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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * 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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * 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__softmax_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp8 = tl.load(in_ptr1 + (8 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp12 = tl.load(in_ptr1 + (12 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x4, tmp14, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 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((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        triton_poi_fused__softmax_mul_sum_2[grid(64)](primals_2, buf2, buf3,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
    return buf3, primals_2, buf0


class AttentionNew(nn.Module):

    def __init__(self, hidden):
        super(AttentionNew, self).__init__()
        self.linear = nn.Linear(hidden, 1, bias=False)

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


Measured latency_ms: 0.046168