

'''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			:				List[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(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')

					lowerCamelCase			:				Tuple 			=					tf.keras.preprocessing.image.ImageDataGenerator(
					    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
					)

					lowerCamelCase			:				Union[str, Any] 			=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

					lowerCamelCase			:				Tuple 			=					train_datagen.flow_from_directory(
					    "dataset/training_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
					)

					lowerCamelCase			:				Dict 			=					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

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



'''simple docstring'''
from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt


def 	_lowerCAmelCase (						_UpperCamelCase						:					int    )				->      bool:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =int(number**0.5    )
     return number == sq * sq


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					int    )				->      tuple[int, int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     _SCREAMING_SNAKE_CASE							   =x_den * y_den * z_den
     _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
     top //= hcf
     bottom //= hcf
     return top, bottom


def 	_lowerCAmelCase (						_UpperCamelCase						:					int = 35    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =set()
     _SCREAMING_SNAKE_CASE							   =42
     _SCREAMING_SNAKE_CASE							   =Fraction(0    )
     _SCREAMING_SNAKE_CASE							   =42

     for x_num in range(1							,			order + 1    ):
          for x_den in range(x_num + 1							,			order + 1    ):
               for y_num in range(1							,			order + 1    ):
                    for y_den in range(y_num + 1							,			order + 1    ):
                         # n=1
                         _SCREAMING_SNAKE_CASE							   =x_num * y_den + x_den * y_num
                         _SCREAMING_SNAKE_CASE							   =x_den * y_den
                         _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              _SCREAMING_SNAKE_CASE							   =add_three(
                                  _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                              unique_s.add(_UpperCamelCase    )

                         # n=2
                         _SCREAMING_SNAKE_CASE							   =(
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         _SCREAMING_SNAKE_CASE							   =x_den * x_den * y_den * y_den
                         if is_sq(_UpperCamelCase    ) and is_sq(_UpperCamelCase    ):
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   _SCREAMING_SNAKE_CASE							   =add_three(
                                       _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                                   unique_s.add(_UpperCamelCase    )

                    # n=-1
                         _SCREAMING_SNAKE_CASE							   =x_num * y_num
                         _SCREAMING_SNAKE_CASE							   =x_den * y_num + x_num * y_den
                         _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              _SCREAMING_SNAKE_CASE							   =add_three(
                                  _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                              unique_s.add(_UpperCamelCase    )

                         # n=2
                         _SCREAMING_SNAKE_CASE							   =x_num * x_num * y_num * y_num
                         _SCREAMING_SNAKE_CASE							   =(
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(_UpperCamelCase    ) and is_sq(_UpperCamelCase    ):
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =int(sqrt(_UpperCamelCase    )    )
                              _SCREAMING_SNAKE_CASE							   =gcd(_UpperCamelCase							,			_UpperCamelCase    )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   _SCREAMING_SNAKE_CASE							   =add_three(
                                       _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
                                   unique_s.add(_UpperCamelCase    )

     for num, den in unique_s:
          total += Fraction(_UpperCamelCase							,			_UpperCamelCase    )

     return total.denominator + total.numerator


if __name__ == "__main__":
     print(f'''{solution() = }''')
