"""Deep Learning Auto-Encoder using Attention Bidirectional GRU Network."""

__maintainer__ = []
__all__ = ["AEAttentionBiGRUClusterer"]

import gc
import os
import time
from copy import deepcopy

from sklearn.utils import check_random_state

from aeon.clustering import DummyClusterer
from aeon.clustering.deep_learning.base import BaseDeepClusterer
from aeon.networks import AEAttentionBiGRUNetwork


class AEAttentionBiGRUClusterer(BaseDeepClusterer):
    """
    Auto-Encoder based Attention Bidirectional GRU Network.

    Adapted from the implementation used in [1]_.

    Parameters
    ----------
    estimator : aeon clusterer, default=None
        An aeon estimator to be built using the transformed data.
        Defaults to aeon TimeSeriesKMeans() with euclidean distance
        and mean averaging method and n_clusters set to 2.
    latent_space_dim : int, default=128
        Dimension of the latent space of the auto-encoder.
    n_layers_encoder : int, default = 2
        Number of Attention Bidirectional GRU Layers in the encoder.
    n_layers_encoder : int, default = 2
        Number of Attention Bidirectional GRU Layers in the decoder.
    activation_encoder : str or list of str, default = "relu"
        Activation used after the Attention Bidirectional GRU Layer of the encoder.
    activation_encoder : str or list of str, default = "relu"
        Activation used after the Attention Bidirectional GRU Layer of the decoder.
    n_epochs : int, default = 2000
        The number of epochs to train the model.
    batch_size : int, default = 16
        The number of samples per gradient update.
    validation_split: float, default = 0
        Fraction of the training data to be used as validation data.
    use_mini_batch_size : bool, default = True,
        Whether or not to use the mini batch size formula.
    random_state : int, RandomState instance or None, default=None
        If `int`, random_state is the seed used by the random number generator;
        If `RandomState` instance, random_state is the random number generator;
        If `None`, the random number generator is the `RandomState` instance used
        by `np.random`.
        Seeded random number generation can only be guaranteed on CPU processing,
        GPU processing will be non-deterministic.
    verbose : boolean, default = False
        Whether to output extra information.
    loss : str, default="mean_squared_error"
        Fit parameter for the keras model.
    metrics : str, default=["mean_squared_error"]
        Metrics to evaluate model predictions.
    optimizer : keras.optimizers object, default = Adam(lr=0.01)
        Specify the optimizer and the learning rate to be used.
    file_path : str, default = "./"
        File path to save best model.
    save_best_model : bool, default = False
        Whether or not to save the best model, if the
        modelcheckpoint callback is used by default,
        this condition, if True, will prevent the
        automatic deletion of the best saved model from
        file and the user can choose the file name.
    save_last_model : bool, default = False
        Whether or not to save the last model, last
        epoch trained, using the base class method
        save_last_model_to_file.
    save_init_model : bool, default = False
        Whether to save the initialization of the  model.
    best_file_name : str, default = "best_model"
        The name of the file of the best model, if
        save_best_model is set to False, this parameter
        is discarded.
    last_file_name : str, default = "last_model"
        The name of the file of the last model, if
        save_last_model is set to False, this parameter
        is discarded.
    init_file_name : str, default = "init_model"
        The name of the file of the init model, if
        save_init_model is set to False,
        this parameter is discarded.
    callbacks : keras.callbacks, default = None
        List of keras callbacks.

    Examples
    --------
    >>> from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer
    >>> from aeon.clustering import DummyClusterer
    >>> from aeon.datasets import load_unit_test
    >>> X_train, y_train = load_unit_test(split="train")
    >>> X_test, y_test = load_unit_test(split="test")
    >>> _clst = DummyClusterer(n_clusters=2)
    >>> abgruc=AEAttentionBiGRUClusterer(estimator=_clst, n_epochs=20,
    ... batch_size=4) # doctest: +SKIP
    >>> abgruc.fit(X_train)  # doctest: +SKIP
    AEAttentionBiGRUClusterer(...)

    Notes
    -----
    While originally based on [1]_, this implementation
    derives from the adaptation in [2]_, this
    implementation only uses the reconstruction training
    (pretext loss) phase and not the cluster optimization

    References
    ----------
    .. [1] Ienco & Interdonato, Deep Multivariate Time Series Embedding
    Clustering via Attentive-Gated Autoencoder, Advances in Knowledge
    Discovery and Data Mining, 2020.

    .. [2] Lafabregue et. al, End-to-end deep representation learning
    for time series clustering: a comparative study, Data Mining
    and Knowledge Discovery, 2022.

    """

    def __init__(
        self,
        estimator=None,
        latent_space_dim=128,
        n_layers_encoder=2,
        n_layers_decoder=2,
        activation_encoder="relu",
        activation_decoder="relu",
        n_epochs=2000,
        batch_size=32,
        validation_split=0,
        use_mini_batch_size=False,
        random_state=None,
        verbose=False,
        loss="mse",
        metrics=None,
        optimizer="Adam",
        file_path="./",
        save_best_model=False,
        save_last_model=False,
        save_init_model=False,
        best_file_name="best_model",
        last_file_name="last_model",
        init_file_name="init_model",
        callbacks=None,
    ):
        self.latent_space_dim = latent_space_dim
        self.n_layers_encoder = n_layers_encoder
        self.n_layers_decoder = n_layers_decoder
        self.activation_encoder = activation_encoder
        self.activation_decoder = activation_decoder
        self.optimizer = optimizer
        self.loss = loss
        self.metrics = metrics
        self.verbose = verbose
        self.use_mini_batch_size = use_mini_batch_size
        self.callbacks = callbacks
        self.file_path = file_path
        self.n_epochs = n_epochs
        self.validation_split = validation_split
        self.save_best_model = save_best_model
        self.save_last_model = save_last_model
        self.save_init_model = save_init_model
        self.best_file_name = best_file_name
        self.init_file_name = init_file_name
        self.random_state = random_state

        super().__init__(
            estimator=estimator,
            batch_size=batch_size,
            last_file_name=last_file_name,
        )

        self._network = AEAttentionBiGRUNetwork(
            latent_space_dim=self.latent_space_dim,
            n_layers_encoder=self.n_layers_encoder,
            n_layers_decoder=self.n_layers_decoder,
            activation_encoder=self.activation_encoder,
            activation_decoder=self.activation_decoder,
        )

    def build_model(self, input_shape, **kwargs):
        """Construct a compiled, un-trained, keras model that is ready for training.

        In aeon, time series are stored in numpy arrays of shape
        (n_channels,n_timepoints). Keras/tensorflow assume
        data is in shape (n_timepoints,n_channels). This method also assumes
        (n_timepoints,n_channels). Transpose should happen in fit.

        Parameters
        ----------
        input_shape : tuple
            The shape of the data fed into the input layer, should be
            (n_timepoints,n_channels).

        Returns
        -------
        output : a compiled Keras Model.
        """
        import numpy as np
        import tensorflow as tf

        rng = check_random_state(self.random_state)
        self.random_state_ = rng.randint(0, np.iinfo(np.int32).max)
        tf.keras.utils.set_random_seed(self.random_state_)
        encoder, decoder = self._network.build_network(input_shape, **kwargs)

        input_layer = tf.keras.layers.Input(input_shape, name="input layer")
        encoder_output = encoder(input_layer)
        decoder_output = decoder(encoder_output)
        output_layer = decoder_output

        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)

        self.optimizer_ = (
            tf.keras.optimizers.Adam() if self.optimizer is None else self.optimizer
        )

        if self.metrics is None:
            self._metrics = ["mean_squared_error"]
        elif isinstance(self.metrics, list):
            self._metrics = self.metrics
        elif isinstance(self.metrics, str):
            self._metrics = [self.metrics]
        else:
            raise ValueError("Metrics should be a list, string, or None.")

        model.compile(optimizer=self.optimizer_, loss=self.loss, metrics=self._metrics)

        return model

    def _fit(self, X):
        """Fit the classifier on the training set (X, y).

        Parameters
        ----------
        X : np.ndarray of shape = (n_cases (n), n_channels (d), n_timepoints (m))
            The training input samples.

        Returns
        -------
        self : object
        """
        import tensorflow as tf

        # Transpose to conform to Keras input style.
        X = X.transpose(0, 2, 1)

        self.input_shape = X.shape[1:]
        self.training_model_ = self.build_model(self.input_shape)

        if self.save_init_model:
            self.training_model_.save(self.file_path + self.init_file_name + ".keras")

        if self.verbose:
            self.training_model_.summary()

        if self.use_mini_batch_size:
            mini_batch_size = min(self.batch_size, X.shape[0] // 10)
        else:
            mini_batch_size = self.batch_size

        self.file_name_ = (
            self.best_file_name if self.save_best_model else str(time.time_ns())
        )

        if self.callbacks is None:
            self.callbacks_ = [
                tf.keras.callbacks.ReduceLROnPlateau(
                    monitor="loss", factor=0.5, patience=50, min_lr=0.0001
                ),
                tf.keras.callbacks.ModelCheckpoint(
                    filepath=self.file_path + self.file_name_ + ".keras",
                    monitor="val_loss" if self.validation_split > 0 else "loss",
                    save_best_only=True,
                ),
            ]
        else:
            self.callbacks_ = self._get_model_checkpoint_callback(
                callbacks=self.callbacks,
                file_path=self.file_path,
                file_name=self.file_name_,
            )

        self.history = self.training_model_.fit(
            X,
            X,
            batch_size=mini_batch_size,
            epochs=self.n_epochs,
            verbose=self.verbose,
            callbacks=self.callbacks_,
            validation_split=self.validation_split,
        )

        try:
            self.model_ = tf.keras.models.load_model(
                self.file_path + self.file_name_ + ".keras",
                compile=False,
            )
            if not self.save_best_model:
                os.remove(self.file_path + self.file_name_ + ".keras")
        except FileNotFoundError:
            self.model_ = deepcopy(self.training_model_)

        self._fit_clustering(X=X)

        if self.save_last_model:
            self.save_last_model_to_file(file_path=self.file_path)

        gc.collect()

        return self

    @classmethod
    def _get_test_params(cls, parameter_set="default"):
        """Return testing parameter settings for the estimator.

        Parameters
        ----------
        parameter_set : str, default="default"
            Name of the set of test parameters to return, for use in tests. If no
            special parameters are defined for a value, will return `"default"` set.
            For classifiers, a "default" set of parameters should be provided for
            general testing, and a "results_comparison" set for comparing against
            previously recorded results if the general set does not produce suitable
            probabilities to compare against.

        Returns
        -------
        params : dict or list of dict, default={}
            Parameters to create testing instances of the class.
            Each dict are parameters to construct an "interesting" test instance, i.e.,
            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
            `create_test_instance` uses the first (or only) dictionary in `params`.
        """
        param1 = {
            "estimator": DummyClusterer(n_clusters=2),
            "n_epochs": 1,
            "batch_size": 4,
            "n_layers_encoder": 1,
            "n_layers_decoder": 1,
        }

        return [param1]
