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
import torch.nn.init
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_exp_mul_sub_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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')
    tmp10 = tl.load(in_ptr1 + x2, xmask)
    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)
    tmp11 = tmp9 * tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + r2, None)
    tmp11 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tmp9 = -tmp8
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp15 + tmp17
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp10 - tmp22
    tmp24 = tmp9 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    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, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_exp_mul_sub_0[grid(16)](arg0_1,
            arg1_1, buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused__log_softmax_div_mul_neg_sum_1[grid(1)](buf4, buf0,
            buf1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf4,


class softCENew(nn.Module):
    """
    The objective function for the distant supervised typing.

    Parameters
    ----------
    if_average : ``bool``, optional, (default = True).
        Whether to average over batches or not.
    """

    def __init__(self, if_average=True):
        super(softCENew, self).__init__()
        self.logSoftmax = nn.LogSoftmax(dim=1)
        self.if_average = if_average

    @staticmethod
    def soft_max(vec, mask):
        """
        Calculate the softmax for the input with regard to a mask.

        Parameters
        ----------
        vec : ``torch.FloatTensor``, required.
            The input of the softmax.
        mask : ``torch.ByteTensor`` , required.
            The mask for the softmax input.
        """
        batch_size = vec.size(0)
        max_score, _idx = torch.max(vec, 1, keepdim=True)
        exp_score = torch.exp(vec - max_score.expand_as(vec))
        exp_score = exp_score * mask
        exp_score_sum = torch.sum(exp_score, 1).view(batch_size, 1).expand_as(
            exp_score)
        prob_score = exp_score / exp_score_sum
        return prob_score

    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: 0.030194