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