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
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_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 256
    x0 = xindex % 4
    x3 = xindex // 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(out_ptr0 + x6, tmp7, xmask)


@triton.jit
def triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1(in_ptr0,
    in_ptr1, out_ptr0, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tmp0 != 0
    tmp2 = tmp1 == 0
    tmp4 = float('-inf')
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr2 + x0, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(1024)](buf0, primals_2, buf1,
            primals_6, buf2, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        del primals_6
        buf4 = reinterpret_tensor(buf1, (256, 1), (1, 1), 0)
        del buf1
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1[grid(256)
            ](primals_9, buf4, buf5, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_9
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), buf2, buf5, primals_7


class AdditiveAttentionNew(nn.Module):
    """
    Originally from:
        https://arxiv.org/pdf/1409.0473v5.pdf

    Also referenced to as Content Based Attention:
        https://arxiv.org/pdf/1506.03134v1.pdf

    Attention is learned for a query vector over a set of vectors. 
    If we have a query vector and then a key matrix with the size (n,k), 
    we will create attention vector over n

    
    NOTE!
    We mask out the attention scores for the positions which its impossible for 
    the segments to attend to. I.e. the padding.


    """

    def __init__(self, input_dim: 'int'):
        super().__init__()
        self.W1 = nn.Linear(input_dim, input_dim)
        self.W2 = nn.Linear(input_dim, input_dim)
        self.v = nn.Linear(input_dim, 1)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W1.weight
        primals_2 = self.W1.bias
        primals_5 = self.W2.weight
        primals_6 = self.W2.bias
        primals_7 = self.v.weight
        primals_8 = self.v.bias
        primals_3 = input_0
        primals_4 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.014183