import torch
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.utils.data
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_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 = 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, xmask)


@triton.jit
def triton_per_fused__to_copy_max_nll_loss2d_forward_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp13 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp23 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp42 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp44 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp47 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp50 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tmp0.to(tl.int64)
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tmp1 > tmp3
    tmp5 = tmp1 == tmp3
    tmp6 = tl.full([1, 1], 0, tl.int64)
    tmp7 = tl.full([1, 1], 1, tl.int64)
    tmp8 = tmp6 < tmp7
    tmp9 = tmp5 & tmp8
    tmp10 = tmp4 | tmp9
    tmp11 = tl.where(tmp10, tmp1, tmp3)
    tmp12 = tl.where(tmp10, tmp6, tmp7)
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp11 > tmp14
    tmp16 = tmp11 == tmp14
    tmp17 = tl.full([1, 1], 2, tl.int64)
    tmp18 = tmp12 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tmp15 | tmp19
    tmp21 = tl.where(tmp20, tmp11, tmp14)
    tmp22 = tl.where(tmp20, tmp12, tmp17)
    tmp24 = tmp23.to(tl.int64)
    tmp25 = tmp21 > tmp24
    tmp26 = tmp21 == tmp24
    tmp27 = tl.full([1, 1], 3, tl.int64)
    tmp28 = tmp22 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tmp25 | tmp29
    tl.where(tmp30, tmp21, tmp24)
    tmp32 = tl.where(tmp30, tmp22, tmp27)
    tmp33 = tl.full([1, 1], -100, tl.int64)
    tmp34 = tmp32 != tmp33
    tmp35 = tl.where(tmp34, tmp32, tmp6)
    tmp36 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp35 < 0
    tmp39 = tl.where(tmp38, tmp37, tmp35)
    tl.device_assert((0 <= tmp39) & (tmp39 < 4),
        'index out of bounds: 0 <= tmp39 < 4')
    tmp41 = tl.load(in_ptr1 + (r0 + 16 * tmp39 + 64 * r1), None)
    tmp43 = tl_math.exp(tmp42)
    tmp45 = tl_math.exp(tmp44)
    tmp46 = tmp43 + tmp45
    tmp48 = tl_math.exp(tmp47)
    tmp49 = tmp46 + tmp48
    tmp51 = tl_math.exp(tmp50)
    tmp52 = tmp49 + tmp51
    tmp53 = tl_math.log(tmp52)
    tmp54 = tmp41 - tmp53
    tmp55 = -tmp54
    tmp56 = 0.0
    tmp57 = tl.where(tmp34, tmp55, tmp56)
    tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
    tmp60 = tl.sum(tmp58, 1)[:, None]
    tmp61 = tmp34.to(tl.int64)
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = tmp64.to(tl.float32)
    tmp66 = tmp60 / tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp66, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused__to_copy_max_nll_loss2d_forward_1[grid(1)](buf4,
            arg0_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf1
    return buf4,


class CrossEntropyLossNew(nn.Module):

    def __init__(self, label_nc):
        super(CrossEntropyLossNew, self).__init__()
        self.softmax = nn.LogSoftmax(dim=1)
        self.criterion = nn.NLLLoss2d()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.015787