

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)
			UpperCAmelCase_							:					Optional[Any]		 =   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=128, 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')

			UpperCAmelCase_							:					Union[str, Any]		 =   tf.keras.preprocessing.image.ImageDataGenerator(
			    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
			)

			UpperCAmelCase_							:					List[Any]		 =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

			UpperCAmelCase_							:					Dict		 =   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

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












__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
