



'''simple docstring'''


import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{'''vocab_file''': '''spiece.model'''}

lowercase							       =			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase							       =			{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowercase							       =			'''▁'''







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      VOCAB_FILES_NAMES
 __magic_name__					: Any  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: str  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: Dict  =      ["input_ids", "attention_mask"]




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_="</s>"							,  lowerCAmelCase_="<unk>"							,  lowerCAmelCase_="<pad>"							,  lowerCAmelCase_=1_0_0							,  lowerCAmelCase_=None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_=True							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      if extra_ids > 0 and additional_special_tokens is None:
           a_       		=[f"""<extra_id_{i}>""" for i in range(lowerCAmelCase_)]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           a_       		=len(set(filter(lambda lowerCAmelCase_: bool("extra_id" in str(lowerCAmelCase_))							,  lowerCAmelCase_)))
           if extra_tokens != extra_ids:
                raise ValueError(
                    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                    " tokens")
      if legacy:
           logger.warning_once(
               f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
               " read the related pull request available at https://github.com/huggingface/transformers/pull/24565")

      a_       		=legacy
      a_       		={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowerCAmelCase_							,  unk_token=lowerCAmelCase_							,  pad_token=lowerCAmelCase_							,  extra_ids=lowerCAmelCase_							,  additional_special_tokens=lowerCAmelCase_							,  sp_model_kwargs=self.sp_model_kwargs							,  legacy=lowerCAmelCase_							,  **lowerCAmelCase_							,  )

      a_       		=vocab_file
      a_       		=extra_ids

      a_       		=spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)




 @staticmethod
 def       lowercase_  (   lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           a_       		=TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    "This tokenizer was incorrectly instantiated with a model max length of"
                    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                    " instantiate this tokenizer with `model_max_length` set to your preferred value."							,  lowerCAmelCase_							,  )

      return max_model_length




 @property
 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      return self.sp_model.get_piece_size() + self._extra_ids




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		={self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = False) ->      List[int]:



      """simple docstring"""







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

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(lowerCAmelCase_)) + [1]
      return ([0] * len(lowerCAmelCase_)) + [1] + ([0] * len(lowerCAmelCase_)) + [1]




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return list(
          set(filter(lambda lowerCAmelCase_: bool(re.search(r"<extra_id_\d+>"							,  lowerCAmelCase_)) is not None							,  self.additional_special_tokens)))




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return [self._convert_token_to_id(lowerCAmelCase_) for token in self.get_sentinel_tokens()]




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[int]:



      """simple docstring"""







      if len(lowerCAmelCase_) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
               " eos tokens being added.")
           return token_ids
      else:
           return token_ids + [self.eos_token_id]




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      List[int]:



      """simple docstring"""







      a_       		=[self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos) * [0]
      return len(token_ids_a + eos + token_ids_a + eos) * [0]




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      List[int]:



      """simple docstring"""







      a_       		=self._add_eos_if_not_present(lowerCAmelCase_)
      if token_ids_a is None:
           return token_ids_a
      else:
           a_       		=self._add_eos_if_not_present(lowerCAmelCase_)
           return token_ids_a + token_ids_a




 def __getstate__(   self) ->      Any:



      """simple docstring"""







      a_       		=self.__dict__.copy()
      a_       		=None
      return state




 def __setstate__(   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=d

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

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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      if not self.legacy:
           a_       		=SPIECE_UNDERLINE + text.replace(lowerCAmelCase_							,  " ")
      return super().tokenize(lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      int:



      """simple docstring"""







      if not self.legacy:
           a_       		=text.startswith(lowerCAmelCase_)
           if is_first:
                a_       		=text[1:]

      a_       		=self.sp_model.encode(lowerCAmelCase_							,  out_type=lowerCAmelCase_)

      if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(lowerCAmelCase_):
           a_       		=([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
      return tokens




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      if token.startswith("<extra_id_"):
           a_       		=re.match(r"<extra_id_(\d+)>"							,  lowerCAmelCase_)
           a_       		=int(match.group(1))
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      if index < self.sp_model.get_piece_size():
           a_       		=self.sp_model.IdToPiece(lowerCAmelCase_)
      else:
           a_       		=f"""<extra_id_{self.vocab_size - 1 - index}>"""
      return token




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



      """simple docstring"""







      a_       		=[]
      a_       		=""
      a_       		=False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(lowerCAmelCase_) + token
                a_       		=True
                a_       		=[]
           else:
                current_sub_tokens.append(lowerCAmelCase_)
                a_       		=False
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Tuple[str]:



      """simple docstring"""







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

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

      return (out_vocab_file,)







'''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  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''simple docstring'''







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

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

     a_       		=ConfigClass(**checkpoint["model_args"]       )
     a_       		=ModelClass(config=lowercase__       )
     a_       		=GenerationConfigClass()

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

               a_       		=state_dict.pop(lowercase__       )

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

     return model






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''simple docstring'''







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

     a_       		="cpu"  # do conversion on cpu

     a_       		=_get_ckpt_path(lowercase__       ,	use_small=lowercase__       )
     a_       		=_load_model(lowercase__       ,	lowercase__       ,	model_type=lowercase__       ,	use_small=lowercase__       )

     # load bark initial model
     a_       		=_bark_load_model(lowercase__       ,	"cpu"       ,	model_type=lowercase__       ,	use_small=lowercase__       )

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

     if model.num_parameters(exclude_embeddings=lowercase__       ) != 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_       		=5
     a_       		=1_0

     if model_type in ["text", "coarse"]:
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length)       ,	dtype=torch.int       )
          a_       		=bark_model(lowercase__       )[0]

          a_       		=model(lowercase__       )

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

     else:
          a_       		=3
          a_       		=8
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length, n_codes_total)       ,	dtype=torch.int       )

          a_       		=model(lowercase__       ,	lowercase__       )
          a_       		=bark_model(lowercase__       ,	lowercase__       )

          a_       		=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(lowercase__       ).mkdir(exist_ok=lowercase__       )
     model.save_pretrained(lowercase__       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	):


     '''simple docstring'''







     a_       		=os.path.join(lowercase__       ,	lowercase__       )

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

     a_       		=BarkSemanticModel.from_pretrained(lowercase__       )
     a_       		=BarkCoarseModel.from_pretrained(lowercase__       )
     a_       		=BarkFineModel.from_pretrained(lowercase__       )
     a_       		=EncodecModel.from_pretrained("facebook/encodec_24khz"       )

     a_       		=BarkConfig.from_sub_model_configs(
         lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       )

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

     a_       		=BarkModel(lowercase__       )

     a_       		=semantic
     a_       		=coarseAcoustic
     a_       		=fineAcoustic
     a_       		=codec

     a_       		=bark_generation_config

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


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)


