






"""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


lowercase_    					=					logging.get_logger(__name__)


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

lowercase_    					=					'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''


class  snake_case		(							__lowerCAmelCase						):


   '''simple docstring'''

   A_						:     int									=			'''bark_module'''
   A_						:     Optional[int]									=			['''past_key_values''']

   A_						:     Any									=			{
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
       '''vocab_size''': '''input_vocab_size''',
       '''window_size''': '''block_size''',
   }


   def __init__(     self			:					List[str],	_lowerCamelCase			:					List[str]=10_24,	_lowerCamelCase			:					Dict=1_00_48,	_lowerCamelCase			:					Tuple=1_00_48,	_lowerCamelCase			:					Optional[int]=12,	_lowerCamelCase			:					Union[str, Any]=12,	_lowerCamelCase			:					Optional[Any]=7_68,	_lowerCamelCase			:					str=0.0,	_lowerCamelCase			:					Any=True,	_lowerCamelCase			:					Union[str, Any]=0.02,	_lowerCamelCase			:					Tuple=True,	**_lowerCamelCase			:					Dict,	):

     '''simple docstring'''
     __A						      =			block_size
     __A						      =			input_vocab_size
     __A						      =			output_vocab_size
     __A						      =			num_layers
     __A						      =			num_heads
     __A						      =			hidden_size
     __A						      =			dropout
     __A						      =			bias
     __A						      =			use_cache
     __A						      =			initializer_range

     super().__init__(**__SCREAMING_SNAKE_CASE  )






   @classmethod
   def     _SCREAMING_SNAKE_CASE    (     cls			:					List[Any],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any] = None,	_lowerCamelCase			:					List[str] = False,	_lowerCamelCase			:					Dict = False,	_lowerCamelCase			:					Tuple = None,	_lowerCamelCase			:					List[str] = "main",	**_lowerCamelCase			:					int,	):

     '''simple docstring'''
     __A						      =			cache_dir
     __A						      =			force_download
     __A						      =			local_files_only
     __A						      =			revision

     cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE,	__SCREAMING_SNAKE_CASE  )

     __A      ,   __A						      =			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":
       __A						      =			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  snake_case		(							__lowerCAmelCase						):


   '''simple docstring'''

   A_						:     int									=			'''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  snake_case		(							__lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Dict									=			'''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  snake_case		(							__lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Optional[int]									=			'''fine_acoustics'''


   def __init__(     self			:					List[str],	_lowerCamelCase			:					str=True,	_lowerCamelCase			:					Any=8,	_lowerCamelCase			:					List[Any]=1,	**_lowerCamelCase			:					Optional[Any]  ):

     '''simple docstring'''
     __A						      =			n_codes_total
     __A						      =			n_codes_given

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





class  snake_case		(							__lowerCAmelCase						):


   '''simple docstring'''

   A_						:     List[str]									=			'''bark'''
   A_						:     Any									=			True


   def __init__(     self			:					str,	_lowerCamelCase			:					Union[str, Any] = None,	_lowerCamelCase			:					Dict = None,	_lowerCamelCase			:					str = None,	_lowerCamelCase			:					List[Any] = None,	_lowerCamelCase			:					str=0.02,	**_lowerCamelCase			:					Union[str, Any],	):

     '''simple docstring'''




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

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

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

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

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

     __A						      =			initializer_range

     super().__init__(**__SCREAMING_SNAKE_CASE  )


   @classmethod
   def     _SCREAMING_SNAKE_CASE    (     cls			:					Any,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					str,	_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					Dict,	):

     '''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     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			copy.deepcopy(self.__dict__  )

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

     __A						      =			self.__class__.model_type
     return output












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




