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)
      UpperCamelCase__    							= 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')

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

      UpperCamelCase__    							= tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

      UpperCamelCase__    							= train_datagen.flow_from_directory(
          'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
      )

      UpperCamelCase__    							= 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

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

from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



