






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = ["image_processor", "tokenizer"]
		snake_case			 = "AutoImageProcessor"
		snake_case			 = "AutoTokenizer"
		def __init__(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							List[str]=None    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=None    ,**_SCREAMING_SNAKE_CASE		:							List[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'    ,a__    ,)
										A									=   kwargs.pop('feature_extractor'		)

						A									=   image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'		)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'		)

						super().__init__(a__    ,a__		)
						A									=   self.image_processor
						A									=   False
		def __call__(	self		:							Tuple    ,*_SCREAMING_SNAKE_CASE		:							List[str]    ,**_SCREAMING_SNAKE_CASE		:							Optional[Any]		)     -> List[str]:


						'''simple docstring'''
						# For backward compatibility
						if self._in_target_context_manager:
										return self.current_processor(*a__    ,**a__		)

						A									=   kwargs.pop('images'    ,a__		)
						A									=   kwargs.pop('text'    ,a__		)
						if len(a__		) > 0:
										A									=   args[0]
										A									=   args[1:]

						if images is None and text is None:
										raise ValueError('You need to specify either an `images` or `text` input to process.'		)

						if images is not None:
										A									=   self.image_processor(a__    ,*a__    ,**a__		)
						if text is not None:
										A									=   self.tokenizer(a__    ,**a__		)

						if text is None:
										return inputs
						elif images is None:
										return encodings
						else:
										A									=   encodings['input_ids']
										return inputs
		def    A(	self		:							Any    ,*_SCREAMING_SNAKE_CASE		:							Tuple    ,**_SCREAMING_SNAKE_CASE		:							Dict		)     -> Union[str, Any]:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*a__    ,**a__		)
		def    A(	self		:							Tuple    ,*_SCREAMING_SNAKE_CASE		:							str    ,**_SCREAMING_SNAKE_CASE		:							Dict		)     -> List[str]:


						'''simple docstring'''
						return self.tokenizer.decode(*a__    ,**a__		)
		@contextmanager
		def    A(	self		:							Dict		)     -> Optional[int]:


						'''simple docstring'''
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your images inputs, or in a separate call.'		)
						A									=   True
						A									=   self.tokenizer
						yield
						A									=   self.image_processor
						A									=   False
		@property
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,a__    ,)
						return self.image_processor_class







		@property
		def    A(	self		:							str		)     -> List[str]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,a__    ,)
						return self.image_processor












"""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)






