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_add_tanh_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + x3, tmp5, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](primals_2, buf0, primals_4,
            buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        del primals_4
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(4)](buf3, buf4, buf5, buf6, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 16), (16, 0, 1),
            0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 4, 1), 0),
            out=buf7)
        del buf6
    return reinterpret_tensor(buf7, (4, 4), (4, 1), 0
        ), primals_5, buf1, buf3, buf4, buf5, reinterpret_tensor(primals_1,
        (4, 4, 16), (64, 1, 4), 0), primals_6


class AttentionNew(nn.Module):

    def __init__(self, opt):
        super(AttentionNew, self).__init__()
        self.rnn_size = opt.rnn_size
        self.att_hid_size = opt.att_hid_size
        self.h2att = nn.Linear(self.rnn_size, self.att_hid_size)
        self.alpha_net = nn.Linear(self.att_hid_size, 1)

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.h2att.weight
        primals_4 = self.h2att.bias
        primals_6 = self.alpha_net.weight
        primals_7 = self.alpha_net.bias
        primals_5 = input_0
        primals_1 = input_1
        primals_2 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.01591