

"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase_       :       str							   =		logging.getLogger()


def   a__      (		)->	Any:
						UpperCAmelCase__						=		argparse.ArgumentParser()
						parser.add_argument("""-f"""			)
						UpperCAmelCase__						=		parser.parse_args()
						return args.f







class 			_lowercase		(   __lowerCamelCase  ):

				def        A (     self       :    List[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		logging.StreamHandler(sys.stdout     )
										logger.addHandler(_lowercase     )

										UpperCAmelCase__						=		self.get_auto_remove_tmp_dir()
										UpperCAmelCase__						=		f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(_lowercase       ,    """argv"""       ,    _lowercase     ):
																UpperCAmelCase__						=		run_glue_with_pabee.main()
																for value in result.values():
																						self.assertGreaterEqual(_lowercase       ,    0.75     )


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		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
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

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

