





'''simple docstring'''


class       UpperCamelCase__  :







	def __init__(				self			:   List[Any]		):




				'''simple docstring'''
				a__     = ""
				a__     = ""
				a__     = []







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   int		):




				'''simple docstring'''







				if m == -1:
							return n + 1
				elif n == -1:
							return m + 1
				elif self.dp[m][n] > -1:
							return self.dp[m][n]
				else:
							if self.worda[m] == self.worda[n]:
										a__     = self.__min_dist_top_down_dp(m - 1 , n - 1		)
							else:
										a__     = self.__min_dist_top_down_dp(lowerCamelCase , n - 1		)
										a__     = self.__min_dist_top_down_dp(m - 1 , lowerCamelCase		)
										a__     = self.__min_dist_top_down_dp(m - 1 , n - 1		)
										a__     = 1 + min(lowerCamelCase , lowerCamelCase , lowerCamelCase		)

							return self.dp[m][n]







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = worda
				a__     = worda
				a__     = [[-1 for _ in range(len(lowerCamelCase		)		)] for _ in range(len(lowerCamelCase		)		)]

				return self.__min_dist_top_down_dp(len(lowerCamelCase		) - 1 , len(lowerCamelCase		) - 1		)







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = worda
				a__     = worda
				a__     = len(lowerCamelCase		)
				a__     = len(lowerCamelCase		)
				a__     = [[0 for _ in range(n + 1		)] for _ in range(m + 1		)]

				for i in range(m + 1		):
							for j in range(n + 1		):
										if i == 0:  # first string is empty
													a__     = j
										elif j == 0:  # second string is empty
													a__     = i
										elif worda[i - 1] == worda[j - 1]:  # last characters are equal
													a__     = self.dp[i - 1][j - 1]
										else:
													a__     = self.dp[i][j - 1]
													a__     = self.dp[i - 1][j]
													a__     = self.dp[i - 1][j - 1]
													a__     = 1 + min(lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				return self.dp[m][n]


if __name__ == "__main__":
		lowerCAmelCase_   :   str       =					EditDistance()

		print("****************** Testing Edit Distance DP Algorithm ******************")
		print()

		lowerCAmelCase_   :   str       =					input("Enter the first string: ").strip()
		lowerCAmelCase_   :   int       =					input("Enter the second string: ").strip()

		print()
		print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
		print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
		print()
		print("*************** End of Testing Edit Distance DP Algorithm ***************")










'''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)
		lowerCAmelCase_   :   str       =					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')

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

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

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

		lowerCAmelCase_   :   Any       =					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

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



