# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/tc/ctcljh7q36b3xg5ofxo7xvjxbezjdewz3orlw4zhiiekkfy33ssc.py
# Topologically Sorted Source Nodes: [mean, var, mul, attention, mul_1, mul_2, mul_3], Original ATen: [aten.elu, aten.relu, aten.mul, aten.exp]
# Source node to ATen node mapping:
#   attention => exp
#   mean => expm1, gt, mul, mul_2, where
#   mul => mul_3
#   mul_1 => mul_4
#   mul_2 => mul_5
#   mul_3 => mul_6
#   var => relu
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mm_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%relu, -1.0), kwargs = {})
#   %exp : [num_users=3] = call_function[target=torch.ops.aten.exp.default](args = (%mul_3,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %exp), kwargs = {})
#   %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%relu, %exp), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %exp), kwargs = {})
triton_poi_fused_elu_exp_mul_relu_0 = async_compile.triton('triton_poi_fused_elu_exp_mul_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_exp_mul_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_exp_mul_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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)
    tmp8 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = -1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp7 * tmp13
    tmp15 = tmp10 * tmp13
    tmp16 = tmp15 * tmp13
    tl.store(out_ptr0 + (x0), tmp14, xmask)
    tl.store(out_ptr1 + (x0), tmp15, xmask)
    tl.store(out_ptr2 + (x0), tmp16, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, var, mul, attention, mul_1, mul_2, mul_3], Original ATen: [aten.elu, aten.relu, aten.mul, aten.exp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_elu_exp_mul_relu_0.run(buf0, buf1, buf2, buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, var, mul, attention, mul_1, mean_1], Original ATen: [aten.elu, aten.relu, aten.mul, aten.exp, aten.mm]
        extern_kernels.mm(primals_5, buf2, out=buf3)
        buf6 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [var, mul, attention, mul_3, var_1], Original ATen: [aten.relu, aten.mul, aten.exp, aten.mm]
        extern_kernels.mm(primals_6, buf5, out=buf6)
        del buf5
    return (buf3, buf6, primals_2, primals_4, buf0, buf1, buf4, reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn.functional as F
import torch.nn as nn


class GaussionConvD(nn.Module):
    """The subsequent layer in `RobustGCN` that takes node distribution (mean, var) as input"""

    def __init__(self, in_features, out_features, bias=False, gamma=1.0):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.w_mean = nn.Linear(in_features, out_features, bias=bias)
        self.w_var = nn.Linear(in_features, out_features, bias=bias)
        self.gamma = gamma

    def reset_parameters(self):
        self.w_mean.reset_parameters()
        self.w_var.reset_parameters()

    def forward(self, mean, var, adj_mean, adj_var):
        mean = F.elu(self.w_mean(mean))
        var = F.relu(self.w_var(var))
        attention = torch.exp(-self.gamma * var)
        mean = adj_mean.mm(mean * attention)
        var = adj_var.mm(var * attention * attention)
        return mean, var

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
            )


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
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_elu_exp_mul_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, 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)
    tmp8 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = -1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp7 * tmp13
    tmp15 = tmp10 * tmp13
    tmp16 = tmp15 * tmp13
    tl.store(out_ptr0 + x0, tmp14, xmask)
    tl.store(out_ptr1 + x0, tmp15, xmask)
    tl.store(out_ptr2 + x0, tmp16, 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, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (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_2, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_exp_mul_relu_0[grid(16)](buf0, buf1, buf2,
            buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_5, buf2, out=buf3)
        buf6 = buf2
        del buf2
        extern_kernels.mm(primals_6, buf5, out=buf6)
        del buf5
    return (buf3, buf6, primals_2, primals_4, buf0, buf1, buf4,
        reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_5, (4, 4), (1, 4), 0))


class GaussionConvDNew(nn.Module):
    """The subsequent layer in `RobustGCN` that takes node distribution (mean, var) as input"""

    def __init__(self, in_features, out_features, bias=False, gamma=1.0):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.w_mean = nn.Linear(in_features, out_features, bias=bias)
        self.w_var = nn.Linear(in_features, out_features, bias=bias)
        self.gamma = gamma

    def reset_parameters(self):
        self.w_mean.reset_parameters()
        self.w_var.reset_parameters()

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features})'
            )

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.w_mean.weight
        primals_2 = self.w_var.weight
        primals_3 = input_0
        primals_4 = input_1
        primals_5 = input_2
        primals_6 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
