code: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[Any], lowercase__   :Optional[int]=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 			a_   (  lowercase__   :Optional[int], lowercase__   :Optional[Any]=0    ):
  			__lowerCamelCase        =			[]
  			for old_item in old_list:
  						__lowerCamelCase        =			old_item
  						__lowerCamelCase        =			new_item.replace("""block.""", """resnets."""    )
  						__lowerCamelCase        =			new_item.replace("""conv_shorcut""", """conv1"""    )
  						__lowerCamelCase        =			new_item.replace("""in_shortcut""", """conv_shortcut"""    )
  						__lowerCamelCase        =			new_item.replace("""temb_proj""", """time_emb_proj"""    )
  
  						__lowerCamelCase        =			shave_segments(lowercase__, n_shave_prefix_segments=lowercase__    )
  
  						mapping.append({"""old""": old_item, """new""": new_item}    )
  
  			return mapping
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str], lowercase__   :Optional[Any]=0, lowercase__   :Dict=False    ):
  			__lowerCamelCase        =			[]
  			for old_item in old_list:
  						__lowerCamelCase        =			old_item
  
  						# In `model.mid`, the layer is called `attn`.
  						if not in_mid:
  									__lowerCamelCase        =			new_item.replace("""attn""", """attentions"""    )
  						__lowerCamelCase        =			new_item.replace(""".k.""", """.key."""    )
  						__lowerCamelCase        =			new_item.replace(""".v.""", """.value."""    )
  						__lowerCamelCase        =			new_item.replace(""".q.""", """.query."""    )
  
  						__lowerCamelCase        =			new_item.replace("""proj_out""", """proj_attn"""    )
  						__lowerCamelCase        =			new_item.replace("""norm""", """group_norm"""    )
  
  						__lowerCamelCase        =			shave_segments(lowercase__, n_shave_prefix_segments=lowercase__    )
  						mapping.append({"""old""": old_item, """new""": new_item}    )
  
  			return mapping
  
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :Optional[Any], lowercase__   :int, lowercase__   :Dict=None, lowercase__   :Union[str, Any]=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():
  									__lowerCamelCase        =			old_checkpoint[path]
  									__lowerCamelCase        =			old_tensor.shape[0] // 3
  
  									__lowerCamelCase        =			(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)
  
  									__lowerCamelCase        =			old_tensor.shape[0] // config.get("""num_head_channels""", 1    ) // 3
  
  									__lowerCamelCase        =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
  									__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			old_tensor.split(channels // num_heads, dim=1    )
  
  									__lowerCamelCase        =			query.reshape(lowercase__    ).squeeze()
  									__lowerCamelCase        =			key.reshape(lowercase__    ).squeeze()
  									__lowerCamelCase        =			value.reshape(lowercase__    ).squeeze()
  
  			for path in paths:
  						__lowerCamelCase        =			path["""new"""]
  
  						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  									continue
  
  						__lowerCamelCase        =			new_path.replace("""down.""", """down_blocks."""    )
  						__lowerCamelCase        =			new_path.replace("""up.""", """up_blocks."""    )
  
  						if additional_replacements is not None:
  									for replacement in additional_replacements:
  												__lowerCamelCase        =			new_path.replace(replacement["""old"""], replacement["""new"""]    )
  
  						if "attentions" in new_path:
  									__lowerCamelCase        =			old_checkpoint[path["""old"""]].squeeze()
  						else:
  									__lowerCamelCase        =			old_checkpoint[path["""old"""]]
  
  
  
  
  
  def 			a_   (  lowercase__   :Union[str, Any], lowercase__   :Tuple    ):
  			__lowerCamelCase        =			{}
  
  			__lowerCamelCase        =			checkpoint["""temb.dense.0.weight"""]
  			__lowerCamelCase        =			checkpoint["""temb.dense.0.bias"""]
  			__lowerCamelCase        =			checkpoint["""temb.dense.1.weight"""]
  			__lowerCamelCase        =			checkpoint["""temb.dense.1.bias"""]
  
  			__lowerCamelCase        =			checkpoint["""norm_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""norm_out.bias"""]
  
  			__lowerCamelCase        =			checkpoint["""conv_in.weight"""]
  			__lowerCamelCase        =			checkpoint["""conv_in.bias"""]
  			__lowerCamelCase        =			checkpoint["""conv_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""conv_out.bias"""]
  
  			__lowerCamelCase        =			len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """down""" in layer}    )
  			__lowerCamelCase        =			{
  			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowercase__    )
  			}
  
  			__lowerCamelCase        =			len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """up""" in layer}    )
  			__lowerCamelCase        =			{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__    ):
  						__lowerCamelCase        =			(i - 1) // (config["""layers_per_block"""] + 1)
  
  						if any("""downsample""" in layer for layer in down_blocks[i]    ):
  									__lowerCamelCase        =			checkpoint[
  									    f'down.{i}.downsample.op.weight'
  									]
  									__lowerCamelCase        =			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        =			len(
  									    {""".""".join(shave_segments(lowercase__, 2    ).split("""."""    )[:2]    ) for layer in down_blocks[i] if """block""" in layer}    )
  									__lowerCamelCase        =			{
  									    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"""]    ):
  															__lowerCamelCase        =			renew_resnet_paths(blocks[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__    )
  
  						if any("""attn""" in layer for layer in down_blocks[i]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 2    ).split("""."""    )[:2]    ) for layer in down_blocks[i] if """attn""" in layer}    )
  									__lowerCamelCase        =			{
  									    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"""]    ):
  															__lowerCamelCase        =			renew_attention_paths(attns[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__, config=lowercase__    )
  
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.block_1""" in key]
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.block_2""" in key]
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.attn_1""" in key]
  
  			# Mid new 2
  			__lowerCamelCase        =			renew_resnet_paths(lowercase__    )
  			assign_to_checkpoint(
  			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}], )
  
  			__lowerCamelCase        =			renew_resnet_paths(lowercase__    )
  			assign_to_checkpoint(
  			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}], )
  
  			__lowerCamelCase        =			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__    ):
  						__lowerCamelCase        =			num_up_blocks - 1 - i
  
  						if any("""upsample""" in layer for layer in up_blocks[i]    ):
  									__lowerCamelCase        =			checkpoint[
  									    f'up.{i}.upsample.conv.weight'
  									]
  									__lowerCamelCase        =			checkpoint[f'up.{i}.upsample.conv.bias']
  
  						if any("""block""" in layer for layer in up_blocks[i]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 2    ).split("""."""    )[:2]    ) for layer in up_blocks[i] if """block""" in layer}    )
  									__lowerCamelCase        =			{
  									    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    ):
  															__lowerCamelCase        =			{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
  															__lowerCamelCase        =			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]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 2    ).split("""."""    )[:2]    ) for layer in up_blocks[i] if """attn""" in layer}    )
  									__lowerCamelCase        =			{
  									    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    ):
  															__lowerCamelCase        =			{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
  															__lowerCamelCase        =			renew_attention_paths(attns[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__, additional_replacements=[replace_indices]    )
  
  			__lowerCamelCase        =			{k.replace("""mid_new_2""", """mid_block"""    ): v for k, v in new_checkpoint.items()}
  			return new_checkpoint
  
  
  
  
  
  def 			a_   (  lowercase__   :List[Any], lowercase__   :Optional[int]    ):
  			__lowerCamelCase        =			{}
  
  			__lowerCamelCase        =			checkpoint["""encoder.norm_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""encoder.norm_out.bias"""]
  
  			__lowerCamelCase        =			checkpoint["""encoder.conv_in.weight"""]
  			__lowerCamelCase        =			checkpoint["""encoder.conv_in.bias"""]
  			__lowerCamelCase        =			checkpoint["""encoder.conv_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""encoder.conv_out.bias"""]
  
  			__lowerCamelCase        =			checkpoint["""decoder.norm_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""decoder.norm_out.bias"""]
  
  			__lowerCamelCase        =			checkpoint["""decoder.conv_in.weight"""]
  			__lowerCamelCase        =			checkpoint["""decoder.conv_in.bias"""]
  			__lowerCamelCase        =			checkpoint["""decoder.conv_out.weight"""]
  			__lowerCamelCase        =			checkpoint["""decoder.conv_out.bias"""]
  
  			__lowerCamelCase        =			len({""".""".join(layer.split("""."""    )[:3]    ) for layer in checkpoint if """down""" in layer}    )
  			__lowerCamelCase        =			{
  			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowercase__    )
  			}
  
  			__lowerCamelCase        =			len({""".""".join(layer.split("""."""    )[:3]    ) for layer in checkpoint if """up""" in layer}    )
  			__lowerCamelCase        =			{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__    ):
  						__lowerCamelCase        =			(i - 1) // (config["""layers_per_block"""] + 1)
  
  						if any("""downsample""" in layer for layer in down_blocks[i]    ):
  									__lowerCamelCase        =			checkpoint[
  									    f'encoder.down.{i}.downsample.conv.weight'
  									]
  									__lowerCamelCase        =			checkpoint[
  									    f'encoder.down.{i}.downsample.conv.bias'
  									]
  
  						if any("""block""" in layer for layer in down_blocks[i]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 3    ).split("""."""    )[:3]    ) for layer in down_blocks[i] if """block""" in layer}    )
  									__lowerCamelCase        =			{
  									    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"""]    ):
  															__lowerCamelCase        =			renew_resnet_paths(blocks[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__    )
  
  						if any("""attn""" in layer for layer in down_blocks[i]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 3    ).split("""."""    )[:3]    ) for layer in down_blocks[i] if """attn""" in layer}    )
  									__lowerCamelCase        =			{
  									    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"""]    ):
  															__lowerCamelCase        =			renew_attention_paths(attns[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__, config=lowercase__    )
  
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.block_1""" in key]
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.block_2""" in key]
  			__lowerCamelCase        =			[key for key in checkpoint if """mid.attn_1""" in key]
  
  			# Mid new 2
  			__lowerCamelCase        =			renew_resnet_paths(lowercase__    )
  			assign_to_checkpoint(
  			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}], )
  
  			__lowerCamelCase        =			renew_resnet_paths(lowercase__    )
  			assign_to_checkpoint(
  			    lowercase__, lowercase__, lowercase__, additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}], )
  
  			__lowerCamelCase        =			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__    ):
  						__lowerCamelCase        =			num_up_blocks - 1 - i
  
  						if any("""upsample""" in layer for layer in up_blocks[i]    ):
  									__lowerCamelCase        =			checkpoint[
  									    f'decoder.up.{i}.upsample.conv.weight'
  									]
  									__lowerCamelCase        =			checkpoint[
  									    f'decoder.up.{i}.upsample.conv.bias'
  									]
  
  						if any("""block""" in layer for layer in up_blocks[i]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 3    ).split("""."""    )[:3]    ) for layer in up_blocks[i] if """block""" in layer}    )
  									__lowerCamelCase        =			{
  									    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    ):
  															__lowerCamelCase        =			{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
  															__lowerCamelCase        =			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]    ):
  									__lowerCamelCase        =			len(
  									    {""".""".join(shave_segments(lowercase__, 3    ).split("""."""    )[:3]    ) for layer in up_blocks[i] if """attn""" in layer}    )
  									__lowerCamelCase        =			{
  									    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    ):
  															__lowerCamelCase        =			{"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
  															__lowerCamelCase        =			renew_attention_paths(attns[j]    )
  															assign_to_checkpoint(lowercase__, lowercase__, lowercase__, additional_replacements=[replace_indices]    )
  
  			__lowerCamelCase        =			{k.replace("""mid_new_2""", """mid_block"""    ): v for k, v in new_checkpoint.items()}
  			__lowerCamelCase        =			checkpoint["""quant_conv.weight"""]
  			__lowerCamelCase        =			checkpoint["""quant_conv.bias"""]
  			if "quantize.embedding.weight" in checkpoint:
  						__lowerCamelCase        =			checkpoint["""quantize.embedding.weight"""]
  			__lowerCamelCase        =			checkpoint["""post_quant_conv.weight"""]
  			__lowerCamelCase        =			checkpoint["""post_quant_conv.bias"""]
  
  			return new_checkpoint
  
  
  if __name__ == "__main__":
  					__magic_name__	:   Dict          =							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.')
  
  					__magic_name__	:   int          =							parser.parse_args()
  					__magic_name__	:   List[str]          =							torch.load(args.checkpoint_path)
  
  					with open(args.config_file) as f:
  										__magic_name__	:   Union[str, Any]          =							json.loads(f.read())
  
  					# unet case
  					__magic_name__	:   List[Any]          =							{key.split('.')[0] for key in checkpoint.keys()}
  					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  										__magic_name__	:   List[str]          =							convert_vq_autoenc_checkpoint(checkpoint, config)
  					else:
  										__magic_name__	:   str          =							convert_ddpm_checkpoint(checkpoint, config)
  
  					if "ddpm" in config:
  										del config["ddpm"]
  
  					if config["_class_name"] == "VQModel":
  										__magic_name__	:   Union[str, Any]          =							VQModel(**config)
  										model.load_state_dict(converted_checkpoint)
  										model.save_pretrained(args.dump_path)
  					elif config["_class_name"] == "AutoencoderKL":
  										__magic_name__	:   List[Any]          =							AutoencoderKL(**config)
  										model.load_state_dict(converted_checkpoint)
  										model.save_pretrained(args.dump_path)
  					else:
  										__magic_name__	:   List[Any]          =							UNetaDModel(**config)
  										model.load_state_dict(converted_checkpoint)
  
  										__magic_name__	:   str          =							DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
  										__magic_name__	:   str          =							DDPMPipeline(unet=model, scheduler=scheduler)
  										pipe.save_pretrained(args.dump_path)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  import re
  
  
  
  
  
  def 			a_   (  lowercase__   :list[str]    ):
  
  			def alphanum_key(lowercase__   :int    ):
  						return [int(lowercase__    ) if s.isdigit() else s.lower() for s in re.split("""([0-9]+)""", lowercase__    )]
  
  			return sorted(lowercase__, key=lowercase__    )
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  
  
  
  
  