

'''simple docstring'''



import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


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







def      a__ (						a__		):




     """simple docstring"""


     random.seed(a__		)
     np.random.seed(a__		)
     torch.manual_seed(a__		)
     torch.cuda.manual_seed_all(a__		)







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     model.eval()
     __SCREAMING_SNAKE_CASE						     =   next(model.parameters()		).device
     __SCREAMING_SNAKE_CASE						     =   1
     __SCREAMING_SNAKE_CASE						     =   torch.zeros((eval_batch_size, context_len)   ,					dtype=torch.long   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)
     __SCREAMING_SNAKE_CASE						     =   torch.zeros(1   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   0
     for batch in eval_dataloader:
          batch.to(a__		)
          # pad
          context.zero_()
          for i in range(a__		):
               __SCREAMING_SNAKE_CASE						     =   batch[i]
          __SCREAMING_SNAKE_CASE						     =   model(a__   ,					labels=a__		)
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0		)
     __SCREAMING_SNAKE_CASE						     =   eval_loss / nb_eval_examples
     __SCREAMING_SNAKE_CASE						     =   torch.exp(a__		)
     model.train()
     return perplexity







def      a__ (						a__="gpt2"		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   GPTaLMHeadModel.from_pretrained(a__   ,					output_hidden_states=a__		)
     torch.save(model.state_dict()   ,					model_name + """local.pt"""		)
     return model







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(a__		)
     model.to(a__		)

     __SCREAMING_SNAKE_CASE						     =   ["""bias""", """LayerNorm.weight"""]
     __SCREAMING_SNAKE_CASE						     =   [
         {
             """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
             """weight_decay""": 0.0,
         },
         {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], """weight_decay""": 0.0},
     ]
     __SCREAMING_SNAKE_CASE						     =   AdamW(a__   ,					lr=5E-5   ,					eps=1E-8		)
     __SCREAMING_SNAKE_CASE						     =   get_linear_schedule_with_warmup(a__   ,					0   ,					a__		)
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler







def      a__ (						a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   np.array(a__		).mean()
     __SCREAMING_SNAKE_CASE						     =   np.array(a__		).std()
     __SCREAMING_SNAKE_CASE						     =   (real_perps - avg) / std
     __SCREAMING_SNAKE_CASE						     =   list(zip(a__   ,					a__   ,					a__		)		)
     joblib.dump(a__   ,					a__		)







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__="dev.jbl"   ,					a__=recopy_gpta   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   []
     __SCREAMING_SNAKE_CASE						     =   []
     __SCREAMING_SNAKE_CASE						     =   []

     # Initialize the transformer model
     __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(a__		)
     orig_model.to(device="""cpu"""		)
     torch.cuda.empty_cache()

     # Compute perplexity of initial transformer model for comparison
     model.train()
     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   recopy_model(a__   ,					a__   ,					a__		)

     for step in tqdm(range(a__		)		):
          __SCREAMING_SNAKE_CASE						     =   torch.zeros((1, context_len)   ,					dtype=torch.long   ,					device=a__		)
          __SCREAMING_SNAKE_CASE						     =   random.choice(a__		)
          __SCREAMING_SNAKE_CASE						     =   random.randint(0   ,					len(story[0]		) - context_len - 1		)
          __SCREAMING_SNAKE_CASE						     =   story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          __SCREAMING_SNAKE_CASE						     =   model(a__   ,					labels=a__		)
          __SCREAMING_SNAKE_CASE						     =   outputs[0]
          __SCREAMING_SNAKE_CASE						     =   compute_perplexity(a__   ,					a__   ,					a__		)
          model.train()
          lm_loss.backward()
          # Do LM backprop
          torch.nn.utils.clip_grad_norm_(model.parameters()   ,					3.0		)
          lm_optimizer.step()
          lm_scheduler.step()  # Update learning rate schedule

          # Compute perplexity after back-propagating on the selected context
          __SCREAMING_SNAKE_CASE						     =   compute_perplexity(a__   ,					a__   ,					a__		)

          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
               intermittent_save(a__   ,					a__   ,					a__   ,					a__		)

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   recopy_model(a__   ,					a__   ,					a__		)

          past_perps.append(past_perp.item()		)
          real_perps.append(orig_perp - real_perp.item()		)
          contexts.append(np.array(context.cpu()		)		)

     intermittent_save(a__   ,					a__   ,					a__   ,					a__		)







def      a__ (						a__   ,					a__="data/tokenized_stories_train_wikitext103.jbl"   ,					a__=1_00   ,					a__=10_26   ,					a__=True		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   joblib.load(a__		)
     print("""data loaded"""		)
     __SCREAMING_SNAKE_CASE						     =   []
     if trim:
          for i, example in enumerate(a__		):
               if len(example[0]		) > min_len:
                    __SCREAMING_SNAKE_CASE						     =   random.randint(0   ,					len(example[0]		) - context_len - 1		)
                    objective_set.append(example[0, start : start + context_len]		)
               if len(a__		) >= number:
                    break
          __SCREAMING_SNAKE_CASE						     =   []
          for j in range(i + 1   ,					len(a__		)		):
               if len(data[j][0]		) > min_len:
                    train_data.append(data[j]		)
     else:
          __SCREAMING_SNAKE_CASE						     =   data[0:number]
          __SCREAMING_SNAKE_CASE						     =   data[number:]

     joblib.dump(a__   ,					"""objective_set.jbl"""		)
     print("""objective set saved"""		)
     return train_data, objective_set







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__=50   ,					a__="secondary_learner.pt"		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""		)
     # We will use the first 512 pairs from our dataset as a test set for
     # our secondary learner and the rest to train
     __SCREAMING_SNAKE_CASE						     =   train_dataset[:5_12]
     __SCREAMING_SNAKE_CASE						     =   train_dataset[5_12:]
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)

     # secondary learner model set up
     __SCREAMING_SNAKE_CASE						     =   nn.MSELoss()
     __SCREAMING_SNAKE_CASE						     =   nn.MSELoss(reduction="""sum"""		)
     secondary_learner.to(a__		)
     __SCREAMING_SNAKE_CASE						     =   torch.optim.Adam(secondary_learner.parameters()   ,					lr=0.00_001		)
     secondary_learner.train()

     # TODO in original code this is written as number of actual batches seen
     # not number of items seen but other places it is number of items instead.
     # improve consistency! changed this to epochs for clarity
     __SCREAMING_SNAKE_CASE						     =   float("""inf"""		)
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(a__		)		):
          __SCREAMING_SNAKE_CASE						     =   0.0
          secondary_learner.train()
          for step, batch in enumerate(a__		):
               __SCREAMING_SNAKE_CASE						     =   batch[0].to(a__		)
               __SCREAMING_SNAKE_CASE						     =   batch[1].to(a__		)
               __SCREAMING_SNAKE_CASE						     =   secondary_learner(a__		)
               q_optimizer.zero_grad()
               __SCREAMING_SNAKE_CASE						     =   loss(a__   ,					real_q.float()		)
               q_loss.backward()
               q_optimizer.step()
               tr_q_loss += q_loss.item()

               # model trains fairly quickly so we won't wait for a full epoch
               # eval is triggered at eval_freq and end of epochs
               if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(a__		)):
                    __SCREAMING_SNAKE_CASE						     =   tr_q_loss / (step + 1)

                    secondary_learner.eval()
                    __SCREAMING_SNAKE_CASE						     =   0.0
                    __SCREAMING_SNAKE_CASE						     =   0.0
                    __SCREAMING_SNAKE_CASE						     =   []
                    __SCREAMING_SNAKE_CASE						     =   []
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(a__		):
                         __SCREAMING_SNAKE_CASE						     =   batcha[0].to(a__		)
                         __SCREAMING_SNAKE_CASE						     =   batcha[1].to(a__		)
                         __SCREAMING_SNAKE_CASE						     =   secondary_learner(a__		)
                         q_lossa += test_loss(a__   ,					a__		).item()
                         sum_qa += torch.sum(a__		).item()
                         for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
                              predicted.append(i.item()		)
                         for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
                              actual.append(i.item()		)

                    q_lossa /= len(a__		)
                    print(
                        """Epoch: """   ,					a__   ,					"""step: """   ,					a__   ,					"""Avg. q:"""   ,					sum_qa / len(a__		)   ,					"""Train Loss: """   ,					a__   ,					"""Test Loss: """   ,					a__   ,					)
                    if q_lossa < best_test_loss:
                         joblib.dump((predicted, actual)   ,					"""pred_vs_actual.jbl"""		)
                         torch.save(secondary_learner.state_dict()   ,					a__		)
                         __SCREAMING_SNAKE_CASE						     =   q_lossa

               secondary_learner.train()
     return secondary_learner







class 		lowerCAmelCase__     (   nn.Module      ):



 """simple docstring"""



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


      """simple docstring"""




      super(__SCREAMING_SNAKE_CASE      ,							self    ).__init__()
      __SCREAMING_SNAKE_CASE						     =   model.transformer.wte
      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(model.transformer.wte.weight    )
      __SCREAMING_SNAKE_CASE						     =   nn.Convad(self.embeddings.weight.size(1    )      ,							256      ,							3      ,							padding=1    )
      __SCREAMING_SNAKE_CASE						     =   nn.Sequential(nn.Linear(256      ,							32    )      ,							nn.Dropout(p=0.1    )      ,							nn.Linear(32      ,							32    )      ,							nn.Linear(32      ,							1    )    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   torch.max(self.conv(self.embeddings(__SCREAMING_SNAKE_CASE    ).squeeze(1    ).transpose(1      ,							2    )    )      ,							2    )[0]
      __SCREAMING_SNAKE_CASE						     =   self.fc(__SCREAMING_SNAKE_CASE    )
      return qs.squeeze(1    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : int      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : int    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cls(__SCREAMING_SNAKE_CASE    )  # this calls __init__
      __SCREAMING_SNAKE_CASE						     =   torch.load(__SCREAMING_SNAKE_CASE    )
      secondary_learner.load_state_dict(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model.transformer.wte
      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(model.transformer.wte.weight    )
      return secondary_learner









'''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






