
"""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(8_00_00))
														lowerCAmelCase								=						val_dataset.shuffle().select(range(80_00))

							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=40_96)

							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)

"""simple docstring"""


from math import cos, sin






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__     :					List[Any]		=float(snake_case_      )
			return [[scaling_factor * int(x == y      ) for x in range(2      )] for y in range(2      )]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					Union[str, Any]		=cos(snake_case_      ), sin(snake_case_      )
			return [[c, -s], [s, c]]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					Optional[Any]		=cos(snake_case_      ), sin(snake_case_      )
			lowerCamelCase__     :					Dict		=c * s
			return [[c * c, cs], [cs, s * s]]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					str		=cos(snake_case_      ), sin(snake_case_      )
			lowerCamelCase__     :					Dict		=c * s
			return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f"""    {scaling(5) = }""")
print(f"""  {rotation(45) = }""")
print(f"""{projection(45) = }""")

print(f"""{reflection(45) = }""")