



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				:					int     					=logging.get_logger(__name__)


_lowercase				:					Dict     					={
    """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				:					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 lowerCAmelCase_       (   lowerCAmelCase_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		"bark_module"
	A_       :     List[str]									=		["past_key_values"]

	A_       :     Optional[int]									=		{
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	    "vocab_size": "input_vocab_size",
	    "window_size": "block_size",
	}

	def __init__(	self   ,       lowerCamelCase=1024   ,       lowerCamelCase=1_0048   ,       lowerCamelCase=1_0048   ,       lowerCamelCase=12   ,       lowerCamelCase=12   ,       lowerCamelCase=768   ,       lowerCamelCase=0.0   ,       lowerCamelCase=True   ,       lowerCamelCase=0.0_2   ,       lowerCamelCase=True   ,       **lowerCamelCase   ,       ):



				'''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__(**lowerCamelCase__       )







	@classmethod
	def 				_A			(	cls   ,       lowerCamelCase   ,       lowerCamelCase = None   ,       lowerCamelCase = False   ,       lowerCamelCase = False   ,       lowerCamelCase = None   ,       lowerCamelCase = "main"   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  cache_dir
				a__					=  force_download
				a__					=  local_files_only
				a__					=  revision

				cls._set_token_in_kwargs(lowerCamelCase__   ,       lowerCamelCase__       )

				a__				,       a__					=  cls.get_config_dict(lowerCamelCase__   ,       **lowerCamelCase__       )

				# 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(lowerCamelCase__   ,       **lowerCamelCase__       )


@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_       (   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 lowerCAmelCase_       (   lowerCAmelCase_			):







	'''simple docstring'''




	A_       :     Tuple									=		"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_       (   lowerCAmelCase_			):







	'''simple docstring'''




	A_       :     int									=		"fine_acoustics"

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



				'''simple docstring'''


				a__					=  n_codes_total
				a__					=  n_codes_given

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


class lowerCAmelCase_       (   lowerCAmelCase_			):







	'''simple docstring'''




	A_       :     Optional[int]									=		"bark"
	A_       :     int									=		True

	def __init__(	self   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       lowerCamelCase = None   ,       lowerCamelCase=0.0_2   ,       **lowerCamelCase   ,       ):



				'''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(**lowerCamelCase__       )
				a__					=  BarkCoarseConfig(**lowerCamelCase__       )
				a__					=  BarkFineConfig(**lowerCamelCase__       )
				a__					=  AutoConfig.for_model(**lowerCamelCase__       )

				a__					=  initializer_range

				super().__init__(**lowerCamelCase__       )

	@classmethod
	def 				_A			(	cls   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       **lowerCamelCase   ,       ):



				'''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()   ,       **lowerCamelCase__   ,       )







	def 				_A			(	self       ):



				'''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








import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


