




'''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_       				=      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_0_0_0_0))
								UpperCAmelCase_       				=      val_dataset.shuffle().select(range(8_0_0_0))

				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=4_0_9_6)

				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_				,		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)






'''simple docstring'''




import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase_       				=      2 << 2_0  # 2mb
UpperCAmelCase_       				=      get_logger(__name__)

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self._options.get_all_options().get("""runner""" )
   return runner in [None, "DirectRunner", "PortableRunner"]

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Any=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if fs.exists(SCREAMING_SNAKE_CASE__       ):
  if force_upload:
   logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.create(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""rb"""       ) as local_file:
   UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    remote_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    local_file.read(SCREAMING_SNAKE_CASE__       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Dict=False       ):
 '''simple docstring'''





 UpperCAmelCase__										=    FileSystems
 if os.path.exists(SCREAMING_SNAKE_CASE__       ):
  if force_download:
   logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''       )
  else:
   logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''       )
   return
 with fs.open(SCREAMING_SNAKE_CASE__       ) as remote_file:
  with open(SCREAMING_SNAKE_CASE__       ,						"""wb"""       ) as local_file:
   UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
   while chunk:
    local_file.write(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    remote_file.read(SCREAMING_SNAKE_CASE__       )
