


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

       __UpperCamelCase					:	Union[str, Any]          =				load_dataset('json', data_files=args.tr_data_path)['train']
       __UpperCamelCase					:	Dict          =				load_dataset('json', data_files=args.val_data_path)['train']

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

       if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
              __UpperCamelCase					:	Optional[Any]          =				tr_dataset.shuffle().select(range(8_0000))
              __UpperCamelCase					:	Any          =				val_dataset.shuffle().select(range(8000))

       print(tr_dataset)
       print(val_dataset)

       __UpperCamelCase					:	int          =				FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       __UpperCamelCase					:	Optional[int]          =				BigBirdTokenizerFast.from_pretrained(args.model_id)
       __UpperCamelCase					:	str          =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

       __UpperCamelCase					:	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,
       }
       __UpperCamelCase       ,       __UpperCamelCase					:	str          =				build_tx(**tx_args)

       __UpperCamelCase					:	Optional[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,
       )

       __UpperCamelCase					:	Tuple          =				None
       __UpperCamelCase					:	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`')
       __UpperCamelCase					:	str          =				jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers.activations_tf import get_tf_activation





@require_tf
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]						)
       UpperCAmelCase_				:   List[Any]							= get_tf_activation('''gelu'''						)
       UpperCAmelCase_				:   Any							= get_tf_activation('''gelu_10'''						)

       UpperCAmelCase_				:   int							= gelu(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= geluaa(__snake_case						)

       UpperCAmelCase_				:   Union[str, Any]							= tf.where(y_gelu_aa < 10.0							, 1.0							, 0.0						)

       self.assertEqual(tf.math.reduce_max(__snake_case						).numpy().item()							, 10.0						)
       self.assertTrue(np.allclose(y_gelu * clipped_mask							, y_gelu_aa * clipped_mask						)						)





    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       get_tf_activation('''gelu'''						)
       get_tf_activation('''gelu_10'''						)
       get_tf_activation('''gelu_fast'''						)
       get_tf_activation('''gelu_new'''						)
       get_tf_activation('''glu'''						)
       get_tf_activation('''mish'''						)
       get_tf_activation('''quick_gelu'''						)
       get_tf_activation('''relu'''						)
       get_tf_activation('''sigmoid'''						)
       get_tf_activation('''silu'''						)
       get_tf_activation('''swish'''						)
       get_tf_activation('''tanh'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation('''bogus'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation(__snake_case						)