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
from torch import 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_per_fused__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 2
    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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused__softmax_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp9 = tl.load(in_ptr3 + x0, xmask)
    tmp11 = tl.load(in_ptr0 + 1)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp5 / tmp7
    tmp10 = tmp8 * tmp9
    tmp13 = tmp12 - tmp3
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp14 / tmp7
    tmp16 = tmp15 * tmp9
    tmp17 = tmp10 + tmp16
    tl.store(out_ptr0 + x0, tmp17, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (2, 1, 1, 1, 1), (1, 1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 1, 1, 1), (1, 1, 1, 1, 1), torch.
            float32)
        buf1 = empty_strided_cuda((1, 1, 1, 1, 1), (1, 1, 1, 1, 1), torch.
            float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(1)](primals_1, buf0, buf1, 1, 2,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_1[grid(256)](primals_1, buf0,
            buf1, primals_2, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
    return buf2, primals_1, primals_2


class SoftMaxWeightedSumNew(nn.Module):

    def __init__(self, op_number=2):
        super(SoftMaxWeightedSumNew, self).__init__()
        shape = op_number, 1, 1, 1, 1
        self.weights = nn.Parameter(torch.ones(shape), requires_grad=True)

    def forward(self, input_0):
        primals_1 = self.weights
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.028205