'''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


snake_case__	:     Tuple      =    logging.get_logger(__name__)


snake_case__	:     Optional[Any]      =    {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

snake_case__	:     Union[str, Any]      =    '''
    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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''bark_module'''
  lowerCamelCase_						:Optional[int]		       =						['''past_key_values''']

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


  def __init__(					self ,						snake_case_=1_0_2_4 ,						snake_case_=1_0_0_4_8 ,						snake_case_=1_0_0_4_8 ,						snake_case_=1_2 ,						snake_case_=1_2 ,						snake_case_=7_6_8 ,						snake_case_=0.0 ,						snake_case_=True ,						snake_case_=0.02 ,						snake_case_=True ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = block_size
       UpperCAmelCase_				:  Dict  = input_vocab_size
       UpperCAmelCase_				:  List[str]  = output_vocab_size
       UpperCAmelCase_				:  Any  = num_layers
       UpperCAmelCase_				:  Tuple  = num_heads
       UpperCAmelCase_				:  Optional[int]  = hidden_size
       UpperCAmelCase_				:  Optional[Any]  = dropout
       UpperCAmelCase_				:  Optional[Any]  = bias
       UpperCAmelCase_				:  int  = use_cache
       UpperCAmelCase_				:  Any  = initializer_range

       super().__init__(**snake_case_		)






  @classmethod
  def _UpperCamelCase	(					cls ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = False ,						snake_case_ = False ,						snake_case_ = None ,						snake_case_ = "main" ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = cache_dir
       UpperCAmelCase_				:  Dict  = force_download
       UpperCAmelCase_				:  Optional[Any]  = local_files_only
       UpperCAmelCase_				:  List[Any]  = revision

       cls._set_token_in_kwargs(snake_case_ ,						snake_case_		)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = cls.get_config_dict(snake_case_ ,						**snake_case_		)

       # get the config dict if we are loading from Bark
       if config_dict.get('model_type'		) == "bark":
            UpperCAmelCase_				:  Dict  = 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(snake_case_ ,						**snake_case_		)







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkSemanticConfig'''		,      model='''BarkSemanticModel'''			)		,      '''
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						'''semantic'''







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''		,      model='''BarkCoarseModel'''			)		,      '''
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''coarse_acoustics'''







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''		,      model='''BarkFineModel'''			)		,      '''
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						'''fine_acoustics'''


  def __init__(					self ,						snake_case_=True ,						snake_case_=8 ,						snake_case_=1 ,						**snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = n_codes_total
       UpperCAmelCase_				:  List[str]  = n_codes_given

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







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Tuple		       =						'''bark'''
  lowerCamelCase_						:Optional[Any]		       =						True


  def __init__(					self ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_=0.02 ,						**snake_case_ ,						):




       '''simple docstring'''

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

       if coarse_acoustics_config is None:
            UpperCAmelCase_				:  List[str]  = {}
            logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'		)

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

       if codec_config is None:
            UpperCAmelCase_				:  Optional[Any]  = {}
            logger.info('codec_config is None. initializing the codec model with default values.'		)

       UpperCAmelCase_				:  Union[str, Any]  = BarkSemanticConfig(**snake_case_		)
       UpperCAmelCase_				:  Any  = BarkCoarseConfig(**snake_case_		)
       UpperCAmelCase_				:  Dict  = BarkFineConfig(**snake_case_		)
       UpperCAmelCase_				:  Dict  = AutoConfig.for_model(**snake_case_		)

       UpperCAmelCase_				:  Any  = initializer_range

       super().__init__(**snake_case_		)


  @classmethod
  def _UpperCamelCase	(					cls ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						**snake_case_ ,						):




       '''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() ,						**snake_case_ ,						)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = copy.deepcopy(self.__dict__		)

       UpperCAmelCase_				:  Optional[int]  = self.semantic_config.to_dict()
       UpperCAmelCase_				:  Union[str, Any]  = self.coarse_acoustics_config.to_dict()
       UpperCAmelCase_				:  str  = self.fine_acoustics_config.to_dict()
       UpperCAmelCase_				:  List[str]  = self.codec_config.to_dict()

       UpperCAmelCase_				:  List[Any]  = self.__class__.model_type
       return output










'''simple docstring'''






import math
from decimal import Decimal, getcontext






def 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'		)
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


if __name__ == "__main__":
 print(f'''{solution() = }''')

