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
import torch.nn.functional as F
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_div_mean_std_sub_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-16
    tmp26 = tmp24 + tmp25
    tmp27 = tmp10 / tmp26
    tl.store(out_ptr0 + x3, tmp27, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 8192
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + (x0 + 32768 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (8192 + x0 + 32768 * x2), None,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (16384 + x0 + 32768 * x2), None,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (24576 + x0 + 32768 * x2), None,
        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 + x3, tmp8, None)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 8192
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + (x0 + 32768 * x2), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (8192 + x0 + 32768 * x2), None,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (16384 + x0 + 32768 * x2), None,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (24576 + x0 + 32768 * x2), None,
        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 + x3, tmp13, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (2048,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_std_sub_0[grid(256)](primals_1, buf0,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_2, (4, 2048), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1),
            torch.float32)
        triton_poi_fused__log_softmax_1[grid(131072)](buf1, buf2, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 2048), (32768, 8192, 2048,
            1), 0)
        del buf1
        triton_poi_fused__log_softmax_2[grid(131072)](buf2, buf3, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del buf2
    return buf3, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf3


class NetNew(nn.Module):

    def __init__(self, input_dim, n_classes):
        super(NetNew, self).__init__()
        self.n_classes = n_classes
        self.fc = nn.Linear(input_dim, 2048)

    def _forward2(self, x):
        x = self.fc(x)
        x = F.log_softmax(x, dim=1)
        return x

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


Measured latency_ms: 0.04912