import torch
import torch.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn


class ConvRelu(nn.Module):
    """3x3 convolution followed by ReLU activation building block."""

    def __init__(self, num_in, num_out):
        super().__init__()
        self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
            bias=False)

    def forward(self, x):
        return nn.functional.relu(self.block(x), inplace=True)


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


def get_init_inputs():
    return [[], {'num_in': 4, 'num_out': 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.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn
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_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, buf2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf1, primals_1, primals_2, buf2


class ConvReluNew(nn.Module):
    """3x3 convolution followed by ReLU activation building block."""

    def __init__(self, num_in, num_out):
        super().__init__()
        self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
            bias=False)

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