


"""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()
__lowercase    					=      logging.get_logger(__name__)

set_seed(770)


__lowercase    					=      {
    """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""",
}


__lowercase    					=      {
    """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""",
    },
}

__lowercase    					=      os.path.dirname(os.path.abspath(__file__))
__lowercase    					=      os.path.join(os.path.expanduser("""~"""), """.cache""")
__lowercase    					=      os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def      lowercase			(				A_  ,   A_=False       )->      Dict:
  '''simple docstring'''







  a						:		str      							=			model_type
  if use_small:
    key += "_small"
  return os.path.join(A_  ,   REMOTE_MODEL_PATHS[key]["file_name"]       )


def      lowercase			(				A_  ,   A_       )->      Any:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_  ,   A_=False  ,   A_="text"       )->      int:
  '''simple docstring'''







  if model_type == "text":
    a						:		Any      							=			BarkSemanticModel
    a						:		List[Any]      							=			BarkSemanticConfig
    a						:		List[Any]      							=			BarkSemanticGenerationConfig
  elif model_type == "coarse":
    a						:		Optional[Any]      							=			BarkCoarseModel
    a						:		Tuple      							=			BarkCoarseConfig
    a						:		List[str]      							=			BarkCoarseGenerationConfig
  elif model_type == "fine":
    a						:		Optional[Any]      							=			BarkFineModel
    a						:		Dict      							=			BarkFineConfig
    a						:		List[str]      							=			BarkFineGenerationConfig
  else:
    raise NotImplementedError()
  a						:		Dict      							=			F'''{model_type}_small''' if use_small else model_type
  a						:		Any      							=			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"]       )
  a						:		Optional[int]      							=			torch.load(A_  ,   map_location=A_       )
  # this is a hack
  a						:		List[str]      							=			checkpoint["model_args"]
  if "input_vocab_size" not in model_args:
    a						:		str      							=			model_args["vocab_size"]
    a						:		Union[str, Any]      							=			model_args["vocab_size"]
    del model_args["vocab_size"]

  # convert Bark model arguments to HF Bark model arguments
  a						:		Tuple      							=			model_args.pop("n_head"       )
  a						:		Tuple      							=			model_args.pop("n_embd"       )
  a						:		int      							=			model_args.pop("n_layer"       )

  a						:		List[Any]      							=			ConfigClass(**checkpoint["model_args"]       )
  a						:		Tuple      							=			ModelClass(config=A_       )
  a						:		int      							=			GenerationConfigClass()

  a						:		Optional[int]      							=			model_generation_config
  a						:		Any      							=			checkpoint["model"]
  # fixup checkpoint
  a						:		Optional[Any]      							=			"_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
      a						:		Tuple      							=			k[len(A_       ) :]
      for old_layer_name in new_layer_name_dict:
        a						:		Tuple      							=			new_k.replace(A_  ,   new_layer_name_dict[old_layer_name]       )

      a						:		Tuple      							=			state_dict.pop(A_       )

  a						:		Dict      							=			set(state_dict.keys()       ) - set(model.state_dict().keys()       )
  a						:		Any      							=			{k for k in extra_keys if not k.endswith(".attn.bias"       )}
  a						:		Any      							=			set(model.state_dict().keys()       ) - set(state_dict.keys()       )
  a						:		Optional[Any]      							=			{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_       )
  a						:		List[Any]      							=			model.num_parameters(exclude_embeddings=A_       )
  a						:		int      							=			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      lowercase			(				A_  ,   A_=False  ,   A_="text"       )->      Dict:
  '''simple docstring'''







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

  a						:		Dict      							=			"cpu"  # do conversion on cpu

  a						:		Optional[Any]      							=			_get_ckpt_path(A_  ,   use_small=A_       )
  a						:		Optional[int]      							=			_load_model(A_  ,   A_  ,   model_type=A_  ,   use_small=A_       )

  # load bark initial model
  a						:		Optional[Any]      							=			_bark_load_model(A_  ,   "cpu"  ,   model_type=A_  ,   use_small=A_       )

  if model_type == "text":
    a						:		Dict      							=			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
  a						:		Dict      							=			5
  a						:		List[Any]      							=			10

  if model_type in ["text", "coarse"]:
    a						:		List[Any]      							=			torch.randint(256  ,   (batch_size, sequence_length)  ,   dtype=torch.int       )
    a						:		Optional[Any]      							=			bark_model(A_       )[0]

    a						:		str      							=			model(A_       )

    # take last logits
    a						:		Tuple      							=			output_new_model_total.logits[:, [-1], :]

  else:
    a						:		int      							=			3
    a						:		Any      							=			8
    a						:		Tuple      							=			torch.randint(256  ,   (batch_size, sequence_length, n_codes_total)  ,   dtype=torch.int       )

    a						:		Union[str, Any]      							=			model(A_  ,   A_       )
    a						:		str      							=			bark_model(A_  ,   A_       )

    a						:		Optional[int]      							=			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      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   )->      Any:
  '''simple docstring'''







  a						:		Tuple      							=			os.path.join(A_  ,   A_       )

  a						:		int      							=			BarkSemanticConfig.from_pretrained(os.path.join(A_  ,   "config.json"       )       )
  a						:		Tuple      							=			BarkCoarseConfig.from_pretrained(os.path.join(A_  ,   "config.json"       )       )
  a						:		Tuple      							=			BarkFineConfig.from_pretrained(os.path.join(A_  ,   "config.json"       )       )
  a						:		List[str]      							=			EncodecConfig.from_pretrained("facebook/encodec_24khz"       )

  a						:		Tuple      							=			BarkSemanticModel.from_pretrained(A_       )
  a						:		Tuple      							=			BarkCoarseModel.from_pretrained(A_       )
  a						:		Any      							=			BarkFineModel.from_pretrained(A_       )
  a						:		Tuple      							=			EncodecModel.from_pretrained("facebook/encodec_24khz"       )

  a						:		List[str]      							=			BarkConfig.from_sub_model_configs(
      A_  ,   A_  ,   A_  ,   A_       )

  a						:		str      							=			BarkGenerationConfig.from_sub_model_configs(
      semantic.generation_config  ,   coarseAcoustic.generation_config  ,   fineAcoustic.generation_config       )

  a						:		Optional[int]      							=			BarkModel(A_       )

  a						:		Tuple      							=			semantic
  a						:		int      							=			coarseAcoustic
  a						:		List[str]      							=			fineAcoustic
  a						:		Dict      							=			codec

  a						:		str      							=			bark_generation_config

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


if __name__ == "__main__":
 __lowercase    					=      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.""")

 __lowercase    					=      parser.parse_args()

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










"""simple docstring"""







def      lowercase			(				A_  ,   A_       )->      float:
  '''simple docstring'''







  if mass < 0:
    raise ValueError("The mass of a body cannot be negative"       )
  return 0.5 * mass * abs(A_       ) * abs(A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)






