			Dict    			=      checkpoint['encoder.conv_in.bias']
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['encoder.conv_out.weight']
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['encoder.conv_out.bias']
  
      lowerCAmelCase				:			List[str]    			=      checkpoint['decoder.norm_out.weight']
      lowerCAmelCase				:			str    			=      checkpoint['decoder.norm_out.bias']
  
      lowerCAmelCase				:			int    			=      checkpoint['decoder.conv_in.weight']
      lowerCAmelCase				:			Optional[int]    			=      checkpoint['decoder.conv_in.bias']
      lowerCAmelCase				:			Union[str, Any]    			=      checkpoint['decoder.conv_out.weight']
      lowerCAmelCase				:			List[str]    			=      checkpoint['decoder.conv_out.bias']
  
      lowerCAmelCase				:			Optional[Any]    			=      len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'down' in layer}				)
      lowerCAmelCase				:			Any    			=      {
          layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
      }
  
      lowerCAmelCase				:			Dict    			=      len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'up' in layer}				)
      lowerCAmelCase				:			int    			=      {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)}
  
      for i in range(_SCREAMING_SNAKE_CASE				):
          lowerCAmelCase				:			Any    			=      (i - 1) // (config['layers_per_block'] + 1)
  
          if any('downsample' in layer for layer in down_blocks[i]				):
              lowerCAmelCase				:			Optional[int]    			=      checkpoint[
                  f"encoder.down.{i}.downsample.conv.weight"
              ]
              lowerCAmelCase				:			Union[str, Any]    			=      checkpoint[
                  f"encoder.down.{i}.downsample.conv.bias"
              ]
  
          if any('block' in layer for layer in down_blocks[i]				):
              lowerCAmelCase				:			Optional[Any]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'block' in layer}				)
              lowerCAmelCase				:			Union[str, Any]    			=      {
                  layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                  for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_blocks > 0:
                  for j in range(config['layers_per_block']				):
                      lowerCAmelCase				:			Dict    			=      renew_resnet_paths(blocks[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE				)
  
          if any('attn' in layer for layer in down_blocks[i]				):
              lowerCAmelCase				:			int    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'attn' in layer}				)
              lowerCAmelCase				:			Tuple    			=      {
                  layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                  for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_attn > 0:
                  for j in range(config['layers_per_block']				):
                      lowerCAmelCase				:			int    			=      renew_attention_paths(attns[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      config=_SCREAMING_SNAKE_CASE				)
  
      lowerCAmelCase				:			int    			=      [key for key in checkpoint if 'mid.block_1' in key]
      lowerCAmelCase				:			Tuple    			=      [key for key in checkpoint if 'mid.block_2' in key]
      lowerCAmelCase				:			Tuple    			=      [key for key in checkpoint if 'mid.attn_1' in key]
  
      # Mid new 2
      lowerCAmelCase				:			Optional[int]    			=      renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}],      )
  
      lowerCAmelCase				:			Any    			=      renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}],      )
  
      lowerCAmelCase				:			Tuple    			=      renew_attention_paths(_SCREAMING_SNAKE_CASE,      in_mid=_SCREAMING_SNAKE_CASE				)
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}],      )
  
      for i in range(_SCREAMING_SNAKE_CASE				):
          lowerCAmelCase				:			Dict    			=      num_up_blocks - 1 - i
  
          if any('upsample' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			Any    			=      checkpoint[
                  f"decoder.up.{i}.upsample.conv.weight"
              ]
              lowerCAmelCase				:			Any    			=      checkpoint[
                  f"decoder.up.{i}.upsample.conv.bias"
              ]
  
          if any('block' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			Optional[int]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'block' in layer}				)
              lowerCAmelCase				:			Tuple    			=      {
                  layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_blocks > 0:
                  for j in range(config['layers_per_block'] + 1				):
                      lowerCAmelCase				:			List[Any]    			=      {'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                      lowerCAmelCase				:			Union[str, Any]    			=      renew_resnet_paths(blocks[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[replace_indices]				)
  
          if any('attn' in layer for layer in up_blocks[i]				):
              lowerCAmelCase				:			Optional[int]    			=      len(
                  {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE,      3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'attn' in layer}				)
              lowerCAmelCase				:			str    			=      {
                  layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE				)
              }
  
              if num_attn > 0:
                  for j in range(config['layers_per_block'] + 1				):
                      lowerCAmelCase				:			Union[str, Any]    			=      {'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                      lowerCAmelCase				:			Optional[Any]    			=      renew_attention_paths(attns[j]				)
                      assign_to_checkpoint(_SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      _SCREAMING_SNAKE_CASE,      additional_replacements=[replace_indices]				)
  
      lowerCAmelCase				:			Any    			=      {k.replace('mid_new_2',      'mid_block'				): v for k, v in new_checkpoint.items()}
      lowerCAmelCase				:			List[Any]    			=      checkpoint['quant_conv.weight']
      lowerCAmelCase				:			str    			=      checkpoint['quant_conv.bias']
      if "quantize.embedding.weight" in checkpoint:
          lowerCAmelCase				:			Optional[Any]    			=      checkpoint['quantize.embedding.weight']
      lowerCAmelCase				:			List[str]    			=      checkpoint['post_quant_conv.weight']
      lowerCAmelCase				:			int    			=      checkpoint['post_quant_conv.bias']
  
      return new_checkpoint
  
  
  if __name__ == "__main__":
   __A   :     int      =			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.''')
  
   __A   :     List[Any]      =			parser.parse_args()
   __A   :     str      =			torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    __A   :     int      =			json.loads(f.read())
  
   # unet case
   __A   :     Optional[Any]      =			{key.split('''.''')[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    __A   :     Dict      =			convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    __A   :     List[Any]      =			convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    __A   :     Optional[int]      =			VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    __A   :     Optional[Any]      =			AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    __A   :     Union[str, Any]      =			UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    __A   :     List[Any]      =			DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
    __A   :     Optional[Any]      =			DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
style_context: 
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			Any    			=      torch.cat(
              [
                  torch.zeros(
                      (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),      device=states.device,      ),
                  states,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			int    			=      torch.cat(
              [
                  torch.zeros(
                      (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),      device=actions.device,      ),
                  actions,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Union[str, Any]    			=      torch.cat(
              [
                  torch.zeros(
                      (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),      device=returns_to_go.device,      ),
                  returns_to_go,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Tuple    			=      torch.cat(
              [
                  torch.zeros(
                      (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),      device=timesteps.device				),
                  timesteps,
              ],      dim=1,      ).to(dtype=torch.long				)
      else:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
          states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )
  
      return action_preds[0, -1]
  
  
  # build the environment
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    __A   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     List[Any]      =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
    episode_return += reward
    episode_length += 1
  
    if done:
     break
  
  
  
  
  
  