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 math
import torch.nn as nn
from scipy.sparse import *
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 = 16
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(16)](buf2, primals_4, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_4
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, primals_5, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, primals_3, buf3, alpha=1, beta=1,
            out=buf4)
        del buf3
        del primals_6
    return buf2, buf4, buf2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


def dropout(x, drop_prob, shared_axes=[], training=False):
    """
    Apply dropout to input tensor.
    Parameters
    ----------
    input_tensor: ``torch.FloatTensor``
        A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)``
    Returns
    -------
    output: ``torch.FloatTensor``
        A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied.
    """
    if drop_prob == 0 or drop_prob is None or not training:
        return x
    sz = list(x.size())
    for i in shared_axes:
        sz[i] = 1
    mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob)
    mask = mask.expand_as(x)
    return x * mask


class GraphConvolution(nn.Module):

    def __init__(self, in_features, out_features, with_bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features)
            )
        if with_bias:
            self.bias = nn.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 init_params(self):
        for param in self.parameters():
            if len(param.size()) == 2:
                nn.init.xavier_uniform_(param)
            else:
                nn.init.constant_(param, 0.0)

    def forward(self, input, adj):
        """ Graph Convolutional Layer forward function
        """
        if input.data.is_sparse:
            support = torch.spmm(input, self.weight)
        else:
            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) + ')'


class GCN2New(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout=0.5, with_bias=True):
        super(GCN2New, self).__init__()
        self.nfeat = nfeat
        self.hidden_sizes = [nhid]
        self.nclass = nclass
        self.gc1 = GraphConvolution(nfeat, nhid, with_bias=with_bias)
        self.gc2 = GraphConvolution(nhid, nclass, with_bias=with_bias)
        self.dropout = dropout
        self.with_bias = with_bias

    def initialize(self):
        """Initialize parameters of GCN.
        """
        self.gc1.reset_parameters()
        self.gc2.reset_parameters()

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


Measured latency_ms: 0.038286