# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.


class TransformerMixin:
    """Mixin class for all transformers in scikit-learn."""

    def fit_transform(self, X, y=None, **fit_params):
        """Fit to data, then transform it.

        Fits transformer to ``X`` and ``y`` with optional parameters
        ``fit_params``, and returns a transformed version of ``X``.

        Parameters
        ----------
        X : array, shape (n_samples, n_features)
            Training set.
        y : array, shape (n_samples,)
            Target values or class labels.
        **fit_params : dict
            Additional fitting parameters passed to the ``fit`` method..

        Returns
        -------
        X_new : array, shape (n_samples, n_features_new)
            Transformed array.
        """
        # non-optimized default implementation; override when a better
        # method is possible for a given clustering algorithm
        if y is None:
            # fit method of arity 1 (unsupervised transformation)
            return self.fit(X, **fit_params).transform(X)
        else:
            # fit method of arity 2 (supervised transformation)
            return self.fit(X, y, **fit_params).transform(X)


class EstimatorMixin:
    """Mixin class for estimators."""

    def get_params(self, deep=True):
        """Get the estimator params.

        Parameters
        ----------
        deep : bool
            Deep.
        """
        return

    def set_params(self, **params):
        """Set parameters (mimics sklearn API).

        Parameters
        ----------
        **params : dict
            Extra parameters.

        Returns
        -------
        inst : object
            The instance.
        """
        if not params:
            return self
        valid_params = self.get_params(deep=True)
        for key, value in params.items():
            split = key.split("__", 1)
            if len(split) > 1:
                # nested objects case
                name, sub_name = split
                if name not in valid_params:
                    raise ValueError(
                        f"Invalid parameter {name} for estimator {self}. Check the list"
                        " of available parameters with `estimator.get_params().keys()`."
                    )
                sub_object = valid_params[name]
                sub_object.set_params(**{sub_name: value})
            else:
                # simple objects case
                if key not in valid_params:
                    raise ValueError(
                        f"Invalid parameter {key} for estimator "
                        f"{self.__class__.__name__}. Check the list of available "
                        "parameters with `estimator.get_params().keys()`."
                    )
                setattr(self, key, value)
        return self