





'''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
    A_				     =							Args()
    A_				     =							wandb.init(project="bigbird-natural-questions", config=args.__dict__)
    A_				     =							dict(logger.config)
    del wandb_args["batch_size"]
    A_				     =							replace(args, **wandb_args)
    A_				     =							args.base_dir + """-""" + wandb.run.id
    A_				     =							replace(args, base_dir=base_dir)
    print(args)

    A_				     =							load_dataset("json", data_files=args.tr_data_path)["""train"""]
    A_				     =							load_dataset("json", data_files=args.val_data_path)["""train"""]

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

    if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
        A_				     =							tr_dataset.shuffle().select(range(8_00_00))
        A_				     =							val_dataset.shuffle().select(range(80_00))

    print(tr_dataset)
    print(val_dataset)

    A_				     =							FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    A_				     =							BigBirdTokenizerFast.from_pretrained(args.model_id)
    A_				     =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

    A_				     =							{
        """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,
    }
    A_				     =							build_tx(**tx_args)

    A_				     =							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,
    )

    A_				     =							None
    A_				     =							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`")
    A_				     =							jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)










from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
