"""Module containing an interface to trained Keras Tensorflow model."""

from dice_ml.model_interfaces.base_model import BaseModel
import tensorflow as tf
from tensorflow import keras


class KerasTensorFlowModel(BaseModel):

    def __init__(self, model=None, model_path='', backend='TF1', func=None, kw_args=None):
        """Init method

        :param model: trained Keras Sequential Model.
        :param model_path: path to trained model.
        :param backend: "TF1" for TensorFlow 1 and "TF2" for TensorFlow 2.
        :param func: function transformation required for ML model. If func is None, then func will be the identity function.
        :param kw_args: Dictionary of additional keyword arguments to pass to func. DiCE's data_interface is appended to the
                        dictionary of kw_args, by default.
        """

        super().__init__(model, model_path, backend)

    def load_model(self):
        if self.model_path != '':
            self.model = keras.models.load_model(self.model_path)

    def get_output(self, input_tensor, training=False, transform_data=False):
        """returns prediction probabilities

        :param input_tensor: test input.
        :param training: to determine training mode in TF2.
        :param transform_data: boolean to indicate if data transformation is required.
        """
        if transform_data:
            input_tensor = tf.constant(self.transformer.transform(input_tensor), dtype=tf.float32)

        if self.backend == 'TF2':
            return self.model(input_tensor, training=training)
        else:
            return self.model(input_tensor)

    def get_gradient(self, input):
        # Future Support
        raise NotImplementedError("Future Support")

    def get_num_output_nodes(self, inp_size):
        temp_input = tf.convert_to_tensor([tf.random.uniform([inp_size])], dtype=tf.float32)
        return self.get_output(temp_input)
