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 = 32
    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_add_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    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 + (8 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 * tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_tanh_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, out_ptr1, 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 + (4 + x0 + 8 * x1), xmask)
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp0 * tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.tanh(tmp7)
    tmp9 = tmp2 * tmp8
    tmp10 = tmp4 + tmp9
    tl.store(out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr1 + x2, tmp10, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (8, 8), (8, 1))
    assert_size_stride(primals_4, (8,), (1,))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf0, primals_3, out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_add_sigmoid_1[grid(32)](buf2, primals_4, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(32)](primals_1, buf2, primals_2, buf3, 
            32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, primals_5, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_tanh_3[grid(16)](buf2, primals_2,
            buf4, primals_6, buf5, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf6, primals_2, primals_6, buf2, buf4, buf5, reinterpret_tensor(
        buf3, (8, 4), (1, 8), 0), reinterpret_tensor(primals_5, (4, 8), (1,
        4), 0), reinterpret_tensor(buf0, (8, 4), (1, 8), 0)


class GRUCellNew(nn.Module):

    def __init__(self, input_size, hidden_size):
        super(GRUCellNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self._W = nn.Parameter(torch.FloatTensor(input_size + hidden_size, 
            2 * hidden_size))
        self._W_b = nn.Parameter(torch.FloatTensor(2 * hidden_size))
        self._U = nn.Parameter(torch.FloatTensor(input_size + hidden_size,
            hidden_size))
        self._U_b = nn.Parameter(torch.FloatTensor(hidden_size))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self._W.data)
        nn.init.xavier_uniform_(self._U.data)
        nn.init.constant_(self._W_b.data, 0)
        nn.init.constant_(self._U_b.data, 0)

    def forward(self, input_0, input_1):
        primals_3 = self._W
        primals_4 = self._W_b
        primals_5 = self._U
        primals_6 = self._U_b
        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: 0.030966