import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init


class MeanAggregator(nn.Module):

    def forward(self, features, A):
        x = torch.bmm(A, features)
        return x


class GraphConv(nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = nn.Parameter(torch.FloatTensor(in_dim * 2, out_dim))
        self.bias = nn.Parameter(torch.FloatTensor(out_dim))
        init.xavier_uniform_(self.weight)
        init.constant_(self.bias, 0)
        self.aggregator = MeanAggregator()

    def forward(self, features, A):
        _b, _n, d = features.shape
        assert d == self.in_dim
        agg_feats = self.aggregator(features, A)
        cat_feats = torch.cat([features, agg_feats], dim=2)
        out = torch.einsum('bnd,df->bnf', cat_feats, self.weight)
        out = F.relu(out + self.bias)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (8, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_2, primals_1, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, buf0, buf1, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = reinterpret_tensor(buf0, (1, 16, 4), (64, 4, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf1, (1, 16, 8), (0, 8, 1), 
            0), reinterpret_tensor(primals_3, (1, 8, 4), (32, 4, 1), 0),
            out=buf2)
        del primals_3
        buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(64)](buf3,
            primals_4, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
    return buf3, buf4, reinterpret_tensor(buf1, (1, 8, 16), (128, 1, 8), 0)


class MeanAggregator(nn.Module):

    def forward(self, features, A):
        x = torch.bmm(A, features)
        return x


class GraphConvNew(nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = nn.Parameter(torch.FloatTensor(in_dim * 2, out_dim))
        self.bias = nn.Parameter(torch.FloatTensor(out_dim))
        init.xavier_uniform_(self.weight)
        init.constant_(self.bias, 0)
        self.aggregator = MeanAggregator()

    def forward(self, input_0, input_1):
        primals_3 = self.weight
        primals_4 = self.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
