
'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     _lowerCAmelCase					(					a_   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		['image_processor', 'tokenizer']
							lowerCAmelCase_      		=		'AutoImageProcessor'
							lowerCAmelCase_      		=		'AutoTokenizer'

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						**UpperCAmelCase						)  ->     Union[str, Any]:
														_snake_case     =						None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""						,						UpperCAmelCase						,						)
																					_snake_case     =						kwargs.pop("""feature_extractor"""						)

														_snake_case     =						image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("""You have to specify an image_processor."""						)
														if tokenizer is None:
																					raise ValueError("""You have to specify a tokenizer."""						)

														super().__init__(UpperCAmelCase						,						UpperCAmelCase						)
														_snake_case     =						self.image_processor

							def __call__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						**UpperCAmelCase						)  ->     Dict:

														if text is None and images is None:
																					raise ValueError("""You have to specify either text or images. Both cannot be none."""						)

														if text is not None:
																					_snake_case     =						self.tokenizer(UpperCAmelCase						,						return_tensors=UpperCAmelCase						,						**UpperCAmelCase						)

														if images is not None:
																					_snake_case     =						self.image_processor(UpperCAmelCase						,						return_tensors=UpperCAmelCase						,						**UpperCAmelCase						)

														if text is not None and images is not None:
																					_snake_case     =						image_features.pixel_values
																					return encoding
														elif text is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**UpperCAmelCase						)						,						tensor_type=UpperCAmelCase						)

							def  lowercase							(self						,						*UpperCAmelCase						,						**UpperCAmelCase						)  ->     int:
														return self.tokenizer.batch_decode(*UpperCAmelCase						,						**UpperCAmelCase						)

							def  lowercase							(self						,						*UpperCAmelCase						,						**UpperCAmelCase						)  ->     Any:
														return self.tokenizer.decode(*UpperCAmelCase						,						**UpperCAmelCase						)

							@property
							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						self.tokenizer.model_input_names
														_snake_case     =						self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)

							@property
							def  lowercase							(self						)  ->     Union[str, Any]:
														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,						UpperCAmelCase						,						)
														return self.image_processor_class





							@property
							def  lowercase							(self						)  ->     Dict:
														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,						UpperCAmelCase						,						)
														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)