
"""simple docstring"""
from .number_theory.prime_numbers import next_prime






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = None    ,     ):
      _snake_case  :    Optional[int]            = size_table
      _snake_case  :    Optional[int]            = [None] * self.size_table
      _snake_case  :    int            = 0.75 if lim_charge is None else lim_charge
      _snake_case  :    List[Any]            = 1 if charge_factor is None else charge_factor
      _snake_case  :    list            = []
      _snake_case  :    dict            = {}
    def        lowerCamelCase__						(     self  ):
      return self._keys
    def        lowerCamelCase__						(     self  ):
      return sum(1 for slot in self.values if slot is not None  ) / (
          self.size_table * self.charge_factor
      )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return key % self.size_table
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      print(F'step {step_ord}'  )
      print(list(range(len(self.values  )  )  )  )
      print(self.values  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = 1
      _snake_case  :    Tuple            = values
      for value in values:
        self.insert_data(snake_case_  )
        self._step_by_step(snake_case_  )
        i += 1
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Tuple            = data
      _snake_case  :    str            = data
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None  ):
      _snake_case  :    Union[str, Any]            = self.hash_function(key + 1  )

      while self.values[new_key] is not None and self.values[new_key] != key:
        if self.values.count(snake_case_  ) > 0:
          _snake_case  :    Optional[int]            = self.hash_function(new_key + 1  )
        else:
          _snake_case  :    List[str]            = None
          break

      return new_key
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = [value for value in self.values if value is not None]
      _snake_case  :    Tuple            = next_prime(self.size_table    ,     factor=2  )
      self._keys.clear()
      _snake_case  :    int            = [None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
        self.insert_data(snake_case_  )


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[str]            = self.hash_function(snake_case_  )

      if self.values[key] is None:
        self._set_value(snake_case_    ,     snake_case_  )

      elif self.values[key] == data:
        pass

      else:
        _snake_case  :    int            = self._collision_resolution(snake_case_    ,     snake_case_  )
        if collision_resolution is not None:
          self._set_value(snake_case_    ,     snake_case_  )
        else:
          self.rehashing()
          self.insert_data(snake_case_  )








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

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

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

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

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

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






