






'''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__	=     logging.get_logger(__name__)


UpperCamelCase__	=     {
    '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__	=     '\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     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				Optional[int]						    =      'bark_module'
    __lowerCamelCase:				Tuple						    =      ['past_key_values']

    __lowerCamelCase:				str						    =      {
        '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]					,       a					:    Union[str, Any]=1_0_2_4					,       a					:    Optional[Any]=1_0_0_4_8					,       a					:    str=1_0_0_4_8					,       a					:    Optional[Any]=1_2					,       a					:    Any=1_2					,       a					:    Any=7_6_8					,       a					:    int=0.0					,       a					:    Optional[Any]=True					,       a					:    Dict=0.02					,       a					:    str=True					,       **a					:    List[Any]					,       ):






       '''simple docstring'''






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

       super().__init__(**a     )





    @classmethod
    def 			lowerCAmelCase__			(     cls					:    Dict					,       a					:    Union[str, os.PathLike]					,       a					:    Optional[Union[str, os.PathLike]] = None					,       a					:    bool = False					,       a					:    bool = False					,       a					:    Optional[Union[str, bool]] = None					,       a					:    str = "main"					,       **a					:    Dict					,       ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       cache_dir
       lowercase_		:      Dict          =       force_download
       lowercase_		:      int          =       local_files_only
       lowercase_		:      Tuple          =       revision

       cls._set_token_in_kwargs(a					,       a     )

       lowercase_      ,						lowercase_		:      Any          =       cls.get_config_dict(a					,       **a     )

       # get the config dict if we are loading from Bark
       if config_dict.get("model_type"     ) == "bark":
          lowercase_		:      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(a					,       **a     )


@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     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[Any]						    =      '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     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				Union[str, Any]						    =      '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     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				Optional[int]						    =      'fine_acoustics'




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






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       n_codes_total
       lowercase_		:      Any          =       n_codes_given

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


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[str]						    =      'bark'
    __lowerCamelCase:				List[str]						    =      True




    def __init__(     self					:    Optional[int]					,       a					:    Dict = None					,       a					:    Dict = None					,       a					:    Dict = None					,       a					:    Dict = None					,       a					:    Any=0.02					,       **a					:    Tuple					,       ):






       '''simple docstring'''






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

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

       if fine_acoustics_config is None:
          lowercase_		:      Optional[int]          =       {}
          logger.info("fine_acoustics_config is None. initializing the fine model with default values."     )

       if codec_config is None:
          lowercase_		:      Optional[int]          =       {}
          logger.info("codec_config is None. initializing the codec model with default values."     )

       lowercase_		:      Optional[int]          =       BarkSemanticConfig(**a     )
       lowercase_		:      Any          =       BarkCoarseConfig(**a     )
       lowercase_		:      List[Any]          =       BarkFineConfig(**a     )
       lowercase_		:      Any          =       AutoConfig.for_model(**a     )

       lowercase_		:      List[str]          =       initializer_range

       super().__init__(**a     )




    @classmethod
    def 			lowerCAmelCase__			(     cls					:    str					,       a					:    BarkSemanticConfig					,       a					:    BarkCoarseConfig					,       a					:    BarkFineConfig					,       a					:    AutoConfig					,       **a					:    Tuple					,       ):






       '''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()					,       **a					,       )





    def 			lowerCAmelCase__			(     self					:    List[Any]     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       copy.deepcopy(self.__dict__     )

       lowercase_		:      List[Any]          =       self.semantic_config.to_dict()
       lowercase_		:      Optional[int]          =       self.coarse_acoustics_config.to_dict()
       lowercase_		:      Tuple          =       self.fine_acoustics_config.to_dict()
       lowercase_		:      int          =       self.codec_config.to_dict()

       lowercase_		:      int          =       self.__class__.model_type
       return output












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




