# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s):       Marc Glisse
#
# Copyright (C) 2020 Inria
#
# Modification(s):
#   - YYYY/MM Author: Description of the modification

import numpy


class KNN:
    """
    Class wrapping several implementations for computing the k nearest neighbors in a point set.
    """

    def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs):
        """
        Args:
            k (int): number of neighbors (possibly including the point itself).
            return_index (bool): if True, return the index of each neighbor.
            return_distance (bool): if True, return the distance to each neighbor.
            implementation (str): choice of the library that does the real work.

                * 'keops' for a brute-force, CUDA implementation through pykeops. Useful when the dimension becomes large (10+) but the number of points remains low (less than a million). Only "minkowski" and its aliases are supported.
                * 'ckdtree' for scipy's cKDTree. Only "minkowski" and its aliases are supported.
                * 'sklearn' for scikit-learn's NearestNeighbors. Note that this provides in particular an option algorithm="brute".
                * 'hnsw' for hnswlib.Index. It can be very fast but does not provide guarantees. Only supports "euclidean" for now.
                * None will try to select a sensible one (scipy if possible, scikit-learn otherwise).
            metric (str): see `sklearn.neighbors.NearestNeighbors`.
            eps (float): relative error when computing nearest neighbors with the cKDTree.
            p (float): norm L^p on input points (including numpy.inf) if metric is "minkowski". 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.
            sort_results (bool): if True, then distances and indices of each point are
                sorted on return, so that the first column contains the closest points.
                Otherwise, neighbors are returned in an arbitrary order. Defaults to True.
            kwargs: additional parameters are forwarded to the backends.
        """
        self.k = k
        self.return_index = return_index
        self.return_distance = return_distance
        self.metric = metric
        self.params = kwargs
        # canonicalize
        if metric == "euclidean":
            self.params["p"] = 2
            self.metric = "minkowski"
        elif metric == "manhattan":
            self.params["p"] = 1
            self.metric = "minkowski"
        elif metric == "chebyshev":
            self.params["p"] = numpy.inf
            self.metric = "minkowski"
        elif metric == "minkowski":
            self.params["p"] = kwargs.get("p", 2)
        if self.params.get("implementation") in {"keops", "ckdtree"}:
            assert self.metric == "minkowski"
        if self.params.get("implementation") == "hnsw":
            assert self.metric == "minkowski" and self.params["p"] == 2
        if not self.params.get("implementation"):
            if self.metric == "minkowski":
                self.params["implementation"] = "ckdtree"
            else:
                self.params["implementation"] = "sklearn"

    def fit_transform(self, X, y=None):
        return self.fit(X).transform(X)

    def fit(self, X, y=None):
        """
        Args:
            X (numpy.array): coordinates for reference points.
        """
        self.ref_points = X
        if self.params["implementation"] == "ckdtree":
            # sklearn could handle this, but it is much slower
            from scipy.spatial import cKDTree

            self.kdtree = cKDTree(X)

        if self.params["implementation"] == "sklearn" and self.metric != "precomputed":
            # FIXME: sklearn badly handles "precomputed"
            from sklearn.neighbors import NearestNeighbors

            nargs = {
                k: v for k, v in self.params.items() if k in {"p", "n_jobs", "metric_params", "algorithm", "leaf_size"}
            }
            self.nn = NearestNeighbors(self.k, metric=self.metric, **nargs)
            self.nn.fit(X)

        if self.params["implementation"] == "hnsw":
            import hnswlib

            self.graph = hnswlib.Index("l2", len(X[0]))  # Actually returns squared distances
            self.graph.init_index(
                len(X), **{k: v for k, v in self.params.items() if k in {"ef_construction", "M", "random_seed"}}
            )
            n = self.params.get("num_threads")
            if n is None:
                n = self.params.get("n_jobs", 1)
                self.params["num_threads"] = n
            self.graph.add_items(X, num_threads=n)

        return self

    def transform(self, X):
        """
        Args:
            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed".
        """
        metric = self.metric
        k = self.k

        if metric == "precomputed":
            # scikit-learn could handle that, but they insist on calling fit() with an unused square array, which is too unnatural.
            X = numpy.array(X)
            if self.return_index:
                neighbors = numpy.argpartition(X, k - 1)[:, 0:k]
                if self.params.get("sort_results", True):
                    X = numpy.take_along_axis(X, neighbors, axis=-1)
                    ngb_order = numpy.argsort(X, axis=-1)
                    neighbors = numpy.take_along_axis(neighbors, ngb_order, axis=-1)
                else:
                    ngb_order = neighbors
                if self.return_distance:
                    distances = numpy.take_along_axis(X, ngb_order, axis=-1)
                    return neighbors, distances
                else:
                    return neighbors
            if self.return_distance:
                distances = numpy.partition(X, k - 1)[:, 0:k]
                if self.params.get("sort_results"):
                    # partition is not guaranteed to sort the lower half, although it often does
                    distances.sort(axis=-1)
                return distances
            return None

        if self.params["implementation"] == "hnsw":
            ef = self.params.get("ef")
            if ef is not None:
                self.graph.set_ef(ef)
            neighbors, distances = self.graph.knn_query(X, k, num_threads=self.params["num_threads"])
            # The k nearest neighbors are always sorted. I couldn't find it in the doc, but the code calls searchKnn,
            # which returns a priority_queue, and then fills the return array backwards with top/pop on the queue.
            if self.return_index:
                if self.return_distance:
                    return neighbors, numpy.sqrt(distances)
                else:
                    return neighbors
            if self.return_distance:
                return numpy.sqrt(distances)
            return None

        if self.params["implementation"] == "keops":
            import torch
            from pykeops.torch import LazyTensor

            # 'float64' is slow except on super expensive GPUs. Allow it with some param?
            XX = torch.tensor(X, dtype=torch.float32)
            if X is self.ref_points:
                YY = XX
            else:
                YY = torch.tensor(self.ref_points, dtype=torch.float32)

            p = self.params["p"]
            if p == numpy.inf:
                # Requires pykeops 1.4 or later
                mat = (LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])).abs().max(-1)
            elif p == 2:  # Any even integer?
                mat = ((LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])) ** p).sum(-1)
            else:
                mat = ((LazyTensor(XX[:, None, :]) - LazyTensor(YY[None, :, :])).abs() ** p).sum(-1)

            if self.return_index:
                if self.return_distance:
                    distances, neighbors = mat.Kmin_argKmin(k, dim=1)
                    if p != numpy.inf:
                        distances = distances ** (1.0 / p)
                    return neighbors, distances
                else:
                    neighbors = mat.argKmin(k, dim=1)
                    return neighbors
            if self.return_distance:
                distances = mat.Kmin(k, dim=1)
                if p != numpy.inf:
                    distances = distances ** (1.0 / p)
                return distances
            return None
        # FIXME: convert everything back to numpy arrays or not?

        if self.params["implementation"] == "ckdtree":
            qargs = {key: val for key, val in self.params.items() if key in {"p", "eps", "n_jobs"}}
            distances, neighbors = self.kdtree.query(X, k=self.k, **qargs)
            if self.return_index:
                if self.return_distance:
                    return neighbors, distances
                else:
                    return neighbors
            if self.return_distance:
                return distances
            return None

        assert self.params["implementation"] == "sklearn"
        if self.return_distance:
            distances, neighbors = self.nn.kneighbors(X, return_distance=True)
            if self.return_index:
                return neighbors, distances
            else:
                return distances
        if self.return_index:
            neighbors = self.nn.kneighbors(X, return_distance=False)
            return neighbors
        return None