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 libdevice, math as tl_math
import torch.nn as nn
from torch.autograd import *
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_stack_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 12
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 3 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp1 - tmp5
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp2 - tmp5
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp13 = tmp4 - tmp5
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp16 = tmp7 / tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 4), (4, 1))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(48)](primals_1, primals_2, primals_3,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((12, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (12, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_8, reinterpret_tensor(primals_6, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_6
        buf3 = reinterpret_tensor(buf1, (4, 3, 4), (12, 4, 1), 0)
        del buf1
        triton_poi_fused_add_tanh_1[grid(48)](buf3, buf2, primals_7,
            primals_5, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        del primals_7
        buf5 = empty_strided_cuda((12, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf3, (12, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_10
        buf6 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(12)](buf5, buf6, 12, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 3), (3, 0, 1), 0
            ), reinterpret_tensor(buf0, (4, 3, 4), (12, 4, 1), 0), out=buf7)
        del buf6
    return reinterpret_tensor(buf7, (4, 4), (4, 1), 0
        ), primals_8, reinterpret_tensor(buf0, (4, 3, 4), (12, 4, 1), 0
        ), buf3, buf5, primals_9


class OutputSPNew(nn.Module):

    def __init__(self, opt):
        super(OutputSPNew, self).__init__()
        self.rnn_size = opt.rnn_size
        self.att_hid_size = opt.att_hid_size
        self.wv = nn.Linear(self.rnn_size, self.att_hid_size)
        self.wh = nn.Linear(self.rnn_size, self.att_hid_size)
        self.wa = nn.Linear(self.att_hid_size, 1)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.wv.weight
        primals_5 = self.wv.bias
        primals_2 = self.wh.weight
        primals_7 = self.wh.bias
        primals_9 = self.wa.weight
        primals_10 = self.wa.bias
        primals_3 = input_0
        primals_4 = input_1
        primals_6 = input_2
        primals_8 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.028109