





'''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(80000))
		__UpperCamelCase										=  val_dataset.shuffle().select(range(8000))

	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=4096)

	__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'''






def 				_a	(					_lowerCamelCase   )   ->							bool:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    [list(_lowerCamelCase   ) for row in matrix]
			if matrix and isinstance(_lowerCamelCase						,  _lowerCamelCase   ):
						if isinstance(matrix[0]						,  _lowerCamelCase   ):
									__snake_case				:			str       			=    0
									for row in matrix:
												if prev_len == 0:
															__snake_case				:			Dict       			=    len(_lowerCamelCase   )
															__snake_case				:			List[Any]       			=    True
												else:
															__snake_case				:			Union[str, Any]       			=    prev_len == len(_lowerCamelCase   )
						else:
									__snake_case				:			List[Any]       			=    True
			else:
						__snake_case				:			List[str]       			=    False

			return result







def 				_a	(					_lowerCamelCase   )   ->							None:



			"""simple docstring"""


			if check_matrix(_lowerCamelCase   ) and len(_lowerCamelCase   ) > 0:
						__snake_case				:			List[str]       			=    [list(_lowerCamelCase   ) for row in a]
						__snake_case				:			Optional[Any]       			=    len(_lowerCamelCase   )
						if isinstance(a[0]						,  _lowerCamelCase   ):
									__snake_case				:			Tuple       			=    len(a[0]   )
						else:
									for dat in a:
												print(_lowerCamelCase   )
									return

						# horizotal printing increasing
						for i in range(0						,  _lowerCamelCase   ):
									print(a[0][i]   )
						# vertical printing down
						for i in range(1						,  _lowerCamelCase   ):
									print(a[i][mat_col - 1]   )
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2						,  -1						,  -1   ):
												print(a[mat_row - 1][i]   )
        # vertical printing up
						for i in range(mat_row - 2						,  0						,  -1   ):
									print(a[i][0]   )
						__snake_case				:			Union[str, Any]       			=    [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(_lowerCamelCase   ) > 0:
									spiral_print_clockwise(_lowerCamelCase   )
						else:
									return
			else:
						print("""Not a valid matrix"""   )
						return







def 				_a	(					_lowerCamelCase   )   ->							list[int]:



			"""simple docstring"""


			if matrix:
						return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*_lowerCamelCase   )   )[::-1]   )
			else:
						return []


# driver code
if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase										=  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
	spiral_print_clockwise(a)


