





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

			__UpperCamelCase:  Optional[Any]   =   load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
			__UpperCamelCase:  Any   =   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:  Dict   =   tr_dataset.shuffle().select(indices)
			__UpperCamelCase:  List[str]   =   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:  str   =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						__UpperCamelCase:  Optional[int]   =   val_dataset.shuffle().select(range(8_0_0_0))

			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:  Dict   =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			__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:  str   =   build_tx(**tx_args)

			__UpperCamelCase:  Union[str, 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:  Dict   =   None
			__UpperCamelCase:  Union[str, 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)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

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


