
'''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__   :		Tuple 		=logging.get_logger(__name__)

set_seed(770)


A__   :		List[Any] 		={
    '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__   :		Tuple 		={
    '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__   :		Any 		=os.path.dirname(os.path.abspath(__file__))
A__   :		int 		=os.path.join(os.path.expanduser('~'), '.cache')
A__   :		Optional[int] 		=os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]=False )				->       List[str]:





  """simple docstring"""

  __A		:     List[Any]												=		model_type
  if use_small:
    key += "_small"
  return os.path.join(__SCREAMING_SNAKE_CASE			,     REMOTE_MODEL_PATHS[key]["""file_name"""] )







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Dict:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[str]=False			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]="text" )				->       Optional[Any]:





  """simple docstring"""

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

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

  __A		:     int												=		ConfigClass(**checkpoint["""model_args"""] )
  __A		:     Optional[Any]												=		ModelClass(config=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		GenerationConfigClass()

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

      __A		:     Union[str, Any]												=		state_dict.pop(__SCREAMING_SNAKE_CASE )

  __A		:     Any												=		set(state_dict.keys() ) - set(model.state_dict().keys() )
  __A		:     str												=		{k for k in extra_keys if not k.endswith(""".attn.bias""" )}
  __A		:     Tuple												=		set(model.state_dict().keys() ) - set(state_dict.keys() )
  __A		:     int												=		{k for k in missing_keys if not k.endswith(""".attn.bias""" )}
  if len(__SCREAMING_SNAKE_CASE ) != 0:
    raise ValueError(F"extra keys found: {extra_keys}" )
  if len(__SCREAMING_SNAKE_CASE ) != 0:
    raise ValueError(F"missing keys: {missing_keys}" )
  model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		checkpoint["""best_val_loss"""].item()
  logger.info(F"model loaded: {round(n_params/1e6			,     1 )}M params, {round(__SCREAMING_SNAKE_CASE			,     3 )} loss" )
  model.eval()
  model.to(__SCREAMING_SNAKE_CASE )
  del checkpoint, state_dict

  return model







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Any=False			,     __SCREAMING_SNAKE_CASE						:  str="text" )				->       Optional[int]:





  """simple docstring"""

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

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

  __A		:     str												=		_get_ckpt_path(__SCREAMING_SNAKE_CASE			,     use_small=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		_load_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     model_type=__SCREAMING_SNAKE_CASE			,     use_small=__SCREAMING_SNAKE_CASE )

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

  if model_type == "text":
    __A		:     Optional[int]												=		bark_model["""model"""]

  if model.num_parameters(exclude_embeddings=__SCREAMING_SNAKE_CASE ) != 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		:     Optional[int]												=		5
  __A		:     Dict												=		10

  if model_type in ["text", "coarse"]:
    __A		:     Dict												=		torch.randint(256			,     (batch_size, sequence_length)			,     dtype=torch.int )
    __A		:     Tuple												=		bark_model(__SCREAMING_SNAKE_CASE )[0]

    __A		:     Tuple												=		model(__SCREAMING_SNAKE_CASE )

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

  else:
    __A		:     int												=		3
    __A		:     Any												=		8
    __A		:     List[str]												=		torch.randint(256			,     (batch_size, sequence_length, n_codes_total)			,     dtype=torch.int )

    __A		:     Dict												=		model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    __A		:     List[str]												=		bark_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

    __A		:     Optional[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(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
  model.save_pretrained(__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     )				->       List[str]:





  """simple docstring"""

  __A		:     Optional[Any]												=		os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  __A		:     Optional[Any]												=		BarkSemanticConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE			,     """config.json""" ) )
  __A		:     int												=		BarkCoarseConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE			,     """config.json""" ) )
  __A		:     Optional[Any]												=		BarkFineConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE			,     """config.json""" ) )
  __A		:     Any												=		EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

  __A		:     int												=		BarkSemanticModel.from_pretrained(__SCREAMING_SNAKE_CASE )
  __A		:     List[str]												=		BarkCoarseModel.from_pretrained(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		BarkFineModel.from_pretrained(__SCREAMING_SNAKE_CASE )
  __A		:     str												=		EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

  __A		:     Optional[int]												=		BarkConfig.from_sub_model_configs(
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		BarkGenerationConfig.from_sub_model_configs(
      semantic.generation_config			,     coarseAcoustic.generation_config			,     fineAcoustic.generation_config )

  __A		:     List[Any]												=		BarkModel(__SCREAMING_SNAKE_CASE )

  __A		:     Dict												=		semantic
  __A		:     List[Any]												=		coarseAcoustic
  __A		:     Dict												=		fineAcoustic
  __A		:     Optional[Any]												=		codec

  __A		:     Union[str, Any]												=		bark_generation_config

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


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__   :		Optional[Any] 		=parser.parse_args()

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





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



