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.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__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


@triton.jit
def triton_red_fused__log_softmax__softmax_div_mul_neg_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.
    constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp25 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp9 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp10 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp12 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp15 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp18 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp5 = tmp3 + tmp4
        tmp7 = tmp5 + tmp6
        tmp8 = tmp0 / tmp7
        tmp11 = tl_math.exp(tmp10)
        tmp13 = tl_math.exp(tmp12)
        tmp14 = tmp11 + tmp13
        tmp16 = tl_math.exp(tmp15)
        tmp17 = tmp14 + tmp16
        tmp19 = tl_math.exp(tmp18)
        tmp20 = tmp17 + tmp19
        tmp21 = tl_math.log(tmp20)
        tmp22 = tmp9 - tmp21
        tmp23 = tmp8 * tmp22
        tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
        tmp26 = _tmp25 + tmp24
        _tmp25 = tl.where(rmask, tmp26, _tmp25)
    tmp25 = tl.sum(_tmp25, 1)[:, None]
    tmp27 = -tmp25
    tmp28 = 0.25
    tmp29 = tmp27 * tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp29, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg0_1, buf0,
            buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_red_fused__log_softmax__softmax_div_mul_neg_sum_1[grid(1)](buf4,
            buf0, buf1, 1, 256, XBLOCK=1, RBLOCK=256, num_warps=8, num_stages=1
            )
        del buf0
        del buf1
    return buf4,


class EntMinLossNew(nn.Module):

    def __init__(self):
        super().__init__()

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


Measured latency_ms: 0.029429