

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

# Load iris file
A_			  =				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_			  =				train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

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

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

A_			  =				knn.predict(X_new)

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







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    {
        "sld_warmup_steps": 15,
        "sld_guidance_scale": 20,
        "sld_threshold": 0.0,
        "sld_momentum_scale": 0.0,
        "sld_mom_beta": 0.0,
    }
    a__       							=    {
        "sld_warmup_steps": 10,
        "sld_guidance_scale": 10_00,
        "sld_threshold": 0.01,
        "sld_momentum_scale": 0.3,
        "sld_mom_beta": 0.4,
    }
    a__       							=    {
        "sld_warmup_steps": 7,
        "sld_guidance_scale": 20_00,
        "sld_threshold": 0.0_25,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }
    a__       							=    {
        "sld_warmup_steps": 0,
        "sld_guidance_scale": 50_00,
        "sld_threshold": 1.0,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }






@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    42
    a__       							=    42
    a__       							=    42
    a__       							=    42


if is_transformers_available() and is_torch_available():
       from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
       from .safety_checker import SafeStableDiffusionSafetyChecker




