import torch
import torch.nn as nn


class DownSampleConv(nn.Module):

    def __init__(self, in_feature, out_feature, kernel):
        super(DownSampleConv, self).__init__()
        self.conv = nn.Conv1d(in_feature, out_feature, kernel_size=kernel,
            stride=2, padding=kernel // 2, groups=in_feature)
        self._init_weigth()

    def _init_weigth(self):
        nn.init.xavier_uniform_(self.conv.weight)
        nn.init.zeros_(self.conv.bias)

    def forward(self, x):
        x = self.conv(x)
        return nn.ReLU()(x)


def get_inputs():
    return [torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_feature': 4, 'out_feature': 4, 'kernel': 4}]

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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4), (4, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_1, stride=(2,), padding=(2,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=4,
            bias=None)
        assert_size_stride(buf0, (1, 4, 3), (12, 3, 1))
        buf1 = reinterpret_tensor(buf0, (4, 3), (3, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 3), (3, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(12)](buf1,
            primals_2, buf2, 12, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf1, primals_1, reinterpret_tensor(primals_3, (1, 4, 4), (16, 4,
        1), 0), buf2


class DownSampleConvNew(nn.Module):

    def __init__(self, in_feature, out_feature, kernel):
        super(DownSampleConvNew, self).__init__()
        self.conv = nn.Conv1d(in_feature, out_feature, kernel_size=kernel,
            stride=2, padding=kernel // 2, groups=in_feature)
        self._init_weigth()

    def _init_weigth(self):
        nn.init.xavier_uniform_(self.conv.weight)
        nn.init.zeros_(self.conv.bias)

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