





"""simple docstring"""
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[int]						=     Args()
       __UpperCamelCase    :	List[str]						=     wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
       __UpperCamelCase    :	Any						=     dict(logger.config)
       del wandb_args["batch_size"]
       __UpperCamelCase    :	List[Any]						=     replace(args, **wandb_args)
       __UpperCamelCase    :	List[str]						=     args.base_dir + '''-''' + wandb.run.id
       __UpperCamelCase    :	Union[str, Any]						=     replace(args, base_dir=base_dir)
       print(args)

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

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

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

       print(tr_dataset)
       print(val_dataset)

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

       __UpperCamelCase    :	List[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[Any]						=     build_tx(**tx_args)

       __UpperCamelCase    :	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    :	int						=     None
       __UpperCamelCase    :	int						=     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    :	int						=     jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :	Dict						=     logging.get_logger(__name__)

__UpperCamelCase    :	List[Any]						=     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}







class 			a       (      a__     ):
 snake_case__								=					'''mpnet'''



 def __init__(    self							,					_snake_case=3_05_27							,					_snake_case=7_68							,					_snake_case=12							,					_snake_case=12							,					_snake_case=30_72							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=0.02							,					_snake_case=1E-12							,					_snake_case=32							,					_snake_case=1							,					_snake_case=0							,					_snake_case=2							,					**_snake_case							,					):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case							,					bos_token_id=_snake_case							,					eos_token_id=_snake_case							,					**_snake_case    )

  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   layer_norm_eps
  lowerCAmelCase													=   relative_attention_num_buckets





