



'''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)
							__snake_case       :	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(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')

							__snake_case       :	List[Any]            =tf.keras.preprocessing.image.ImageDataGenerator(
							    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
							)

							__snake_case       :	List[str]            =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

							__snake_case       :	List[Any]            =train_datagen.flow_from_directory(
							    'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
							)

							__snake_case       :	Tuple            =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

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






from bisect import bisect
from itertools import accumulate






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  sorted(zip(lowerCamelCase_ ,lowerCamelCase_) ,key=lambda lowerCamelCase_: x[0] / x[1] ,reverse=lowerCamelCase_)
				lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__  :       Tuple				=  list(accumulate(lowerCamelCase_))
				lowerCAmelCase__  :       str				=  bisect(lowerCamelCase_ ,lowerCamelCase_)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k])
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

