






'''simple docstring'''

# Algorithm for the pigeonhole sorting





def 				snake_case       (  a_				:			str						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				min(a_						)  # min() finds the minimum value
      UpperCamelCase_					: int            =				max(a_						)  # max() finds the maximum value

      UpperCamelCase_					: Dict            =				max_val - min_val + 1  # size is difference of max and min values plus one

      # list of pigeonholes of size equal to the variable size
      UpperCamelCase_					: Optional[int]            =				[0] * size

      # Populate the pigeonholes.
      for x in a:
            assert isinstance(a_     , a_						), "integers only please"
            holes[x - min_val] += 1

      # Putting the elements back into the array in an order.
      UpperCamelCase_					: Optional[Any]            =				0
      for count in range(a_						):
            while holes[count] > 0:
                  holes[count] -= 1
                  UpperCamelCase_					: List[str]            =				count + min_val
                  i += 1



def 				snake_case       (  )							->					int:





      """simple docstring"""

      UpperCamelCase_					: int            =				[8, 3, 2, 7, 4, 6, 8]
      pigeonhole_sort(a_						)
      print("""Sorted order is:"""     , """ """.join(a_						)						)


if __name__ == "__main__":
      main()














'''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)
      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(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')

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

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

      UpperCamelCase      					=train_datagen.flow_from_directory(
          "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      UpperCamelCase      					=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

      UpperCamelCase      					=tf.keras.preprocessing.image.load_img(
          "dataset/single_prediction/image.png", target_size=(64, 64)
      )
      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"






