



import requests

_lowercase: Optional[int]      			=		""  # <-- Put your OpenWeatherMap appid here!
_lowercase: Any      			=		"https://api.openweathermap.org/data/2.5/"






def     a(      A       :    str = "Chicago" ,     A       :    str = APPID       )		->       dict:
					"""simple docstring"""





					return requests.get(URL_BASE + "weather" ,     params=locals()       ).json()






def     a(      A       :    str = "Kolkata, India" ,     A       :    str = APPID       )		->       dict:
					"""simple docstring"""





					return requests.get(URL_BASE + "forecast" ,     params=locals()       ).json()






def     a(      A       :    float = 55.68 ,     A       :    float = 12.57 ,     A       :    str = APPID       )		->       dict:
					"""simple docstring"""





					return requests.get(URL_BASE + "onecall" ,     params=locals()       ).json()


if __name__ == "__main__":
				from pprint import pprint

				while True:
								_lowercase: Union[str, Any]      			=		input("Enter a location:").strip()
								if location:
												pprint(current_weather(location))
								else:
												break





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





					return (data["data"], data["target"])






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    A ,     A ,     A ,     display_labels=A ,     cmap="Blues" ,     normalize="true" ,     )
					plt.title("Normalized Confusion Matrix - IRIS Dataset"       )
					plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
