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

			lowercase		:						int =					load_dataset('json', data_files=args.tr_data_path)['train']
			lowercase		:						Tuple =					load_dataset('json', data_files=args.val_data_path)['train']

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

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

			print(tr_dataset)
			print(val_dataset)

			lowercase		:						List[Any] =					FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			lowercase		:						Any =					BigBirdTokenizerFast.from_pretrained(args.model_id)
			lowercase		:						Dict =					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

			lowercase		:						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,
			}
			lowercase		:						List[str] =					build_tx(**tx_args)

			lowercase		:						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,
			)

			lowercase		:						Optional[Any] =					None
			lowercase		:						str =					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`')
			lowercase		:						int =					jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)


"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
