
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


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


__lowerCAmelCase   :     List[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",
}

__lowerCAmelCase   :     Dict      =						"\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		(		UpperCAmelCase__ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      '''bark_module'''
      A__		:     Any       				=      ['''past_key_values''']

      A__		:     List[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]			,    _snake_case						:    Optional[int]=1024			,    _snake_case						:    Tuple=1_0048			,    _snake_case						:    str=1_0048			,    _snake_case						:    Dict=12			,    _snake_case						:    List[str]=12			,    _snake_case						:    Dict=768			,    _snake_case						:    Optional[int]=0.0			,    _snake_case						:    str=True			,    _snake_case						:    Union[str, Any]=0.02			,    _snake_case						:    Any=True			,    **_snake_case						:    Union[str, Any]			,    ):
          __lowercase						:     Optional[Any]		      =    block_size
          __lowercase						:     Optional[Any]		      =    input_vocab_size
          __lowercase						:     str		      =    output_vocab_size
          __lowercase						:     List[str]		      =    num_layers
          __lowercase						:     Any		      =    num_heads
          __lowercase						:     Optional[int]		      =    hidden_size
          __lowercase						:     Any		      =    dropout
          __lowercase						:     Union[str, Any]		      =    bias
          __lowercase						:     Tuple		      =    use_cache
          __lowercase						:     Union[str, Any]		      =    initializer_range

          super().__init__(**__lowercase			)




      @classmethod
      def 						snake_case_     ( cls						:    str			,    _snake_case						:    Union[str, os.PathLike]			,    _snake_case						:    Optional[Union[str, os.PathLike]] = None			,    _snake_case						:    bool = False			,    _snake_case						:    bool = False			,    _snake_case						:    Optional[Union[str, bool]] = None			,    _snake_case						:    str = "main"			,    **_snake_case						:    Union[str, Any]			,    ):
          __lowercase						:     Dict		      =    cache_dir
          __lowercase						:     Optional[int]		      =    force_download
          __lowercase						:     Any		      =    local_files_only
          __lowercase						:     Union[str, Any]		      =    revision

          cls._set_token_in_kwargs(__lowercase			,    __lowercase			)

          __lowercase      , __lowercase						:     Tuple		      =    cls.get_config_dict(__lowercase			,    **__lowercase			)

          # get the config dict if we are loading from Bark
          if config_dict.get('''model_type'''			) == "bark":
              __lowercase						:     Optional[Any]		      =    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(__lowercase			,    **__lowercase			)






@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   __lowerCAmelCase		(		UpperCAmelCase__ ):



      """simple docstring"""







      A__		:     List[Any]       				=      '''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   __lowerCAmelCase		(		UpperCAmelCase__ ):



      """simple docstring"""







      A__		:     Tuple       				=      '''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   __lowerCAmelCase		(		UpperCAmelCase__ ):



      """simple docstring"""







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




      def __init__( self						:    List[Any]			,    _snake_case						:    str=True			,    _snake_case						:    str=8			,    _snake_case						:    List[str]=1			,    **_snake_case						:    Union[str, Any]			):
          __lowercase						:     str		      =    n_codes_total
          __lowercase						:     Tuple		      =    n_codes_given

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






class   __lowerCAmelCase		(		UpperCAmelCase__ ):



      """simple docstring"""







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




      def __init__( self						:    int			,    _snake_case						:    Dict = None			,    _snake_case						:    Dict = None			,    _snake_case						:    Dict = None			,    _snake_case						:    Dict = None			,    _snake_case						:    List[Any]=0.02			,    **_snake_case						:    int			,    ):






          if semantic_config is None:
              __lowercase						:     Optional[Any]		      =    {}
              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						:     str		      =    {}
              logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''			)

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

          __lowercase						:     Optional[int]		      =    BarkSemanticConfig(**__lowercase			)
          __lowercase						:     Tuple		      =    BarkCoarseConfig(**__lowercase			)
          __lowercase						:     Optional[Any]		      =    BarkFineConfig(**__lowercase			)
          __lowercase						:     int		      =    AutoConfig.for_model(**__lowercase			)

          __lowercase						:     List[Any]		      =    initializer_range

          super().__init__(**__lowercase			)




      @classmethod
      def 						snake_case_     ( cls						:    List[Any]			,    _snake_case						:    BarkSemanticConfig			,    _snake_case						:    BarkCoarseConfig			,    _snake_case						:    BarkFineConfig			,    _snake_case						:    AutoConfig			,    **_snake_case						:    Dict			,    ):
          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()			,    **__lowercase			,    )




      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     List[str]		      =    copy.deepcopy(self.__dict__			)

          __lowercase						:     Any		      =    self.semantic_config.to_dict()
          __lowercase						:     Dict		      =    self.coarse_acoustics_config.to_dict()
          __lowercase						:     List[Any]		      =    self.fine_acoustics_config.to_dict()
          __lowercase						:     List[Any]		      =    self.codec_config.to_dict()

          __lowercase						:     Union[str, Any]		      =    self.__class__.model_type
          return output




from __future__ import annotations

from collections import Counter


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  list[int]:
    __lowercase						:     Any		      =    [True] * n
    __lowercase						:     str		      =    False
    __lowercase						:     int		      =    False
    __lowercase						:     List[Any]		      =    True

    for i in range(3		,   int(n**0.5 + 1		)		,   2		):
        __lowercase						:     List[str]		      =    i * 2
        while index < n:
            __lowercase						:     List[Any]		      =    False
            __lowercase						:     Dict		      =    index + i

    __lowercase						:     Tuple		      =    [2]

    for i in range(3		,   __lowerCAmelCase		,   2		):
        if is_prime[i]:
            primes.append(__lowerCAmelCase		)

    return primes


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  list[list[int]]:
    __lowercase						:     Optional[int]		      =    str(__lowerCAmelCase		)
    __lowercase						:     List[str]		      =    []
    __lowercase						:     int		      =    ['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']

    for duplicate in Counter(__lowerCAmelCase		) - Counter(set(__lowerCAmelCase		)		):
        __lowercase						:     Union[str, Any]		      =    [int(number_str.replace(__lowerCAmelCase		,   __lowerCAmelCase		)		) for digit in digits]
        replacements.append(__lowerCAmelCase		)

    return replacements


def       UpperCAmelCase_					(  __lowerCAmelCase = 8		)	->  int:
    __lowercase						:     Tuple		      =    set()

    # Filter primes with less than 3 replaceable digits
    __lowercase						:     int		      =    {
        x for x in set(prime_sieve(1_000_000		)		) if len(str(__lowerCAmelCase		)		) - len(set(str(__lowerCAmelCase		)		)		) >= 3
    }

    for prime in primes:
        if prime in numbers_checked:
            continue

        __lowercase						:     str		      =    digit_replacements(__lowerCAmelCase		)

        for family in replacements:
            numbers_checked.update(__lowerCAmelCase		)
            __lowercase						:     List[Any]		      =    primes.intersection(__lowerCAmelCase		)

            if len(__lowerCAmelCase		) != family_length:
                continue

            return min(__lowerCAmelCase		)

    return -1


if __name__ == "__main__":
 print(solution())


