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 import triton_helpers
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_convolution_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 4
    x3 = xindex // 4
    y0 = yindex % 4
    y1 = yindex // 4
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (x5 + 16 * y4), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (x5 + 16 * y4), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_relu_sigmoid_threshold_backward_1(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 8 % 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')
    tmp8 = tl.load(in_ptr2 + x3, xmask)
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp2)
    tmp7 = tmp5 + tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = 0.0
    tmp15 = tmp13 <= tmp14
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp13, xmask)
    tl.store(out_ptr0 + x3, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = 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)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 16)](primals_1, buf0, buf2,
            buf5, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 2), (32, 8, 2, 1))
        del buf0
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 2), (32, 8, 2, 1))
        del buf2
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 2), (32, 8, 2, 1))
        del buf5
        buf4 = buf3
        del buf3
        buf7 = buf1
        del buf1
        buf8 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_sigmoid_threshold_backward_1[grid
            (128)](buf4, buf7, primals_5, primals_3, buf6, primals_7, buf8,
            128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        del primals_3
        del primals_5
        del primals_7
    return reinterpret_tensor(buf7, (4, 2, 4, 4), (32, 1, 2, 8), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        4, 4, 4, 4), (64, 1, 4, 16), 0), buf4, buf8


class TemporalConvNew(nn.Module):
    """Temporal convolution block applied to nodes in the STGCN Layer
    For details see: `"Spatio-Temporal Graph Convolutional Networks:
    A Deep Learning Framework for Traffic Forecasting" 
    <https://arxiv.org/abs/1709.04875>`_

    Based off the temporal convolution introduced in "Convolutional 
    Sequence to Sequence Learning"  <https://arxiv.org/abs/1709.04875>`_

    NB. Given an input sequence of length m and a kernel size of k
    the output sequence will have length m-(k-1)

    Args:
        in_channels (int): Number of input features.
        out_channels (int): Number of output features.
        kernel_size (int): Convolutional kernel size.
    """

    def __init__(self, in_channels, out_channels, kernel_size=3):
        super(TemporalConvNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))
        self.conv2 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))
        self.conv3 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.061774