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

							__a  :					Optional[int]          =  load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
							__a  :					List[str]          =  load_dataset("""json""", data_files=args.val_data_path)["""train"""]

							# drop extra batch for now
							__a  :					Any          =  range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
							__a  :					List[str]          =  tr_dataset.shuffle().select(indices)
							__a  :					Any          =  range(len(val_dataset) - len(val_dataset) % args.batch_size)
							__a  :					str          =  val_dataset.shuffle().select(indices)

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

							print(tr_dataset)
							print(val_dataset)

							__a  :					Union[str, Any]          =  FlaxBigBirdForNaturalQuestions.from_pretrained(
							    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
							)
							__a  :					List[Any]          =  BigBirdTokenizerFast.from_pretrained(args.model_id)
							__a  :					int          =  DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

							__a  :					Optional[Any]          =  {
							    """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  :					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  :					Any          =  None
							__a  :					Optional[int]          =  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  :					Any          =  jax_utils.unreplicate(state.params)
							model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


							return F"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							1

				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							len(node.outbound							)

				for i in range(lowercase							):
								print(F"======= Iteration {i + 1} ======="							)
								for _, node in enumerate(lowercase							):
												__lowercase			      =							(1 - d) + d * sum(
												    ranks[ib] / outbounds[ib] for ib in node.inbound							)
								print(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							list(input('''Enter Names of the Nodes: '''							).split()							)

				__lowercase			      =							[Node(lowercase							) for name in names]

				for ri, row in enumerate(lowercase							):
								for ci, col in enumerate(lowercase							):
												if col == 1:
																nodes[ci].add_inbound(names[ri]							)
																nodes[ri].add_outbound(names[ci]							)

				print('''======= Nodes ======='''							)
				for node in nodes:
								print(lowercase							)

				page_rank(lowercase							)


if __name__ == "__main__":
		main()