




"""simple docstring"""




from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
				# Adjacency List of graph
				print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))









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

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

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

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    '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_            =   build_tx(**tx_args)

			a_            =   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_            =   None
			a_            =   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_            =   jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



