import numpy
from ._tomato import *

# The fit/predict interface is not so well suited...
class Tomato:
    """
    Clustering

    This clustering algorithm needs a neighborhood graph on the points, and an estimation of the density at each point. A few possible graph constructions and density estimators are provided for convenience, but it is perfectly natural to provide your own. In particular, we do not provide anything specific to cluster pixels on images yet.

    Attributes
    ----------
    n_clusters_: int
        The number of clusters. Writing to it automatically adjusts labels_.
    merge_threshold_: float
        minimum prominence of a cluster so it doesn't get merged. Writing to it automatically adjusts labels_.
    n_leaves_: int
        number of leaves in the hierarchical tree
    leaf_labels_: ndarray of shape (n_samples)
        cluster labels for each point, at the very bottom of the hierarchy
    labels_: ndarray of shape (n_samples)
        cluster labels for each point
    diagram_: ndarray of shape (n_leaves_,2)
        persistence diagram (only the finite points)
    max_density_per_cc_: ndarray os shape (n_connected_components)
        maximum of the density function on each connected component
    children_: ndarray of shape (n_leaves_-1,2)
        The children of each non-leaf node. Values less than n_leaves_ correspond to leaves of the tree. A node i greater than or equal to n_leaves_ is a non-leaf node and has children children_[i - n_leaves_]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_leaves_ + i
    params_: dict
        Parameters like input_type, metric, etc
    """

    def __init__(
        self,
        input_type="points",
        metric=None,
        graph_type=None,
        density_type="manual",
        n_clusters=None,
        merge_threshold=None,
        eliminate_threshold=None,
        **params
    ):
        """
        Each parameter has a corresponding attribute, like self.merge_threshold_, that can be changed later.

        Args:
            input_type (str): 'points', 'distance_matrix' or 'neighbors'.
            metric (str or callable): FIXME ???
            graph_type (str): 'manual', 'knn' or 'radius'. Ignored if input_type is 'neighbors'.
            density_type (str): 'manual', 'DTM', 'logDTM' or 'kde'.
            kde_params (dict): if density_type is 'kde', additional parameters passed directly to sklearn.neighbors.KernelDensity.
            k (int): number of neighbors for a knn graph (including the vertex itself). Defaults to 10.
            k_DTM (int): number of neighbors for the DTM density estimation (including the vertex itself). Defaults to k.
            r (float): size of a neighborhood if graph_type is 'radius'
            eps (float): approximation factor when computing nearest neighbors without a GPU
            gpu (bool): enable use of CUDA (through pykeops) to compute k nearest neighbors. This is useful when the dimension becomes large (10+) but the number of points remains low (less than a million).
            n_clusters (int): number of clusters requested. Defaults to ???
            merge_threshold (float): minimum prominence of a cluster so it doesn't get merged.
            eliminate_threshold (float): minimum height of a cluster so it doesn't get eliminated
            symmetrize_graph (bool): whether we should add edges to make the neighborhood graph symmetric. This can be useful with k-NN for small k. Defaults to false.
            p (float): norm L^p on input points (numpy.inf is supported without gpu). Defaults to 2.
            p_DTM (float): order used to compute the distance to measure. Defaults to 2.
            n_jobs (int): Number of jobs to schedule for parallel processing of nearest neighbors on the CPU. If -1 is given all processors are used. Default: 1.
        """
        self.input_type_ = input_type
        self.metric_ = metric
        self.graph_type_ = graph_type
        self.density_type_ = density_type
        self.params_ = params
        self.__n_clusters = n_clusters
        self.__merge_threshold = merge_threshold
        self.eliminate_threshold_ = eliminate_threshold
        if n_clusters and merge_threshold:
            raise ValueError("Cannot specify both a merge threshold and a number of clusters")

    def fit(self, X, y=None, weights=None):
        """
        Args:
            X (?): points or distance_matrix or list of neighbors
            weights (ndarray of shape (n_samples)): if density_type == 'manual', a density estimate at each point
        """
        # TODO: First detect if this is a new call with the same data (only threshold changed?)
        # TODO: less code duplication (subroutines?), less spaghetti, but don't compute neighbors twice if not needed. Clear error message for missing or contradictory parameters.
        if weights:
            density_type = "manual"
        else:
            density_type = self.density_type_
            assert density_type != "manual"
            if density_type == "manual":
                raise ValueError("If density_type is 'manual', you must provide weights to fit()")

        if self.input_type_ == "distance_matrix" and self.graph_type_ == "radius":
            X = numpy.array(X)
            r = self.params_["r"]
            self.neighbors_ = [numpy.nonzero(l <= r) for l in X]

        if self.input_type_ == "distance_matrix" and self.graph_type_ == "knn":
            k = self.params_["k"]
            self.neighbors_ = numpy.argpartition(X, k - 1)[:, 0:k]

        if self.input_type_ == "neighbors":
            self.neighbors_ = X
            assert density_type == "manual"

        if self.input_type_ == "points" and self.graph_type_ == "knn" and self.density_type_ in {"DTM", "logDTM"}:
            self.points_ = X
            q = self.params_.get("p_DTM", 2)
            p = self.params_.get("p", 2)
            k = self.params_.get("k", 10)
            k_DTM = self.params_.get("k_DTM", k)
            kmax = max(k, k_DTM)
            if self.params_.get("gpu"):
                import torch
                from pykeops.torch import LazyTensor

                # 'float64' is slow except on super expensive GPUs. Allow it with some param?
                XX = torch.tensor(self.points_, dtype=torch.float32)
                if p == numpy.inf:
                    # Requires a version of pykeops strictly more recent than 1.3
                    dd, nn = (
                        (LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :]))
                        .abs()
                        .max(-1)
                        .Kmin_argKmin(kmax, dim=1)
                    )
                elif p == 2:  # Any even integer?
                    dd, nn = (
                        ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])) ** p)
                        .sum(-1)
                        .Kmin_argKmin(kmax, dim=1)
                    )
                else:
                    dd, nn = (
                        ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs() ** p)
                        .sum(-1)
                        .Kmin_argKmin(kmax, dim=1)
                    )
                if k < kmax:
                    nn = nn[:, 0:k]
                if k_DTM < kmax:
                    dd = dd[:, 0:k_DTM]
                assert q != numpy.inf  # for now
                if p != numpy.inf:
                    qp = float(q) / p
                else:
                    qp = q
                if qp != 1:
                    dd = dd ** qp
                weights = dd.sum(-1)
                # **1/q is a waste of time, whether we take another **-.25 or a log

                # Back to the CPU. Not sure this is necessary, or the right way to do it.
                weights = numpy.array(weights)
                self.neighbors_ = numpy.array(nn)
            else:  # CPU
                from scipy.spatial import cKDTree

                kdtree = cKDTree(self.points_)
                qargs = {k: v for k, v in self.params_.items() if k in {"eps", "n_jobs"}}
                dd, self.neighbors_ = kdtree.query(self.points_, k=kmax, p=p, **qargs)
                if k < kmax:
                    self.neighbors_ = self.neighbors_[:, 0:k]
                if k_DTM < kmax:
                    dd = dd[:, 0:k_DTM]
                # weights = numpy.linalg.norm(dd, axis=1, ord=q)
                weights = (dd ** q).sum(-1)

            # TODO: check the formula in Fred's paper
            if self.density_type_ == "DTM":
                weights = weights ** (-0.25 / q)
            else:
                weights = -numpy.log(weights)

        if self.input_type_ == "points" and self.graph_type_ == "knn" and self.density_type_ not in {"DTM", "logDTM"}:
            self.points_ = X
            p = self.params_.get("p", 2)
            k = self.params_.get("k", 10)
            if self.params_.get("gpu"):
                import torch
                from pykeops.torch import LazyTensor

                # 'float64' is slow except on super expensive GPUs. Allow it with some param?
                XX = torch.tensor(self.points_, dtype=torch.float32)
                if p == numpy.inf:
                    # Requires a version of pykeops strictly more recent than 1.3
                    nn = (LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs().max(-1).argKmin(k, dim=1)
                elif p == 2:  # Any even integer?
                    nn = ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])) ** p).sum(-1).argKmin(k, dim=1)
                else:
                    nn = (
                        ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs() ** p).sum(-1).argKmin(k, dim=1)
                    )
                # Back to the CPU. Not sure this is necessary, or the right way to do it.
                self.neighbors_ = numpy.array(nn)
            else:  # CPU
                from scipy.spatial import cKDTree

                kdtree = cKDTree(self.points_)
                # FIXME 'p'
                qargs = {k: v for k, v in self.params_.items() if k in {"eps", "n_jobs"}}
                _, self.neighbors_ = kdtree.query(self.points_, k=k, p=p, **qargs)

        if self.input_type_ == "points" and self.graph_type_ != "knn" and self.density_type_ in {"DTM", "logDTM"}:
            self.points_ = X
            q = self.params_.get("p_DTM", 2)
            p = self.params_.get("p", 2)
            k = self.params_.get("k", 10)
            k_DTM = self.params_.get("k_DTM", k)
            if self.params_.get("gpu"):
                import torch
                from pykeops.torch import LazyTensor

                # 'float64' is slow except on super expensive GPUs. Allow it with some param?
                XX = torch.tensor(self.points_, dtype=torch.float32)
                if p == numpy.inf:
                    assert False  # Not supported???
                    dd = (LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs().max(-1).Kmin(k_DTM, dim=1)
                elif p == 2:  # Any even integer?
                    dd = ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])) ** p).sum(-1).Kmin(k_DTM, dim=1)
                else:
                    dd = (
                        ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs() ** p)
                        .sum(-1)
                        .Kmin(k_DTM, dim=1)
                    )
                assert q != numpy.inf  # for now
                if p != numpy.inf:
                    qp = float(q) / p
                else:
                    qp = q
                if qp != 1:
                    dd = dd ** qp
                weights = dd.sum(-1)
                # **1/q is a waste of time, whether we take another **-.25 or a log

                # Back to the CPU. Not sure this is necessary, or the right way to do it.
                weights = numpy.array(weights)
            else:  # CPU
                from scipy.spatial import cKDTree

                kdtree = cKDTree(self.points_)
                qargs = {k: v for k, v in self.params_.items() if k in {"eps", "n_jobs"}}
                dd, _ = kdtree.query(self.points_, k=k_DTM, p=p, **qargs)
                # weights = numpy.linalg.norm(dd, axis=1, ord=q)
                weights = (dd ** q).sum(-1)

            # TODO: check the formula in Fred's paper
            if self.density_type_ == "DTM":
                weights = weights ** (-0.25 / q)
            else:
                weights = -numpy.log(weights)

        if self.input_type_ == "distance_matrix" and self.density_type_ in {"DTM", "logDTM"}:
            q = self.params_.get("p_DTM", 2)
            X = numpy.array(X)
            k = self.params_.get("k_DTM")
            if not k:
                k = self.params_["k"]
            q = self.params_.get("p_DTM", 2)
            weights = (numpy.partition(X) ** q, k - 1).sum(-1)
            # TODO: check the formula in Fred's paper
            if self.density_type_ == "DTM":
                weights = weights ** (-0.25 / q)
            else:
                weights = -numpy.log(weights)

        if self.density_type_ == "kde":
            # FIXME: replace most assert with raise ValueError("blabla")
            assert self.input_type_ == "points"
            kde_params = self.params_.get("kde_params", dict())
            from sklearn.neighbors import KernelDensity

            weights = KernelDensity(**kde_params).fit(X).score_samples(X)

        if self.params_.get("symmetrize_graph"):
            self.neighbors_ = [set(line) for line in self.neighbors_]
            for i, line in enumerate(self.neighbors_):
                line.discard(i)
                for j in line:
                    self.neighbors_[j].add(i)

        self.weights_ = weights  # TODO remove
        self.leaf_labels_, self.children_, self.diagram_, self.max_density_per_cc_ = tomato.doit(
            list(self.neighbors_), weights
        )
        self.n_leaves_ = len(self.max_density_per_cc_) + len(self.children_)
        assert self.leaf_labels_.max() + 1 == len(self.max_density_per_cc_) + len(self.children_)
        if self.__n_clusters:
            renaming = tomato.merge(self.children_, self.n_leaves_, self.__n_clusters)
            self.labels_ = renaming[self.leaf_labels_]
        else:
            self.labels_ = self.leaf_labels_
            self.__n_clusters = self.n_leaves_

    def fit_predict(self, X, y=None):
        """
        """
        self.fit(X)
        return labels_

    # TODO: add argument k or threshold? Have a version where you can click and it shows the line and the corresponding k?
    def plot_diagram(self):
        """
        """
        import matplotlib.pyplot as plt

        plt.plot(self.diagram_[:, 0], self.diagram_[:, 1], "ro")
        l = self.diagram_[:, 1].min()
        r = max(self.diagram_[:, 0].max(), self.max_density_per_cc_.max())
        plt.plot([l, r], [l, r])
        plt.plot(
            self.max_density_per_cc_, numpy.full(self.max_density_per_cc_.shape, 1.1 * l - 0.1 * r), "ro", color="green"
        )
        plt.show()

    #    def predict(self, X):
    #        # X had better be the same as in fit()
    #        return labels_

    # Use set_params instead?
    @property
    def n_clusters_(self):
        return self.__n_clusters

    @n_clusters_.setter
    def n_clusters_(self, n_clusters):
        if n_clusters == self.__n_clusters:
            return
        self.__n_clusters = n_clusters
        self.__merge_threshold = None
        if hasattr(self, "leaf_labels_"):
            renaming = tomato.merge(self.children_, self.n_leaves_, self.__n_clusters)
            self.labels_ = renaming[self.leaf_labels_]

    @property
    def merge_threshold_(self):
        return self.__merge_threshold

    @merge_threshold_.setter
    def merge_threshold_(self, merge_threshold):
        if merge_threshold == self.__merge_threshold:
            return
        if hasattr(self, "leaf_labels_"):
            self.n_clusters_ = numpy.count_nonzero(self.diagram_[1] - self.diagram_[0] > merge_threshold) + len(
                max_density_per_cc_
            )
        else:
            self.__n_clusters = None
        self.__merge_threshold = merge_threshold


if __name__ == "__main__":
    import sys

    a = [(1, 2), (1.1, 1.9), (0.9, 1.8), (10, 0), (10.1, 0.05), (10.2, -0.1), (5.4, 0)]
    a = numpy.random.rand(500, 2)
    t = Tomato(
        input_type="points",
        metric="Euclidean",
        graph_type="knn",
        density_type="DTM",
        n_clusters=2,
        k=4,
        n_jobs=-1,
        eps=0.05,
    )
    t.fit(a)
    # print("neighbors\n",t.neighbors_)
    # print()
    # print("weights\n",t.weights_)
    # print()
    # print("diagram\n",t.diagram_)
    # print()
    print("max\n", t.max_density_per_cc_, file=sys.stderr)
    # print()
    print("leaf labels\n", t.leaf_labels_)
    # print()
    print("labels\n", t.labels_)
    # print()
    print("children\n", t.children_)
    # print()
    t.n_clusters_ = 2
    print("labels\n", t.labels_)
    t.plot_diagram()