"""Random interval features.

A transformer for the extraction of features on randomly selected intervals.
"""

__maintainer__ = []
__all__ = ["RandomIntervals"]

import numpy as np
from joblib import Parallel, delayed
from sklearn.utils import check_random_state

from aeon.base._base import _clone_estimator
from aeon.transformations.base import BaseTransformer
from aeon.transformations.collection.base import BaseCollectionTransformer
from aeon.utils.numba.stats import (
    row_mean,
    row_median,
    row_numba_max,
    row_numba_min,
    row_quantile25,
    row_quantile75,
    row_std,
)
from aeon.utils.validation import check_n_jobs


class RandomIntervals(BaseCollectionTransformer):
    """Random interval feature transformer.

    Extracts intervals with random length, position and dimension from series in fit.
    Transforms each interval subseries using the given transformer(s)/features and
    concatenates them into a feature vector in transform.

    Identical intervals are pruned at the end of fit, as such the number of features may
    be less than expected from n_intervals.

    The output type is a 2D numpy array where rows are input cases and columns are
    the concatenated interval features.

    Parameters
    ----------
    n_intervals : int, default=100,
        The number of intervals of random length, position and dimension to be
        extracted.
    min_interval_length : int, default=3
        The minimum length of extracted intervals. Minimum value of 3.
    max_interval_length : int, default=3
        The maximum length of extracted intervals. Minimum value of min_interval_length.
    features : aeon transformer, a function taking a 2d numpy array parameter, or list
            of said transformers and functions, default=None
        Transformers and functions used to extract features from selected intervals.
        If None, defaults to [mean, median, min, max, std, 25% quantile, 75% quantile]
    dilation : int, list or None, default=None
        Add dilation to extracted intervals. No dilation is added if None or 1. If a
        list of ints, a random dilation value is selected from the list for each
        interval.

        The dilation value is selected after the interval star and end points. If the
        number of values in the dilated interval is less than the min_interval_length,
        the amount of dilation applied is reduced.
    random_state : None, int or instance of RandomState, default=None
        Seed or RandomState object used for random number generation.
        If random_state is None, use the RandomState singleton used by np.random.
        If random_state is an int, use a new RandomState instance seeded with seed.
    n_jobs : int, default=1
        The number of jobs to run in parallel for both `fit` and `transform` functions.
        `-1` means using all processors.
    parallel_backend : str, ParallelBackendBase instance or None, default=None
        Specify the parallelisation backend implementation in joblib, if None a 'prefer'
        value of "threads" is used by default.
        Valid options are "loky", "multiprocessing", "threading" or a custom backend.
        See the joblib Parallel documentation for more details.

    Attributes
    ----------
    n_cases_ : int
        The number of train cases.
    n_channels_ : int
        The number of dimensions per case.
    n_timepoints_ : int
        The length of each series.
    n_intervals_ : int
        The number of intervals extracted after pruning identical intervals.
    intervals_ : list of tuples
        Contains information for each feature extracted in fit. Each tuple contains the
        interval start, interval end, interval dimension, the feature(s) extracted and
        the dilation.
        Length will be n_intervals*len(features).

    See Also
    --------
    SupervisedIntervals

    Examples
    --------
    >>> from aeon.transformations.collection.interval_based import RandomIntervals
    >>> from aeon.testing.data_generation import make_example_3d_numpy
    >>> X = make_example_3d_numpy(n_cases=4, n_channels=1, n_timepoints=8,
    ...                           return_y=False, random_state=0)
    >>> tnf = RandomIntervals(n_intervals=2, random_state=0)
    >>> tnf.fit(X)
    RandomIntervals(...)
    >>> print(tnf.transform(X)[0])
    [1.04753424 0.14925939 0.8473096  1.20552675 1.08976637 0.96853798
     1.14764656 1.07628806 0.18170775 0.8473096  1.29178823 1.08976637
     0.96853798 1.1907773 ]
    """

    _tags = {
        "output_data_type": "Tabular",
        "capability:multivariate": True,
        "capability:multithreading": True,
        "fit_is_empty": False,
        "algorithm_type": "interval",
    }

    def __init__(
        self,
        n_intervals=100,
        min_interval_length=3,
        max_interval_length=np.inf,
        features=None,
        dilation=None,
        random_state=None,
        n_jobs=1,
        parallel_backend=None,
    ):
        self.n_intervals = n_intervals
        self.min_interval_length = min_interval_length
        self.max_interval_length = max_interval_length
        self.features = features
        self.dilation = dilation
        self.random_state = random_state
        self.n_jobs = n_jobs
        self.parallel_backend = parallel_backend

        super().__init__()

    transformer_feature_skip = ["transform_features_", "_transform_features"]

    def _fit_transform(self, X, y=None):
        X, rng = self._fit_setup(X)

        fit = Parallel(
            n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads"
        )(
            delayed(self._generate_interval)(
                X,
                y,
                rng.randint(np.iinfo(np.int32).max),
                True,
            )
            for _ in range(self.n_intervals)
        )

        (
            intervals,
            transformed_intervals,
        ) = zip(*fit)

        current = []
        removed_idx = []
        self.n_intervals_ = 0
        for i, interval in enumerate(intervals):
            new_interval = (
                interval[0][0],
                interval[0][1],
                interval[0][2],
                interval[0][4],
            )
            if new_interval not in current:
                current.append(new_interval)
                self.intervals_.extend(interval)
                self.n_intervals_ += 1
            else:
                removed_idx.append(i)

        Xt = transformed_intervals[0]
        for i in range(1, self.n_intervals):
            if i not in removed_idx:
                Xt = np.hstack((Xt, transformed_intervals[i]))

        return Xt

    def _fit(self, X, y=None):
        X, rng = self._fit_setup(X)

        fit = Parallel(
            n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads"
        )(
            delayed(self._generate_interval)(
                X,
                y,
                rng.randint(np.iinfo(np.int32).max),
                False,
            )
            for _ in range(self.n_intervals)
        )

        (
            intervals,
            _,
        ) = zip(*fit)

        current = []
        self.n_intervals_ = 0
        for i in intervals:
            interval = (i[0][0], i[0][1], i[0][2], i[0][4])
            if interval not in current:
                current.append(interval)
                self.intervals_.extend(i)
                self.n_intervals_ += 1

        return self

    def _transform(self, X, y=None):
        if self._transform_features is None:
            transform_features = [None] * len(self.intervals_)
        else:
            count = 0
            transform_features = []
            for _ in range(self.n_intervals_):
                for feature in self._features:
                    if isinstance(feature, BaseTransformer):
                        nf = feature.n_transformed_features
                        transform_features.append(
                            self._transform_features[count : count + nf]
                        )
                        count += nf
                    else:
                        transform_features.append(self._transform_features[count])
                        count += 1

        transform = Parallel(
            n_jobs=self._n_jobs, backend=self.parallel_backend, prefer="threads"
        )(
            delayed(self._transform_interval)(
                X,
                i,
                transform_features[i],
            )
            for i in range(len(self.intervals_))
        )

        Xt = transform[0]
        for i in range(1, len(self.intervals_)):
            Xt = np.hstack((Xt, transform[i]))

        return Xt

    def _fit_setup(self, X):
        self.intervals_ = []
        self._transform_features = None

        self.n_cases_, self.n_channels_, self.n_timepoints_ = X.shape
        self._n_jobs = check_n_jobs(self.n_jobs)

        self._min_interval_length = self.min_interval_length
        if self.min_interval_length < 3:
            self._min_interval_length = 3

        self._max_interval_length = self.max_interval_length
        if self.max_interval_length < self._min_interval_length:
            self._max_interval_length = self._min_interval_length
        elif self.max_interval_length > self.n_timepoints_:
            self._max_interval_length = self.n_timepoints_

        self._features = self.features
        if self.features is None:
            self._features = [
                row_mean,
                row_std,
                row_numba_min,
                row_numba_max,
                row_median,
                row_quantile25,
                row_quantile75,
            ]
        elif not isinstance(self.features, list):
            self._features = [self.features]

        li = []
        for feature in self._features:
            if isinstance(feature, BaseTransformer):
                li.append(
                    _clone_estimator(
                        feature,
                        self.random_state,
                    )
                )
            elif callable(feature):
                li.append(feature)
            else:
                raise ValueError(
                    "Input features must be a list of callables or aeon transformers."
                )
        self._features = li

        if self.dilation is None:
            self._dilation = [1]
        elif isinstance(self.dilation, list):
            self._dilation = self.dilation
        else:
            self._dilation = [self.dilation]

        rng = check_random_state(self.random_state)

        return X, rng

    def _generate_interval(self, X, y, seed, transform):
        rng = check_random_state(seed)

        dim = rng.randint(self.n_channels_)

        if rng.random() < 0.5:
            interval_start = (
                rng.randint(0, self.n_timepoints_ + 1 - self._min_interval_length)
                if self.n_timepoints_ > self._min_interval_length
                else 0
            )
            len_range = min(
                self.n_timepoints_ + 1 - interval_start,
                self._max_interval_length,
            )
            length = (
                rng.randint(0, len_range - self._min_interval_length)
                + self._min_interval_length
                if len_range > self._min_interval_length
                else self._min_interval_length
            )
            interval_end = interval_start + length
        else:
            interval_end = (
                rng.randint(0, self.n_timepoints_ + 1 - self._min_interval_length)
                + self._min_interval_length
                if self.n_timepoints_ > self._min_interval_length
                else self._min_interval_length
            )
            len_range = min(interval_end, self._max_interval_length)
            length = (
                rng.randint(0, len_range - self._min_interval_length)
                + self._min_interval_length
                if len_range > self._min_interval_length
                else self._min_interval_length
            )
            interval_start = interval_end - length

        interval_length = interval_end - interval_start
        dilation = rng.choice(self._dilation)
        while interval_length / dilation < self._min_interval_length:
            dilation -= 1

        Xt = np.empty((self.n_cases_, 0)) if transform else None
        intervals = []

        for feature in self._features:
            if isinstance(feature, BaseTransformer):
                if transform:
                    feature = _clone_estimator(
                        feature,
                        seed,
                    )

                    t = feature.fit_transform(
                        np.expand_dims(
                            X[:, dim, interval_start:interval_end:dilation], axis=1
                        ),
                        y,
                    )

                    if t.ndim == 3 and t.shape[1] == 1:
                        t = t.reshape((t.shape[0], t.shape[2]))

                    Xt = np.hstack((Xt, t))
                else:
                    feature.fit(
                        np.expand_dims(
                            X[:, dim, interval_start:interval_end:dilation], axis=1
                        ),
                        y,
                    )
            elif transform:
                t = [
                    [f]
                    for f in feature(X[:, dim, interval_start:interval_end:dilation])
                ]
                Xt = np.hstack((Xt, t))

            intervals.append((interval_start, interval_end, dim, feature, dilation))

        return intervals, Xt

    def _transform_interval(self, X, idx, keep_transform):
        interval_start, interval_end, dim, feature, dilation = self.intervals_[idx]

        if keep_transform is not None:
            if isinstance(feature, BaseTransformer):
                for n in self.transformer_feature_skip:
                    if hasattr(feature, n):
                        setattr(feature, n, keep_transform)
                        break
            elif not keep_transform:
                return [[0] for _ in range(X.shape[0])]

        if isinstance(feature, BaseTransformer):
            Xt = feature.transform(
                np.expand_dims(X[:, dim, interval_start:interval_end:dilation], axis=1)
            )

            if Xt.ndim == 3:
                Xt = Xt.reshape((Xt.shape[0], Xt.shape[2]))
        else:
            Xt = [[f] for f in feature(X[:, dim, interval_start:interval_end:dilation])]

        return Xt

    def set_features_to_transform(self, arr, raise_error=True):
        """Set transform_features to the given array.

        Each index in the list corresponds to the index of an interval, True intervals
        are included in the transform, False intervals skipped and are set to 0.

        If any transformers are in features, they must also have a "transform_features"
        or "_transform_features" attribute as well as a "n_transformed_features"
        attribute. The input array should contain an item for each of the transformers
        "n_transformed_features" output features.

        Parameters
        ----------
        arr : list of bools
             A list of intervals to skip.
        raise_error : bool, default=True
             Whether to raise and error or return None if input or transformers are
             invalid.

        Returns
        -------
        completed: bool
            Whether the operation was successful.
        """
        length = 0
        for feature in self._features:
            if isinstance(feature, BaseTransformer):
                if not any(
                    hasattr(feature, n) for n in self.transformer_feature_skip
                ) or not hasattr(feature, "n_transformed_features"):
                    if raise_error:
                        raise ValueError(
                            "Transformer must have one of {} as an attribute and a "
                            "n_transformed_features attribute.".format(
                                self.transformer_feature_skip
                            )
                        )
                    else:
                        return False

                length += feature.n_transformed_features
            else:
                length += 1

        if len(arr) != length * self.n_intervals_ or not all(
            isinstance(b, bool) for b in arr
        ):
            if raise_error:
                raise ValueError(
                    "Input must be a list bools, matching the length of the transform "
                    "output."
                )
            else:
                return False

        self._transform_features = arr

        return True

    @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.

        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.
        """
        if parameter_set == "results_comparison":
            return {"n_intervals": 3}
        else:
            return {"n_intervals": 2}
