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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    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
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    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 = tl.load(in_ptr1 + (8 + 12 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr2 + (8 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tmp5 * tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp4, tmp10, tmp11)
    tmp13 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp16 = tl.load(in_ptr3 + (4 * x1 + (-4 + x0)), tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tl.load(in_ptr1 + (8 + 12 * x1 + (-4 + x0)), tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr2 + (8 + (-4 + x0)), tmp13 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tmp16 * tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp13, tmp21, tmp22)
    tmp24 = tl.where(tmp4, tmp12, tmp23)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 12 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp5 = tl.load(in_ptr3 + x2, xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 12 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = libdevice.tanh(tmp12)
    tmp14 = tmp13 * tmp3
    tmp15 = tmp11 + tmp14
    tmp16 = 1.0
    tmp17 = tmp16 - tmp10
    tmp18 = tmp10 * tmp17
    tl.store(out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr2 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_sigmoid_sigmoid_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 + (8 + x0 + 12 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = 1.0
    tmp5 = tmp4 - tmp3
    tmp6 = tmp3 * tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (12, 8), (8, 1))
    assert_size_stride(primals_4, (12,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 12), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](primals_1, buf1, primals_4,
            primals_2, buf2, 512, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf2, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_2[grid(256)](
            buf1, primals_4, primals_1, primals_2, buf3, buf4, buf5, buf6, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_sigmoid_backward_3[grid(256)](buf1,
            primals_4, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_4
    return buf5, primals_1, primals_2, reinterpret_tensor(buf0, (64, 8), (8,
        1), 0), reinterpret_tensor(buf2, (64, 8), (8, 1), 0
        ), buf3, buf4, buf6, primals_5, buf7


class BinaryTreeGRULayerNew(nn.Module):

    def __init__(self, hidden_dim):
        super(BinaryTreeGRULayerNew, self).__init__()
        self.fc1 = nn.Linear(in_features=2 * hidden_dim, out_features=3 *
            hidden_dim)
        self.fc2 = nn.Linear(in_features=2 * hidden_dim, out_features=
            hidden_dim)

    def forward(self, input_0, input_1):
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.05064