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, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(80)](buf2, primals_3, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 5), (20, 5, 1))
        buf4 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(80)](buf3,
            primals_5, buf4, buf5, 80, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_5
    return reinterpret_tensor(buf5, (4, 5, 4), (20, 1, 5), 0
        ), primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4, 4), (
        16, 1, 4), 0), buf2, buf4


class DepthwiseSeparableConvNew(nn.Module):
    """
    Depth-wise separable convolution uses less parameters to generate output by convolution.
    :Examples:
        >>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
        >>> input_tensor = torch.randn(32, 300, 20)
        >>> output = m(input_tensor)
    """

    def __init__(self, in_ch, out_ch, k, dim=1, relu=True):
        """
        :param in_ch: input hidden dimension size
        :param out_ch: output hidden dimension size
        :param k: kernel size
        :param dim: default 1. 1D conv or 2D conv
        """
        super(DepthwiseSeparableConvNew, self).__init__()
        self.relu = relu
        if dim == 1:
            self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
                =in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
            self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
                =out_ch, kernel_size=1, padding=0)
        elif dim == 2:
            self.depthwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
                =in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
            self.pointwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
                =out_ch, kernel_size=1, padding=0)
        else:
            raise Exception('Incorrect dimension!')

    def forward(self, input_0):
        primals_2 = self.depthwise_conv.weight
        primals_3 = self.depthwise_conv.bias
        primals_4 = self.pointwise_conv.weight
        primals_5 = self.pointwise_conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.014233