
"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_a   :      Union[str, Any]       =				load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
_a,      _a,      _a,      _a   :      Union[str, Any]       =				train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

_a   :      int       =				KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_a   :      Optional[int]       =				[[1, 2, 1, 4], [2, 3, 4, 5]]

_a   :      int       =				knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






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

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

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

				print(tr_dataset)
				print(val_dataset)

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

				snake_case_								=			{
				    """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,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			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,
				)

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





