
'''simple docstring'''







import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]=1							):



						"""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       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]=0							):



						"""simple docstring"""





						lowercase_			:  Tuple									= []
						for old_item in old_list:
												lowercase_			:  Tuple									= old_item
												lowercase_			:  Any									= new_item.replace('''block.'''   ,   '''resnets.'''							)
												lowercase_			:  Dict									= new_item.replace('''conv_shorcut'''   ,   '''conv1'''							)
												lowercase_			:  Dict									= new_item.replace('''in_shortcut'''   ,   '''conv_shortcut'''							)
												lowercase_			:  Dict									= new_item.replace('''temb_proj'''   ,   '''time_emb_proj'''							)

												lowercase_			:  List[Any]									= shave_segments(__SCREAMING_SNAKE_CASE   ,   n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)

												mapping.append({'''old''': old_item, '''new''': new_item}							)

						return mapping
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				Union[str, Any]=0   ,   __SCREAMING_SNAKE_CASE      :				Union[str, Any]=False							):



						"""simple docstring"""





						lowercase_			:  int									= []
						for old_item in old_list:
												lowercase_			:  List[Any]									= old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowercase_			:  Optional[Any]									= new_item.replace('''attn'''   ,   '''attentions'''							)
												lowercase_			:  Any									= new_item.replace('''.k.'''   ,   '''.key.'''							)
												lowercase_			:  str									= new_item.replace('''.v.'''   ,   '''.value.'''							)
												lowercase_			:  str									= new_item.replace('''.q.'''   ,   '''.query.'''							)

												lowercase_			:  Tuple									= new_item.replace('''proj_out'''   ,   '''proj_attn'''							)
												lowercase_			:  Tuple									= new_item.replace('''norm'''   ,   '''group_norm'''							)

												lowercase_			:  Dict									= shave_segments(__SCREAMING_SNAKE_CASE   ,   n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)
												mapping.append({'''old''': old_item, '''new''': new_item}							)

						return mapping
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]=None   ,   __SCREAMING_SNAKE_CASE      :				int=None   ,   __SCREAMING_SNAKE_CASE      :				str=None							):



						"""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():
																		lowercase_			:  Dict									= old_checkpoint[path]
																		lowercase_			:  str									= old_tensor.shape[0] // 3

																		lowercase_			:  Union[str, Any]									= (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

																		lowercase_			:  Union[str, Any]									= old_tensor.shape[0] // config.get('''num_head_channels'''   ,   1							) // 3

																		lowercase_			:  List[str]									= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
																		lowercase_		,				lowercase_		,				lowercase_			:  Optional[int]									= old_tensor.split(channels // num_heads   ,   dim=1							)

																		lowercase_			:  Tuple									= query.reshape(__SCREAMING_SNAKE_CASE							).squeeze()
																		lowercase_			:  Union[str, Any]									= key.reshape(__SCREAMING_SNAKE_CASE							).squeeze()
																		lowercase_			:  int									= value.reshape(__SCREAMING_SNAKE_CASE							).squeeze()

						for path in paths:
												lowercase_			:  Tuple									= path['''new''']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowercase_			:  int									= new_path.replace('''down.'''   ,   '''down_blocks.'''							)
												lowercase_			:  str									= new_path.replace('''up.'''   ,   '''up_blocks.'''							)

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowercase_			:  Optional[int]									= new_path.replace(replacement['''old''']   ,   replacement['''new''']							)

												if "attentions" in new_path:
																		lowercase_			:  Tuple									= old_checkpoint[path['''old''']].squeeze()
												else:
																		lowercase_			:  str									= old_checkpoint[path['''old''']]
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Tuple   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						lowercase_			:  int									= {}

						lowercase_			:  int									= checkpoint['''temb.dense.0.weight''']
						lowercase_			:  List[Any]									= checkpoint['''temb.dense.0.bias''']
						lowercase_			:  Tuple									= checkpoint['''temb.dense.1.weight''']
						lowercase_			:  str									= checkpoint['''temb.dense.1.bias''']

						lowercase_			:  str									= checkpoint['''norm_out.weight''']
						lowercase_			:  Dict									= checkpoint['''norm_out.bias''']

						lowercase_			:  Any									= checkpoint['''conv_in.weight''']
						lowercase_			:  Any									= checkpoint['''conv_in.bias''']
						lowercase_			:  int									= checkpoint['''conv_out.weight''']
						lowercase_			:  int									= checkpoint['''conv_out.bias''']

						lowercase_			:  int									= len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''down''' in layer}							)
						lowercase_			:  str									= {
						    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(__SCREAMING_SNAKE_CASE							)
						}

						lowercase_			:  Any									= len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''up''' in layer}							)
						lowercase_			:  str									= {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							):
												lowercase_			:  Union[str, Any]									= (i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]							):
																		lowercase_			:  int									= checkpoint[
																		    F'''down.{i}.downsample.op.weight'''
																		]
																		lowercase_			:  List[Any]									= 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_			:  Optional[int]									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''block''' in layer}							)
																		lowercase_			:  Optional[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''']							):
																														lowercase_			:  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]							):
																		lowercase_			:  Union[str, Any]									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''attn''' in layer}							)
																		lowercase_			:  Optional[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''']							):
																														lowercase_			:  Tuple									= renew_attention_paths(attns[j]							)
																														assign_to_checkpoint(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   config=__SCREAMING_SNAKE_CASE							)

						lowercase_			:  Tuple									= [key for key in checkpoint if '''mid.block_1''' in key]
						lowercase_			:  Dict									= [key for key in checkpoint if '''mid.block_2''' in key]
						lowercase_			:  Dict									= [key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						lowercase_			:  Tuple									= 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'''}]   ,   )

						lowercase_			:  Dict									= 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'''}]   ,   )

						lowercase_			:  Dict									= 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							):
												lowercase_			:  Any									= num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]							):
																		lowercase_			:  Optional[Any]									= checkpoint[
																		    F'''up.{i}.upsample.conv.weight'''
																		]
																		lowercase_			:  Tuple									= checkpoint[F'''up.{i}.upsample.conv.bias''']

												if any('''block''' in layer for layer in up_blocks[i]							):
																		lowercase_			:  List[Any]									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''block''' in layer}							)
																		lowercase_			:  Any									= {
																		    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							):
																														lowercase_			:  List[str]									= {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
																														lowercase_			:  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]							):
																		lowercase_			:  Union[str, Any]									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''attn''' in layer}							)
																		lowercase_			:  Optional[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							):
																														lowercase_			:  Optional[Any]									= {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
																														lowercase_			:  List[str]									= renew_attention_paths(attns[j]							)
																														assign_to_checkpoint(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]							)

						lowercase_			:  Any									= {k.replace('''mid_new_2'''   ,   '''mid_block'''							): v for k, v in new_checkpoint.items()}
						return new_checkpoint
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



						"""simple docstring"""





						lowercase_			:  Any									= {}

						lowercase_			:  Any									= checkpoint['''encoder.norm_out.weight''']
						lowercase_			:  List[Any]									= checkpoint['''encoder.norm_out.bias''']

						lowercase_			:  Optional[int]									= checkpoint['''encoder.conv_in.weight''']
						lowercase_			:  Any									= checkpoint['''encoder.conv_in.bias''']
						lowercase_			:  Optional[Any]									= checkpoint['''encoder.conv_out.weight''']
						lowercase_			:  List[str]									= checkpoint['''encoder.conv_out.bias''']

						lowercase_			:  int									= checkpoint['''decoder.norm_out.weight''']
						lowercase_			:  Optional[Any]									= checkpoint['''decoder.norm_out.bias''']

						lowercase_			:  Union[str, Any]									= checkpoint['''decoder.conv_in.weight''']
						lowercase_			:  List[Any]									= checkpoint['''decoder.conv_in.bias''']
						lowercase_			:  List[str]									= checkpoint['''decoder.conv_out.weight''']
						lowercase_			:  Union[str, Any]									= checkpoint['''decoder.conv_out.bias''']

						lowercase_			:  str									= len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''down''' in layer}							)
						lowercase_			:  List[str]									= {
						    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(__SCREAMING_SNAKE_CASE							)
						}

						lowercase_			:  Tuple									= len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''up''' in layer}							)
						lowercase_			:  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							):
												lowercase_			:  Optional[int]									= (i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]							):
																		lowercase_			:  List[Any]									= checkpoint[
																		    F'''encoder.down.{i}.downsample.conv.weight'''
																		]
																		lowercase_			:  List[Any]									= checkpoint[
																		    F'''encoder.down.{i}.downsample.conv.bias'''
																		]

												if any('''block''' in layer for layer in down_blocks[i]							):
																		lowercase_			:  List[Any]									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''block''' in layer}							)
																		lowercase_			:  List[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''']							):
																														lowercase_			:  Union[str, Any]									= 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]							):
																		lowercase_			:  Dict									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''attn''' in layer}							)
																		lowercase_			:  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''']							):
																														lowercase_			:  str									= renew_attention_paths(attns[j]							)
																														assign_to_checkpoint(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   config=__SCREAMING_SNAKE_CASE							)

						lowercase_			:  str									= [key for key in checkpoint if '''mid.block_1''' in key]
						lowercase_			:  Dict									= [key for key in checkpoint if '''mid.block_2''' in key]
						lowercase_			:  List[str]									= [key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						lowercase_			:  Optional[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_1''', '''new''': '''resnets.0'''}]   ,   )

						lowercase_			:  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'''}]   ,   )

						lowercase_			:  Optional[Any]									= 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							):
												lowercase_			:  Optional[Any]									= num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]							):
																		lowercase_			:  Tuple									= checkpoint[
																		    F'''decoder.up.{i}.upsample.conv.weight'''
																		]
																		lowercase_			:  Dict									= checkpoint[
																		    F'''decoder.up.{i}.upsample.conv.bias'''
																		]

												if any('''block''' in layer for layer in up_blocks[i]							):
																		lowercase_			:  Tuple									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''block''' in layer}							)
																		lowercase_			:  Optional[Any]									= {
																		    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							):
																														lowercase_			:  Dict									= {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
																														lowercase_			:  str									= 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]							):
																		lowercase_			:  int									= len(
																		    {'''.'''.join(shave_segments(__SCREAMING_SNAKE_CASE   ,   3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''attn''' in layer}							)
																		lowercase_			:  Union[str, Any]									= {
																		    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							):
																														lowercase_			:  str									= {'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
																														lowercase_			:  Any									= renew_attention_paths(attns[j]							)
																														assign_to_checkpoint(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]							)

						lowercase_			:  List[str]									= {k.replace('''mid_new_2'''   ,   '''mid_block'''							): v for k, v in new_checkpoint.items()}
						lowercase_			:  Optional[int]									= checkpoint['''quant_conv.weight''']
						lowercase_			:  List[str]									= checkpoint['''quant_conv.bias''']
						if "quantize.embedding.weight" in checkpoint:
												lowercase_			:  Tuple									= checkpoint['''quantize.embedding.weight''']
						lowercase_			:  int									= checkpoint['''post_quant_conv.weight''']
						lowercase_			:  Optional[int]									= checkpoint['''post_quant_conv.bias''']

						return new_checkpoint


if __name__ == "__main__":
							_lowercase				:						Any												= 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				:						Union[str, Any]												= parser.parse_args()
							_lowercase				:						Optional[Any]												= torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														_lowercase				:						Dict												= json.loads(f.read())

							# unet case
							_lowercase				:						List[str]												= {key.split(".")[0] for key in checkpoint.keys()}
							if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
														_lowercase				:						str												= convert_vq_autoenc_checkpoint(checkpoint, config)
							else:
														_lowercase				:						str												= convert_ddpm_checkpoint(checkpoint, config)

							if "ddpm" in config:
														del config["ddpm"]

							if config["_class_name"] == "VQModel":
														_lowercase				:						List[str]												= VQModel(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							elif config["_class_name"] == "AutoencoderKL":
														_lowercase				:						Any												= AutoencoderKL(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							else:
														_lowercase				:						str												= UNetaDModel(**config)
														model.load_state_dict(converted_checkpoint)

														_lowercase				:						Dict												= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

														_lowercase				:						Optional[Any]												= DDPMPipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)





'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 998001							):



						"""simple docstring"""





						lowercase_			:  Dict									= 0
						for i in range(999   ,   99   ,   -1							):  # 3 digit numbers range from 999 down to 100
												for j in range(999   ,   99   ,   -1							):
																		lowercase_			:  int									= str(i * j							)
																		if product_string == product_string[::-1] and i * j < n:
																								lowercase_			:  str									= max(__SCREAMING_SNAKE_CASE   ,   i * j							)
						return answer


if __name__ == "__main__":
							print(f"""{solution() = }""")



