



"""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      =		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')

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

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

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

  lowerCamelCase      =		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

  lowerCamelCase      =		tf.keras.preprocessing.image.load_img(
      """dataset/single_prediction/image.png""", target_size=(64, 64)
  )
  lowerCamelCase      =		tf.keras.preprocessing.image.img_to_array(test_image)
  lowerCamelCase      =		np.expand_dims(test_image, axis=0)
  lowerCamelCase      =		classifier.predict(test_image)
  # training_set.class_indices
  if result[0][0] == 0:
    lowerCamelCase      =		"""Normal"""
  if result[0][0] == 1:
    lowerCamelCase      =		"""Abnormality detected"""









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


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

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




