


import math
from collections.abc import Generator

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=(i for i in range(1     ,	(max_n + 1)							))
     for i in (n for n in numbers if n > 1):
          for j in range(2     ,	lowerCAmelCase_							):
               if (i % j) == 0:
                    break
          else:
               yield i

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     __UpperCamelCase       				=(i for i in range(1     ,	(max_n + 1)							))
     for i in (n for n in numbers if n > 1):
          # only need to check for factors up to sqrt(i)
          __UpperCamelCase       				=int(math.sqrt(lowerCAmelCase_							)							) + 1
          for j in range(2     ,	lowerCAmelCase_							):
               if (i % j) == 0:
                    break
          else:
               yield i

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     __UpperCamelCase       				=(i for i in range(1     ,	(max_n + 1)     ,	2							))
     # It's useless to test even numbers as they will not be prime
     if max_n > 2:
          yield 2  # Because 2 will not be tested, it's necessary to yield it now
     for i in (n for n in numbers if n > 1):
          __UpperCamelCase       				=int(math.sqrt(lowerCAmelCase_							)							) + 1
          for j in range(3     ,	lowerCAmelCase_     ,	2							):
               # As we removed the even numbers, we don't need them now
               if (i % j) == 0:
                    break
          else:
               yield i

def 		_UpperCAmelCase							(     ):
     from timeit import timeit

     __UpperCamelCase       				='from __main__ import slow_primes, primes, fast_primes'
     print(timeit('slow_primes(1_000_000_000_000)'     ,	setup=lowerCAmelCase_     ,	number=1_00_00_00							)							)
     print(timeit('primes(1_000_000_000_000)'     ,	setup=lowerCAmelCase_     ,	number=1_00_00_00							)							)
     print(timeit('fast_primes(1_000_000_000_000)'     ,	setup=lowerCAmelCase_     ,	number=1_00_00_00							)							)


if __name__ == "__main__":
   _A									=		int(input('Calculate primes up to:\n>> ').strip())
   for ret in primes(number):
      print(ret)
   benchmark()






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(80_000))
            UpperCAmelCase_    		=				val_dataset.shuffle().select(range(8_000))

      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_096)

      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)