



import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=1  )							->	int:
    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       (						lowerCAmelCase_   ,       lowerCAmelCase_=0  )							->	List[Any]:
    SCREAMING_SNAKE_CASE__       							= []
    for old_item in old_list:
        SCREAMING_SNAKE_CASE__       							= old_item
        SCREAMING_SNAKE_CASE__       							= new_item.replace('''block.'''   ,       '''resnets.'''  )
        SCREAMING_SNAKE_CASE__       							= new_item.replace('''conv_shorcut'''   ,       '''conv1'''  )
        SCREAMING_SNAKE_CASE__       							= new_item.replace('''in_shortcut'''   ,       '''conv_shortcut'''  )
        SCREAMING_SNAKE_CASE__       							= new_item.replace('''temb_proj'''   ,       '''time_emb_proj'''  )

        SCREAMING_SNAKE_CASE__       							= 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       (						lowerCAmelCase_   ,       lowerCAmelCase_=0   ,       lowerCAmelCase_=False  )							->	List[str]:
    SCREAMING_SNAKE_CASE__       							= []
    for old_item in old_list:
        SCREAMING_SNAKE_CASE__       							= old_item

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

        SCREAMING_SNAKE_CASE__       							= new_item.replace('''proj_out'''   ,       '''proj_attn'''  )
        SCREAMING_SNAKE_CASE__       							= new_item.replace('''norm'''   ,       '''group_norm'''  )

        SCREAMING_SNAKE_CASE__       							= 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       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None  )							->	Dict:
    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():
            SCREAMING_SNAKE_CASE__       							= old_checkpoint[path]
            SCREAMING_SNAKE_CASE__       							= old_tensor.shape[0] // 3

            SCREAMING_SNAKE_CASE__       							= (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

            SCREAMING_SNAKE_CASE__       							= old_tensor.shape[0] // config.get('''num_head_channels'''   ,       1  ) // 3

            SCREAMING_SNAKE_CASE__       							= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
            SCREAMING_SNAKE_CASE__       							= old_tensor.split(channels // num_heads   ,       dim=1  )

            SCREAMING_SNAKE_CASE__       							= query.reshape(_SCREAMING_SNAKE_CASE  ).squeeze()
            SCREAMING_SNAKE_CASE__       							= key.reshape(_SCREAMING_SNAKE_CASE  ).squeeze()
            SCREAMING_SNAKE_CASE__       							= value.reshape(_SCREAMING_SNAKE_CASE  ).squeeze()

    for path in paths:
        SCREAMING_SNAKE_CASE__       							= path['new']

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        SCREAMING_SNAKE_CASE__       							= new_path.replace('''down.'''   ,       '''down_blocks.'''  )
        SCREAMING_SNAKE_CASE__       							= new_path.replace('''up.'''   ,       '''up_blocks.'''  )

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

        if "attentions" in new_path:
            SCREAMING_SNAKE_CASE__       							= old_checkpoint[path['old']].squeeze()
        else:
            SCREAMING_SNAKE_CASE__       							= old_checkpoint[path['old']]


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	int:
    SCREAMING_SNAKE_CASE__       							= {}

    SCREAMING_SNAKE_CASE__       							= checkpoint['temb.dense.0.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['temb.dense.0.bias']
    SCREAMING_SNAKE_CASE__       							= checkpoint['temb.dense.1.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['temb.dense.1.bias']

    SCREAMING_SNAKE_CASE__       							= checkpoint['norm_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['norm_out.bias']

    SCREAMING_SNAKE_CASE__       							= checkpoint['conv_in.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['conv_in.bias']
    SCREAMING_SNAKE_CASE__       							= checkpoint['conv_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['conv_out.bias']

    SCREAMING_SNAKE_CASE__       							= len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''down''' in layer}  )
    SCREAMING_SNAKE_CASE__       							= {
        layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE  )
    }

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

        if any('''downsample''' in layer for layer in down_blocks[i]  ):
            SCREAMING_SNAKE_CASE__       							= checkpoint[
                f'''down.{i}.downsample.op.weight'''
            ]
            SCREAMING_SNAKE_CASE__       							= 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]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       2  ).split('''.'''  )[:2]  ) for layer in down_blocks[i] if '''block''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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''']  ):
                    SCREAMING_SNAKE_CASE__       							= 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]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       2  ).split('''.'''  )[:2]  ) for layer in down_blocks[i] if '''attn''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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''']  ):
                    SCREAMING_SNAKE_CASE__       							= renew_attention_paths(attns[j]  )
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       config=_SCREAMING_SNAKE_CASE  )

    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.block_1' in key]
    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.block_2' in key]
    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    SCREAMING_SNAKE_CASE__       							= 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'''}]   ,       )

    SCREAMING_SNAKE_CASE__       							= 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'''}]   ,       )

    SCREAMING_SNAKE_CASE__       							= 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  ):
        SCREAMING_SNAKE_CASE__       							= num_up_blocks - 1 - i

        if any('''upsample''' in layer for layer in up_blocks[i]  ):
            SCREAMING_SNAKE_CASE__       							= checkpoint[
                f'''up.{i}.upsample.conv.weight'''
            ]
            SCREAMING_SNAKE_CASE__       							= checkpoint[f'''up.{i}.upsample.conv.bias''']

        if any('''block''' in layer for layer in up_blocks[i]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       2  ).split('''.'''  )[:2]  ) for layer in up_blocks[i] if '''block''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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  ):
                    SCREAMING_SNAKE_CASE__       							= {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
                    SCREAMING_SNAKE_CASE__       							= 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]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       2  ).split('''.'''  )[:2]  ) for layer in up_blocks[i] if '''attn''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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  ):
                    SCREAMING_SNAKE_CASE__       							= {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
                    SCREAMING_SNAKE_CASE__       							= renew_attention_paths(attns[j]  )
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       additional_replacements=[replace_indices]  )

    SCREAMING_SNAKE_CASE__       							= {k.replace('''mid_new_2'''   ,       '''mid_block'''  ): v for k, v in new_checkpoint.items()}
    return new_checkpoint


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Union[str, Any]:
    SCREAMING_SNAKE_CASE__       							= {}

    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.norm_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.norm_out.bias']

    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.conv_in.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.conv_in.bias']
    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.conv_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['encoder.conv_out.bias']

    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.norm_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.norm_out.bias']

    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.conv_in.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.conv_in.bias']
    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.conv_out.weight']
    SCREAMING_SNAKE_CASE__       							= checkpoint['decoder.conv_out.bias']

    SCREAMING_SNAKE_CASE__       							= len({'''.'''.join(layer.split('''.'''  )[:3]  ) for layer in checkpoint if '''down''' in layer}  )
    SCREAMING_SNAKE_CASE__       							= {
        layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(_SCREAMING_SNAKE_CASE  )
    }

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

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

        if any('''block''' in layer for layer in down_blocks[i]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       3  ).split('''.'''  )[:3]  ) for layer in down_blocks[i] if '''block''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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''']  ):
                    SCREAMING_SNAKE_CASE__       							= 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]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       3  ).split('''.'''  )[:3]  ) for layer in down_blocks[i] if '''attn''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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''']  ):
                    SCREAMING_SNAKE_CASE__       							= renew_attention_paths(attns[j]  )
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       config=_SCREAMING_SNAKE_CASE  )

    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.block_1' in key]
    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.block_2' in key]
    SCREAMING_SNAKE_CASE__       							= [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    SCREAMING_SNAKE_CASE__       							= 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'''}]   ,       )

    SCREAMING_SNAKE_CASE__       							= 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'''}]   ,       )

    SCREAMING_SNAKE_CASE__       							= 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  ):
        SCREAMING_SNAKE_CASE__       							= num_up_blocks - 1 - i

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

        if any('''block''' in layer for layer in up_blocks[i]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       3  ).split('''.'''  )[:3]  ) for layer in up_blocks[i] if '''block''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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  ):
                    SCREAMING_SNAKE_CASE__       							= {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
                    SCREAMING_SNAKE_CASE__       							= 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]  ):
            SCREAMING_SNAKE_CASE__       							= len(
                {'''.'''.join(shave_segments(_SCREAMING_SNAKE_CASE   ,       3  ).split('''.'''  )[:3]  ) for layer in up_blocks[i] if '''attn''' in layer}  )
            SCREAMING_SNAKE_CASE__       							= {
                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  ):
                    SCREAMING_SNAKE_CASE__       							= {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
                    SCREAMING_SNAKE_CASE__       							= renew_attention_paths(attns[j]  )
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       additional_replacements=[replace_indices]  )

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







from __future__ import annotations

from collections.abc import Generator


def        __snake_case       (						lowerCAmelCase_  )							->	Generator[int, None, None]:
    if not isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ) or n < 1:
        raise Exception('''Sequence only defined for positive integers'''  )

    yield n
    while n != 1:
        if n % 2 == 0:
            n //= 2
        else:
            SCREAMING_SNAKE_CASE__       							= 3 * n + 1
        yield n


def        __snake_case       (						)							->	str:
    SCREAMING_SNAKE_CASE__       							= 4_3
    SCREAMING_SNAKE_CASE__       							= tuple(collatz_sequence(lowerCAmelCase_  )  )
    print(lowerCAmelCase_  )
    print(f'''Collatz sequence from {n} took {len(lowerCAmelCase_  )} steps.'''  )


if __name__ == "__main__":
  main()
