



'''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											=      '''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			List[Any]	=   "bark_module"
 __magic_name__       :			Optional[Any]	=   ["past_key_values"]

 __magic_name__       :			int	=   {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
     "vocab_size": "input_vocab_size",
     "window_size": "block_size",
 }




 def __init__( self				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=1_0_0_4_8				,	lowerCAmelCase_=1_0_0_4_8				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=7_6_8				,	lowerCAmelCase_=0.0				,	lowerCAmelCase_=True				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_=True				,	**lowerCAmelCase_				,	)			->   int:



      """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    lowercase_	( cls				,	lowerCAmelCase_				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = "main"				,	**lowerCAmelCase_				,	)			->   "PretrainedConfig":



      """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 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Optional[int]	=   "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 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   "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 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Optional[int]	=   "fine_acoustics"




 def __init__( self				,	lowerCAmelCase_=True				,	lowerCAmelCase_=8				,	lowerCAmelCase_=1				,	**lowerCAmelCase_)			->   Union[str, Any]:



      """simple docstring"""







      a_									=n_codes_total
      a_									=n_codes_given

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







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			Any	=   "bark"
 __magic_name__       :			str	=   True




 def __init__( self				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_=0.0_2				,	**lowerCAmelCase_				,	)			->   Any:



      """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    lowercase_	( cls				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	**lowerCAmelCase_				,	)			->   Union[str, 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    lowercase_	( self)			->   Dict:



      """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'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


