

'''simple docstring'''



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

import sentencepiece as spm

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

UpperCAmelCase				: Tuple         =  logging.get_logger(__name__)

UpperCAmelCase				: Tuple         =  {'vocab_file': 'tokenizer.model'}

UpperCAmelCase				: str         =  {
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
UpperCAmelCase				: Tuple         =  {
    'hf-internal-testing/llama-tokenizer': 2_0_4_8,
}
UpperCAmelCase				: str         =  '▁'

UpperCAmelCase				: List[Any]         =  '[INST]', '[/INST]'
UpperCAmelCase				: Optional[Any]         =  '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
UpperCAmelCase				: List[Any]         =  'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'







class 		lowerCAmelCase__     (   a      ):



  """simple docstring"""



  lowerCAmelCase__						      =   VOCAB_FILES_NAMES
  lowerCAmelCase__						      =   PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase__						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase__						      =   ["input_ids", "attention_mask"]
  def __init__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : int="<unk>"      ,							__SCREAMING_SNAKE_CASE      : Dict="<s>"      ,							__SCREAMING_SNAKE_CASE      : List[Any]="</s>"      ,							__SCREAMING_SNAKE_CASE      : Dict=None      ,							__SCREAMING_SNAKE_CASE      : Optional[Dict[str, Any]] = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Any=False      ,							__SCREAMING_SNAKE_CASE      : List[str]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=True      ,							**__SCREAMING_SNAKE_CASE      : List[Any]      ,							)						->				str:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   {} if sp_model_kwargs is None else sp_model_kwargs
       __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else bos_token
       __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else eos_token
       __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else unk_token
       __SCREAMING_SNAKE_CASE						     =   AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) else pad_token
       super().__init__(
           bos_token=__SCREAMING_SNAKE_CASE      ,							eos_token=__SCREAMING_SNAKE_CASE      ,							unk_token=__SCREAMING_SNAKE_CASE      ,							pad_token=__SCREAMING_SNAKE_CASE      ,							add_bos_token=__SCREAMING_SNAKE_CASE      ,							add_eos_token=__SCREAMING_SNAKE_CASE      ,							sp_model_kwargs=self.sp_model_kwargs      ,							clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      ,							legacy=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
       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"""    )
       __SCREAMING_SNAKE_CASE						     =   legacy
       __SCREAMING_SNAKE_CASE						     =   vocab_file
       __SCREAMING_SNAKE_CASE						     =   add_bos_token
       __SCREAMING_SNAKE_CASE						     =   add_eos_token
       __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(__SCREAMING_SNAKE_CASE    )
  def __getstate__(	self      : Any    )						->				Dict:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.__dict__.copy()
       __SCREAMING_SNAKE_CASE						     =   None
       __SCREAMING_SNAKE_CASE						     =   self.sp_model.serialized_model_proto()
       return state
  def __setstate__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[int]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   d
       __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
  @property
  def    UpperCAmelCase__	(	self      : Tuple    )						->				Dict:


       """simple docstring"""




       return self.sp_model.get_piece_size()
  def    UpperCAmelCase__	(	self      : Any    )						->				Optional[Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab
  def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				List[str]:


       """simple docstring"""




       if not self.legacy:
            __SCREAMING_SNAKE_CASE						     =   SPIECE_UNDERLINE + text.replace(__SCREAMING_SNAKE_CASE      ,							""" """    )
       return super().tokenize(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )
  def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				int:


       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE						     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,							out_type=__SCREAMING_SNAKE_CASE    )

       if not self.legacy and not is_first and not text.startswith(""" """    ) and tokens[0].startswith(__SCREAMING_SNAKE_CASE    ):
            __SCREAMING_SNAKE_CASE						     =   ([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
       return tokens
  def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : int    )						->				str:


       """simple docstring"""




       return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE    )
  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Any:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE    )
       return token
  def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Tuple    )						->				Any:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   []
       __SCREAMING_SNAKE_CASE						     =   """"""
       __SCREAMING_SNAKE_CASE						     =   False
       for i, token in enumerate(__SCREAMING_SNAKE_CASE    ):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 if not prev_is_special and i != 0:
                      out_string += " "
                 out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    ) + token
                 __SCREAMING_SNAKE_CASE						     =   True
                 __SCREAMING_SNAKE_CASE						     =   []
            else:
                 current_sub_tokens.append(__SCREAMING_SNAKE_CASE    )
                 __SCREAMING_SNAKE_CASE						     =   False
       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    )
       return out_string
  def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[str] = None    )						->				Tuple[str]:


       """simple docstring"""




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

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

       return (out_vocab_file,)
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Any=None    )						->				Tuple:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   [self.bos_token_id] if self.add_bos_token else []
       __SCREAMING_SNAKE_CASE						     =   [self.eos_token_id] if self.add_eos_token else []

       __SCREAMING_SNAKE_CASE						     =   bos_token_id + token_ids_a + eos_token_id

       if token_ids_a is not None:
            __SCREAMING_SNAKE_CASE						     =   output + bos_token_id + token_ids_a + eos_token_id

       return output
  def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False    )						->				List[int]:


       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE						     =   [1] if self.add_bos_token else []
       __SCREAMING_SNAKE_CASE						     =   [1] if self.add_eos_token else []

       if token_ids_a is None:
            return bos_token_id + ([0] * len(__SCREAMING_SNAKE_CASE    )) + eos_token_id
       return (
           bos_token_id
           + ([0] * len(__SCREAMING_SNAKE_CASE    ))
           + eos_token_id
           + bos_token_id
           + ([0] * len(__SCREAMING_SNAKE_CASE    ))
           + eos_token_id
       )
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   [self.bos_token_id] if self.add_bos_token else []
       __SCREAMING_SNAKE_CASE						     =   [self.eos_token_id] if self.add_eos_token else []

       __SCREAMING_SNAKE_CASE						     =   [0] * len(bos_token_id + token_ids_a + eos_token_id    )

       if token_ids_a is not None:
            output += [1] * len(bos_token_id + token_ids_a + eos_token_id    )

       return output


  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : "Conversation"    )						->				List[int]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   list(conversation.iter_texts()    )
       if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
           [not is_user for is_user, msg in dialogue[1::2]]    ):
            raise ValueError(
                """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""    )

       __SCREAMING_SNAKE_CASE						     =   []
       if len(conversation.past_user_inputs    ) > 0:
            if not conversation.past_user_inputs[0].startswith(__SCREAMING_SNAKE_CASE    ) or E_SYS not in conversation.past_user_inputs[0]:
                 __SCREAMING_SNAKE_CASE						     =   (
                     B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                 )
       elif not dialogue[0][1].startswith(__SCREAMING_SNAKE_CASE    ) or E_SYS not in dialogue[0][1]:
            __SCREAMING_SNAKE_CASE						     =   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

       dialog_tokens += sum(
           [
               [self.bos_token_id]
               + self.encode(
                   f'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '      ,							add_special_tokens=__SCREAMING_SNAKE_CASE    )
               + [self.eos_token_id]
               for prompt, answer in zip(dialogue[::2]      ,							dialogue[1::2]    )
           ]      ,							[]      ,							)
       if not (dialogue[-1][0]):
            raise ValueError(f'Last message must be from user, got {dialogue[-1]["role"]}'    )
       dialog_tokens += [self.bos_token_id] + self.encode(
           f'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'      ,							add_special_tokens=__SCREAMING_SNAKE_CASE    )
       return dialog_tokens









'''simple docstring'''



import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


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


UpperCAmelCase				: int         =  {
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

UpperCAmelCase				: Optional[Any]         =  '\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark_module"
 lowerCAmelCase__						      =   ["past_key_values"]

 lowerCAmelCase__						      =   {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
     "vocab_size": "input_vocab_size",
     "window_size": "block_size",
 }
 def __init__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=1_024      ,							__SCREAMING_SNAKE_CASE      : Dict=10_048      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=10_048      ,							__SCREAMING_SNAKE_CASE      : Tuple=12      ,							__SCREAMING_SNAKE_CASE      : Dict=12      ,							__SCREAMING_SNAKE_CASE      : List[str]=768      ,							__SCREAMING_SNAKE_CASE      : Tuple=0.0      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   block_size
      __SCREAMING_SNAKE_CASE						     =   input_vocab_size
      __SCREAMING_SNAKE_CASE						     =   output_vocab_size
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   dropout
      __SCREAMING_SNAKE_CASE						     =   bias
      __SCREAMING_SNAKE_CASE						     =   use_cache
      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, os.PathLike]      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, os.PathLike]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, bool]] = None      ,							__SCREAMING_SNAKE_CASE      : str = "main"      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				"PretrainedConfig":


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cache_dir
      __SCREAMING_SNAKE_CASE						     =   force_download
      __SCREAMING_SNAKE_CASE						     =   local_files_only
      __SCREAMING_SNAKE_CASE						     =   revision

      cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   cls.get_config_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      # get the config dict if we are loading from Bark
      if config_dict.get("""model_type"""    ) == "bark":
           __SCREAMING_SNAKE_CASE						     =   config_dict[f'{cls.model_type}_config']

      if "model_type" in config_dict and hasattr(cls      ,							"""model_type"""    ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

      return cls.from_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"				,      model="BarkSemanticModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "semantic"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"				,      model="BarkCoarseModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "coarse_acoustics"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"				,      model="BarkFineModel"      )				,      "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "fine_acoustics"
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple=8      ,							__SCREAMING_SNAKE_CASE      : Tuple=1      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   n_codes_total
      __SCREAMING_SNAKE_CASE						     =   n_codes_given

      super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark"
 lowerCAmelCase__						      =   True
 def __init__(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=0.02      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				Union[str, Any]:


      """simple docstring"""




      if semantic_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

      if coarse_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""    )

      if fine_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""    )

      if codec_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""codec_config is None. initializing the codec model with default values."""    )

      __SCREAMING_SNAKE_CASE						     =   BarkSemanticConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkCoarseConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkFineConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   AutoConfig.for_model(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )
 @classmethod
 def    UpperCAmelCase__	(	cls      : str      ,							__SCREAMING_SNAKE_CASE      : BarkSemanticConfig      ,							__SCREAMING_SNAKE_CASE      : BarkCoarseConfig      ,							__SCREAMING_SNAKE_CASE      : BarkFineConfig      ,							__SCREAMING_SNAKE_CASE      : AutoConfig      ,							**__SCREAMING_SNAKE_CASE      : int      ,							)						->				Optional[int]:


      """simple docstring"""




      return cls(
          semantic_config=semantic_config.to_dict()      ,							coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,							fine_acoustics_config=fine_acoustics_config.to_dict()      ,							codec_config=codec_config.to_dict()      ,							**__SCREAMING_SNAKE_CASE      ,							)


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(self.__dict__    )

      __SCREAMING_SNAKE_CASE						     =   self.semantic_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.coarse_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.fine_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.codec_config.to_dict()

      __SCREAMING_SNAKE_CASE						     =   self.__class__.model_type
      return output






