






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

       a	: str      =				load_dataset("json", data_files=args.tr_data_path)["train"]
       a	: List[Any]      =				load_dataset("json", data_files=args.val_data_path)["train"]

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

       if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
              a	: Dict      =				tr_dataset.shuffle().select(range(80_000))
              a	: int      =				val_dataset.shuffle().select(range(8_000))

       print(tr_dataset)
       print(val_dataset)

       a	: Tuple      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       a	: Optional[int]      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
       a	: Union[str, Any]      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

       a	: List[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,
       }
       a  ,a	: Optional[Any]      =				build_tx(**tx_args)

       a	: Optional[int]      =				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	: List[str]      =				None
       a	: 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`")
       a	: List[Any]      =				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_torch_available, is_vision_available


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "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

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


