import dgl
import dgl.function as fn
from collections import Counter
import numpy as np
import scipy.sparse as ssp
import itertools
from itertools import product
import backend as F
import networkx as nx
import unittest, pytest
from dgl import DGLError
import test_utils
from test_utils import parametrize_dtype, get_cases
from scipy.sparse import rand
rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
feat_size = 2

@unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')

def create_test_heterograph(idtype):
    # test heterograph from the docstring, plus a user -- wishes -- game relation
    # 3 users, 2 games, 2 developers
    # metagraph:
    #    ('user', 'follows', 'user'),
    #    ('user', 'plays', 'game'),
    #    ('user', 'wishes', 'game'),
    #    ('developer', 'develops', 'game')])

    g = dgl.heterograph({
        ('user', 'follows', 'user'):  ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'plays', 'game'): ([0, 1, 2, 1], [0, 0, 1, 1]),
        ('user', 'wishes', 'game'): ([0, 1, 1], [0, 0, 1]),
        ('developer', 'develops', 'game'): ([0, 1, 0], [0, 1, 1]),
    }, idtype=idtype, device=F.ctx())
    assert g.idtype == idtype
    assert g.device == F.ctx()
    return g

def create_test_heterograph_2(idtype):

    src = np.random.randint(0, 50, 25)
    dst = np.random.randint(0, 50, 25)
    src1 = np.random.randint(0, 25, 10)
    dst1 = np.random.randint(0, 25, 10)
    src2 = np.random.randint(0, 100, 1000)
    dst2 = np.random.randint(0, 100, 1000)
    g = dgl.heterograph({
        ('user', 'becomes', 'player'):  (src, dst),
        ('user', 'follows', 'user'):  (src, dst),
        ('user', 'plays', 'game'): (src, dst),
        ('user', 'wishes', 'game'): (src1, dst1),
        ('developer', 'develops', 'game'): (src2, dst2),
    }, idtype=idtype, device=F.ctx())
    assert g.idtype == idtype
    assert g.device == F.ctx()
    return g

def create_test_heterograph_large(idtype):

    src = np.random.randint(0, 50, 2500)
    dst = np.random.randint(0, 50, 2500)
    g = dgl.heterograph({
        ('user', 'follows', 'user'):  (src, dst),
        ('user', 'plays', 'game'): (src, dst),
        ('user', 'wishes', 'game'): (src, dst),
        ('developer', 'develops', 'game'): (src, dst),
    }, idtype=idtype, device=F.ctx())
    assert g.idtype == idtype
    assert g.device == F.ctx()
    return g

@parametrize_dtype
def test_unary_copy_u(idtype):
    def _test(mfunc, rfunc):
        g = create_test_heterograph_2(idtype)
        g0 = create_test_heterograph(idtype)
        g1 = create_test_heterograph_large(idtype)
        cross_reducer = rfunc.__name__
        x1 = F.randn((g.num_nodes('user'), feat_size))
        x2 = F.randn((g.num_nodes('developer'), feat_size))
        F.attach_grad(x1)
        F.attach_grad(x2)
        g.nodes['user'].data['h'] = x1
        g.nodes['developer'].data['h'] = x2

        #################################################################
        #  multi_update_all(): call msg_passing separately for each etype
        #################################################################

        with F.record_grad():
            g.multi_update_all(
                {etype : (mfunc('h', 'm'), rfunc('m', 'y'))
                    for etype in g.canonical_etypes},
                cross_reducer)
            r1 = g.nodes['game'].data['y'].clone()
            r2 = g.nodes['user'].data['y'].clone()
            r3 = g.nodes['player'].data['y'].clone()
            loss = r1.sum() + r2.sum() + r3.sum()
            F.backward(loss)
            n_grad1 = F.grad(g.nodes['user'].data['h']).clone()
            n_grad2 = F.grad(g.nodes['developer'].data['h']).clone()

        g.nodes['user'].data.clear()
        g.nodes['developer'].data.clear()
        g.nodes['game'].data.clear()
        g.nodes['player'].data.clear()

        #################################################################
        #  update_all(): call msg_passing for all etypes
        #################################################################

        F.attach_grad(x1)
        F.attach_grad(x2)
        g.nodes['user'].data['h'] = x1
        g.nodes['developer'].data['h'] = x2

        with F.record_grad():
            g.update_all(mfunc('h', 'm'), rfunc('m', 'y'))
            r4 = g.nodes['game'].data['y']
            r5 = g.nodes['user'].data['y']
            r6 = g.nodes['player'].data['y']
            loss = r4.sum() + r5.sum() + r6.sum()
            F.backward(loss)
            n_grad3 = F.grad(g.nodes['user'].data['h'])
            n_grad4 = F.grad(g.nodes['developer'].data['h'])

        assert F.allclose(r1, r4)
        assert F.allclose(r2, r5)
        assert F.allclose(r3, r6)
        assert(F.allclose(n_grad1, n_grad3))
        assert(F.allclose(n_grad2, n_grad4))
    _test(fn.copy_u, fn.sum)
    _test(fn.copy_u, fn.max)
    _test(fn.copy_u, fn.min)
    # _test('copy_u', 'mean')

@parametrize_dtype
def test_unary_copy_e(idtype):
    def _test(mfunc, rfunc):

        g = create_test_heterograph_large(idtype)
        g0 = create_test_heterograph_2(idtype)
        g1 = create_test_heterograph(idtype)
        cross_reducer = rfunc.__name__
        x1 = F.randn((g.num_edges('plays'),feat_size))
        x2 = F.randn((g.num_edges('follows'),feat_size))
        x3 = F.randn((g.num_edges('develops'),feat_size))
        x4 = F.randn((g.num_edges('wishes'),feat_size))
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)
        g['plays'].edata['eid'] = x1
        g['follows'].edata['eid'] = x2
        g['develops'].edata['eid'] = x3
        g['wishes'].edata['eid'] = x4

        #################################################################
        #  multi_update_all(): call msg_passing separately for each etype
        #################################################################

        with F.record_grad():
            g.multi_update_all(
                {'plays' : (mfunc('eid', 'm'), rfunc('m', 'y')),
                'follows': (mfunc('eid', 'm'), rfunc('m', 'y')),
                'develops': (mfunc('eid', 'm'), rfunc('m', 'y')),
                'wishes': (mfunc('eid', 'm'), rfunc('m', 'y'))},
                cross_reducer)
            r1 = g.nodes['game'].data['y'].clone()
            r2 = g.nodes['user'].data['y'].clone()
            loss = r1.sum() + r2.sum()
            F.backward(loss)
            e_grad1 = F.grad(g['develops'].edata['eid']).clone()
            e_grad2 = F.grad(g['plays'].edata['eid']).clone()
            e_grad3 = F.grad(g['wishes'].edata['eid']).clone()
            e_grad4 = F.grad(g['follows'].edata['eid']).clone()
        {etype : (g[etype].edata.clear())
            for _, etype, _ in g.canonical_etypes},

        #################################################################
        #  update_all(): call msg_passing for all etypes
        #################################################################

        # TODO(Israt): output type can be None in multi_update and empty
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)

        g['plays'].edata['eid'] = x1
        g['follows'].edata['eid'] = x2
        g['develops'].edata['eid'] = x3
        g['wishes'].edata['eid'] = x4

        with F.record_grad():
            g.update_all(mfunc('eid', 'm'), rfunc('m', 'y'))
            r3 = g.nodes['game'].data['y']
            r4 = g.nodes['user'].data['y']
            loss = r3.sum() + r4.sum()
            F.backward(loss)
            e_grad5 = F.grad(g['develops'].edata['eid'])
            e_grad6 = F.grad(g['plays'].edata['eid'])
            e_grad7 = F.grad(g['wishes'].edata['eid'])
            e_grad8 = F.grad(g['follows'].edata['eid'])
        # # correctness check
        def _print_error(a, b):
            for i, (x, y) in enumerate(zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())):
                if not np.allclose(x, y):
                   print('@{} {} v.s. {}'.format(i, x, y))

        assert F.allclose(r1, r3)
        assert F.allclose(r2, r4)
        assert(F.allclose(e_grad1, e_grad5))
        assert(F.allclose(e_grad2, e_grad6))
        assert(F.allclose(e_grad3, e_grad7))
        assert(F.allclose(e_grad4, e_grad8))
    _test(fn.copy_e, fn.sum)
    _test(fn.copy_e, fn.max)
    _test(fn.copy_e, fn.min)
    # _test('copy_e', 'mean')

@parametrize_dtype
def test_binary_op(idtype):
    def _test(lhs, rhs, binary_op, reducer):

        g = create_test_heterograph(idtype)

        x1 = F.randn((g.num_nodes('user'), feat_size))
        x2 = F.randn((g.num_nodes('developer'), feat_size))
        x3 = F.randn((g.num_nodes('game'), feat_size))

        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        g.nodes['user'].data['h'] = x1
        g.nodes['developer'].data['h'] = x2
        g.nodes['game'].data['h'] = x3

        x1 = F.randn((4,feat_size))
        x2 = F.randn((4,feat_size))
        x3 = F.randn((3,feat_size))
        x4 = F.randn((3,feat_size))
        F.attach_grad(x1)
        F.attach_grad(x2)
        F.attach_grad(x3)
        F.attach_grad(x4)
        g['plays'].edata['h'] = x1
        g['follows'].edata['h'] = x2
        g['develops'].edata['h'] = x3
        g['wishes'].edata['h'] = x4

        builtin_msg_name = "{}_{}_{}".format(lhs, binary_op, rhs)
        builtin_msg = getattr(fn, builtin_msg_name)
        builtin_red = getattr(fn, reducer)

        #################################################################
        #  multi_update_all(): call msg_passing separately for each etype
        #################################################################

        with F.record_grad():
            g.multi_update_all(
                {etype : (builtin_msg('h', 'h', 'm'), builtin_red('m', 'y'))
                    for etype in g.canonical_etypes},
                'sum')
            r1 = g.nodes['game'].data['y']
            F.backward(r1, F.ones(r1.shape))
            n_grad1 = F.grad(r1)

        #################################################################
        #  update_all(): call msg_passing for all etypes
        #################################################################

        g.update_all(builtin_msg('h', 'h', 'm'), builtin_red('m', 'y'))
        r2 = g.nodes['game'].data['y']
        F.backward(r2, F.ones(r2.shape))
        n_grad2 = F.grad(r2)
        # correctness check
        def _print_error(a, b):
            for i, (x, y) in enumerate(zip(F.asnumpy(a).flatten(), F.asnumpy(b).flatten())):
                if not np.allclose(x, y):
                    print('@{} {} v.s. {}'.format(i, x, y))

        if not F.allclose(r1, r2):
            _print_error(r1, r2)
        assert F.allclose(r1, r2)
        # TODO (Israt): r1 and r2 have different frad func associated with
        # if not F.allclose(n_grad1, n_grad2):
        #     print('node grad')
        #     _print_error(n_grad1, n_grad2)
        # assert(F.allclose(n_grad1, n_grad2))

    target = ["u", "v", "e"]
    for lhs, rhs in product(target, target):
        if lhs == rhs:
            continue
        for binary_op in ["add", "sub", "mul", "div"]:
            # TODO(Israt) :Add support for reduce func "max", "min", "mean"
            for reducer in ["sum"]:
                print(lhs, rhs, binary_op, reducer)
                _test(lhs, rhs, binary_op, reducer)


if __name__ == '__main__':
    test_unary_copy_u()
    test_unary_copy_e()
    test_binary_op()

