






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

			lowercase    			=       tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

			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 __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



