


'''simple docstring'''






import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__A   :    List[Any]											= logging.get_logger(__name__)

set_seed(770)


__A   :    str											= {
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


__A   :    Any											= {
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

__A   :    Optional[int]											= os.path.dirname(os.path.abspath(__file__))
__A   :    Any											= os.path.join(os.path.expanduser("~"), ".cache")
__A   :    str											= os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[Any]=False   ):
  '''simple docstring'''





  lowerCAmelCase_     : Union[str, Any]     =  model_type
  if use_small:
    key += "_small"
  return os.path.join(A__				,       REMOTE_MODEL_PATHS[key]["""file_name"""]   )







def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  os.makedirs(A__				,       exist_ok=A__   )
  hf_hub_download(repo_id=A__				,       filename=A__				,       local_dir=A__   )







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[str]				,       A__   :  str=False				,       A__   :  Optional[Any]="text"   ):
  '''simple docstring'''





  if model_type == "text":
    lowerCAmelCase_     : Union[str, Any]     =  BarkSemanticModel
    lowerCAmelCase_     : List[str]     =  BarkSemanticConfig
    lowerCAmelCase_     : Optional[int]     =  BarkSemanticGenerationConfig
  elif model_type == "coarse":
    lowerCAmelCase_     : int     =  BarkCoarseModel
    lowerCAmelCase_     : Any     =  BarkCoarseConfig
    lowerCAmelCase_     : List[Any]     =  BarkCoarseGenerationConfig
  elif model_type == "fine":
    lowerCAmelCase_     : Optional[Any]     =  BarkFineModel
    lowerCAmelCase_     : Any     =  BarkFineConfig
    lowerCAmelCase_     : Any     =  BarkFineGenerationConfig
  else:
    raise NotImplementedError()
  lowerCAmelCase_     : str     =  f'{model_type}_small' if use_small else model_type
  lowerCAmelCase_     : Optional[int]     =  REMOTE_MODEL_PATHS[model_key]
  if not os.path.exists(A__   ):
    logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'   )
    _download(model_info["""repo_id"""]				,       model_info["""file_name"""]   )
  lowerCAmelCase_     : Any     =  torch.load(A__				,       map_location=A__   )
  # this is a hack
  lowerCAmelCase_     : Optional[Any]     =  checkpoint["""model_args"""]
  if "input_vocab_size" not in model_args:
    lowerCAmelCase_     : Optional[Any]     =  model_args["""vocab_size"""]
    lowerCAmelCase_     : Dict     =  model_args["""vocab_size"""]
    del model_args["vocab_size"]

  # convert Bark model arguments to HF Bark model arguments
  lowerCAmelCase_     : Any     =  model_args.pop("""n_head"""   )
  lowerCAmelCase_     : Union[str, Any]     =  model_args.pop("""n_embd"""   )
  lowerCAmelCase_     : List[str]     =  model_args.pop("""n_layer"""   )

  lowerCAmelCase_     : List[str]     =  ConfigClass(**checkpoint["""model_args"""]   )
  lowerCAmelCase_     : int     =  ModelClass(config=A__   )
  lowerCAmelCase_     : Union[str, Any]     =  GenerationConfigClass()

  lowerCAmelCase_     : Tuple     =  model_generation_config
  lowerCAmelCase_     : int     =  checkpoint["""model"""]
  # fixup checkpoint
  lowerCAmelCase_     : Dict     =  """_orig_mod."""
  for k, v in list(state_dict.items()   ):
    if k.startswith(A__   ):
      # replace part of the key with corresponding layer name in HF implementation
      lowerCAmelCase_     : int     =  k[len(A__   ) :]
      for old_layer_name in new_layer_name_dict:
        lowerCAmelCase_     : int     =  new_k.replace(A__				,       new_layer_name_dict[old_layer_name]   )

      lowerCAmelCase_     : Union[str, Any]     =  state_dict.pop(A__   )

  lowerCAmelCase_     : Optional[int]     =  set(state_dict.keys()   ) - set(model.state_dict().keys()   )
  lowerCAmelCase_     : Dict     =  {k for k in extra_keys if not k.endswith(""".attn.bias"""   )}
  lowerCAmelCase_     : Optional[int]     =  set(model.state_dict().keys()   ) - set(state_dict.keys()   )
  lowerCAmelCase_     : str     =  {k for k in missing_keys if not k.endswith(""".attn.bias"""   )}
  if len(A__   ) != 0:
    raise ValueError(f'extra keys found: {extra_keys}'   )
  if len(A__   ) != 0:
    raise ValueError(f'missing keys: {missing_keys}'   )
  model.load_state_dict(A__				,       strict=A__   )
  lowerCAmelCase_     : int     =  model.num_parameters(exclude_embeddings=A__   )
  lowerCAmelCase_     : Union[str, Any]     =  checkpoint["""best_val_loss"""].item()
  logger.info(f'model loaded: {round(n_params/1E6				,       1   )}M params, {round(A__				,       3   )} loss'   )
  model.eval()
  model.to(A__   )
  del checkpoint, state_dict

  return model







def 							UpperCamelCase_     (       A__   :  Union[str, Any]				,       A__   :  str=False				,       A__   :  Union[str, Any]="text"   ):
  '''simple docstring'''





  if model_type not in ("text", "coarse", "fine"):
    raise NotImplementedError()

  lowerCAmelCase_     : List[str]     =  """cpu"""  # do conversion on cpu

  lowerCAmelCase_     : int     =  _get_ckpt_path(A__				,       use_small=A__   )
  lowerCAmelCase_     : Optional[Any]     =  _load_model(A__				,       A__				,       model_type=A__				,       use_small=A__   )

  # load bark initial model
  lowerCAmelCase_     : str     =  _bark_load_model(A__				,       """cpu"""				,       model_type=A__				,       use_small=A__   )

  if model_type == "text":
    lowerCAmelCase_     : Tuple     =  bark_model["""model"""]

  if model.num_parameters(exclude_embeddings=A__   ) != bark_model.get_num_params():
    raise ValueError("""initial and new models don't have the same number of parameters"""   )

  # check if same output as the bark model
  lowerCAmelCase_     : Tuple     =  5
  lowerCAmelCase_     : List[Any]     =  10

  if model_type in ["text", "coarse"]:
    lowerCAmelCase_     : Optional[Any]     =  torch.randint(2_56				,       (batch_size, sequence_length)				,       dtype=torch.int   )
    lowerCAmelCase_     : Any     =  bark_model(A__   )[0]

    lowerCAmelCase_     : Optional[int]     =  model(A__   )

    # take last logits
    lowerCAmelCase_     : int     =  output_new_model_total.logits[:, [-1], :]

  else:
    lowerCAmelCase_     : List[Any]     =  3
    lowerCAmelCase_     : int     =  8
    lowerCAmelCase_     : Optional[Any]     =  torch.randint(2_56				,       (batch_size, sequence_length, n_codes_total)				,       dtype=torch.int   )

    lowerCAmelCase_     : Dict     =  model(A__				,       A__   )
    lowerCAmelCase_     : Tuple     =  bark_model(A__				,       A__   )

    lowerCAmelCase_     : Union[str, Any]     =  output_new_model_total.logits

  # output difference should come from the difference of self-attention implementation design
  if output_new_model.shape != output_old_model.shape:
    raise ValueError("""initial and new outputs don't have the same shape"""   )
  if (output_new_model - output_old_model).abs().max().item() > 1E-3:
    raise ValueError("""initial and new outputs are not equal"""   )

  Path(A__   ).mkdir(exist_ok=A__   )
  model.save_pretrained(A__   )







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  Union[str, Any]				,       A__   :  Dict				,       A__   :  int				,       A__   :  Union[str, Any]				,       ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  os.path.join(A__				,       A__   )

  lowerCAmelCase_     : int     =  BarkSemanticConfig.from_pretrained(os.path.join(A__				,       """config.json"""   )   )
  lowerCAmelCase_     : str     =  BarkCoarseConfig.from_pretrained(os.path.join(A__				,       """config.json"""   )   )
  lowerCAmelCase_     : str     =  BarkFineConfig.from_pretrained(os.path.join(A__				,       """config.json"""   )   )
  lowerCAmelCase_     : Union[str, Any]     =  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""   )

  lowerCAmelCase_     : List[Any]     =  BarkSemanticModel.from_pretrained(A__   )
  lowerCAmelCase_     : List[Any]     =  BarkCoarseModel.from_pretrained(A__   )
  lowerCAmelCase_     : Tuple     =  BarkFineModel.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""   )

  lowerCAmelCase_     : List[Any]     =  BarkConfig.from_sub_model_configs(
      A__				,       A__				,       A__				,       A__   )

  lowerCAmelCase_     : Optional[int]     =  BarkGenerationConfig.from_sub_model_configs(
      semantic.generation_config				,       coarseAcoustic.generation_config				,       fineAcoustic.generation_config   )

  lowerCAmelCase_     : Tuple     =  BarkModel(A__   )

  lowerCAmelCase_     : int     =  semantic
  lowerCAmelCase_     : Tuple     =  coarseAcoustic
  lowerCAmelCase_     : Dict     =  fineAcoustic
  lowerCAmelCase_     : int     =  codec

  lowerCAmelCase_     : Any     =  bark_generation_config

  Path(A__   ).mkdir(exist_ok=A__   )
  bark.save_pretrained(A__				,       repo_id=A__				,       push_to_hub=A__   )


if __name__ == "__main__":
  __A   :    Tuple											= argparse.ArgumentParser()
  # Required parameters

  parser.add_argument("model_type", type=str, help="text, coarse or fine.")
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")

  __A   :    List[str]											= parser.parse_args()

  load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)







'''simple docstring'''






from collections.abc import Callable






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Callable | None = None						)  ->    None:
      # Stores actual heap items.
      lowerCAmelCase_     : list     =  []
      # Stores indexes of each item for supporting updates and deletion.
      lowerCAmelCase_     : dict     =  {}
      # Stores current size of heap.
      lowerCAmelCase_     : List[Any]     =  0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      lowerCAmelCase_     : Tuple     =  key or (lambda lowerCamelCase						: x)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      return int((i - 1) / 2						) if i > 0 else None

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[str]     =  int(2 * i + 1						)
      return left if 0 < left < self.size else None

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[Any]     =  int(2 * i + 2						)
      return right if 0 < right < self.size else None

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  self.arr[j], self.arr[i]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    bool:
      return self.arr[i][1] < self.arr[j][1]

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self._left(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  self._right(lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  i

      if left is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : int     =  left
      if right is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  right

      return valid_parent

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Tuple     =  self._parent(lowerCamelCase						)
      while parent is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : str     =  parent, self._parent(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Optional[Any]     =  self._get_valid_parent(lowerCamelCase						)
      while valid_parent != index:
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  valid_parent, self._get_valid_parent(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : Dict     =  self.pos_map[item]
      lowerCAmelCase_     : Dict     =  [item, self.key(lowerCamelCase						)]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(lowerCamelCase						)
      self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : List[str]     =  self.pos_map[item]
      del self.pos_map[item]
      lowerCAmelCase_     : Tuple     =  self.arr[self.size - 1]
      lowerCAmelCase_     : List[str]     =  index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
        self._heapify_up(lowerCamelCase						)
        self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Any     =  len(self.arr						)
      if arr_len == self.size:
        self.arr.append([item, self.key(lowerCamelCase						)]						)
      else:
        lowerCAmelCase_     : str     =  [item, self.key(lowerCamelCase						)]
      lowerCAmelCase_     : Optional[Any]     =  self.size
      self.size += 1
      self._heapify_up(self.size - 1						)

    def __lowercase  (  self       :    str						)  ->    tuple | None:
      return self.arr[0] if self.size else None





    def __lowercase  (  self       :    Optional[Any]						)  ->    tuple | None:
      lowerCAmelCase_     : int     =  self.get_top()
      if top_item_tuple:
        self.delete_item(top_item_tuple[0]						)
      return top_item_tuple







def 							UpperCamelCase_     (       ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



