import torch
from torch._inductor.select_algorithm import extern_kernels
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 40
    x2 = xindex // 160
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (40 + x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (80 + x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (120 + x0 + 160 * 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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 40
    x2 = xindex // 160
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (40 + x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (80 + x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (120 + x0 + 160 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 40
    x1 = xindex // 40
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 160 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 160 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (40 + x0 + 160 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (40 + x0 + 160 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (80 + x0 + 160 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (80 + x0 + 160 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (120 + x0 + 160 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (120 + x0 + 160 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = 1e-07
    tmp19 = tmp17 + tmp18
    tmp20 = tmp14 / tmp19
    tl.store(out_ptr0 + x2, tmp20, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (10, 4), (4, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 10), (160, 40, 10, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 10), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(640)](buf0, buf1, 640, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 10), (160, 40, 10, 1), torch.
            float32)
        triton_poi_fused__softmax_1[grid(640)](buf1, buf2, 640, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 10), (40, 10, 1), torch.float32)
        triton_poi_fused_add_div_mul_sum_2[grid(160)](primals_4, buf2, buf3,
            160, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
    return buf3, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0


class AttentionPoolNew(nn.Module):
    """docstring for AttentionPool"""

    def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
        super().__init__()
        self.inputdim = inputdim
        self.outputdim = outputdim
        self.pooldim = pooldim
        self.transform = nn.Linear(inputdim, outputdim)
        self.activ = nn.Softmax(dim=self.pooldim)
        self.eps = 1e-07

    def forward(self, input_0, input_1):
        primals_1 = self.transform.weight
        primals_2 = self.transform.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.049036