



import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
				# Initialising the CNN
				# (Sequential- Building the model layer by layer)
				lowercase_													=      models.Sequential()

				# Step 1 - Convolution
				# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
				# (3,3) is the kernel size (filter matrix)
				classifier.add(
				    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='''relu''')
				)

				# Step 2 - Pooling
				classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

				# Adding a second convolutional layer
				classifier.add(layers.ConvaD(32, (3, 3), activation='''relu'''))
				classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

				# Step 3 - Flattening
				classifier.add(layers.Flatten())

				# Step 4 - Full connection
				classifier.add(layers.Dense(units=1_28, activation='''relu'''))
				classifier.add(layers.Dense(units=1, activation='''sigmoid'''))

				# Compiling the CNN
				classifier.compile(
				    optimizer='''adam''', loss='''binary_crossentropy''', metrics=['''accuracy''']
				)

				# Part 2 - Fitting the CNN to the images

				# Load Trained model weights

				# from keras.models import load_model
				# regressor=load_model('cnn.h5')

				lowercase_													=      tf.keras.preprocessing.image.ImageDataGenerator(
				    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
				)

				lowercase_													=      tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

				lowercase_													=      train_datagen.flow_from_directory(
				    '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
				)

				lowercase_													=      test_datagen.flow_from_directory(
				    '''dataset/test_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
				)

				classifier.fit_generator(
				    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
				)

				classifier.save('''cnn.h5''')

				# Part 3 - Making new predictions

				lowercase_													=      tf.keras.preprocessing.image.load_img(
				    '''dataset/single_prediction/image.png''', target_size=(64, 64)
				)
				lowercase_													=      tf.keras.preprocessing.image.img_to_array(test_image)
				lowercase_													=      np.expand_dims(test_image, axis=0)
				lowercase_													=      classifier.predict(test_image)
				# training_set.class_indices
				if result[0][0] == 0:
								lowercase_													=      '''Normal'''
				if result[0][0] == 1:
								lowercase_													=      '''Abnormality detected'''











"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__  :	Tuple		 =			prime_factors(lowerCAmelCase__			)
			if is_square_free(lowerCAmelCase__			):
						return -1 if len(lowerCAmelCase__			) % 2 else 1
			return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()