import itertools

import math

import os

from functools import partial

import keras

from keras import backend as K

from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler, ReduceLROnPlateau, EarlyStopping, \

    LambdaCallback

from keras.models import load_model, Model

import fetal_net.model

from fetal_net.metrics import (dice_coefficient, dice_coefficient_loss, dice_coef, dice_coef_loss,

                               weighted_dice_coefficient_loss, weighted_dice_coefficient,

                               vod_coefficient, vod_coefficient_loss, focal_loss, dice_and_xent, double_dice_loss)

K.set_image_dim_ordering('th')

from multiprocessing import cpu_count

# learning rate schedule

def step_decay(epoch, initial_lrate, drop, epochs_drop):

    return initial_lrate * math.pow(drop, math.floor((1 + epoch) / float(epochs_drop)))

def get_callbacks(model_file, initial_learning_rate=0.0001, learning_rate_drop=0.5, learning_rate_epochs=None,

                  learning_rate_patience=50, logging_file="training.log", verbosity=1,

                  early_stopping_patience=None):

    callbacks = list()

    callbacks.append(

        ModelCheckpoint(model_file + '-epoch{epoch:02d}-loss{val_loss:.3f}-acc{val_binary_accuracy:.3f}.h5',

                        save_best_only=True, verbose=verbosity, monitor='val_loss'))

    callbacks.append(CSVLogger(logging_file, append=True))

    if learning_rate_epochs:

        callbacks.append(LearningRateScheduler(partial(step_decay, initial_lrate=initial_learning_rate,

                                                       drop=learning_rate_drop, epochs_drop=learning_rate_epochs)))

    else:

        callbacks.append(ReduceLROnPlateau(factor=learning_rate_drop, patience=learning_rate_patience,

                                           verbose=verbosity))

    if early_stopping_patience:

        callbacks.append(EarlyStopping(verbose=verbosity, patience=early_stopping_patience))

    return callbacks

def load_old_model(model_file, verbose=True, config=None) -> Model:

    print("Loading pre-trained model")

    custom_objects = {'dice_coefficient_loss': dice_coefficient_loss, 'dice_coefficient': dice_coefficient,

                      'dice_coef': dice_coef, 'dice_coef_loss': dice_coef_loss,

                      'weighted_dice_coefficient': weighted_dice_coefficient,

                      'weighted_dice_coefficient_loss': weighted_dice_coefficient_loss,

                      'vod_coefficient': vod_coefficient,

                      'vod_coefficient_loss': vod_coefficient_loss,

                      'focal_loss': focal_loss,

                      'focal_loss_fixed': focal_loss,

                      'dice_and_xent': dice_and_xent,

                      'double_dice_loss': double_dice_loss }

    try:

        from keras_contrib.layers import InstanceNormalization

        custom_objects["InstanceNormalization"] = InstanceNormalization

    except ImportError:

        pass

    try:

        if verbose:

            print('Loading model from {}...'.format(model_file))

        return load_model(model_file, custom_objects=custom_objects)

    except ValueError as error:

        print(error)

        if 'InstanceNormalization' in str(error):

            raise ValueError(str(error) + "\n\nPlease install keras-contrib to use InstanceNormalization:\n"

                                          "'pip install git+https://www.github.com/keras-team/keras-contrib.git'")

        else:

            if config is not None:

                print('Trying to build model manually...')

                loss_func = getattr(fetal_net.metrics, config['loss'])

                model_func = getattr(fetal_net.model, config['model_name'])

                model = model_func(input_shape=config["input_shape"],

                                   initial_learning_rate=config["initial_learning_rate"],

                                   **{'dropout_rate': config['dropout_rate'],

                                      'loss_function': loss_func,

                                      'mask_shape': None if config["weight_mask"] is None else config["input_shape"],

                                      # TODO: change to output shape

                                      'old_model_path': config['old_model']})

                model.load_weights(model_file)

                return model

            else:

                raise

def train_model(model, model_file, training_generator, validation_generator, steps_per_epoch, validation_steps,

                initial_learning_rate=0.001, learning_rate_drop=0.5, learning_rate_epochs=None, n_epochs=500,

                learning_rate_patience=20, early_stopping_patience=None, output_folder='.'):

    """

    Train a Keras model.

    :param early_stopping_patience: If set, training will end early if the validation loss does not improve after the

    specified number of epochs.

    :param learning_rate_patience: If learning_rate_epochs is not set, the learning rate will decrease if the validation

    loss does not improve after the specified number of epochs. (default is 20)

    :param model: Keras model that will be trained.

    :param model_file: Where to save the Keras model.

    :param training_generator: Generator that iterates through the training data.

    :param validation_generator: Generator that iterates through the validation data.

    :param steps_per_epoch: Number of batches that the training generator will provide during a given epoch.

    :param validation_steps: Number of batches that the validation generator will provide during a given epoch.

    :param initial_learning_rate: Learning rate at the beginning of training.

    :param learning_rate_drop: How much at which to the learning rate will decay.

    :param learning_rate_epochs: Number of epochs after which the learning rate will drop.

    :param n_epochs: Total number of epochs to train the model.

    :return: 

    """

    model.fit_generator(generator=training_generator,

                        steps_per_epoch=steps_per_epoch,

                        epochs=n_epochs,

                        validation_data=validation_generator,

                        validation_steps=validation_steps,

                        max_queue_size=15,

                        workers=1,

                        use_multiprocessing=False,

                        callbacks=get_callbacks(model_file,

                                                initial_learning_rate=initial_learning_rate,

                                                learning_rate_drop=learning_rate_drop,

                                                learning_rate_epochs=learning_rate_epochs,

                                                learning_rate_patience=learning_rate_patience,

                                                early_stopping_patience=early_stopping_patience,

                                                logging_file=os.path.join(output_folder, 'training')))