






import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase				:	Optional[int]    						=    logging.get_logger(__name__)


class 		lowerCAmelCase_							(     UpperCamelCase   ):





     def __init__(  self       ,   *_UpperCAmelCase       ,   **_UpperCAmelCase     )		->							None:
        warnings.warn(
            """The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
            """ Please use ConvNextImageProcessor instead."""       ,   _UpperCAmelCase       ,   )
        super().__init__(*_UpperCAmelCase       ,   **_UpperCAmelCase     )







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				:	List[Any]    						=    train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCAmelCase				:	Optional[int]    						=    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCAmelCase				:	List[Any]    						=    [[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCAmelCase				:	Tuple    						=    knn.predict(X_new)

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