






import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax





@require_flax
class     __a						:







 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__    :     Any        =						self.model_class(**SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[Any]        =						model.init(inputs_dict["prng_key"]							,     inputs_dict["sample"]		)
      jax.lax.stop_gradient(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     List[str]        =						model.apply(SCREAMING_SNAKE_CASE							,     inputs_dict["sample"]		)

      if isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		):
           UpperCamelCase__    :     Any        =						output.sample

      self.assertIsNotNone(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Any        =						inputs_dict["sample"].shape
      self.assertEqual(output.shape							,     SCREAMING_SNAKE_CASE							,     "Input and output shapes do not match"		)







 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__    :     List[str]        =						16
      UpperCamelCase__    :     Optional[int]        =						(16, 32)

      UpperCamelCase__    :     Optional[Any]        =						self.model_class(**SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Tuple        =						model.init(inputs_dict["prng_key"]							,     inputs_dict["sample"]		)
      jax.lax.stop_gradient(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Any        =						model.apply(SCREAMING_SNAKE_CASE							,     inputs_dict["sample"]		)

      if isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		):
           UpperCamelCase__    :     List[Any]        =						output.sample

      self.assertIsNotNone(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     str        =						inputs_dict["sample"].shape
      self.assertEqual(output.shape							,     SCREAMING_SNAKE_CASE							,     "Input and output shapes do not match"		)







 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     Tuple        =						"kwargs" in inspect.signature(self.model_class.__init__		).parameters
      UpperCamelCase__    :     Optional[int]        =						len(self.model_class._deprecated_kwargs		) > 0

      if has_kwarg_in_model_class and not has_deprecated_kwarg:
           raise ValueError(
               F'{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'
               " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
               " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
               " [<deprecated_argument>]`"		)

      if not has_kwarg_in_model_class and has_deprecated_kwarg:
           raise ValueError(
               F'{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'
               " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
               F' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'
               " from `_deprecated_kwargs = [<deprecated_argument>]`"		)







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

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

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

 if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
  lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
  lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))

 print(tr_dataset)
 print(val_dataset)

 lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
 lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

 lowerCamelCase			:							List[str]       ={
     '''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			:							Tuple       =build_tx(**tx_args)

 lowerCamelCase			:							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,
 )

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