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
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


@triton.jit
def triton_poi_fused__log_softmax_cat_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp20 = tl.load(in_ptr1 + 1)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp32 = tl.load(in_ptr1 + 2)
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK])
    tmp44 = tl.load(in_ptr1 + 3)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1)), tmp11 & xmask, other=0.0)
    tmp15 = tmp14 + tmp7
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp10, tmp17)
    tmp19 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tl.load(in_ptr0 + (4 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp26 = tmp25 + tmp21
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp11, tmp26, tmp27)
    tmp29 = tl.where(tmp4, tmp24, tmp28)
    tmp30 = triton_helpers.maximum(tmp18, tmp29)
    tmp31 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp34 = tmp31 + tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp4, tmp34, tmp35)
    tmp37 = tl.load(in_ptr0 + (8 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp38 = tmp37 + tmp33
    tmp39 = tl.full(tmp38.shape, 0.0, tmp38.dtype)
    tmp40 = tl.where(tmp11, tmp38, tmp39)
    tmp41 = tl.where(tmp4, tmp36, tmp40)
    tmp42 = triton_helpers.maximum(tmp30, tmp41)
    tmp43 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp46 = tmp43 + tmp45
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp4, tmp46, tmp47)
    tmp49 = tl.load(in_ptr0 + (12 + x0 + 16 * (-4 + x1)), tmp11 & xmask,
        other=0.0)
    tmp50 = tmp49 + tmp45
    tmp51 = tl.full(tmp50.shape, 0.0, tmp50.dtype)
    tmp52 = tl.where(tmp11, tmp50, tmp51)
    tmp53 = tl.where(tmp4, tmp48, tmp52)
    tmp54 = triton_helpers.maximum(tmp42, tmp53)
    tmp55 = tmp18 - tmp54
    tmp56 = tl_math.exp(tmp55)
    tmp57 = tmp29 - tmp54
    tmp58 = tl_math.exp(tmp57)
    tmp59 = tmp56 + tmp58
    tmp60 = tmp41 - tmp54
    tmp61 = tl_math.exp(tmp60)
    tmp62 = tmp59 + tmp61
    tmp63 = tmp53 - tmp54
    tmp64 = tl_math.exp(tmp63)
    tmp65 = tmp62 + tmp64
    tl.store(out_ptr0 + x2, tmp54, xmask)
    tl.store(out_ptr1 + x2, tmp65, xmask)


@triton.jit
def triton_poi_fused__log_softmax_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp19 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x3 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr0 + (x3 + 16 * (-4 + x2)), tmp10 & xmask, other=0.0)
    tmp14 = tl.load(in_ptr1 + x1, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp20 = tmp18 - tmp19
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp20 - tmp22
    tl.store(out_ptr0 + x4, tmp23, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (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 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        buf2 = empty_strided_cuda((8, 1, 2, 2), (4, 32, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_cat_0[grid(32)](buf0, primals_2, buf1,
            buf2, 32, XBLOCK=32, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((8, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused__log_softmax_cat_1[grid(128)](buf0, primals_2,
            buf1, buf2, buf3, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del primals_2
    return buf3, primals_1, primals_3, buf3


class NetNew(nn.Module):

    def __init__(self, Cin, Cout):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(Cin, Cout, (3, 3))

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.039779