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.triton_helpers import libdevice, 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_add_exp_sum_tanh_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp0 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp4 + tmp8
    tmp11 = tmp0 + tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp9 + tmp13
    tmp16 = tmp0 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp14 + tmp18
    tmp20 = 1e-07
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_mul_sum_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex // 4
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x4 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr1 + (4 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr3 + (16 + x4 + 64 * x2), xmask)
    tmp17 = tl.load(in_ptr1 + (8 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr3 + (32 + x4 + 64 * x2), xmask)
    tmp25 = tl.load(in_ptr1 + (12 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr3 + (48 + x4 + 64 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 / tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp0 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp12 / tmp5
    tmp15 = tmp13 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp0 + tmp17
    tmp19 = libdevice.tanh(tmp18)
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp5
    tmp23 = tmp21 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp0 + tmp25
    tmp27 = libdevice.tanh(tmp26)
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp28 / tmp5
    tmp31 = tmp29 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + x5, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 70, 1), (70, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_sum_tanh_0[grid(16)](primals_4, buf1, buf2,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_exp_mul_sum_tanh_1[grid(64)](primals_4,
            buf1, buf2, primals_3, buf3, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf2
    return buf3, primals_3, buf1, reinterpret_tensor(primals_4, (1, 4, 1),
        (70, 1, 1), 0)


class AttentionNew(nn.Module):

    def __init__(self, feature_dim, maxlen=70):
        super().__init__()
        self.attention_fc = nn.Linear(feature_dim, 1)
        self.bias = nn.Parameter(torch.zeros(1, maxlen, 1, requires_grad=True))

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


Measured latency_ms: 0.027846