from torch.nn import Module
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class GraphConvolution(Module):
    """
    Simple GCN layer
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, 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) + ')'


class KGCN(Module):
    """
    A bit more complex GNN to deal with non-convex feature space.
    """

    def __init__(self, nhidden, nfeat, nclass, degree):
        super(KGCN, self).__init__()
        self.Wx = GraphConvolution(nfeat, nhidden)
        self.W = nn.Linear(nhidden, nclass)
        self.d = degree

    def forward(self, x, adj):
        h = F.relu(self.Wx(x, adj))
        for i in range(self.d):
            h = torch.spmm(adj, h)
        return self.W(h)


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


def get_init_inputs():
    return [[], {'nhidden': 4, 'nfeat': 4, 'nclass': 4, 'degree': 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
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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_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)
    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, 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_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1
        del buf1
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_threshold_backward_0[grid(16)](buf2,
            primals_4, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_4
        buf3 = buf0
        del buf0
        extern_kernels.mm(primals_3, buf2, out=buf3)
        buf4 = buf2
        del buf2
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = buf3
        del buf3
        extern_kernels.mm(primals_3, buf4, out=buf5)
        buf6 = buf4
        del buf4
        extern_kernels.mm(primals_3, buf5, out=buf6)
        buf7 = buf5
        del buf5
        extern_kernels.addmm(primals_6, buf6, reinterpret_tensor(primals_5,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_6
    return buf7, buf6, primals_5, reinterpret_tensor(primals_3, (4, 4), (1,
        4), 0), buf8, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GraphConvolution(Module):
    """
    Simple GCN layer
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, 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) + ')'


class KGCNNew(Module):
    """
    A bit more complex GNN to deal with non-convex feature space.
    """

    def __init__(self, nhidden, nfeat, nclass, degree):
        super(KGCNNew, self).__init__()
        self.Wx = GraphConvolution(nfeat, nhidden)
        self.W = nn.Linear(nhidden, nclass)
        self.d = degree

    def forward(self, input_0, input_1):
        primals_1 = self.Wx.weight
        primals_4 = self.Wx.bias
        primals_2 = self.W.weight
        primals_6 = self.W.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]
