code: 
  
  
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=1				)    ->  Union[str, Any]:
  						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				)    ->  List[Any]:
  						_a     =			[]
  						for old_item in old_list:
  												_a     =			old_item
  												_a     =			new_item.replace('block.'							,     'resnets.'				)
  												_a     =			new_item.replace('conv_shorcut'							,     'conv1'				)
  												_a     =			new_item.replace('in_shortcut'							,     'conv_shortcut'				)
  												_a     =			new_item.replace('temb_proj'							,     'time_emb_proj'				)
  
  												_a     =			shave_segments(_UpperCAmelCase							,     n_shave_prefix_segments=_UpperCAmelCase				)
  
  												mapping.append({'old': old_item, 'new': new_item}				)
  
  						return mapping
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=0							,     _UpperCAmelCase=False				)    ->  Dict:
  						_a     =			[]
  						for old_item in old_list:
  												_a     =			old_item
  
  												# In `model.mid`, the layer is called `attn`.
  												if not in_mid:
  																		_a     =			new_item.replace('attn'							,     'attentions'				)
  												_a     =			new_item.replace('.k.'							,     '.key.'				)
  												_a     =			new_item.replace('.v.'							,     '.value.'				)
  												_a     =			new_item.replace('.q.'							,     '.query.'				)
  
  												_a     =			new_item.replace('proj_out'							,     'proj_attn'				)
  												_a     =			new_item.replace('norm'							,     'group_norm'				)
  
  												_a     =			shave_segments(_UpperCAmelCase							,     n_shave_prefix_segments=_UpperCAmelCase				)
  												mapping.append({'old': old_item, 'new': new_item}				)
  
  						return mapping
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=None							,     _UpperCAmelCase=None							,     _UpperCAmelCase=None				)    ->  Any:
  						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():
  																		_a     =			old_checkpoint[path]
  																		_a     =			old_tensor.shape[0] // 3
  
  																		_a     =			(-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
  																		_a     =			old_tensor.shape[0] // config.get('num_head_channels'							,     1				) // 3
  
  																		_a     =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
  																		_a ,	_a ,	_a     =			old_tensor.split(channels // num_heads							,     dim=1				)
  
  																		_a     =			query.reshape(_UpperCAmelCase				).squeeze()
  																		_a     =			key.reshape(_UpperCAmelCase				).squeeze()
  																		_a     =			value.reshape(_UpperCAmelCase				).squeeze()
  
  						for path in paths:
  												_a     =			path['new']
  
  												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  																		continue
  
  												_a     =			new_path.replace('down.'							,     'down_blocks.'				)
  												_a     =			new_path.replace('up.'							,     'up_blocks.'				)
  
  												if additional_replacements is not None:
  																		for replacement in additional_replacements:
  																								_a     =			new_path.replace(replacement['old']							,     replacement['new']				)
  
  												if "attentions" in new_path:
  																		_a     =			old_checkpoint[path['old']].squeeze()
  												else:
  																		_a     =			old_checkpoint[path['old']]
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
  						_a     =			{}
  
  						_a     =			checkpoint['temb.dense.0.weight']
  						_a     =			checkpoint['temb.dense.0.bias']
  						_a     =			checkpoint['temb.dense.1.weight']
  						_a     =			checkpoint['temb.dense.1.bias']
  
  						_a     =			checkpoint['norm_out.weight']
  						_a     =			checkpoint['norm_out.bias']
  
  						_a     =			checkpoint['conv_in.weight']
  						_a     =			checkpoint['conv_in.bias']
  						_a     =			checkpoint['conv_out.weight']
  						_a     =			checkpoint['conv_out.bias']
  
  						_a     =			len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'down' in layer}				)
  						_a     =			{
  						    layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase				)
  						}
  
  						_a     =			len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'up' in layer}				)
  						_a     =			{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				):
  												_a     =			(i - 1) // (config['layers_per_block'] + 1)
  
  												if any('downsample' in layer for layer in down_blocks[i]				):
  																		_a     =			checkpoint[
  																		    f"""down.{i}.downsample.op.weight"""
  																		]
  																		_a     =			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]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'block' in layer}				)
  																		_a     =			{
  																		    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']				):
  																														_a     =			renew_resnet_paths(blocks[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  
  												if any('attn' in layer for layer in down_blocks[i]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'attn' in layer}				)
  																		_a     =			{
  																		    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']				):
  																														_a     =			renew_attention_paths(attns[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase				)
  
  						_a     =			[key for key in checkpoint if 'mid.block_1' in key]
  						_a     =			[key for key in checkpoint if 'mid.block_2' in key]
  						_a     =			[key for key in checkpoint if 'mid.attn_1' in key]
  
  						# Mid new 2
  						_a     =			renew_resnet_paths(_UpperCAmelCase				)
  						assign_to_checkpoint(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,     )
  
  						_a     =			renew_resnet_paths(_UpperCAmelCase				)
  						assign_to_checkpoint(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,     )
  
  						_a     =			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				):
  												_a     =			num_up_blocks - 1 - i
  
  												if any('upsample' in layer for layer in up_blocks[i]				):
  																		_a     =			checkpoint[
  																		    f"""up.{i}.upsample.conv.weight"""
  																		]
  																		_a     =			checkpoint[f"""up.{i}.upsample.conv.bias"""]
  
  												if any('block' in layer for layer in up_blocks[i]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'block' in layer}				)
  																		_a     =			{
  																		    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				):
  																														_a     =			{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
  																														_a     =			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]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'attn' in layer}				)
  																		_a     =			{
  																		    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				):
  																														_a     =			{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
  																														_a     =			renew_attention_paths(attns[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[replace_indices]				)
  
  						_a     =			{k.replace('mid_new_2'							,     'mid_block'				): v for k, v in new_checkpoint.items()}
  						return new_checkpoint
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  str:
  						_a     =			{}
  
  						_a     =			checkpoint['encoder.norm_out.weight']
  						_a     =			checkpoint['encoder.norm_out.bias']
  
  						_a     =			checkpoint['encoder.conv_in.weight']
  						_a     =			checkpoint['encoder.conv_in.bias']
  						_a     =			checkpoint['encoder.conv_out.weight']
  						_a     =			checkpoint['encoder.conv_out.bias']
  
  						_a     =			checkpoint['decoder.norm_out.weight']
  						_a     =			checkpoint['decoder.norm_out.bias']
  
  						_a     =			checkpoint['decoder.conv_in.weight']
  						_a     =			checkpoint['decoder.conv_in.bias']
  						_a     =			checkpoint['decoder.conv_out.weight']
  						_a     =			checkpoint['decoder.conv_out.bias']
  
  						_a     =			len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'down' in layer}				)
  						_a     =			{
  						    layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase				)
  						}
  
  						_a     =			len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'up' in layer}				)
  						_a     =			{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				):
  												_a     =			(i - 1) // (config['layers_per_block'] + 1)
  
  												if any('downsample' in layer for layer in down_blocks[i]				):
  																		_a     =			checkpoint[
  																		    f"""encoder.down.{i}.downsample.conv.weight"""
  																		]
  																		_a     =			checkpoint[
  																		    f"""encoder.down.{i}.downsample.conv.bias"""
  																		]
  
  												if any('block' in layer for layer in down_blocks[i]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'block' in layer}				)
  																		_a     =			{
  																		    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']				):
  																														_a     =			renew_resnet_paths(blocks[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  
  												if any('attn' in layer for layer in down_blocks[i]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'attn' in layer}				)
  																		_a     =			{
  																		    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']				):
  																														_a     =			renew_attention_paths(attns[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase				)
  
  						_a     =			[key for key in checkpoint if 'mid.block_1' in key]
  						_a     =			[key for key in checkpoint if 'mid.block_2' in key]
  						_a     =			[key for key in checkpoint if 'mid.attn_1' in key]
  
  						# Mid new 2
  						_a     =			renew_resnet_paths(_UpperCAmelCase				)
  						assign_to_checkpoint(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,     )
  
  						_a     =			renew_resnet_paths(_UpperCAmelCase				)
  						assign_to_checkpoint(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,     )
  
  						_a     =			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				):
  												_a     =			num_up_blocks - 1 - i
  
  												if any('upsample' in layer for layer in up_blocks[i]				):
  																		_a     =			checkpoint[
  																		    f"""decoder.up.{i}.upsample.conv.weight"""
  																		]
  																		_a     =			checkpoint[
  																		    f"""decoder.up.{i}.upsample.conv.bias"""
  																		]
  
  												if any('block' in layer for layer in up_blocks[i]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'block' in layer}				)
  																		_a     =			{
  																		    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				):
  																														_a     =			{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
  																														_a     =			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]				):
  																		_a     =			len(
  																		    {'.'.join(shave_segments(_UpperCAmelCase							,     3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'attn' in layer}				)
  																		_a     =			{
  																		    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				):
  																														_a     =			{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
  																														_a     =			renew_attention_paths(attns[j]				)
  																														assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[replace_indices]				)
  
  						_a     =			{k.replace('mid_new_2'							,     'mid_block'				): v for k, v in new_checkpoint.items()}
  						_a     =			checkpoint['quant_conv.weight']
  						_a     =			checkpoint['quant_conv.bias']
  						if "quantize.embedding.weight" in checkpoint:
  												_a     =			checkpoint['quantize.embedding.weight']
  						_a     =			checkpoint['post_quant_conv.weight']
  						_a     =			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: 
  
  
  import math
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						_a     =			math.log(2							,     10				) * number
  						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)
  
  						return difference
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
  						_a     =			90
  						_a     =			0
  
  						_a     =			math.log(1.23							,     10				)
  						_a     =			math.log(1.24							,     10				)
  						_a     =			0
  
  						while position < number:
  												_a     =			log_difference(_UpperCAmelCase				)
  
  												if difference >= upper_limit:
  																		power_iterator += 93
  
  												elif difference < lower_limit:
  																		power_iterator += 196
  
  												else:
  																		_a     =			power_iterator
  																		power_iterator += 196
  																		position += 1
  
  						return previous_power
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		print(f"""{solution() = }""")
  
  
  