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
import torch.optim
import torch.multiprocessing
from torch.nn.parameter import Parameter
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_add_clamp_clone_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -20.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 3.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_add_clone_ge_le_logical_and_3(in_ptr0, in_ptr1,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -20.0
    tmp4 = tmp2 >= tmp3
    tmp5 = 3.0
    tmp6 = tmp2 <= tmp5
    tmp7 = tmp4 & tmp6
    tl.store(out_ptr0 + (y0 + 4 * x2 + 16 * y1), tmp7, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 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, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](buf0, buf1, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_clone_1[grid(64, 4)](buf2, primals_4, buf3, 64,
            4, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            primals_5, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf4, buf5, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_clamp_clone_2[grid(64, 4)](buf6, primals_7,
            buf7, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_clone_ge_le_logical_and_3[grid(64, 4)](buf6,
            primals_7, buf8, 64, 4, XBLOCK=4, YBLOCK=64, num_warps=4,
            num_stages=1)
        del buf6
        del primals_7
    return buf3, buf7, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf5, (64, 4), (4, 1), 0
        ), buf8, primals_6, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0
        ), primals_3


class GraphConvolution(nn.Module):

    def __init__(self, in_features, out_features, bias=True, node_n=48,
        out_node_n=None):
        super(GraphConvolution, self).__init__()
        if out_node_n is None:
            out_node_n = node_n
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        self.att = Parameter(torch.FloatTensor(out_node_n, node_n))
        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)
        self.att.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input):
        support = torch.matmul(input, self.weight)
        output = torch.matmul(self.att, 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 GraphGaussianBlockNew(nn.Module):

    def __init__(self, in_nodes, in_features, n_z_nodes, n_z_features):
        """
        :param input_feature: num of input feature
        :param n_z: dim of distribution
        """
        super(GraphGaussianBlockNew, self).__init__()
        self.in_features = in_features
        self.in_nodes = in_nodes
        self.n_z_features = n_z_features
        self.n_z_nodes = n_z_nodes
        self.z_mu_graphconv = GraphConvolution(in_features, n_z_features,
            bias=True, node_n=in_nodes, out_node_n=n_z_nodes)
        self.z_log_var_graphconv = GraphConvolution(in_features,
            n_z_features, bias=True, node_n=in_nodes, out_node_n=n_z_nodes)

    def forward(self, input_0):
        primals_2 = self.z_mu_graphconv.weight
        primals_3 = self.z_mu_graphconv.att
        primals_4 = self.z_mu_graphconv.bias
        primals_5 = self.z_log_var_graphconv.weight
        primals_6 = self.z_log_var_graphconv.att
        primals_7 = self.z_log_var_graphconv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.044108