#! /usr/bin/env python3

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

"""
Simple distributed kmeans implementation Relies on an abstraction
for the training matrix, that can be sharded over several machines.
"""
import os
import sys
import argparse

import numpy as np

import faiss

from multiprocessing.dummy import Pool as ThreadPool
from faiss.contrib import rpc
from faiss.contrib.datasets import SyntheticDataset
from faiss.contrib.vecs_io import bvecs_mmap, fvecs_mmap
from faiss.contrib.clustering import DatasetAssign, DatasetAssignGPU, kmeans


class DatasetAssignDispatch:
    """dispatches to several other DatasetAssigns and combines the
    results"""

    def __init__(self, xes, in_parallel):
        self.xes = xes
        self.d = xes[0].dim()
        if not in_parallel:
            self.imap = map
        else:
            self.pool = ThreadPool(len(self.xes))
            self.imap = self.pool.imap
        self.sizes = list(map(lambda x: x.count(), self.xes))
        self.cs = np.cumsum([0] + self.sizes)

    def count(self):
        return self.cs[-1]

    def dim(self):
        return self.d

    def get_subset(self, indices):
        res = np.zeros((len(indices), self.d), dtype='float32')
        nos = np.searchsorted(self.cs[1:], indices, side='right')

        def handle(i):
            mask = nos == i
            sub_indices = indices[mask] - self.cs[i]
            subset = self.xes[i].get_subset(sub_indices)
            res[mask] = subset

        list(self.imap(handle, range(len(self.xes))))
        return res

    def assign_to(self, centroids, weights=None):
        src = self.imap(
            lambda x: x.assign_to(centroids, weights),
            self.xes
        )
        I = []
        D = []
        sum_per_centroid = None
        for Ii, Di, sum_per_centroid_i in src:
            I.append(Ii)
            D.append(Di)
            if sum_per_centroid is None:
                sum_per_centroid = sum_per_centroid_i
            else:
                sum_per_centroid += sum_per_centroid_i
        return np.hstack(I), np.hstack(D), sum_per_centroid


class AssignServer(rpc.Server):
    """ Assign version that can be exposed via RPC """

    def __init__(self, s, assign, log_prefix=''):
        rpc.Server.__init__(self, s, log_prefix=log_prefix)
        self.assign = assign

    def __getattr__(self, f):
        return getattr(self.assign, f)




def do_test(todo):

    testdata = '/datasets01_101/simsearch/041218/bigann/bigann_learn.bvecs'

    if os.path.exists(testdata):
        x = bvecs_mmap(testdata)
    else:
        print("using synthetic dataset")
        ds = SyntheticDataset(128, 100000, 0, 0)
        x = ds.get_train()

    # bad distribution to stress-test split code
    xx = x[:100000].copy()
    xx[:50000] = x[0]

    todo = sys.argv[1:]

    if "0" in todo:
        # reference C++ run
        km = faiss.Kmeans(x.shape[1], 1000, niter=20, verbose=True)
        km.train(xx.astype('float32'))

    if "1" in todo:
        # using the Faiss c++ implementation
        data = DatasetAssign(xx)
        kmeans(1000, data, 20)

    if "2" in todo:
        # use the dispatch object (on local datasets)
        data = DatasetAssignDispatch([
            DatasetAssign(xx[20000 * i : 20000 * (i + 1)])
            for i in range(5)
            ], False
        )
        kmeans(1000, data, 20)

    if "3" in todo:
        # same, with GPU
        ngpu = faiss.get_num_gpus()
        print('using %d GPUs' % ngpu)
        data = DatasetAssignDispatch([
            DatasetAssignGPU(xx[100000 * i // ngpu: 100000 * (i + 1) // ngpu], i)
            for i in range(ngpu)
            ], True
        )
        kmeans(1000, data, 20)


def main():
    parser = argparse.ArgumentParser()

    def aa(*args, **kwargs):
        group.add_argument(*args, **kwargs)

    group = parser.add_argument_group('general options')
    aa('--test', default='', help='perform tests (comma-separated numbers)')

    aa('--k', default=0, type=int, help='nb centroids')
    aa('--seed', default=1234, type=int, help='random seed')
    aa('--niter', default=20, type=int, help='nb iterations')
    aa('--gpu', default=-2, type=int, help='GPU to use (-2:none, -1: all)')

    group = parser.add_argument_group('I/O options')
    aa('--indata', default='',
       help='data file to load (supported formats fvecs, bvecs, npy')
    aa('--i0', default=0, type=int, help='first vector to keep')
    aa('--i1', default=-1, type=int, help='last vec to keep + 1')
    aa('--out', default='', help='file to store centroids')
    aa('--store_each_iteration', default=False, action='store_true',
       help='store centroid checkpoints')

    group = parser.add_argument_group('server options')
    aa('--server', action='store_true', default=False, help='run server')
    aa('--port', default=12345, type=int, help='server port')
    aa('--when_ready', default=None, help='store host:port to this file when ready')
    aa('--ipv4', default=False, action='store_true', help='force ipv4')

    group = parser.add_argument_group('client options')
    aa('--client', action='store_true', default=False, help='run client')
    aa('--servers', default='', help='list of server:port separated by spaces')

    args = parser.parse_args()

    if args.test:
        do_test(args.test.split(','))
        return

    # prepare data matrix (either local or remote)
    if args.indata:
        print('loading ', args.indata)
        if args.indata.endswith('.bvecs'):
            x = bvecs_mmap(args.indata)
        elif args.indata.endswith('.fvecs'):
            x = fvecs_mmap(args.indata)
        elif args.indata.endswith('.npy'):
            x = np.load(args.indata, mmap_mode='r')
        else:
            raise AssertionError

        if args.i1 == -1:
            args.i1 = len(x)
        x = x[args.i0:args.i1]
        if args.gpu == -2:
            data = DatasetAssign(x)
        else:
            print('moving to GPU')
            data = DatasetAssignGPU(x, args.gpu)

    elif args.client:
        print('connecting to servers')

        def connect_client(hostport):
            host, port = hostport.split(':')
            port = int(port)
            print('connecting %s:%d' % (host, port))
            client = rpc.Client(host, port, v6=not args.ipv4)
            print('client %s:%d ready' % (host, port))
            return client

        hostports = args.servers.strip().split(' ')
        # pool = ThreadPool(len(hostports))

        data = DatasetAssignDispatch(
            list(map(connect_client, hostports)),
            True
        )
    else:
        raise AssertionError


    if args.server:
        print('starting server')
        log_prefix = f"{rpc.socket.gethostname()}:{args.port}"
        rpc.run_server(
            lambda s: AssignServer(s, data, log_prefix=log_prefix),
            args.port, report_to_file=args.when_ready,
            v6=not args.ipv4)

    else:
        print('running kmeans')
        centroids = kmeans(args.k, data, niter=args.niter, seed=args.seed,
                           checkpoint=args.out if args.store_each_iteration else None)
        if args.out != '':
            print('writing centroids to', args.out)
            np.save(args.out, centroids)


if __name__ == '__main__':
    main()
