


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





def __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Any=False		):
 lowerCamelCase_      =  model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_					,				REMOTE_MODEL_PATHS[key]["file_name"]		)





def __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Dict		):
 os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_		)
 hf_hub_download(repo_id=UpperCAmelCase_					,				filename=UpperCAmelCase_					,				local_dir=UpperCAmelCase_		)





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

 # convert Bark model arguments to HF Bark model arguments
 lowerCamelCase_      =  model_args.pop("n_head"		)
 lowerCamelCase_      =  model_args.pop("n_embd"		)
 lowerCamelCase_      =  model_args.pop("n_layer"		)

 lowerCamelCase_      =  ConfigClass(**checkpoint["model_args"]		)
 lowerCamelCase_      =  ModelClass(config=UpperCAmelCase_		)
 lowerCamelCase_      =  GenerationConfigClass()

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

   lowerCamelCase_      =  state_dict.pop(UpperCAmelCase_		)

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

 return model





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Union[str, Any]=False					,				UpperCAmelCase_						:						List[str]="text"		):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

 lowerCamelCase_      =  "cpu"  # do conversion on cpu

 lowerCamelCase_      =  _get_ckpt_path(UpperCAmelCase_					,				use_small=UpperCAmelCase_		)
 lowerCamelCase_      =  _load_model(UpperCAmelCase_					,				UpperCAmelCase_					,				model_type=UpperCAmelCase_					,				use_small=UpperCAmelCase_		)

 # load bark initial model
 lowerCamelCase_      =  _bark_load_model(UpperCAmelCase_					,				"cpu"					,				model_type=UpperCAmelCase_					,				use_small=UpperCAmelCase_		)

 if model_type == "text":
  lowerCamelCase_      =  bark_model["model"]

 if model.num_parameters(exclude_embeddings=UpperCAmelCase_		) != 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_      =  5
 lowerCamelCase_      =  10

 if model_type in ["text", "coarse"]:
  lowerCamelCase_      =  torch.randint(256					,				(batch_size, sequence_length)					,				dtype=torch.int		)
  lowerCamelCase_      =  bark_model(UpperCAmelCase_		)[0]

  lowerCamelCase_      =  model(UpperCAmelCase_		)

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

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

  lowerCamelCase_      =  model(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  bark_model(UpperCAmelCase_					,				UpperCAmelCase_		)

  lowerCamelCase_      =  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(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
 model.save_pretrained(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Optional[Any]					,				):
 lowerCamelCase_      =  os.path.join(UpperCAmelCase_					,				UpperCAmelCase_		)

 lowerCamelCase_      =  BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase_					,				"config.json"		)		)
 lowerCamelCase_      =  BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase_					,				"config.json"		)		)
 lowerCamelCase_      =  BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase_					,				"config.json"		)		)
 lowerCamelCase_      =  EncodecConfig.from_pretrained("facebook/encodec_24khz"		)

 lowerCamelCase_      =  BarkSemanticModel.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  BarkCoarseModel.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  BarkFineModel.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  EncodecModel.from_pretrained("facebook/encodec_24khz"		)

 lowerCamelCase_      =  BarkConfig.from_sub_model_configs(
     UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 lowerCamelCase_      =  BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config					,				coarseAcoustic.generation_config					,				fineAcoustic.generation_config		)

 lowerCamelCase_      =  BarkModel(UpperCAmelCase_		)

 lowerCamelCase_      =  semantic
 lowerCamelCase_      =  coarseAcoustic
 lowerCamelCase_      =  fineAcoustic
 lowerCamelCase_      =  codec

 lowerCamelCase_      =  bark_generation_config

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


if __name__ == "__main__":
     a_  :				int						= 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[int]						= parser.parse_args()

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







'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {"""vocab_file""": """spiece.model"""}

a_  :				Tuple						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(UpperCamelCase     )

   try:
    import jieba
   except ModuleNotFoundError as error:
    raise error.__class__(
        "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
        "See https://pypi.org/project/jieba/ for installation."     )
   lowerCamelCase_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

   # for backward compatibility
   if not hasattr(self			,  "sp_model_kwargs"     ):
    lowerCamelCase_      =  {}

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  UpperCamelCase     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(UpperCamelCase     )
   lowerCamelCase_      =  self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(UpperCamelCase     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(UpperCamelCase     )
    else:
     new_pieces.append(UpperCamelCase     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [self.cls_token_id]
   if token_ids_a is None:
    return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=UpperCamelCase			,  token_ids_a=UpperCamelCase			,  already_has_special_tokens=UpperCamelCase     )

   if token_ids_a is not None:
    return ([0] * len(UpperCamelCase     )) + [1] + ([0] * len(UpperCamelCase     )) + [1, 1]
   return ([0] * len(UpperCamelCase     )) + [1, 1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [2]

   if token_ids_a is None:
    return len(token_ids_a + sep     ) * [0] + cls_segment_id
   return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  os.path.join(
       UpperCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file			,  UpperCamelCase     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(UpperCamelCase			,  "wb"     ) as fi:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text



