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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
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_add_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_relu_1(in_out_ptr0, in_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 % 16
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__softmax_sigmoid_2(in_ptr0, out_ptr2, out_ptr3, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tl.sigmoid(tmp0)
    tmp10 = tmp5 / tmp8
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)
    tl.store(out_ptr3 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp10, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 32), (32, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 16), (16, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (16, 1), (1, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(128)](buf2, primals_4, 128, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf2, primals_5, out=buf3)
        buf4 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        del buf3
        buf5 = buf4
        del buf4
        triton_poi_fused_add_relu_1[grid(64)](buf5, primals_6, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf5, primals_7, out=buf6)
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, primals_3, buf6, alpha=1, beta=1,
            out=buf8)
        del primals_8
        buf9 = reinterpret_tensor(buf6, (4,), (1,), 0)
        del buf6
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused__softmax_sigmoid_2[grid(1)](buf8, buf9, buf12, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf8
    return buf9, buf12, buf2, buf5, buf9, buf12, reinterpret_tensor(primals_3,
        (4, 4), (1, 4), 0), reinterpret_tensor(primals_7, (1, 16), (1, 1), 0
        ), reinterpret_tensor(primals_5, (16, 32), (1, 16), 0
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GCN(Module):
    """
    Graph Convolutional Network
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GCN, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input, adj):
        support = torch.mm(input, self.weight)
        output = torch.spmm(adj, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'
        return output


class DiscriminatorNew(nn.Module):
    """
    Discriminator network with GCN.
    """

    def __init__(self, input_size, output_size, dropout):
        super(DiscriminatorNew, self).__init__()
        self.gc1 = GCN(input_size, 32)
        self.gc2 = GCN(32, 16)
        self.gc3 = GCN(16, 1)
        self.dropout = dropout

    def forward(self, input_0, input_1):
        primals_1 = self.gc1.weight
        primals_4 = self.gc1.bias
        primals_5 = self.gc2.weight
        primals_6 = self.gc2.bias
        primals_7 = self.gc3.weight
        primals_8 = self.gc3.bias
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]


Measured latency_ms: 0.050327