
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


__magic_name__           =					logging.get_logger(__name__)


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

__magic_name__           =					'''\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    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Optional[int]      		=       'bark_module'
      _A  :						Tuple      		=       ['past_key_values']

      _A  :						str      		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
          'vocab_size': 'input_vocab_size',
          'window_size': 'block_size',
      }
      def __init__(     self						,		lowerCamelCase=10_24						,		lowerCamelCase=1_00_48						,		lowerCamelCase=1_00_48						,		lowerCamelCase=12						,		lowerCamelCase=12						,		lowerCamelCase=7_68						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=0.0_2						,		lowerCamelCase=True						,		**lowerCamelCase						,		):
          snake_case__  =			block_size
          snake_case__  =			input_vocab_size
          snake_case__  =			output_vocab_size
          snake_case__  =			num_layers
          snake_case__  =			num_heads
          snake_case__  =			hidden_size
          snake_case__  =			dropout
          snake_case__  =			bias
          snake_case__  =			use_cache
          snake_case__  =			initializer_range

          super().__init__(**lowerCamelCase	)



      @classmethod
      def A_     (     cls						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = False						,		lowerCamelCase = False						,		lowerCamelCase = None						,		lowerCamelCase = "main"						,		**lowerCamelCase						,		):
          snake_case__  =			cache_dir
          snake_case__  =			force_download
          snake_case__  =			local_files_only
          snake_case__  =			revision

          cls._set_token_in_kwargs(lowerCamelCase						,		lowerCamelCase	)

          snake_case__  =			cls.get_config_dict(lowerCamelCase						,		**lowerCamelCase	)

          # get the config dict if we are loading from Bark
          if config_dict.get("model_type"	) == "bark":
              snake_case__  =			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    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						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    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						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    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Optional[int]      		=       'fine_acoustics'
      def __init__(     self						,		lowerCamelCase=True						,		lowerCamelCase=8						,		lowerCamelCase=1						,		**lowerCamelCase	):
          snake_case__  =			n_codes_total
          snake_case__  =			n_codes_given

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





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						List[str]      		=       'bark'
      _A  :						List[str]      		=       True
      def __init__(     self						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase=0.0_2						,		**lowerCamelCase						,		):






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

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

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

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

          snake_case__  =			BarkSemanticConfig(**lowerCamelCase	)
          snake_case__  =			BarkCoarseConfig(**lowerCamelCase	)
          snake_case__  =			BarkFineConfig(**lowerCamelCase	)
          snake_case__  =			AutoConfig.for_model(**lowerCamelCase	)

          snake_case__  =			initializer_range

          super().__init__(**lowerCamelCase	)
      @classmethod
      def A_     (     cls						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		**lowerCamelCase						,		):
          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	):
          snake_case__  =			copy.deepcopy(self.__dict__	)

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

          snake_case__  =			self.__class__.model_type
          return output





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    if len(__lowerCAmelCase							) > 1:
        snake_case__  =			len(__lowerCAmelCase							) // 2  # Finds the middle of the array
        snake_case__  =			arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        snake_case__  =			arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        snake_case__  =			len(__lowerCAmelCase							)
        snake_case__  =			len(__lowerCAmelCase							)
        merge(__lowerCAmelCase							)  # Starts sorting the left.
        merge(__lowerCAmelCase							)  # Starts sorting the right
        snake_case__  =			0  # Left Counter
        snake_case__  =			0  # Right Counter
        snake_case__  =			0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                snake_case__  =			left_array[left_index]
                left_index += 1
            else:
                snake_case__  =			right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            snake_case__  =			left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            snake_case__  =			right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
     import doctest

     doctest.testmod()



