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.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
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_relu_squeeze_threshold_backward_0(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, 4), (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 = buf0
        del buf0
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_squeeze_threshold_backward_0[grid(16)](buf1,
            buf2, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        extern_kernels.mm(buf2, primals_4, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = buf3
        del buf3
        triton_poi_fused__log_softmax_1[grid(16)](buf4, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__log_softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf5
    return buf6, buf6, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
        ), reinterpret_tensor(buf2, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_4, (4, 4), (1, 4), 0), buf7, reinterpret_tensor(primals_1,
        (4, 4), (1, 4), 0)


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=False, act=lambda x:
        x, dropout=0.0):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.dropout = dropout
        self.act = act
        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))
        torch.nn.init.xavier_uniform_(self.weight)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

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

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


class GCN_classifierNew(Module):

    def __init__(self, feature_dim, hidden_dim, out_dim, dropout=0.2):
        super(GCN_classifierNew, self).__init__()
        self.feature_dim = feature_dim
        self.hidden_dim = hidden_dim
        self.out_dim = out_dim
        self.dropout = dropout
        self.gc1 = GraphConvolution(self.feature_dim, self.hidden_dim,
            dropout=self.dropout, act=F.relu)
        self.gc2 = GraphConvolution(self.hidden_dim, self.out_dim)

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


Measured latency_ms: 0.05025