

'''simple docstring'''



import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase				: int         =  logging.get_logger(__name__)

UpperCAmelCase				: int         =  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCAmelCase				: Dict         =  {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

UpperCAmelCase				: Optional[int]         =  {
    'microsoft/biogpt': 1_0_2_4,
}







def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   set()
     __SCREAMING_SNAKE_CASE						     =   word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)		)
          __SCREAMING_SNAKE_CASE						     =   char
     return pairs







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      : Dict      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Tuple="<unk>"      ,							__SCREAMING_SNAKE_CASE      : int="<s>"      ,							__SCREAMING_SNAKE_CASE      : str="</s>"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]="</s>"      ,							__SCREAMING_SNAKE_CASE      : int="<pad>"      ,							**__SCREAMING_SNAKE_CASE      : Optional[int]      ,							)						->				Tuple:


      """simple docstring"""




      super().__init__(
          bos_token=__SCREAMING_SNAKE_CASE      ,							eos_token=__SCREAMING_SNAKE_CASE      ,							sep_token=__SCREAMING_SNAKE_CASE      ,							unk_token=__SCREAMING_SNAKE_CASE      ,							pad_token=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)

      try:
           import sacremoses
      except ImportError:
           raise ImportError(
               """You need to install sacremoses to use BioGptTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""    )

      __SCREAMING_SNAKE_CASE						     =   """en"""
      __SCREAMING_SNAKE_CASE						     =   sacremoses
      # cache of sm.MosesTokenizer instance
      __SCREAMING_SNAKE_CASE						     =   {}
      __SCREAMING_SNAKE_CASE						     =   {}
      with open(__SCREAMING_SNAKE_CASE      ,							encoding="""utf-8"""    ) as vocab_handle:
           __SCREAMING_SNAKE_CASE						     =   json.load(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   {v: k for k, v in self.encoder.items()}
      with open(__SCREAMING_SNAKE_CASE      ,							encoding="""utf-8"""    ) as merges_handle:
           __SCREAMING_SNAKE_CASE						     =   merges_handle.read().split("""\n"""    )[:-1]
      __SCREAMING_SNAKE_CASE						     =   [tuple(merge.split()[:2]    ) for merge in merges]
      __SCREAMING_SNAKE_CASE						     =   dict(zip(__SCREAMING_SNAKE_CASE      ,							range(len(__SCREAMING_SNAKE_CASE    )    )    )    )
      __SCREAMING_SNAKE_CASE						     =   {}
 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Optional[int]:


      """simple docstring"""




      return len(self.encoder    )
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				List[Any]:


      """simple docstring"""




      return dict(self.encoder      ,							**self.added_tokens_encoder    )
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Optional[int]:


      """simple docstring"""




      if lang not in self.cache_moses_tokenizer:
           __SCREAMING_SNAKE_CASE						     =   self.sm.MosesTokenizer(lang=__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   moses_tokenizer
      return self.cache_moses_tokenizer[lang].tokenize(
          __SCREAMING_SNAKE_CASE      ,							aggressive_dash_splits=__SCREAMING_SNAKE_CASE      ,							return_str=__SCREAMING_SNAKE_CASE      ,							escape=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Any:


      """simple docstring"""




      if lang not in self.cache_moses_detokenizer:
           __SCREAMING_SNAKE_CASE						     =   self.sm.MosesDetokenizer(lang=__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   moses_detokenizer
      return self.cache_moses_detokenizer[lang].detokenize(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : int    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   tuple(token[:-1]    ) + (token[-1] + """</w>""",)
      if token in self.cache:
           return self.cache[token]
      __SCREAMING_SNAKE_CASE						     =   get_pairs(__SCREAMING_SNAKE_CASE    )

      if not pairs:
           return token + "</w>"

      while True:
           __SCREAMING_SNAKE_CASE						     =   min(__SCREAMING_SNAKE_CASE      ,							key=lambda __SCREAMING_SNAKE_CASE    : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE      ,							float("""inf"""    )    )    )
           if bigram not in self.bpe_ranks:
                break
           __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   bigram
           __SCREAMING_SNAKE_CASE						     =   []
           __SCREAMING_SNAKE_CASE						     =   0
           while i < len(__SCREAMING_SNAKE_CASE    ):
                try:
                     __SCREAMING_SNAKE_CASE						     =   word.index(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
                except ValueError:
                     new_word.extend(word[i:]    )
                     break
                else:
                     new_word.extend(word[i:j]    )
                     __SCREAMING_SNAKE_CASE						     =   j

                if word[i] == first and i < len(__SCREAMING_SNAKE_CASE    ) - 1 and word[i + 1] == second:
                     new_word.append(first + second    )
                     i += 2
                else:
                     new_word.append(word[i]    )
                     i += 1
           __SCREAMING_SNAKE_CASE						     =   tuple(__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   new_word
           if len(__SCREAMING_SNAKE_CASE    ) == 1:
                break
           else:
                __SCREAMING_SNAKE_CASE						     =   get_pairs(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   """ """.join(__SCREAMING_SNAKE_CASE    )
      if word == "\n  </w>":
           __SCREAMING_SNAKE_CASE						     =   """\n</w>"""
      __SCREAMING_SNAKE_CASE						     =   word
      return word
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Dict=False    )						->				Optional[int]:


      """simple docstring"""




      if bypass_tokenizer:
           __SCREAMING_SNAKE_CASE						     =   text.split()
      else:
           __SCREAMING_SNAKE_CASE						     =   self.moses_tokenize(__SCREAMING_SNAKE_CASE      ,							self.lang    )

      __SCREAMING_SNAKE_CASE						     =   []
      for token in text:
           if token:
                split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE    ).split(""" """    )    )    )

      return split_tokens
 def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Optional[Any]:


      """simple docstring"""




      return self.encoder.get(__SCREAMING_SNAKE_CASE      ,							self.encoder.get(self.unk_token    )    )
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				int:


      """simple docstring"""




      return self.decoder.get(__SCREAMING_SNAKE_CASE      ,							self.unk_token    )
 def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Tuple    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [t.replace(""" """      ,							""""""    ).replace("""</w>"""      ,							""" """    ) for t in tokens]
      __SCREAMING_SNAKE_CASE						     =   """""".join(__SCREAMING_SNAKE_CASE    ).split()
      # detokenize
      __SCREAMING_SNAKE_CASE						     =   self.moses_detokenize(__SCREAMING_SNAKE_CASE      ,							self.lang    )
      return text
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
      __SCREAMING_SNAKE_CASE						     =   [self.sep_token_id]
      return sep + token_ids_a + sep + token_ids_a
 def    UpperCAmelCase__	(	self      : List[str]      ,							__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    )
      # no bos used in fairseq
      if token_ids_a is not None:
           return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE    ))
      return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    ))
 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.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
           return len(token_ids_a + sep    ) * [0]
      return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : str      ,							__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"""]    )
      __SCREAMING_SNAKE_CASE						     =   os.path.join(
          __SCREAMING_SNAKE_CASE      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]    )

      with open(__SCREAMING_SNAKE_CASE      ,							"""w"""      ,							encoding="""utf-8"""    ) as f:
           f.write(json.dumps(self.encoder      ,							indent=2      ,							sort_keys=__SCREAMING_SNAKE_CASE      ,							ensure_ascii=__SCREAMING_SNAKE_CASE    ) + """\n"""    )

      __SCREAMING_SNAKE_CASE						     =   0
      with open(__SCREAMING_SNAKE_CASE      ,							"""w"""      ,							encoding="""utf-8"""    ) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,							key=lambda __SCREAMING_SNAKE_CASE    : kv[1]    ):
                if index != token_index:
                     logger.warning(
                         f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                         """ Please check that the tokenizer is not corrupted!"""    )
                     __SCREAMING_SNAKE_CASE						     =   token_index
                writer.write(""" """.join(__SCREAMING_SNAKE_CASE    ) + """\n"""    )
                index += 1

      return vocab_file, merge_file
 def __getstate__(	self      : List[Any]    )						->				int:


      """simple docstring"""




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


 def __setstate__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   d

      try:
           import sacremoses
      except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""    )

      __SCREAMING_SNAKE_CASE						     =   sacremoses









'''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






