



"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_								=			{
    """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""DeformableDetrFeatureExtractor"""]
				snake_case_								=			["""DeformableDetrImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DeformableDetrForObjectDetection""",
				    """DeformableDetrModel""",
				    """DeformableDetrPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
								from .image_processing_deformable_detr import DeformableDetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deformable_detr import (
								    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DeformableDetrForObjectDetection,
								    DeformableDetrModel,
								    DeformableDetrPreTrainedModel,
								)

else:
				import sys

				snake_case_								=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










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

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

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

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

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

				snake_case_								=			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,
				)

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





