



'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


_UpperCamelCase							    =						logging.get_logger(__name__)






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def __init__(			self				:     Optional[int]      ,     *_a				:     int      ,     **_a				:     int       ) -> None:
    warnings.warn(
        'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
        ' use ViTImageProcessor instead.'      ,     _a      ,     )
    super().__init__(*_a      ,     **_a       )











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

# KNN

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

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

_UpperCamelCase							    =						knn.predict(X_new)

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






