from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
user_credential = user_secrets.get_gcloud_credential()
user_secrets.set_tensorflow_credential(user_credential)



from kaggle_datasets import KaggleDatasets

GCS_PATH = KaggleDatasets().get_gcs_path('gan-getting-started')

MONET_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/monet_jpg/*.jpg'))
# Selecting first 30
MONET_FILENAMES = MONET_FILENAMES[0:30]
PHOTO_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/photo_jpg/*.jpg'))



import io
def decode_image(image_path):
    image_bytes = tf.io.read_file(image_path)
    image = Image.open(io.BytesIO(image_bytes.numpy()))
    image_array = np.array(image)
    return image_array

if len(tf.config.experimental.list_physical_devices('GPU')) > 0:
    config=tf.compat.v1.ConfigProto(log_device_placement=True, device_count={'GPU': 1})
    tf.compat.v1.Session()
    print("Using GPU for TensorFlow")
    config.gpu_options.allow_growth = True
    session = tf.compat.v1.Session(config=config)
    K.set_session(session)
else:
    print("Using CPU for TensorFlow")

print("Using tf ", tf.__version__)
print("Executing eagerly: ",tf.executing_eagerly())

gc.enable()




def image_loader(file_paths):
    random.shuffle(file_paths)
    while True:
        for file in file_paths:
            yield decode_image(file)


def augmented_image_loader(file_paths):
    while True:
        random_image = random.choice(file_paths)
        random_image = decode_image(random_image)
        yield augment_element(random_image)




monet_dataset = tf.data.Dataset.from_generator(
        lambda: augmented_image_loader(MONET_FILENAMES),
        output_types=tf.float32,
        output_shapes=(tf.TensorShape([None, None, 3])),
    )
photos_dataset = tf.data.Dataset.from_generator(
        lambda: image_loader(PHOTO_FILENAMES),
        output_types=tf.float32,
        output_shapes=(tf.TensorShape([None, None, 3])),
    )