"""simple docstring"""




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	int=1     )	->				Any:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split("""."""     )[n_shave_prefix_segments:]     )
  else:
    return ".".join(path.split("""."""     )[:n_shave_prefix_segments]     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	int=0     )	->				Any:
  _lowerCAmelCase						:	Dict        =					[]
  for old_item in old_list:
    _lowerCAmelCase						:	Union[str, Any]        =					old_item.replace("""in_layers.0"""      ,"""norm1"""     )
    _lowerCAmelCase						:	Tuple        =					new_item.replace("""in_layers.2"""      ,"""conv1"""     )

    _lowerCAmelCase						:	List[Any]        =					new_item.replace("""out_layers.0"""      ,"""norm2"""     )
    _lowerCAmelCase						:	List[str]        =					new_item.replace("""out_layers.3"""      ,"""conv2"""     )

    _lowerCAmelCase						:	List[str]        =					new_item.replace("""emb_layers.1"""      ,"""time_emb_proj"""     )
    _lowerCAmelCase						:	Tuple        =					new_item.replace("""skip_connection"""      ,"""conv_shortcut"""     )

    _lowerCAmelCase						:	Tuple        =					shave_segments(a__      ,n_shave_prefix_segments=a__     )

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

  return mapping





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict      ,_lowerCamelCase						:	int=0     )	->				Tuple:
  _lowerCAmelCase						:	List[Any]        =					[]
  for old_item in old_list:
    _lowerCAmelCase						:	Optional[Any]        =					old_item

    _lowerCAmelCase						:	Union[str, Any]        =					new_item.replace("""norm.weight"""      ,"""group_norm.weight"""     )
    _lowerCAmelCase						:	List[Any]        =					new_item.replace("""norm.bias"""      ,"""group_norm.bias"""     )

    _lowerCAmelCase						:	int        =					new_item.replace("""proj_out.weight"""      ,"""proj_attn.weight"""     )
    _lowerCAmelCase						:	Any        =					new_item.replace("""proj_out.bias"""      ,"""proj_attn.bias"""     )

    _lowerCAmelCase						:	Any        =					shave_segments(a__      ,n_shave_prefix_segments=a__     )

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

  return mapping





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	List[str]=None      ,_lowerCamelCase						:	Tuple=None      ,_lowerCamelCase						:	int=None     )	->				str:
  assert isinstance(a__      ,a__     ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _lowerCAmelCase						:	Optional[Any]        =					old_checkpoint[path]
      _lowerCAmelCase						:	int        =					old_tensor.shape[0] // 3

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

      _lowerCAmelCase						:	Union[str, Any]        =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

      _lowerCAmelCase						:	str        =					query.reshape(a__     )
      _lowerCAmelCase						:	Tuple        =					key.reshape(a__     )
      _lowerCAmelCase						:	Optional[int]        =					value.reshape(a__     )

  for path in paths:
    _lowerCAmelCase						:	int        =					path["""new"""]

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    _lowerCAmelCase						:	List[str]        =					new_path.replace("""middle_block.0"""      ,"""mid_block.resnets.0"""     )
    _lowerCAmelCase						:	str        =					new_path.replace("""middle_block.1"""      ,"""mid_block.attentions.0"""     )
    _lowerCAmelCase						:	Optional[Any]        =					new_path.replace("""middle_block.2"""      ,"""mid_block.resnets.1"""     )

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

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      _lowerCAmelCase						:	Optional[Any]        =					old_checkpoint[path["""old"""]][:, :, 0]
    else:
      _lowerCAmelCase						:	Any        =					old_checkpoint[path["""old"""]]





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	int     )	->				List[str]:
  _lowerCAmelCase						:	List[str]        =					{}

  _lowerCAmelCase						:	Dict        =					checkpoint["""time_embed.0.weight"""]
  _lowerCAmelCase						:	Dict        =					checkpoint["""time_embed.0.bias"""]
  _lowerCAmelCase						:	Tuple        =					checkpoint["""time_embed.2.weight"""]
  _lowerCAmelCase						:	Any        =					checkpoint["""time_embed.2.bias"""]

  _lowerCAmelCase						:	Union[str, Any]        =					checkpoint["""input_blocks.0.0.weight"""]
  _lowerCAmelCase						:	Optional[Any]        =					checkpoint["""input_blocks.0.0.bias"""]

  _lowerCAmelCase						:	Optional[Any]        =					checkpoint["""out.0.weight"""]
  _lowerCAmelCase						:	str        =					checkpoint["""out.0.bias"""]
  _lowerCAmelCase						:	str        =					checkpoint["""out.2.weight"""]
  _lowerCAmelCase						:	Optional[Any]        =					checkpoint["""out.2.bias"""]

  # Retrieves the keys for the input blocks only
  _lowerCAmelCase						:	Any        =					len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """input_blocks""" in layer}     )
  _lowerCAmelCase						:	List[Any]        =					{
      layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
      for layer_id in range(a__     )
  }

  # Retrieves the keys for the middle blocks only
  _lowerCAmelCase						:	Dict        =					len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """middle_block""" in layer}     )
  _lowerCAmelCase						:	Dict        =					{
      layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
      for layer_id in range(a__     )
  }

  # Retrieves the keys for the output blocks only
  _lowerCAmelCase						:	Optional[Any]        =					len({""".""".join(layer.split("""."""     )[:2]     ) for layer in checkpoint if """output_blocks""" in layer}     )
  _lowerCAmelCase						:	Any        =					{
      layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
      for layer_id in range(a__     )
  }

  for i in range(1      ,a__     ):
    _lowerCAmelCase						:	Optional[int]        =					(i - 1) // (config["""num_res_blocks"""] + 1)
    _lowerCAmelCase						:	Optional[Any]        =					(i - 1) % (config["""num_res_blocks"""] + 1)

    _lowerCAmelCase						:	int        =					[key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
    _lowerCAmelCase						:	Optional[Any]        =					[key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

    if f"input_blocks.{i}.0.op.weight" in checkpoint:
      _lowerCAmelCase						:	int        =					checkpoint[
          f"input_blocks.{i}.0.op.weight"
      ]
      _lowerCAmelCase						:	Tuple        =					checkpoint[
          f"input_blocks.{i}.0.op.bias"
      ]
      continue

    _lowerCAmelCase						:	Tuple        =					renew_resnet_paths(a__     )
    _lowerCAmelCase						:	Dict        =					{"""old""": f"input_blocks.{i}.0", """new""": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    _lowerCAmelCase						:	Dict        =					{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
    assign_to_checkpoint(
        a__      ,a__      ,a__      ,additional_replacements=[meta_path, resnet_op]      ,config=a__     )

    if len(a__     ):
      _lowerCAmelCase						:	Tuple        =					renew_attention_paths(a__     )
      _lowerCAmelCase						:	Tuple        =					{
          """old""": f"input_blocks.{i}.1",
          """new""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
      }
      _lowerCAmelCase						:	int        =					{
          f"input_blocks.{i}.1.qkv.bias": {
              """key""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
              """query""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
              """value""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
          },
          f"input_blocks.{i}.1.qkv.weight": {
              """key""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
              """query""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
              """value""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
          },
      }
      assign_to_checkpoint(
          a__      ,a__      ,a__      ,additional_replacements=[meta_path]      ,attention_paths_to_split=a__      ,config=a__      ,)

  _lowerCAmelCase						:	Dict        =					middle_blocks[0]
  _lowerCAmelCase						:	List[str]        =					middle_blocks[1]
  _lowerCAmelCase						:	List[str]        =					middle_blocks[2]

  _lowerCAmelCase						:	Union[str, Any]        =					renew_resnet_paths(a__     )
  assign_to_checkpoint(a__      ,a__      ,a__      ,config=a__     )

  _lowerCAmelCase						:	Any        =					renew_resnet_paths(a__     )
  assign_to_checkpoint(a__      ,a__      ,a__      ,config=a__     )

  _lowerCAmelCase						:	Dict        =					renew_attention_paths(a__     )
  _lowerCAmelCase						:	List[str]        =					{
      """middle_block.1.qkv.bias""": {
          """key""": """mid_block.attentions.0.key.bias""",
          """query""": """mid_block.attentions.0.query.bias""",
          """value""": """mid_block.attentions.0.value.bias""",
      },
      """middle_block.1.qkv.weight""": {
          """key""": """mid_block.attentions.0.key.weight""",
          """query""": """mid_block.attentions.0.query.weight""",
          """value""": """mid_block.attentions.0.value.weight""",
      },
  }
  assign_to_checkpoint(
      a__      ,a__      ,a__      ,attention_paths_to_split=a__      ,config=a__     )

  for i in range(a__     ):
    _lowerCAmelCase						:	Optional[int]        =					i // (config["""num_res_blocks"""] + 1)
    _lowerCAmelCase						:	Any        =					i % (config["""num_res_blocks"""] + 1)
    _lowerCAmelCase						:	List[Any]        =					[shave_segments(a__      ,2     ) for name in output_blocks[i]]
    _lowerCAmelCase						:	int        =					{}

    for layer in output_block_layers:
      _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					layer.split("""."""     )[0], shave_segments(a__      ,1     )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(a__     )
      else:
        _lowerCAmelCase						:	Any        =					[layer_name]

    if len(a__     ) > 1:
      _lowerCAmelCase						:	List[str]        =					[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
      _lowerCAmelCase						:	Tuple        =					[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

      _lowerCAmelCase						:	List[Any]        =					renew_resnet_paths(a__     )
      _lowerCAmelCase						:	int        =					renew_resnet_paths(a__     )

      _lowerCAmelCase						:	Optional[Any]        =					{"""old""": f"output_blocks.{i}.0", """new""": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
      assign_to_checkpoint(a__      ,a__      ,a__      ,additional_replacements=[meta_path]      ,config=a__     )

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _lowerCAmelCase						:	Union[str, Any]        =					list(output_block_list.values()     ).index(["""conv.weight""", """conv.bias"""]     )
        _lowerCAmelCase						:	Optional[int]        =					checkpoint[
            f"output_blocks.{i}.{index}.conv.weight"
        ]
        _lowerCAmelCase						:	Optional[int]        =					checkpoint[
            f"output_blocks.{i}.{index}.conv.bias"
        ]

        # Clear attentions as they have been attributed above.
        if len(a__     ) == 2:
          _lowerCAmelCase						:	Union[str, Any]        =					[]

      if len(a__     ):
        _lowerCAmelCase						:	Optional[Any]        =					renew_attention_paths(a__     )
        _lowerCAmelCase						:	Union[str, Any]        =					{
            """old""": f"output_blocks.{i}.1",
            """new""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        _lowerCAmelCase						:	int        =					{
            f"output_blocks.{i}.1.qkv.bias": {
                """key""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                """query""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                """value""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
            },
            f"output_blocks.{i}.1.qkv.weight": {
                """key""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                """query""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                """value""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
            },
        }
        assign_to_checkpoint(
            a__      ,a__      ,a__      ,additional_replacements=[meta_path]      ,attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions     ) else None      ,config=a__      ,)
    else:
      _lowerCAmelCase						:	Tuple        =					renew_resnet_paths(a__      ,n_shave_prefix_segments=1     )
      for path in resnet_0_paths:
        _lowerCAmelCase						:	Tuple        =					""".""".join(["""output_blocks""", str(a__     ), path["""old"""]]     )
        _lowerCAmelCase						:	List[str]        =					""".""".join(["""up_blocks""", str(a__     ), """resnets""", str(a__     ), path["""new"""]]     )

        _lowerCAmelCase						:	List[str]        =					checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     _a				:		str	   =       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				:		str	   =       parser.parse_args()

     _a				:		int	   =       torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          _a				:		int	   =       json.loads(f.read())

     _a				:		int	   =       convert_ldm_checkpoint(checkpoint, config)

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

     _a				:		str	   =       UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          _a				:		Optional[Any]	   =       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          _a				:		Any	   =       VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          _a				:		List[str]	   =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)





"""simple docstring"""




import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							:
     _UpperCamelCase							:      str		 =		None







@experimental
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[int]     )	->				List[Any]:
  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  return _map_with_joblib(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	str      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Tuple     )	->				Union[str, Any]:
  _lowerCAmelCase						:	int        =					num_proc if num_proc <= len(_lowerCamelCase     ) else len(_lowerCamelCase     )
  _lowerCAmelCase						:	Union[str, Any]        =					[]  # We organize the splits ourselve (contiguous splits)
  for index in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					len(_lowerCamelCase     ) // num_proc
    _lowerCAmelCase						:	str        =					len(_lowerCamelCase     ) % num_proc
    _lowerCAmelCase						:	Tuple        =					div * index + min(_lowerCamelCase      ,_lowerCamelCase     )
    _lowerCAmelCase						:	int        =					start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)     )

  if len(_lowerCamelCase     ) != sum(len(i[1]     ) for i in split_kwds     ):
    raise ValueError(
        f"Error dividing inputs iterable among processes. "
        f"Total number of objects {len(_lowerCamelCase     )}, "
        f"length: {sum(len(i[1]     ) for i in split_kwds     )}"     )

  logger.info(
      f"Spawning {num_proc} processes for {len(_lowerCamelCase     )} objects in slices of {[len(i[1]     ) for i in split_kwds]}"     )
  _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					None, None
  if not disable_tqdm:
    _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					(RLock(),), tqdm.set_lock
  with Pool(_lowerCamelCase      ,initargs=_lowerCamelCase      ,initializer=_lowerCamelCase     ) as pool:
    _lowerCAmelCase						:	str        =					pool.map(_lowerCamelCase      ,_lowerCamelCase     )
  logger.info(f"Finished {num_proc} processes"     )
  _lowerCAmelCase						:	int        =					[obj for proc_res in mapped for obj in proc_res]
  logger.info(f"Unpacked {len(_lowerCamelCase     )} objects"     )

  return mapped





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	str     )	->				Optional[Any]:
  # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
  # and it requires monkey-patching joblib internal classes which is subject to change
  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name      ,n_jobs=_lowerCamelCase     ):
    return joblib.Parallel()(
        joblib.delayed(_lowerCamelCase     )((function, obj, types, None, True, None)     ) for obj in iterable     )






@experimental
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _lowerCAmelCase						:	Optional[Any]        =					None




