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
from torch import 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
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tmp5 = tl_math.exp(tmp4)
    tmp9 = tmp6 + tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp5 + tmp11
    tmp16 = tmp13 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp12 + tmp18
    tmp23 = tmp20 + tmp22
    tmp24 = libdevice.tanh(tmp23)
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp19 + tmp25
    tl.store(out_ptr0 + x0, tmp26, xmask)


@triton.jit
def triton_poi_fused_mul_sum_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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr2 + 1)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp23 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp24 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr2 + 2)
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK])
    tmp34 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp35 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp36 = tl.load(in_ptr2 + 3)
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = 1e-10
    tmp10 = tmp8 + tmp9
    tmp11 = tmp0 * tmp10
    tmp16 = tmp13 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp18 / tmp7
    tmp20 = tmp19 + tmp9
    tmp21 = tmp12 * tmp20
    tmp22 = tmp11 + tmp21
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.tanh(tmp27)
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp29 / tmp7
    tmp31 = tmp30 + tmp9
    tmp32 = tmp23 * tmp31
    tmp33 = tmp22 + tmp32
    tmp38 = tmp35 + tmp37
    tmp39 = libdevice.tanh(tmp38)
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp40 / tmp7
    tmp42 = tmp41 + tmp9
    tmp43 = tmp34 * tmp42
    tmp44 = tmp33 + tmp43
    tl.store(out_ptr0 + x2, tmp44, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (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_1, (64, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_sum_tanh_0[grid(16)](buf0, primals_3, buf1,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(64)](primals_1, buf0, primals_3,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
    return buf2, primals_1, primals_3, buf0


class AttentionNew(nn.Module):

    def __init__(self, feature_dim, K, bias=True, **kwargs):
        super(AttentionNew, self).__init__(**kwargs)
        self.supports_masking = True
        self.bias = bias
        self.feature_dim = feature_dim
        self.K = K
        weight = torch.zeros(feature_dim, 1)
        nn.init.xavier_uniform_(weight)
        self.weight = nn.Parameter(weight)
        if bias:
            self.b = nn.Parameter(torch.zeros(K))

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


Measured latency_ms: 0.036016