import ot
from sklearn.preprocessing import normalize
from lapjv import lapjv
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import euclidean_distances
from sklearn.externals.joblib import Parallel, delayed
from sklearn.utils import check_array
from sklearn.metrics.scorer import check_scoring
from pathos.multiprocessing import ProcessingPool as Pool
from sklearn.metrics import euclidean_distances
import numpy as np

class Wasserstein_Matcher(KNeighborsClassifier):
    """
    Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric.
    Source and target distributions are l_1 normalized before computing the Wasserstein distance.
    Wasserstein is parametrized by the distances between the individual points of the distributions.
    """
    def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1):
        """
        Initialization of the class.
        Arguments
        ---------
        W_embed: embeddings of the words, np.array
        verbose: True/False
        """
        self.sinkhorn = sinkhorn
        self.sinkhorn_reg = sinkhorn_reg
        self.W_embed = W_embed
        self.verbose = verbose
        super(Wasserstein_Matcher, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute')

    def _wmd(self, i, row, X_train):
        union_idx = np.union1d(X_train[i].indices, row.indices)
        W_minimal = self.W_embed[union_idx]
        W_dist = euclidean_distances(W_minimal)
        bow_i = X_train[i, union_idx].A.ravel()
        bow_j = row[:, union_idx].A.ravel()
        if self.sinkhorn:
            return  ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0]
        else:
            return  ot.emd2(bow_i, bow_j, W_dist)

    def _wmd_row(self, row):
        X_train = self._fit_X
        n_samples_train = X_train.shape[0]
        return [self._wmd(i, row, X_train) for i in range(n_samples_train)]

    def _pairwise_wmd(self, X_test, X_train=None):
        n_samples_test = X_test.shape[0]

        if X_train is None:
            X_train = self._fit_X
        pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances
        dist  = pool.map(self._wmd_row, X_test)
        return np.array(dist)

    def fit(self, X, y): # X_train_idf
        X = check_array(X, accept_sparse='csr', copy=True) # check if array is sparse
        X = normalize(X, norm='l1', copy=False)
        return super(Wasserstein_Matcher, self).fit(X, y) # X_train_idf, np_ones(document collection size)

    def predict(self, X):
        X = check_array(X, accept_sparse='csr', copy=True)
        X = normalize(X, norm='l1', copy=False)
        dist = self._pairwise_wmd(X)
        dist = dist * 1000 # for lapjv, small floating point numbers are evil
        return super(Wasserstein_Matcher, self).predict(dist)

    def kneighbors(self, X, n_neighbors=1): # X : X_train_idf
        X = check_array(X, accept_sparse='csr', copy=True)
        X = normalize(X, norm='l1', copy=False)
        dist = self._pairwise_wmd(X)
        dist = dist * 1000 # for lapjv, small floating point numbers are evil
        return lapjv(dist) # and here is the matching part


class Wasserstein_Retriever(KNeighborsClassifier):
    """
    Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric.
    Source and target distributions are l_1 normalized before computing the Wasserstein distance.
    Wasserstein is parametrized by the distances between the individual points of the distributions.
    """
    def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1):
        """
        Initialization of the class.
        Arguments
        ---------
        W_embed: embeddings of the words, np.array
        verbose: True/False
        """
        self.sinkhorn = sinkhorn
        self.sinkhorn_reg = sinkhorn_reg
        self.W_embed = W_embed
        self.verbose = verbose
        super(Wasserstein_Retriever, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute')

    def _wmd(self, i, row, X_train):
        union_idx = np.union1d(X_train[i].indices, row.indices)
        W_minimal = self.W_embed[union_idx]
        W_dist = euclidean_distances(W_minimal)
        bow_i = X_train[i, union_idx].A.ravel()
        bow_j = row[:, union_idx].A.ravel()
        if self.sinkhorn:
            return  ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0]
        else:
            return  ot.emd2(bow_i, bow_j, W_dist)

    def _wmd_row(self, row):
        X_train = self._fit_X
        n_samples_train = X_train.shape[0]
        return [self._wmd(i, row, X_train) for i in range(n_samples_train)]

    def _pairwise_wmd(self, X_test, X_train=None):
        n_samples_test = X_test.shape[0]

        if X_train is None:
            X_train = self._fit_X
        pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances
        dist  = pool.map(self._wmd_row, X_test)
        return np.array(dist)

    def fit(self, X, y):
        X = check_array(X, accept_sparse='csr', copy=True)
        X = normalize(X, norm='l1', copy=False)
        return super(Wasserstein_Retriever, self).fit(X, y)

    def predict(self, X):
        X = check_array(X, accept_sparse='csr', copy=True)
        X = normalize(X, norm='l1', copy=False)
        dist = self._pairwise_wmd(X)
        return super(Wasserstein_Retriever, self).predict(dist)

    def kneighbors(self, X, n_neighbors=1):
        X = check_array(X, accept_sparse='csr', copy=True)
        X = normalize(X, norm='l1', copy=False)
        dist = self._pairwise_wmd(X)
        return super(Wasserstein_Retriever, self).kneighbors(dist, n_neighbors)