



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:							Tuple        =      logging.get_logger(__name__)

lowercase__						:							Dict        =      {
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	"""qdqbert"""



      def __init__( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: Dict=3_0_5_2_2       ,     SCREAMING_SNAKE_CASE_							: Dict=7_6_8       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=1_2       ,     SCREAMING_SNAKE_CASE_							: Tuple=1_2       ,     SCREAMING_SNAKE_CASE_							: Tuple=3_0_7_2       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]="gelu"       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]=0.1       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=0.1       ,     SCREAMING_SNAKE_CASE_							: Tuple=5_1_2       ,     SCREAMING_SNAKE_CASE_							: Any=2       ,     SCREAMING_SNAKE_CASE_							: int=0.02       ,     SCREAMING_SNAKE_CASE_							: str=1E-12       ,     SCREAMING_SNAKE_CASE_							: Dict=True       ,     SCREAMING_SNAKE_CASE_							: int=1       ,     SCREAMING_SNAKE_CASE_							: str=0       ,     SCREAMING_SNAKE_CASE_							: List[Any]=2       ,     **SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,     bos_token_id=SCREAMING_SNAKE_CASE_       ,     eos_token_id=SCREAMING_SNAKE_CASE_       ,     **SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							List[str]								=					vocab_size
        lowerCAmelCase_  :							str								=					max_position_embeddings
        lowerCAmelCase_  :							List[Any]								=					hidden_size
        lowerCAmelCase_  :							List[Any]								=					num_hidden_layers
        lowerCAmelCase_  :							List[Any]								=					num_attention_heads
        lowerCAmelCase_  :							Optional[int]								=					intermediate_size
        lowerCAmelCase_  :							Optional[Any]								=					hidden_act
        lowerCAmelCase_  :							Union[str, Any]								=					hidden_dropout_prob
        lowerCAmelCase_  :							str								=					attention_probs_dropout_prob
        lowerCAmelCase_  :							Tuple								=					initializer_range
        lowerCAmelCase_  :							Tuple								=					type_vocab_size
        lowerCAmelCase_  :							Optional[int]								=					layer_norm_eps
        lowerCAmelCase_  :							Any								=					use_cache










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

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





