





'''simple docstring'''



def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  str				):
			assert x is not None
			assert y is not None

			__lowercase										=							len(lowerCamelCase_				)
			__lowercase										=							len(lowerCamelCase_				)

			# declaring the array for storing the dp values
			__lowercase										=							[[0] * (n + 1) for _ in range(m + 1				)]  # noqa: E741

			for i in range(1		,       m + 1				):
						for j in range(1		,       n + 1				):
									__lowercase										=							1 if x[i - 1] == y[j - 1] else 0

									__lowercase										=							max(l[i - 1][j]		,       l[i][j - 1]		,       l[i - 1][j - 1] + match				)

			__lowercase										=							''''''
			__lowercase						,		__lowercase										=							m, n
			while i > 0 and j > 0:
						__lowercase										=							1 if x[i - 1] == y[j - 1] else 0

						if l[i][j] == l[i - 1][j - 1] + match:
									if match == 1:
												__lowercase										=							x[i - 1] + seq
									i -= 1
									j -= 1
						elif l[i][j] == l[i - 1][j]:
									i -= 1
						else:
									j -= 1

			return l[m][n], seq


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    '''AGGTAB'''
	_SCREAMING_SNAKE_CASE					=    '''GXTXAYB'''
	_SCREAMING_SNAKE_CASE					=    4
	_SCREAMING_SNAKE_CASE					=    '''GTAB'''

	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					=    longest_common_subsequence(a, b)
	print('''len =''', ln, ''', sub-sequence =''', subseq)
	import doctest

	doctest.testmod()












'''simple docstring'''



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)
	_SCREAMING_SNAKE_CASE					=    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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='''relu''')
	)

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

	# Adding a second convolutional layer
	classifier.add(layers.ConvaD(3_2, (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_2_8, 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')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	_SCREAMING_SNAKE_CASE					=    train_datagen.flow_from_directory(
	    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	_SCREAMING_SNAKE_CASE					=    test_datagen.flow_from_directory(
	    '''dataset/test_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	classifier.fit_generator(
	    training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
	)

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

	# Part 3 - Making new predictions

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.img_to_array(test_image)
	_SCREAMING_SNAKE_CASE					=    np.expand_dims(test_image, axis=0)
	_SCREAMING_SNAKE_CASE					=    classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		_SCREAMING_SNAKE_CASE					=    '''Normal'''
	if result[0][0] == 1:
		_SCREAMING_SNAKE_CASE					=    '''Abnormality detected'''





