#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved

import os
import re
import pickle
import logging
from shutil import rmtree
from subprocess import check_output
from tempfile import mkdtemp, NamedTemporaryFile

import numpy as np
from spherecluster import SphericalKMeans
from pyclustering.cluster.xmeans import xmeans
from sklearn.cluster import KMeans as sklearnKMeans
from sklearn.cluster import AgglomerativeClustering, DBSCAN, MeanShift
from sklearn.metrics.pairwise import cosine_similarity, pairwise_distances

from vbdiar.clustering.pldakmeans import PLDAKMeans
from vbdiar.scoring.normalization import Normalization
from vbdiar.utils import mkdir_p
from vbdiar.utils.utils import Utils


CDIR = os.path.dirname(os.path.realpath(__file__))
MD_EVAL_SCRIPT_PATH = os.path.join(CDIR, 'md-eval.pl')
MAX_SRE_CLUSTERS = 5

logger = logging.getLogger(__name__)


def evaluate2rttms(reference_path, hypothesis_path, collar_size=0.25, evaluate_overlaps=False):
    """ Evaluate two rttms.

    Args:
        reference_path (string_types):
        hypothesis_path (string_types):
        collar_size (float):
        evaluate_overlaps (bool):

    Returns:
        float: diarization error rate
    """
    args = [MD_EVAL_SCRIPT_PATH, '{}'.format('' if evaluate_overlaps else '-1'),
            '-c', str(collar_size), '-r', reference_path, '-s', hypothesis_path]
    stdout = check_output(args)

    for line in stdout.decode('utf-8').split(os.linesep):
        if ' OVERALL SPEAKER DIARIZATION ERROR = ' in line:
            return float(line.replace(
                ' OVERALL SPEAKER DIARIZATION ERROR = ', '').replace(
                ' percent of scored speaker time  `(ALL)', ''))
    raise ValueError(f'Command `{" ".join(args)}` failed.')


def evaluate_all(reference_dir, hypothesis_dir, names, collar_size=0.25, evaluate_overlaps=False, rttm_ext='.rttm'):
    """ Evaluate all rttms in directories specified by list of names.

    Args:
        reference_dir (string_types): directory containing reference rttm files
        hypothesis_dir (string_types): directory containing hypothesis rttm files
        names (List[string_types]): list containing relative names
        collar_size (float):
        evaluate_overlaps (bool):
        rttm_ext (string_types): extension of rttm files

    Returns:
        float: diarization error rate
    """
    with NamedTemporaryFile(mode='w') as ref, NamedTemporaryFile(mode='w') as hyp:
        for name in names:
            with open(f'{os.path.join(reference_dir, name)}{rttm_ext}') as f:
                for line in f:
                    ref.write(line)
                ref.write(os.linesep)
            with open(f'{os.path.join(hypothesis_dir, name)}{rttm_ext}') as f:
                for line in f:
                    hyp.write(line)
                hyp.write(os.linesep)
            ref.flush()
            hyp.flush()

        return evaluate2rttms(ref.name, hyp.name, collar_size=collar_size, evaluate_overlaps=evaluate_overlaps)


class Diarization(object):
    """ Diarization class used as main diarization focused implementation.

    """

    def __init__(self, input_list, embeddings, embeddings_mean=None, lda=None, use_l2_norm=True, norm=None, plda=None):
        """ Initialize diarization class.

        Args:
            input_list (string_types): path to list of input files
            embeddings (string_types|List[EmbeddingSet]): path to directory containing embeddings or list
                of EmbeddingSet instances
            embeddings_mean (np.ndarray):
            lda (np.ndarray): linear discriminant analysis - dimensionality reduction
            use_l2_norm (bool): do l2 normalization
            norm (Normalization): instance of class Normalization
            plda (GPLDA): instance of class GPLDA
        """
        self.input_list = input_list
        if isinstance(embeddings, str):
            self.embeddings_dir = embeddings
            self.embeddings = list(self.load_embeddings())
        else:
            self.embeddings = embeddings
        self.lda = lda
        self.use_l2_norm = use_l2_norm
        self.norm = norm
        self.plda = plda

        for embedding_set in self.embeddings:
            for embedding in embedding_set:
                if embeddings_mean is not None:
                    embedding.data = embedding.data - embeddings_mean
                if lda is not None:
                    embedding.data = embedding.data.dot(lda)
                if use_l2_norm:
                    embedding.data = Utils.l2_norm(embedding.data[np.newaxis, :]).flatten()
        if self.norm:
            assert embeddings_mean is not None, 'Expecting usage of mean from normalization set.'
            self.norm.embeddings = self.norm.embeddings - embeddings_mean
            if lda is not None:
                self.norm.embeddings = self.norm.embeddings.dot(lda)
            if use_l2_norm:
                self.norm.embeddings = Utils.l2_norm(self.norm.embeddings)

    def get_embedding(self, name):
        """ Get embedding set by name.

        Args:
            name (string_types):

        Returns:
            EmbeddingSet:
        """
        for ii in self.embeddings:
            if name == ii.name:
                return ii
        raise ValueError(f'Name of the set not found - `{name}`.')

    def load_embeddings(self):
        """ Load embedding from pickled files.

        Returns:
            List[EmbeddingSet]:
        """
        logger.info(f'Loading pickled evaluation embedding from `{self.embeddings_dir}`.')
        with open(self.input_list, 'r') as f:
            for line in f:
                if len(line) > 0:
                    logger.info(f'Loading evaluation pickle file `{line.rstrip().split()[0]}`.')
                    line = line.rstrip()
                    try:
                        if len(line.split()) == 1:
                            with open(os.path.join(self.embeddings_dir, line + '.pkl'), 'rb') as i:
                                yield pickle.load(i)
                        elif len(line.split()) == 2:
                            file_name = line.split()[0]
                            num_spks = int(line.split()[1])
                            with open(os.path.join(self.embeddings_dir, file_name + '.pkl'), 'rb') as i:
                                ivec_set = pickle.load(i)
                                ivec_set.num_speakers = num_spks
                                yield ivec_set
                        else:
                            raise ValueError(f'Unexpected number of columns in input list `{self.input_list}`.')
                    except IOError:
                        logger.warning(f'No pickle file found for `{line.rstrip().split()[0]}`'
                                       f' in `{self.embeddings_dir}`.')

    def score_embeddings(self, min_length, max_num_speakers, mode):
        """ Score embeddings.

        Args:
            min_length (int): minimal length of segment used for clustering in miliseconds
            max_num_speakers (int): maximal number of speakers
            mode (str): running mode, see examples/diarization.py for details

        Returns:
            dict: dictionary with scores for each file
        """
        result_dict = {}
        logger.info('Scoring using `{}`.'.format('PLDA' if self.plda is not None else 'cosine distance'))
        for embedding_set in self.embeddings:
            name = os.path.normpath(embedding_set.name)
            embeddings_all = embedding_set.get_all_embeddings()
            embeddings_long = embedding_set.get_longer_embeddings(min_length)
            if len(embeddings_long) == 0:
                logger.warning(
                    f'No embeddings found longer than {min_length} for embedding set `{name}`.')
                continue
            size = len(embedding_set)
            if size > 0:
                logger.info(f'Clustering `{name}` using {len(embeddings_long)} long embeddings.')
                if mode == 'diarization':
                    if embedding_set.num_speakers is not None:
                        num_speakers = embedding_set.num_speakers
                    else:
                        xm = xmeans(embeddings_long, kmax=max_num_speakers)
                        xm.process()
                        num_speakers = len(xm.get_clusters())

                    centroids = self.run_clustering(num_speakers, embeddings_long)
                    if self.norm is None:
                        if self.plda is None:
                            result_dict[name] = cosine_similarity(embeddings_all, centroids).T
                        else:
                            result_dict[name] = self.plda.score(embeddings_all, centroids)
                    else:
                        result_dict[name] = self.norm.s_norm(embeddings_all, centroids)
                else:
                    clusters = []
                    for k in range(1, MAX_SRE_CLUSTERS + 1):
                        if size >= k:
                            centroids = self.run_clustering(k, embeddings_long)
                            clusters.extend(x for x in centroids)
                    result_dict[name] = np.array(clusters)
            else:
                logger.warning(f'No embeddings to score in `{embedding_set.name}`.')
        return result_dict

    def run_ahc(self, n_clusters, embeddings, scores_matrix):
        """ Run agglomerative hierarchical clustering.

        Returns:
            np.array: means of clusters
        """
        scores_matrix = -((scores_matrix - np.min(scores_matrix)) / (np.max(scores_matrix) - np.min(scores_matrix)))
        ahc = AgglomerativeClustering(affinity='precomputed', linkage='complete', n_clusters=n_clusters)
        labels = ahc.fit_predict(scores_matrix)
        return np.array([np.mean(embeddings[np.where(labels == i)], axis=0) for i in range(n_clusters)])

    def run_clustering(self, num_speakers, embeddings):
        if self.use_l2_norm:
            kmeans_clustering = SphericalKMeans(
                n_clusters=num_speakers, n_init=100, n_jobs=1).fit(embeddings)
        else:
            kmeans_clustering = sklearnKMeans(
                n_clusters=num_speakers, n_init=100, n_jobs=1).fit(embeddings)
        centroids = kmeans_clustering.cluster_centers_
        if self.plda:
            centroids = PLDAKMeans(centroids=kmeans_clustering.cluster_centers_, k=num_speakers,
                                   plda=self.plda, max_iter=100).fit(embeddings)
        return centroids

    def dump_rttm(self, scores, out_dir):
        """ Dump rttm files to output directory. This function requires initialized embeddings.

        Args:
            scores (Dict): dictionary containing scores
            out_dir (string_types): path to output directory
        """
        for embedding_set in self.embeddings:
            if len(embedding_set) > 0:
                name = embedding_set.name
                reg_name = re.sub('/.*', '', embedding_set.name)
                mkdir_p(os.path.join(out_dir, os.path.dirname(name)))
                with open(os.path.join(out_dir, name + '.rttm'), 'w') as f:
                    for i, embedding in enumerate(embedding_set.embeddings):
                        start, end = embedding.window_start, embedding.window_end
                        idx = np.argmax(scores[name][i])
                        f.write(f'SPEAKER {reg_name} 1 {float(start / 1000.0)} {float((end - start) / 1000.0)} '
                                f'<NA> <NA> {reg_name}_spkr_{idx} <NA>\n')
            else:
                logger.warning(f'No embedding to dump in {embedding_set.name}.')

    def evaluate(self, scores, in_rttm_dir, collar_size=0.25, evaluate_overlaps=False, rttm_ext='.rttm'):
        """ At first, separately evaluate each file based on ground truth segmentation. Then evaluate all files.

        Args:
            scores (dict): dictionary containing scores
            in_rttm_dir (string_types): input directory with rttm files
            collar_size (float): collar size for scoring
            evaluate_overlaps (bool): evaluate or ignore overlapping speech segments
            rttm_ext (string_types): extension for rttm files
        """
        tmp_dir = mkdtemp(prefix='rttm_')
        self.dump_rttm(scores, tmp_dir)
        for embedding_set in self.embeddings:
            name = embedding_set.name
            ground_truth_rttm = os.path.join(in_rttm_dir, '{}{}'.format(name, rttm_ext))
            if not os.path.exists(ground_truth_rttm):
                logger.warning(f'Ground truth rttm file not found in `{ground_truth_rttm}`.')
                continue
            # evaluate single rttm
            der = evaluate2rttms(ground_truth_rttm, os.path.join(tmp_dir, '{}{}'.format(name, rttm_ext)),
                                 collar_size=collar_size, evaluate_overlaps=evaluate_overlaps)
            logger.info(f'`{name}` DER={der}')

        # evaluate all rttms
        der = evaluate_all(reference_dir=in_rttm_dir, hypothesis_dir=tmp_dir, names=scores.keys(),
                           collar_size=collar_size, evaluate_overlaps=evaluate_overlaps, rttm_ext=rttm_ext)
        logger.info(f'`Total` DER={der}')
        rmtree(tmp_dir)

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved

import os
import re
import pickle
import logging
from shutil import rmtree
from subprocess import check_output
from tempfile import mkdtemp, NamedTemporaryFile

import numpy as np
from spherecluster import SphericalKMeans
from pyclustering.cluster.xmeans import xmeans
from sklearn.cluster import KMeans as sklearnKMeans
from sklearn.cluster import AgglomerativeClustering, DBSCAN, MeanShift
from sklearn.metrics.pairwise import cosine_similarity, pairwise_distances

from vbdiar.clustering.pldakmeans import PLDAKMeans
from vbdiar.scoring.normalization import Normalization
from vbdiar.utils import mkdir_p
from vbdiar.utils.utils import Utils


CDIR = os.path.dirname(os.path.realpath(__file__))
MD_EVAL_SCRIPT_PATH = os.path.join(CDIR, 'md-eval.pl')
MAX_SRE_CLUSTERS = 5

logger = logging.getLogger(__name__)


def evaluate2rttms(reference_path, hypothesis_path, collar_size=0.25, evaluate_overlaps=False):
    """ Evaluate two rttms.

    Args:
        reference_path (string_types):
        hypothesis_path (string_types):
        collar_size (float):
        evaluate_overlaps (bool):

    Returns:
        float: diarization error rate
    """
    args = [MD_EVAL_SCRIPT_PATH, '{}'.format('' if evaluate_overlaps else '-1'),
            '-c', str(collar_size), '-r', reference_path, '-s', hypothesis_path]
    stdout = check_output(args)

    for line in stdout.decode('utf-8').split(os.linesep):
        if ' OVERALL SPEAKER DIARIZATION ERROR = ' in line:
            return float(line.replace(
                ' OVERALL SPEAKER DIARIZATION ERROR = ', '').replace(
                ' percent of scored speaker time  `(ALL)', ''))
    raise ValueError(f'Command `{" ".join(args)}` failed.')


def evaluate_all(reference_dir, hypothesis_dir, names, collar_size=0.25, evaluate_overlaps=False, rttm_ext='.rttm'):
    """ Evaluate all rttms in directories specified by list of names.

    Args:
        reference_dir (string_types): directory containing reference rttm files
        hypothesis_dir (string_types): directory containing hypothesis rttm files
        names (List[string_types]): list containing relative names
        collar_size (float):
        evaluate_overlaps (bool):
        rttm_ext (string_types): extension of rttm files

    Returns:
        float: diarization error rate
    """
    with NamedTemporaryFile(mode='w') as ref, NamedTemporaryFile(mode='w') as hyp:
        for name in names:
            with open(f'{os.path.join(reference_dir, name)}{rttm_ext}') as f:
                for line in f:
                    ref.write(line)
                ref.write(os.linesep)
            with open(f'{os.path.join(hypothesis_dir, name)}{rttm_ext}') as f:
                for line in f:
                    hyp.write(line)
                hyp.write(os.linesep)
            ref.flush()
            hyp.flush()

        return evaluate2rttms(ref.name, hyp.name, collar_size=collar_size, evaluate_overlaps=evaluate_overlaps)


class Diarization(object):
    """ Diarization class used as main diarization focused implementation.

    """

    def __init__(self, input_list, embeddings, embeddings_mean=None, lda=None, use_l2_norm=True, norm=None, plda=None):
        """ Initialize diarization class.

        Args:
            input_list (string_types): path to list of input files
            embeddings (string_types|List[EmbeddingSet]): path to directory containing embeddings or list
                of EmbeddingSet instances
            embeddings_mean (np.ndarray):
            lda (np.ndarray): linear discriminant analysis - dimensionality reduction
            use_l2_norm (bool): do l2 normalization
            norm (Normalization): instance of class Normalization
            plda (GPLDA): instance of class GPLDA
        """
        self.input_list = input_list
        if isinstance(embeddings, str):
            self.embeddings_dir = embeddings
            self.embeddings = list(self.load_embeddings())
        else:
            self.embeddings = embeddings
        self.lda = lda
        self.use_l2_norm = use_l2_norm
        self.norm = norm
        self.plda = plda

        for embedding_set in self.embeddings:
            for embedding in embedding_set:
                if embeddings_mean is not None:
                    embedding.data = embedding.data - embeddings_mean
                if lda is not None:
                    embedding.data = embedding.data.dot(lda)
                if use_l2_norm:
                    embedding.data = Utils.l2_norm(embedding.data[np.newaxis, :]).flatten()
        if self.norm:
            assert embeddings_mean is not None, 'Expecting usage of mean from normalization set.'
            self.norm.embeddings = self.norm.embeddings - embeddings_mean
            if lda is not None:
                self.norm.embeddings = self.norm.embeddings.dot(lda)
            if use_l2_norm:
                self.norm.embeddings = Utils.l2_norm(self.norm.embeddings)

    def get_embedding(self, name):
        """ Get embedding set by name.

        Args:
            name (string_types):

        Returns:
            EmbeddingSet:
        """
        for ii in self.embeddings:
            if name == ii.name:
                return ii
        raise ValueError(f'Name of the set not found - `{name}`.')

    def load_embeddings(self):
        """ Load embedding from pickled files.

        Returns:
            List[EmbeddingSet]:
        """
        logger.info(f'Loading pickled evaluation embedding from `{self.embeddings_dir}`.')
        with open(self.input_list, 'r') as f:
            for line in f:
                if len(line) > 0:
                    logger.info(f'Loading evaluation pickle file `{line.rstrip().split()[0]}`.')
                    line = line.rstrip()
                    try:
                        if len(line.split()) == 1:
                            with open(os.path.join(self.embeddings_dir, line + '.pkl'), 'rb') as i:
                                yield pickle.load(i)
                        elif len(line.split()) == 2:
                            file_name = line.split()[0]
                            num_spks = int(line.split()[1])
                            with open(os.path.join(self.embeddings_dir, file_name + '.pkl'), 'rb') as i:
                                ivec_set = pickle.load(i)
                                ivec_set.num_speakers = num_spks
                                yield ivec_set
                        else:
                            raise ValueError(f'Unexpected number of columns in input list `{self.input_list}`.')
                    except IOError:
                        logger.warning(f'No pickle file found for `{line.rstrip().split()[0]}`'
                                       f' in `{self.embeddings_dir}`.')

    def score_embeddings(self, min_length, max_num_speakers, mode):
        """ Score embeddings.

        Args:
            min_length (int): minimal length of segment used for clustering in miliseconds
            max_num_speakers (int): maximal number of speakers
            mode (str): running mode, see examples/diarization.py for details

        Returns:
            dict: dictionary with scores for each file
        """
        result_dict = {}
        logger.info('Scoring using `{}`.'.format('PLDA' if self.plda is not None else 'cosine distance'))
        for embedding_set in self.embeddings:
            name = os.path.normpath(embedding_set.name)
            embeddings_all = embedding_set.get_all_embeddings()
            embeddings_long = embedding_set.get_longer_embeddings(min_length)
            if len(embeddings_long) == 0:
                logger.warning(
                    f'No embeddings found longer than {min_length} for embedding set `{name}`.')
                continue
            size = len(embedding_set)
            if size > 0:
                logger.info(f'Clustering `{name}` using {len(embeddings_long)} long embeddings.')
                if mode == 'diarization':
                    if embedding_set.num_speakers is not None:
                        num_speakers = embedding_set.num_speakers
                    else:
                        xm = xmeans(embeddings_long, kmax=max_num_speakers)
                        xm.process()
                        num_speakers = len(xm.get_clusters())

                    centroids = self.run_clustering(num_speakers, embeddings_long)
                    if self.norm is None:
                        if self.plda is None:
                            result_dict[name] = cosine_similarity(embeddings_all, centroids).T
                        else:
                            result_dict[name] = self.plda.score(embeddings_all, centroids)
                    else:
                        result_dict[name] = self.norm.s_norm(embeddings_all, centroids)
                else:
                    clusters = []
                    for k in range(1, MAX_SRE_CLUSTERS + 1):
                        if size >= k:
                            centroids = self.run_clustering(k, embeddings_long)
                            clusters.extend(x for x in centroids)
                    result_dict[name] = np.array(clusters)
            else:
                logger.warning(f'No embeddings to score in `{embedding_set.name}`.')
        return result_dict

    def run_ahc(self, n_clusters, embeddings, scores_matrix):
        """ Run agglomerative hierarchical clustering.

        Returns:
            np.array: means of clusters
        """
        scores_matrix = -((scores_matrix - np.min(scores_matrix)) / (np.max(scores_matrix) - np.min(scores_matrix)))
        ahc = AgglomerativeClustering(affinity='precomputed', linkage='complete', n_clusters=n_clusters)
        labels = ahc.fit_predict(scores_matrix)
        return np.array([np.mean(embeddings[np.where(labels == i)], axis=0) for i in range(n_clusters)])

    def run_clustering(self, num_speakers, embeddings):
        if self.use_l2_norm:
            kmeans_clustering = SphericalKMeans(
                n_clusters=num_speakers, n_init=100, n_jobs=1).fit(embeddings)
        else:
            kmeans_clustering = sklearnKMeans(
                n_clusters=num_speakers, n_init=100, n_jobs=1).fit(embeddings)
        centroids = kmeans_clustering.cluster_centers_
        if self.plda:
            centroids = PLDAKMeans(centroids=kmeans_clustering.cluster_centers_, k=num_speakers,
                                   plda=self.plda, max_iter=100).fit(embeddings)
        return centroids

    def dump_rttm(self, scores, out_dir):
        """ Dump rttm files to output directory. This function requires initialized embeddings.

        Args:
            scores (Dict): dictionary containing scores
            out_dir (string_types): path to output directory
        """
        for embedding_set in self.embeddings:
            if len(embedding_set) > 0:
                name = embedding_set.name
                reg_name = re.sub('/.*', '', embedding_set.name)
                mkdir_p(os.path.join(out_dir, os.path.dirname(name)))
                with open(os.path.join(out_dir, name + '.rttm'), 'w') as f:
                    for i, embedding in enumerate(embedding_set.embeddings):
                        start, end = embedding.window_start, embedding.window_end
                        idx = np.argmax(scores[name][i])
                        f.write(f'SPEAKER {reg_name} 1 {float(start / 1000.0)} {float((end - start) / 1000.0)} '
                                f'<NA> <NA> {reg_name}_spkr_{idx} <NA>\n')
            else:
                logger.warning(f'No embedding to dump in {embedding_set.name}.')

    def evaluate(self, scores, in_rttm_dir, collar_size=0.25, evaluate_overlaps=False, rttm_ext='.rttm'):
        """ At first, separately evaluate each file based on ground truth segmentation. Then evaluate all files.

        Args:
            scores (dict): dictionary containing scores
            in_rttm_dir (string_types): input directory with rttm files
            collar_size (float): collar size for scoring
            evaluate_overlaps (bool): evaluate or ignore overlapping speech segments
            rttm_ext (string_types): extension for rttm files
        """
        tmp_dir = mkdtemp(prefix='rttm_')
        self.dump_rttm(scores, tmp_dir)
        for embedding_set in self.embeddings:
            name = embedding_set.name
            ground_truth_rttm = os.path.join(in_rttm_dir, '{}{}'.format(name, rttm_ext))
            if not os.path.exists(ground_truth_rttm):
                logger.warning(f'Ground truth rttm file not found in `{ground_truth_rttm}`.')
                continue
            # evaluate single rttm
            der = evaluate2rttms(ground_truth_rttm, os.path.join(tmp_dir, '{}{}'.format(name, rttm_ext)),
                                 collar_size=collar_size, evaluate_overlaps=evaluate_overlaps)
            logger.info(f'`{name}` DER={der}')

        # evaluate all rttms
        der = evaluate_all(reference_dir=in_rttm_dir, hypothesis_dir=tmp_dir, names=scores.keys(),
                           collar_size=collar_size, evaluate_overlaps=evaluate_overlaps, rttm_ext=rttm_ext)
        logger.info(f'`Total` DER={der}')
        rmtree(tmp_dir)

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2018 Brno University of Technology FIT # Author: <NAME> <<EMAIL>> # All Rights Reserved Evaluate two rttms.      Args:         reference_path (string_types):         hypothesis_path (string_types):         collar_size (float):         evaluate_overlaps (bool):      Returns:         float: diarization error rate Evaluate all rttms in directories specified by list of names.      Args:         reference_dir (string_types): directory containing reference rttm files         hypothesis_dir (string_types): directory containing hypothesis rttm files         names (List[string_types]): list containing relative names         collar_size (float):         evaluate_overlaps (bool):         rttm_ext (string_types): extension of rttm files      Returns:         float: diarization error rate Diarization class used as main diarization focused implementation. Initialize diarization class.          Args:             input_list (string_types): path to list of input files             embeddings (string_types|List[EmbeddingSet]): path to directory containing embeddings or list                 of EmbeddingSet instances             embeddings_mean (np.ndarray):             lda (np.ndarray): linear discriminant analysis - dimensionality reduction             use_l2_norm (bool): do l2 normalization             norm (Normalization): instance of class Normalization             plda (GPLDA): instance of class GPLDA Get embedding set by name.          Args:             name (string_types):          Returns:             EmbeddingSet: Load embedding from pickled files.          Returns:             List[EmbeddingSet]: Score embeddings.          Args:             min_length (int): minimal length of segment used for clustering in miliseconds             max_num_speakers (int): maximal number of speakers             mode (str): running mode, see examples/diarization.py for details          Returns:             dict: dictionary with scores for each file Run agglomerative hierarchical clustering.          Returns:             np.array: means of clusters Dump rttm files to output directory. This function requires initialized embeddings.          Args:             scores (Dict): dictionary containing scores             out_dir (string_types): path to output directory At first, separately evaluate each file based on ground truth segmentation. Then evaluate all files.          Args:             scores (dict): dictionary containing scores             in_rttm_dir (string_types): input directory with rttm files             collar_size (float): collar size for scoring             evaluate_overlaps (bool): evaluate or ignore overlapping speech segments             rttm_ext (string_types): extension for rttm files # evaluate single rttm # evaluate all rttms