





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

   lowerCamelCase_	:		List[Any]         =   load_dataset('json', data_files=args.tr_data_path)['train']
   lowerCamelCase_	:		Any         =   load_dataset('json', data_files=args.val_data_path)['train']

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

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

   print(tr_dataset)
   print(val_dataset)

   lowerCamelCase_	:		Dict         =   FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCamelCase_	:		str         =   BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCamelCase_	:		Any         =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   lowerCamelCase_	:		int         =   {
       '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,
   }
   lowerCamelCase_   ,   lowerCamelCase_	:		List[Any]         =   build_tx(**tx_args)

   lowerCamelCase_	:		List[Any]         =   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,
   )

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




"""simple docstring"""


from itertools import permutations

def 					_lowerCAmelCase (  lowercase_ = 5		):
						if gon_side < 3 or gon_side > 5:
												raise ValueError('gon_side must be in the range [3, 5]'		)

						# Since it's 16, we know 10 is on the outer ring
						# Put the big numbers at the end so that they are never the first number
						UpperCAmelCase			 =					list(range(gon_side + 1						,      0						,      -1		)		)
						UpperCAmelCase			 =					list(range(gon_side + 2						,      gon_side * 2 + 1		)		)

						for perm in permutations(small_numbers + big_numbers		):
												UpperCAmelCase			 =					generate_gon_ring(lowercase_						,      list(lowercase_		)		)
												if is_magic_gon(lowercase_		):
																		return int(''.join(str(lowercase_		) for n in numbers		)		)

						UpperCAmelCase			 =					F"""Magic {gon_side}-gon ring is impossible"""
						raise ValueError(lowercase_		)

def 					_lowerCAmelCase (  lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					[0] * (gon_side * 3)
						UpperCAmelCase			 =					perm[0:3]
						perm.append(perm[1]		)

						UpperCAmelCase			 =					1 if gon_side < 5 else 2

						for i in range(1						,      len(lowercase_		) // 3 + magic_number		):
												UpperCAmelCase			 =					perm[2 * i + 1]
												UpperCAmelCase			 =					result[3 * i - 1]
												UpperCAmelCase			 =					perm[2 * i + 2]

						return result

def 					_lowerCAmelCase (  lowercase_		):
						if len(lowercase_		) % 3 != 0:
												raise ValueError('a gon ring should have a length that is a multiple of 3'		)

						if min(numbers[::3]		) != numbers[0]:
												return False

						UpperCAmelCase			 =					sum(numbers[:3]		)

						return all(sum(numbers[i : i + 3]		) == total for i in range(3						,      len(lowercase_		)						,      3		)		)


if __name__ == "__main__":
				print(solution())





