"""DisjointCNN classifier."""

__maintainer__ = ["hadifawaz1999"]
__all__ = ["DisjointCNNClassifier"]

import gc
import os
import time
from copy import deepcopy

from sklearn.utils import check_random_state

from aeon.classification.deep_learning.base import BaseDeepClassifier
from aeon.networks import DisjointCNNNetwork


class DisjointCNNClassifier(BaseDeepClassifier):
    """Disjoint Convolutional Neural Netowkr classifier.

    Adapted from the implementation used in [1]_.

    Parameters
    ----------
    n_layers : int, default = 4
        Number of 1+1D Convolution layers.
    n_filters : int or list of int, default = 64
        Number of filters used in convolution layers. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    kernel_size : int or list of int, default = [8, 5, 5, 3]
        Size of convolution kernel. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    dilation_rate : int or list of int, default = 1
        The dilation rate for convolution. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    strides : int or list of int, default = 1
        The strides of the convolution filter. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    padding : str or list of str, default = "same"
        The type of padding used for convolution. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    activation : str or list of str, default = "elu"
        Activation used after the convolution. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    use_bias : bool or list of bool, default = True
        Whether or not to use bias in convolution. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    kernel_initializer: str or list of str, default = "he_uniform"
        The initialization method of convolution layers. If
        input is set to a list, the length should be the same
        as `n_layers`, if input is int the a list of the same
        element is created of length `n_layers`.
    pool_size: int, default = 5
        The size of the one max pool layer at the end of
        the model, default = 5.
    pool_strides: int, default = None
        The strides used for the one max pool layer at
        the end of the model, default = None.
    pool_padding: str, default = "valid"
        The padding method for the one max pool layer at
        the end of the model, default = "valid".
    hidden_fc_units: int, default = 128
        The number of fully connected units.
    activation_fc: str, default = "relu"
        The activation of the fully connected layer.
    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.
    use_mini_batch_size : bool, default = False
        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 = "categorical_crossentropy"
        The name of the keras training loss.
    metrics : str or list[str], default="accuracy"
        The evaluation metrics to use during training. If
        a single string metric is provided, it will be
        used as the only metric. If a list of metrics are
        provided, all will be used for evaluation.
    optimizer : keras.optimizer, default = tf.keras.optimizers.Adam()
        The keras optimizer used for training.
    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 callback or list of callbacks,
        default = None
        The default list of callbacks are set to
        ModelCheckpoint and ReduceLROnPlateau.

    Notes
    -----
    Adapted from the implementation from:
    https://github.com/Navidfoumani/Disjoint-CNN

    References
    ----------
    .. [1] Foumani, Seyed Navid Mohammadi, Chang Wei Tan, and Mahsa Salehi.
    "Disjoint-cnn for multivariate time series classification."
    2021 International Conference on Data Mining Workshops
    (ICDMW). IEEE, 2021.

    Examples
    --------
    >>> from aeon.classification.deep_learning import DisjointCNNClassifier
    >>> 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")
    >>> disjoint_cnn = DisjointCNNClassifier(n_epochs=20,
    ... batch_size=4)  # doctest: +SKIP
    >>> disjoint_cnn.fit(X_train, y_train)  # doctest: +SKIP
    DisjointCNNClassifier(...)
    """

    def __init__(
        self,
        n_layers=4,
        n_filters=64,
        kernel_size=None,
        dilation_rate=1,
        strides=1,
        padding="same",
        activation="elu",
        use_bias=True,
        kernel_initializer="he_uniform",
        pool_size=5,
        pool_strides=None,
        pool_padding="valid",
        hidden_fc_units=128,
        activation_fc="relu",
        n_epochs=2000,
        batch_size=16,
        use_mini_batch_size=False,
        random_state=None,
        verbose=False,
        loss="categorical_crossentropy",
        metrics="accuracy",
        optimizer=None,
        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.n_layers = n_layers
        self.n_filters = n_filters
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.strides = strides
        self.padding = padding
        self.activation = activation
        self.use_bias = use_bias
        self.kernel_initializer = kernel_initializer
        self.pool_size = pool_size
        self.pool_strides = pool_strides
        self.pool_padding = pool_padding
        self.hidden_fc_units = hidden_fc_units
        self.activation_fc = activation_fc

        self.callbacks = callbacks
        self.n_epochs = n_epochs
        self.use_mini_batch_size = use_mini_batch_size
        self.verbose = verbose
        self.loss = loss
        self.metrics = metrics
        self.optimizer = optimizer

        self.file_path = file_path
        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.history = None

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

        self._network = DisjointCNNNetwork(
            n_layers=self.n_layers,
            n_filters=self.n_filters,
            kernel_size=self.kernel_size,
            dilation_rate=self.dilation_rate,
            strides=self.strides,
            padding=self.padding,
            activation=self.activation,
            use_bias=self.use_bias,
            kernel_initializer=self.kernel_initializer,
            pool_size=self.pool_size,
            pool_strides=self.pool_strides,
            pool_padding=self.pool_padding,
            hidden_fc_units=self.hidden_fc_units,
            activation_fc=self.activation_fc,
        )

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

        In aeon, time series are stored in numpy arrays of shape (d,m), where d
        is the number of dimensions, m is the series length. Keras/tensorflow assume
        data is in shape (m,d). This method also assumes (m,d). Transpose should
        happen in fit.

        Parameters
        ----------
        input_shape : tuple
            The shape of the data fed into the input layer, should be (m, d).
        n_classes : int
            The number of classes, which becomes the size of the output layer.

        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_)
        input_layer, output_layer = self._network.build_network(input_shape, **kwargs)

        output_layer = tf.keras.layers.Dense(units=n_classes, activation="softmax")(
            output_layer
        )

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

        model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
        model.compile(
            loss=self.loss,
            optimizer=self.optimizer_,
            metrics=self._metrics,
        )

        return model

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

        Parameters
        ----------
        X : np.ndarray
            The training input samples of shape (n_cases, n_channels, n_timepoints)
        y : np.ndarray
            The training data class labels of shape (n_cases,).

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

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

        if isinstance(self.metrics, list):
            self._metrics = self.metrics
        elif isinstance(self.metrics, str):
            self._metrics = [self.metrics]

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

        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="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,
            y_onehot,
            batch_size=mini_batch_size,
            epochs=self.n_epochs,
            verbose=self.verbose,
            callbacks=self.callbacks_,
        )

        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_)

        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.
        """
        param1 = {
            "n_epochs": 3,
            "batch_size": 4,
            "use_bias": False,
            "n_layers": 2,
            "n_filters": 2,
            "kernel_size": [2, 2],
        }
        param2 = {
            "n_epochs": 3,
            "batch_size": 4,
            "use_bias": False,
            "n_layers": 2,
            "n_filters": 2,
            "kernel_size": [2, 2],
            "verbose": True,
            "metrics": ["accuracy"],
            "use_mini_batch_size": True,
        }

        return [param1, param2]
