
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)

lowerCamelCase__        =						logging.getLogger()
def 					lowerCAmelCase__     (    )							->						Optional[int]:
			lowerCAmelCase__ : Optional[int]   =						argparse.ArgumentParser()
			parser.add_argument('-f'	)
			lowerCAmelCase__ : Optional[int]   =						parser.parse_args()
			return args.f







class 	A__				(	__magic_name__      ):






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						logging.StreamHandler(sys.stdout  )
							logger.addHandler(a  )

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

							with patch.object(a       ,	'argv'       ,	a  ):
										lowerCAmelCase__ : List[str]   =						run_glue_with_pabee.main()
										for value in result.values():
													self.assertGreaterEqual(a       ,	0.7_5  )

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

# Load iris file
lowerCamelCase__        =						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
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

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