


'''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


a_  :    Tuple      =			logging.get_logger(__name__)


a_  :    Tuple      =			{
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

a_  :    str      =			"""\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   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  'bark_module'
  _lowerCamelCase		   =  ['past_key_values']

  _lowerCamelCase		   =  {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
      'vocab_size': 'input_vocab_size',
      'window_size': 'block_size',
  }






  def __init__(   self      ,  UpperCamelCase=1024      ,  UpperCamelCase=1_0048      ,  UpperCamelCase=1_0048      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=768      ,  UpperCamelCase=0.0      ,  UpperCamelCase=True      ,  UpperCamelCase=0.02      ,  UpperCamelCase=True      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  block_size
   lowerCamelCase_								=  input_vocab_size
   lowerCamelCase_								=  output_vocab_size
   lowerCamelCase_								=  num_layers
   lowerCamelCase_								=  num_heads
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  dropout
   lowerCamelCase_								=  bias
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  initializer_range

   super().__init__(**UpperCamelCase					)







  @classmethod
  def   snake_case		(   cls      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  UpperCamelCase = False      ,  UpperCamelCase = False      ,  UpperCamelCase = None      ,  UpperCamelCase = "main"      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  cache_dir
   lowerCamelCase_								=  force_download
   lowerCamelCase_								=  local_files_only
   lowerCamelCase_								=  revision

   cls._set_token_in_kwargs(UpperCamelCase      ,  UpperCamelCase					)

   lowerCamelCase_								=  cls.get_config_dict(UpperCamelCase      ,  **UpperCamelCase					)

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





@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   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  '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   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  '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   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  'fine_acoustics'






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







   """simple docstring"""
   lowerCamelCase_								=  n_codes_total
   lowerCamelCase_								=  n_codes_given

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





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  'bark'
  _lowerCamelCase		   =  True






  def __init__(   self      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase=0.02      ,  **UpperCamelCase      ,  ):







   """simple docstring"""






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

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

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

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

   lowerCamelCase_								=  BarkSemanticConfig(**UpperCamelCase					)
   lowerCamelCase_								=  BarkCoarseConfig(**UpperCamelCase					)
   lowerCamelCase_								=  BarkFineConfig(**UpperCamelCase					)
   lowerCamelCase_								=  AutoConfig.for_model(**UpperCamelCase					)

   lowerCamelCase_								=  initializer_range

   super().__init__(**UpperCamelCase					)






  @classmethod
  def   snake_case		(   cls      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  **UpperCamelCase      ,  ):







   """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()      ,  **UpperCamelCase      ,  )







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  copy.deepcopy(self.__dict__					)

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

   lowerCamelCase_								=  self.__class__.model_type
   return output







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_  :    Union[str, Any]      =			logging.get_logger(__name__)

a_  :    int      =			{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "nystromformer"






  def __init__(   self      ,  UpperCamelCase=3_0000      ,  UpperCamelCase=768      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=3072      ,  UpperCamelCase="gelu_new"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=510      ,  UpperCamelCase=2      ,  UpperCamelCase=64      ,  UpperCamelCase=64      ,  UpperCamelCase=65      ,  UpperCamelCase=False      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-5      ,  UpperCamelCase=1      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  segment_means_seq_len
   lowerCamelCase_								=  num_landmarks
   lowerCamelCase_								=  conv_kernel_size
   lowerCamelCase_								=  inv_coeff_init_option
   lowerCamelCase_								=  layer_norm_eps
   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  eos_token_id=UpperCamelCase      ,  **UpperCamelCase					)



