






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[str]    						=    logging.get_logger(__name__)


_UpperCAmelCase				:	Dict    						=    {
    '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				:	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_							(     UpperCamelCase   ):
     __lowerCamelCase					:						List[Any]     =    '''bark_module'''
     __lowerCamelCase					:						Any     =    ['''past_key_values''']

     __lowerCamelCase					:						Optional[Any]     =    {
         '''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=10048       ,   _UpperCAmelCase=10048       ,   _UpperCAmelCase=12       ,   _UpperCAmelCase=12       ,   _UpperCAmelCase=768       ,   _UpperCAmelCase=0.0       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=0.02       ,   _UpperCAmelCase=True       ,   **_UpperCAmelCase       ,   )		->							List[Any]:
        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 	lowerCamelCase       (  cls       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = "main"       ,   **_UpperCAmelCase       ,   )		->							"PretrainedConfig":
        lowerCamelCase											=cache_dir
        lowerCamelCase											=force_download
        lowerCamelCase											=local_files_only
        lowerCamelCase											=revision

        cls._set_token_in_kwargs(_UpperCAmelCase       ,   _UpperCAmelCase     )

        lowerCamelCase							,					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'''   )							,    '''
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,    )
class 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						int     =    '''semantic'''




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''							,    model='''BarkCoarseModel'''   )							,    '''
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,    )
class 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						int     =    '''coarse_acoustics'''




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''							,    model='''BarkFineModel'''   )							,    '''
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''							,    )
class 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						int     =    '''fine_acoustics'''





     def __init__(  self       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=8       ,   _UpperCAmelCase=1       ,   **_UpperCAmelCase     )		->							Union[str, Any]:
        lowerCamelCase											=n_codes_total
        lowerCamelCase											=n_codes_given

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




class 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						Tuple     =    '''bark'''
     __lowerCamelCase					:						Tuple     =    True





     def __init__(  self       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase=0.02       ,   **_UpperCAmelCase       ,   )		->							str:
        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 	lowerCamelCase       (  cls       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   **_UpperCAmelCase       ,   )		->							Dict:
        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 	lowerCamelCase       (  self     )		->							Tuple:
        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







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   _UpperCAmelCase				:	Optional[Any]    						=    Args()
   _UpperCAmelCase				:	Tuple    						=    wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _UpperCAmelCase				:	Optional[Any]    						=    dict(logger.config)
   del wandb_args["batch_size"]
   _UpperCAmelCase				:	str    						=    replace(args, **wandb_args)
   _UpperCAmelCase				:	Dict    						=    args.base_dir + '-' + wandb.run.id
   _UpperCAmelCase				:	Tuple    						=    replace(args, base_dir=base_dir)
   print(args)

   _UpperCAmelCase				:	List[Any]    						=    load_dataset('json', data_files=args.tr_data_path)['train']
   _UpperCAmelCase				:	Dict    						=    load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _UpperCAmelCase				:	List[str]    						=    range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _UpperCAmelCase				:	List[Any]    						=    tr_dataset.shuffle().select(indices)
   _UpperCAmelCase				:	Any    						=    range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _UpperCAmelCase				:	Union[str, Any]    						=    val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _UpperCAmelCase				:	Optional[Any]    						=    tr_dataset.shuffle().select(range(8_0000))
      _UpperCAmelCase				:	List[Any]    						=    val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   _UpperCAmelCase				:	int    						=    FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _UpperCAmelCase				:	Union[str, Any]    						=    BigBirdTokenizerFast.from_pretrained(args.model_id)
   _UpperCAmelCase				:	List[str]    						=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

   _UpperCAmelCase				:	Any    						=    {
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   _UpperCAmelCase						,						_UpperCAmelCase				:	Optional[int]    						=    build_tx(**tx_args)

   _UpperCAmelCase				:	List[Any]    						=    Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   _UpperCAmelCase				:	Union[str, Any]    						=    None
   _UpperCAmelCase				:	Optional[Any]    						=    trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   _UpperCAmelCase				:	Optional[int]    						=    jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)