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
import torch.nn.init as init
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_mul_rsub_sigmoid_tanh_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x5 = xindex
    x0 = xindex % 4
    x2 = xindex % 16
    x4 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x5, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x5, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x2 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr4 + x5, xmask)
    tmp10 = tl.load(in_ptr5 + x5, xmask)
    tmp17 = tl.load(in_ptr6 + x5, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = libdevice.tanh(tmp12)
    tmp14 = tmp8 * tmp13
    tmp15 = 1.0
    tmp16 = tmp15 - tmp8
    tmp18 = tmp16 * tmp17
    tmp19 = tmp14 + tmp18
    tl.store(in_out_ptr0 + x5, tmp7, xmask)
    tl.store(out_ptr0 + x5, tmp19, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = 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, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4, 4), (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_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf3)
        del primals_7
        del primals_8
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(primals_6, (64,
            4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf4)
        del primals_10
        del primals_9
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_0[grid(256)](buf2,
            primals_2, buf1, primals_5, primals_11, buf3, buf4, primals_6,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_2
        del primals_5
    return buf5, primals_11, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), buf2, buf3, buf4


class AttentionGRUCellNew(nn.Module):

    def __init__(self, input_size, hidden_size):
        super(AttentionGRUCellNew, self).__init__()
        self.hidden_size = hidden_size
        self.Wr = nn.Linear(input_size, hidden_size)
        init.xavier_normal_(self.Wr.state_dict()['weight'])
        self.Ur = nn.Linear(hidden_size, hidden_size)
        init.xavier_normal_(self.Ur.state_dict()['weight'])
        self.W = nn.Linear(input_size, hidden_size)
        init.xavier_normal_(self.W.state_dict()['weight'])
        self.U = nn.Linear(hidden_size, hidden_size)
        init.xavier_normal_(self.U.state_dict()['weight'])

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.Wr.weight
        primals_2 = self.Wr.bias
        primals_4 = self.Ur.weight
        primals_5 = self.Ur.bias
        primals_7 = self.W.weight
        primals_8 = self.W.bias
        primals_9 = self.U.weight
        primals_10 = self.U.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_11 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.024981