

'''simple docstring'''



import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCAmelCase				: List[str]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def __init__(	self      : Union[str, Any]    )						->				Optional[Any]:


       """simple docstring"""




       super().__init__()
       if version.parse(torch.__version__    ) < version.parse("""1.12.0"""    ):
            raise ImportError(
                f'You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '
                """PytorchGELUTanh. Please upgrade torch."""    )


  def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Tuple:


       """simple docstring"""




       return nn.functional.gelu(_lowerCamelCase      ,							approximate="""tanh"""    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Tuple:


       """simple docstring"""




       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.044715 * torch.pow(_lowerCamelCase      ,							3.0    ))    ))







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def __init__(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[Any] = False    )						->				str:


       """simple docstring"""




       super().__init__()
       if use_gelu_python:
            __SCREAMING_SNAKE_CASE						     =   self._gelu_python
       else:
            __SCREAMING_SNAKE_CASE						     =   nn.functional.gelu
  def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[str]:


       """simple docstring"""




       return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0    )    ))


  def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				Any:


       """simple docstring"""




       return self.act(_lowerCamelCase    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				int:


       """simple docstring"""




       return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)    ))







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Tuple:


       """simple docstring"""




       return input * torch.sigmoid(1.702 * input    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def __init__(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Tuple:


       """simple docstring"""




       if min > max:
            raise ValueError(f'min should be < max (got min: {min}, max: {max})'    )

       super().__init__()
       __SCREAMING_SNAKE_CASE						     =   min
       __SCREAMING_SNAKE_CASE						     =   max


  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				int:


       """simple docstring"""




       return torch.clip(gelu(_lowerCamelCase    )      ,							self.min      ,							self.max    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def __init__(	self      : List[str]    )						->				List[Any]:


       """simple docstring"""




       super().__init__()
       __SCREAMING_SNAKE_CASE						     =   math.sqrt(2 / math.pi    )


  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Optional[int]:


       """simple docstring"""




       return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(_lowerCamelCase      ,							3    ))    ))







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : int    )						->				Optional[int]:


       """simple docstring"""




       return nn.functional.silu(_lowerCamelCase    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def __init__(	self      : str    )						->				Tuple:


       """simple docstring"""




       super().__init__()
       if version.parse(torch.__version__    ) < version.parse("""1.9.0"""    ):
            __SCREAMING_SNAKE_CASE						     =   self._mish_python
       else:
            __SCREAMING_SNAKE_CASE						     =   nn.functional.mish
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Tuple:


       """simple docstring"""




       return input * torch.tanh(nn.functional.softplus(_lowerCamelCase    )    )


  def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Optional[int]:


       """simple docstring"""




       return self.act(_lowerCamelCase    )







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : int    )						->				List[str]:


       """simple docstring"""




       return input







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=0.707107      ,							__SCREAMING_SNAKE_CASE      : str=0.282095    )						->				Tuple:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   (input - mu).div(sigma * math.sqrt(2.0    )    )
       return 0.5 * (1.0 + torch.erf(_lowerCamelCase    ))







class 		lowerCAmelCase__     (   nn.Module      ):



  """simple docstring"""



  def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : int    )						->				str:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   nn.functional.relu(_lowerCamelCase    )
       __SCREAMING_SNAKE_CASE						     =   torch.square(_lowerCamelCase    )
       return squared







class 		lowerCAmelCase__     (   lowerCAmelCase__      ):



  """simple docstring"""



  def __getitem__(	self      : int      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				List[Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   super().__getitem__(_lowerCamelCase    )
       __SCREAMING_SNAKE_CASE						     =   content if isinstance(_lowerCamelCase      ,							_lowerCamelCase    ) else (content, {})
       return cls(**_lowerCamelCase    )


UpperCAmelCase				: Dict         =  {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
UpperCAmelCase				: List[Any]         =  ClassInstantier(ACTaCLS)







def      a__ (						a__		):




     """simple docstring"""


     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)


# For backwards compatibility with: from activations import gelu_python
UpperCAmelCase				: int         =  get_activation('gelu_python')
UpperCAmelCase				: Dict         =  get_activation('gelu_new')
UpperCAmelCase				: Optional[Any]         =  get_activation('gelu')
UpperCAmelCase				: Union[str, Any]         =  get_activation('gelu_fast')
UpperCAmelCase				: int         =  get_activation('quick_gelu')
UpperCAmelCase				: Dict         =  get_activation('silu')
UpperCAmelCase				: int         =  get_activation('mish')



UpperCAmelCase				: Union[str, Any]         =  get_activation('linear')









'''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


UpperCAmelCase				: List[Any]         =  logging.get_logger(__name__)


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

UpperCAmelCase				: Optional[Any]         =  '\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__     (   a      ):



 """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      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=1_024      ,							__SCREAMING_SNAKE_CASE      : Dict=10_048      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=10_048      ,							__SCREAMING_SNAKE_CASE      : Tuple=12      ,							__SCREAMING_SNAKE_CASE      : Dict=12      ,							__SCREAMING_SNAKE_CASE      : List[str]=768      ,							__SCREAMING_SNAKE_CASE      : Tuple=0.0      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   block_size
      __SCREAMING_SNAKE_CASE						     =   input_vocab_size
      __SCREAMING_SNAKE_CASE						     =   output_vocab_size
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   dropout
      __SCREAMING_SNAKE_CASE						     =   bias
      __SCREAMING_SNAKE_CASE						     =   use_cache
      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, os.PathLike]      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, os.PathLike]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, bool]] = None      ,							__SCREAMING_SNAKE_CASE      : str = "main"      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				"PretrainedConfig":


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cache_dir
      __SCREAMING_SNAKE_CASE						     =   force_download
      __SCREAMING_SNAKE_CASE						     =   local_files_only
      __SCREAMING_SNAKE_CASE						     =   revision

      cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   cls.get_config_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

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







@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__     (   a      ):



 """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 		lowerCAmelCase__     (   a      ):



 """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 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "fine_acoustics"
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple=8      ,							__SCREAMING_SNAKE_CASE      : Tuple=1      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   n_codes_total
      __SCREAMING_SNAKE_CASE						     =   n_codes_given

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







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark"
 lowerCAmelCase__						      =   True
 def __init__(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=0.02      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				Union[str, Any]:


      """simple docstring"""




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

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

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

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

      __SCREAMING_SNAKE_CASE						     =   BarkSemanticConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkCoarseConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkFineConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   AutoConfig.for_model(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )
 @classmethod
 def    UpperCAmelCase__	(	cls      : str      ,							__SCREAMING_SNAKE_CASE      : BarkSemanticConfig      ,							__SCREAMING_SNAKE_CASE      : BarkCoarseConfig      ,							__SCREAMING_SNAKE_CASE      : BarkFineConfig      ,							__SCREAMING_SNAKE_CASE      : AutoConfig      ,							**__SCREAMING_SNAKE_CASE      : int      ,							)						->				Optional[int]:


      """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()      ,							**__SCREAMING_SNAKE_CASE      ,							)


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(self.__dict__    )

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

      __SCREAMING_SNAKE_CASE						     =   self.__class__.model_type
      return output






