



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

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

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

				if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
								UpperCAmelCase			=    tr_dataset.shuffle().select(range(8_0000))
								UpperCAmelCase			=    val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				UpperCAmelCase			=    FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				UpperCAmelCase			=    BigBirdTokenizerFast.from_pretrained(args.model_id)
				UpperCAmelCase			=    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				UpperCAmelCase			=    {
				    '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			=    build_tx(**tx_args)

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




'''simple docstring'''

from __future__ import annotations

from pathlib import Path






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						tuple[int, int]							,		_SCREAMING_SNAKE_CASE     :						tuple[int, int]							)			->						int:







	"""simple docstring"""
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							)			->						bool:







	"""simple docstring"""
	lowerCAmelCase    =	(xa, ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	-vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	+vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return a > 0 and b > 0 and a + b < 1






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str = "p102_triangles.txt"							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	Path(_SCREAMING_SNAKE_CASE							).parent.joinpath(_SCREAMING_SNAKE_CASE							).read_text(encoding="""utf-8"""							)

	lowerCAmelCase    =	[]
	for line in data.strip().split("""\n"""							):
		triangles.append([int(_SCREAMING_SNAKE_CASE							) for number in line.split(""","""							)]							)

	lowerCAmelCase    =	0
	lowerCAmelCase    =	42

	for triangle in triangles:
		ret += contains_origin(*_SCREAMING_SNAKE_CASE							)

	return ret


if __name__ == "__main__":
				print(F'''{solution() = }''')