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

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

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

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

	print(tr_dataset)
	print(val_dataset)

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

	UpperCAmelCase     						=			{
	    "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     						=			build_tx(**tx_args)

	UpperCAmelCase     						=			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     						=			None
	UpperCAmelCase     						=			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     						=			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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



