

'''simple docstring'''



import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     if isinstance(a__   ,					(list, tuple)		):
          return honor_type(a__   ,					(move_to_device(a__   ,					a__		) for t in state)		)
     elif isinstance(a__   ,					a__		):
          return type(a__		)({k: move_to_device(a__   ,					a__		) for k, v in state.items()}		)
     elif isinstance(a__   ,					torch.Tensor		):
          return state.to(a__		)
     return state







class 		lowerCAmelCase__     (   torch.optim.Optimizer      ):



  """simple docstring"""



  def __init__(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Dict=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				str:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   optimizer
       __SCREAMING_SNAKE_CASE						     =   scaler
       __SCREAMING_SNAKE_CASE						     =   AcceleratorState()
       __SCREAMING_SNAKE_CASE						     =   GradientState()
       __SCREAMING_SNAKE_CASE						     =   device_placement
       __SCREAMING_SNAKE_CASE						     =   False
       __SCREAMING_SNAKE_CASE						     =   None

       # Handle device placement
       if device_placement:
            __SCREAMING_SNAKE_CASE						     =   self.optimizer.state_dict()
            if self.accelerator_state.distributed_type == DistributedType.TPU:
                 xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
            else:
                 __SCREAMING_SNAKE_CASE						     =   move_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
            self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
  @property
  def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


       """simple docstring"""




       return self.optimizer.state
  @state.setter
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   state
  @property
  def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


       """simple docstring"""




       return self.optimizer.param_groups
  @param_groups.setter
  def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[Any]:


       """simple docstring"""




       __SCREAMING_SNAKE_CASE						     =   param_groups
  @property
  def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


       """simple docstring"""




       return self.optimizer.defaults
  @defaults.setter
  def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[Any]:


       """simple docstring"""




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


       """simple docstring"""




       self.optimizer.add_param_group(__SCREAMING_SNAKE_CASE    )
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Dict:


       """simple docstring"""




       if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
            xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
       self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
  def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


       """simple docstring"""




       return self.optimizer.state_dict()
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]=None    )						->				Any:


       """simple docstring"""




       if self.gradient_state.sync_gradients:
            __SCREAMING_SNAKE_CASE						     =   """set_to_none""" in inspect.signature(self.optimizer.zero_grad    ).parameters
            if accept_arg:
                 if set_to_none is None:
                      __SCREAMING_SNAKE_CASE						     =   False
                 self.optimizer.zero_grad(set_to_none=__SCREAMING_SNAKE_CASE    )
            else:
                 if set_to_none is not None:
                      raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""    )
                 self.optimizer.zero_grad()
  def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				Dict:


       """simple docstring"""




       if self.gradient_state.sync_gradients:
            if self.accelerator_state.distributed_type == DistributedType.TPU:
                 __SCREAMING_SNAKE_CASE						     =   {"""closure""": closure} if closure is not None else {}
                 xm.optimizer_step(self.optimizer      ,							optimizer_args=__SCREAMING_SNAKE_CASE    )
            elif self.scaler is not None:
                 __SCREAMING_SNAKE_CASE						     =   False
                 if self._last_scale is None:
                      # `get_scale` is an async operation requiring full synchronization
                      # on CPU and GPUs before finishing. As a result, we store away
                      # the prior one to reduce the call overhead
                      __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                      __SCREAMING_SNAKE_CASE						     =   True
                 self.scaler.step(self.optimizer      ,							__SCREAMING_SNAKE_CASE    )
                 self.scaler.update()
                 __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                 if not new_scale:
                      # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                      __SCREAMING_SNAKE_CASE						     =   scale_after < self._last_scale
                 __SCREAMING_SNAKE_CASE						     =   scale_after

            else:
                 self.optimizer.step(__SCREAMING_SNAKE_CASE    )
  def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Any:


       """simple docstring"""




       for param_group in self.optimizer.param_groups:
            __SCREAMING_SNAKE_CASE						     =   [parameters_map.get(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) for p in param_group["""params"""]]
  @property
  def    UpperCAmelCase__	(	self      : int    )						->				Any:


       """simple docstring"""




       warnings.warn(
           """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
           """`optimizer.step_was_skipped` instead."""      ,							__SCREAMING_SNAKE_CASE      ,							)
       return self._is_overflow
  @property
  def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[int]:


       """simple docstring"""




       return self._is_overflow
  def __getstate__(	self      : Tuple    )						->				str:


       """simple docstring"""




       return self.__dict__.copy()


  def __setstate__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Any:


       """simple docstring"""




       self.__dict__.update(__SCREAMING_SNAKE_CASE    )









'''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






