from tensorflow.keras.callbacks import ModelCheckpoint
import tempfile, pickle, shutil
import numpy as np


class LearningRateSchedule:
    """
    Class for custom learning rate schedules.
    """

    def __init__(self, epoch_to_start=500, last_epoch=999, lr_init=1e-3, lr_final=1e-6):
        self.epoch_to_start = epoch_to_start
        self.last_epoch = last_epoch
        self.lr_init = lr_init
        self.lr_final = lr_final

    def exp_decay(self, epoch, lr):
        """
        Exponential decay.
        """

        decay_duration = self.last_epoch - self.epoch_to_start
        if epoch < self.epoch_to_start:
            return lr
        else:
            # Slope of the decay
            k = -(1 / decay_duration) * np.log(self.lr_final / self.lr_init)

            lr = self.lr_init * np.exp(-k * (epoch - self.epoch_to_start))
            return lr


class ModelAndHistoryCheckpoint(ModelCheckpoint):
    """
    Callback to save all sub-models and training history.
    """

    def __init__(
        self,
        filepath,
        model_dict,
        monitor="val_loss",
        verbose=0,
        save_best_only=False,
        save_weights_only=False,
        mode="auto",
        period=1,
        history={},
    ):

        super().__init__(
            filepath,
            monitor="val_loss",
            verbose=0,
            save_best_only=False,
            save_weights_only=False,
            mode="auto",
            period=1,
        )

        self.period = period
        self.model_dict = model_dict

        self.history = history

    def save_models(self, filepath):
        """
        Save everything in a zip file.
        """

        with tempfile.TemporaryDirectory() as dirpath:

            # Save the Keras models
            try:  # Original DDC
                if self.model_dict["_DDC__mol_to_latent_model"] is not None:
                    self.model_dict["_DDC__mol_to_latent_model"].save(
                        dirpath + "/mol_to_latent_model.h5"
                    )

                self.model_dict["_DDC__latent_to_states_model"].save(
                    dirpath + "/latent_to_states_model.h5"
                )
                self.model_dict["_DDC__batch_model"].save(dirpath + "/batch_model.h5")

            except:  # Unbiased DDC
                self.model_dict["_DDC__model"].save(dirpath + "/model.h5")
                print(dirpath)

            # Exclude un-picklable and un-wanted attributes
            excl_attr = [
                "_DDC__mode",  # mode is excluded because it is defined inside __init__
                "_DDC__train_gen",
                "_DDC__valid_gen",
                "_DDC__mol_to_latent_model",
                "_DDC__latent_to_states_model",
                "_DDC__batch_model",
                "_DDC__sample_model",
                "_DDC__multi_sample_model",
                "_DDC__model",
            ]

            # Cannot deepcopy self.__dict__ because of Keras' thread lock so this is
            # bypassed by popping, saving and re-inserting the un-picklable attributes
            to_add = {}
            # Remove un-picklable attributes
            for attr in excl_attr:
                to_add[attr] = self.model_dict.pop(attr, None)

            # Pickle metadata
            pickle.dump(self.model_dict, open(dirpath + "/metadata.pickle", "wb"))

            # Zip directory with its contents
            shutil.make_archive(filepath, "zip", dirpath)

            # Finally, re-load the popped elements
            for attr in excl_attr:
                # if attr == "_DDC__mol_to_latent_model" and to_add[attr] is None:
                #    continue
                self.model_dict[attr] = to_add[attr]

            print("Model saved in %s." % filepath)

    def on_train_begin(self, logs=None):
        self.epoch = []

    def on_epoch_end(self, epoch, logs=None):
        # Save training history
        logs = logs or {}
        self.epoch.append(epoch)
        for k, v in logs.items():
            self.history.setdefault(k, []).append(v)

        # Save model(s)
        self.epochs_since_last_save += 1
        if self.epochs_since_last_save >= self.period:
            self.epochs_since_last_save = 0
            filepath = self.filepath.format(epoch=epoch + 1, **logs)
            if self.save_best_only:
                current = logs.get(self.monitor)
                if current is None:
                    warnings.warn(
                        "Can save best model only with %s available, "
                        "skipping." % (self.monitor),
                        RuntimeWarning,
                    )
                else:
                    if self.monitor_op(current, self.best):
                        if self.verbose > 0:
                            print(
                                "\nEpoch %05d: %s improved from %0.5f to %0.5f,"
                                " saving model to %s"
                                % (
                                    epoch + 1,
                                    self.monitor,
                                    self.best,
                                    current,
                                    filepath,
                                )
                            )
                        self.best = current
                        if self.save_weights_only:
                            self.model.save_weights(filepath, overwrite=True)
                        else:
                            self.model.save(filepath, overwrite=True)
                    else:
                        if self.verbose > 0:
                            print(
                                "\nEpoch %05d: %s did not improve from %0.5f"
                                % (epoch + 1, self.monitor, self.best)
                            )
            else:
                if self.verbose > 0:
                    print("\nEpoch %05d: saving model to %s" % (epoch + 1, filepath))
                if self.save_weights_only:
                    print("Saving weights of full model, ONLY.")
                    self.model.save_weights(filepath, overwrite=True)
                else:
                    self.save_models(filepath)