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.triton_helpers import math as tl_math
from torch import 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_add_exp_log_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_exp_log_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp4 + tmp2
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_exp_log_2(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_log_0[grid(256)](buf0, buf1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        triton_poi_fused_add_exp_log_1[grid(256)](buf0, buf3, buf4, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        triton_poi_fused_add_exp_log_2[grid(256)](buf0, buf5, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return reinterpret_tensor(buf0, (4, 64), (256, 4), 0
        ), buf2, buf4, buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, buf3, buf5


class LinearNormalGammaNew(nn.Module):

    def __init__(self, in_chanels, out_channels):
        super().__init__()
        self.linear = nn.Linear(in_chanels, out_channels * 4)

    def evidence(self, x):
        return torch.log(torch.exp(x) + 1)

    def forward(self, input_0):
        primals_1 = self.linear.weight
        primals_2 = self.linear.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1], output[2], output[3]


Measured latency_ms: 0.054906