


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							:   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')

				_lowercase							:   str						=						tf.keras.preprocessing.image.ImageDataGenerator(
				    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
				)

				_lowercase							:   Union[str, Any]						=						tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

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







def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Any=False    )			-> str:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) and isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					len(set_a.intersection(UpperCamelCase__    )    )

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
		else:
			A             =					len(set_a.union(UpperCamelCase__    )    )

		return intersection / union

	if isinstance(UpperCamelCase__				,       (list, tuple)    ) and isinstance(UpperCamelCase__				,       (list, tuple)    ):
		A             =					[element for element in set_a if element in set_b]

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
			return len(UpperCamelCase__    ) / union
		else:
			A             =					set_a + [element for element in set_b if element not in set_a]
			return len(UpperCamelCase__    ) / len(UpperCamelCase__    )

		return len(UpperCamelCase__    ) / len(UpperCamelCase__    )
	return None


if __name__ == "__main__":
				_lowercase							:   List[Any]						=						{"a", "b", "c", "d", "e"}
				_lowercase							:   List[Any]						=						{"c", "d", "e", "f", "h", "i"}
				print(jaccard_similarity(set_a, set_b))



