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
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 = 1024
    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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_clamp_div_mul_neg_sum_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x5 = xindex
    x6 = xindex // 4
    x7 = xindex % 256
    x3 = xindex // 64 % 4
    x8 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x5, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x6, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x6), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x6), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x6), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + x7, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (x8 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr1 + (16 + x8 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (32 + x8 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (48 + x8 + 64 * x3), xmask, 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
    tmp14 = -tmp13
    tmp16 = tmp14 * tmp15
    tmp19 = tmp17 + tmp18
    tmp21 = tmp19 + tmp20
    tmp23 = tmp21 + tmp22
    tmp24 = 1.0
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp26 = tmp16 / tmp25
    tl.store(out_ptr0 + x5, tmp26, xmask)


@triton.jit
def triton_per_fused_mean_sum_2(in_out_ptr0, in_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 64
    r1 = rindex // 64
    tmp0 = tl.load(in_ptr0 + (r0 + 256 * r1), None)
    tmp1 = tl.load(in_ptr0 + (64 + r0 + 256 * r1), None)
    tmp3 = tl.load(in_ptr0 + (128 + r0 + 256 * r1), None)
    tmp5 = tl.load(in_ptr0 + (192 + r0 + 256 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = 256.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(1024)](arg0_1, buf0, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused__log_softmax_clamp_div_mul_neg_sum_1[grid(1024)](buf0,
            arg1_1, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_mean_sum_2[grid(1)](buf3, buf1, 1, 256, num_warps=
            2, num_stages=1)
        del buf1
    return buf3,


class LogSoftmaxNew(torch.nn.Module):

    def __init__(self, dim):
        super(LogSoftmaxNew, self).__init__()
        self.dim = dim

    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: 0.028042