





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	:					Dict			  =					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	:					Union[str, Any]			  =					tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
    )

    __lowerCamelCase	:					int			  =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

    __lowerCamelCase	:					Optional[int]			  =					train_datagen.flow_from_directory(
        """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
    )

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







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
