






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)
   A_						:						Tuple						      =					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')

   A_						:						Optional[int]						      =					tf.keras.preprocessing.image.ImageDataGenerator(
       rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
   )

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

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

   A_						:						str						      =					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

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




'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




