




"""simple docstring"""




from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy

class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									="MarkupLMFeatureExtractor"
		UpperCamelCase									=("MarkupLMTokenizer", "MarkupLMTokenizerFast")
		UpperCamelCase									=True
		def __call__(		self							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_ = True							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = 0							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_ = False							,		UpperCamelCase_ = False							,		UpperCamelCase_ = False							,		UpperCamelCase_ = False							,		UpperCamelCase_ = True							,		UpperCamelCase_ = None							,		**UpperCamelCase_							,		)		->   BatchEncoding:
					# first, create nodes and xpaths
					if self.parse_html:
								if html_strings is None:
											raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''   )

								if nodes is not None or xpaths is not None or node_labels is not None:
											raise ValueError(
											    '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''   )

								__lowercase	:       Optional[int]				=   self.feature_extractor(UpperCamelCase_   )
								__lowercase	:       Dict				=   features['''nodes''']
								__lowercase	:       Union[str, Any]				=   features['''xpaths''']
					else:
								if html_strings is not None:
											raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''   )
								if nodes is None or xpaths is None:
											raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''   )

        # # second, apply the tokenizer
					if questions is not None and self.parse_html:
								if isinstance(UpperCamelCase_							,		UpperCamelCase_   ):
											__lowercase	:       Union[str, Any]				=   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

					__lowercase	:       Union[str, Any]				=   self.tokenizer(
					    text=questions if questions is not None else nodes							,		text_pair=nodes if questions is not None else None							,		xpaths=UpperCamelCase_							,		node_labels=UpperCamelCase_							,		add_special_tokens=UpperCamelCase_							,		padding=UpperCamelCase_							,		truncation=UpperCamelCase_							,		max_length=UpperCamelCase_							,		stride=UpperCamelCase_							,		pad_to_multiple_of=UpperCamelCase_							,		return_token_type_ids=UpperCamelCase_							,		return_attention_mask=UpperCamelCase_							,		return_overflowing_tokens=UpperCamelCase_							,		return_special_tokens_mask=UpperCamelCase_							,		return_offsets_mapping=UpperCamelCase_							,		return_length=UpperCamelCase_							,		verbose=UpperCamelCase_							,		return_tensors=UpperCamelCase_							,		**UpperCamelCase_							,		)

					return encoded_inputs
		def 					_lowerCamelCase    (		self							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   List[str]:
					return self.tokenizer.batch_decode(*UpperCamelCase_							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   List[Any]:
					return self.tokenizer.decode(*UpperCamelCase_							,		**UpperCamelCase_   )
		@property
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       int				=   self.tokenizer.model_input_names
					return tokenizer_input_names









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

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

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

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    '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,
			}
			a_       ,	a_            =   build_tx(**tx_args)

			a_            =   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,
			)

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



