





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

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

      # drop extra batch for now
      UpperCAmelCase_       : Dict							=    range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      UpperCAmelCase_       : Dict							=    tr_dataset.shuffle().select(indices)
      UpperCAmelCase_       : Dict							=    range(len(val_dataset) - len(val_dataset) % args.batch_size)
      UpperCAmelCase_       : int							=    val_dataset.shuffle().select(indices)

      if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
            UpperCAmelCase_       : Optional[Any]							=    tr_dataset.shuffle().select(range(8_00_00))
            UpperCAmelCase_       : Optional[Any]							=    val_dataset.shuffle().select(range(80_00))

      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_       : Tuple							=    BigBirdTokenizerFast.from_pretrained(args.model_id)
      UpperCAmelCase_       : Union[str, Any]							=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      UpperCAmelCase_       : str							=    {
          """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_       : List[Any]							=    build_tx(**tx_args)

      UpperCAmelCase_       : Dict							=    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_       : Optional[Any]							=    None
      UpperCAmelCase_       : Tuple							=    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)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_       : Union[str, Any]							=    logging.get_logger(__name__)

UpperCAmelCase_       : int							=    {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""electra"""







  def __init__(						self     ,    _lowercase=30_522     ,    _lowercase=128     ,    _lowercase=256     ,    _lowercase=12     ,    _lowercase=4     ,    _lowercase=1_024     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0.1     ,    _lowercase=512     ,    _lowercase=2     ,    _lowercase=0.02     ,    _lowercase=1e-12     ,    _lowercase="first"     ,    _lowercase=True     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0     ,    _lowercase="absolute"     ,    _lowercase=True     ,    _lowercase=None     ,    **_lowercase     ,    ):
       super().__init__(pad_token_id=_lowercase     ,    **_lowercase    )

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

       lowerCAmelCase_       :							str			 =		summary_type
       lowerCAmelCase_       :							Optional[int]			 =		summary_use_proj
       lowerCAmelCase_       :							Optional[int]			 =		summary_activation
       lowerCAmelCase_       :							int			 =		summary_last_dropout
       lowerCAmelCase_       :							str			 =		position_embedding_type
       lowerCAmelCase_       :							Optional[int]			 =		use_cache
       lowerCAmelCase_       :							Tuple			 =		classifier_dropout





class   lowercase__     (				__A	):







  @property
  def 						UpperCAmelCase__		(						self    ):
       if self.task == "multiple-choice":
            lowerCAmelCase_       :							Optional[int]			 =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_       :							Any			 =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]    )
