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.init
from torch import nn
from torch.autograd import Variable
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@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
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_tanh_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 25 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = libdevice.tanh(tmp2)
    tmp8 = tmp6 * tmp7
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp6, xmask)
    tl.store(out_ptr0 + x3, tmp8, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8, 4, 4), (128, 16, 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, 4, 4), (128, 16, 4, 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)
        del primals_1
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
        buf3 = extern_kernels.convolution(buf0, primals_5, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 5, 5), (100, 25, 5, 1))
        buf2 = buf1
        del buf1
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_tanh_1[grid(400)](buf2,
            buf4, primals_4, primals_6, buf5, 400, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_4
        del primals_6
    return buf5, buf4, primals_2, primals_3, primals_5, buf0, buf2, buf4


class SCS_CellNew(nn.Module):

    def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias,
        p_TD):
        super(SCS_CellNew, self).__init__()
        self.height, self.width = input_size
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.padding = kernel_size[0] // 2, kernel_size[1] // 2
        self.bias = bias
        self.p_TD = p_TD
        self.data_cnn = nn.Conv2d(in_channels=self.input_dim, out_channels=
            self.hidden_dim, kernel_size=self.kernel_size, padding=self.
            padding, bias=self.bias)
        self.ctrl_cnn = nn.Conv2d(in_channels=self.input_dim + self.
            hidden_dim, out_channels=self.hidden_dim, kernel_size=self.
            kernel_size, padding=self.padding, bias=self.bias)

    def init_hidden(self, batch_size):
        return Variable(torch.zeros(batch_size, self.hidden_dim, self.
            height, self.width))

    def forward(self, input_0, input_1):
        primals_1 = self.data_cnn.weight
        primals_4 = self.data_cnn.bias
        primals_5 = self.ctrl_cnn.weight
        primals_6 = self.ctrl_cnn.bias
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]


Measured latency_ms: 0.045941