






"""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


lowercase_    					=					logging.get_logger(__name__)


lowercase_    					=					{
    '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_    					=					'\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  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Dict									=			"bark_module"
   A_						:     Optional[int]									=			["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			:					Union[str, Any],	_lowerCamelCase			:					Tuple=10_24,	_lowerCamelCase			:					List[str]=1_00_48,	_lowerCamelCase			:					str=1_00_48,	_lowerCamelCase			:					int=12,	_lowerCamelCase			:					int=12,	_lowerCamelCase			:					Optional[Any]=7_68,	_lowerCamelCase			:					List[Any]=0.0,	_lowerCamelCase			:					Tuple=True,	_lowerCamelCase			:					List[Any]=0.02,	_lowerCamelCase			:					Union[str, Any]=True,	**_lowerCamelCase			:					Dict,	):

     '''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     _SCREAMING_SNAKE_CASE    (     cls			:					Tuple,	_lowerCamelCase			:					Union[str, os.PathLike],	_lowerCamelCase			:					Optional[Union[str, os.PathLike]] = None,	_lowerCamelCase			:					bool = False,	_lowerCamelCase			:					bool = False,	_lowerCamelCase			:					Optional[Union[str, bool]] = None,	_lowerCamelCase			:					str = "main",	**_lowerCamelCase			:					Tuple,	):

     '''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  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     List[str]									=			"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  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Optional[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  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Any									=			"fine_acoustics"


   def __init__(     self			:					List[str],	_lowerCamelCase			:					Optional[Any]=True,	_lowerCamelCase			:					Dict=8,	_lowerCamelCase			:					List[str]=1,	**_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			n_codes_total
     __A						      =			n_codes_given

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





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Dict									=			"bark"
   A_						:     int									=			True


   def __init__(     self			:					List[str],	_lowerCamelCase			:					Dict = None,	_lowerCamelCase			:					Dict = None,	_lowerCamelCase			:					Dict = None,	_lowerCamelCase			:					Dict = None,	_lowerCamelCase			:					Optional[Any]=0.02,	**_lowerCamelCase			:					Dict,	):

     '''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     _SCREAMING_SNAKE_CASE    (     cls			:					Tuple,	_lowerCamelCase			:					BarkSemanticConfig,	_lowerCamelCase			:					BarkCoarseConfig,	_lowerCamelCase			:					BarkFineConfig,	_lowerCamelCase			:					AutoConfig,	**_lowerCamelCase			:					List[Any],	):

     '''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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''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












"""simple docstring"""


from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if is_torch_available():
    import torch

    if isinstance(__UpperCamelCase		,	torch.Tensor			):
      return True

  return isinstance(__UpperCamelCase		,	np.ndarray			)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			fields(self  )

     # Safety and consistency checks
     if not len(_lowerCamelCase  ):
       raise ValueError(f'{self.__class__.__name__} has no fields.'  )

     __A						      =			getattr(self,	class_fields[0].name  )
     __A						      =			all(getattr(self,	field.name  ) is None for field in class_fields[1:]  )

     if other_fields_are_none and isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       for key, value in first_field.items():
         __A						      =			value
     else:
       for field in class_fields:
         __A						      =			getattr(self,	field.name  )
         if v is not None:
           __A						      =			v


   def __delitem__(     self			:					Dict,	*_lowerCamelCase			:					int,	**_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	*_lowerCamelCase			:					List[str],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	*_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	*_lowerCamelCase			:					Any,	**_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.'  )


   def __getitem__(     self			:					int,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			dict(self.items()  )
       return inner_dict[k]
     else:
       return self.to_tuple()[k]


   def __setattr__(     self			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )


   def __setitem__(     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     # Will raise a KeyException if needed
     super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return tuple(self[k] for k in self.keys()  )




