import torch
import torch.nn as nn
import torch.utils.data


class ResidualBlock(nn.Module):

    def __init__(self, in_channels, out_channels, activation='relu'):
        super().__init__()
        self.in_channels, self.out_channels, self.activation = (in_channels,
            out_channels, activation)
        self.blocks = nn.Identity()
        self.activate = nn.ReLU()
        self.shortcut = nn.Identity()

    def forward(self, x):
        residual = x
        if self.should_apply_shortcut:
            residual = self.shortcut(x)
        x = self.blocks(x)
        x += residual
        x = self.activate(x)
        return x

    @property
    def should_apply_shortcut(self):
        return self.in_channels != self.out_channels


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

import torch
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
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_add_relu_0(in_ptr0, out_ptr0, out_ptr2, 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_ptr0 + x0, xmask)
    tmp1 = tmp0 + tmp0
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = triton_helpers.maximum(tmp2, tmp1)
    tl.store(out_ptr0 + x0, tmp3, xmask)
    tl.store(out_ptr2 + x0, tmp1, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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)
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(256)](arg0_1, buf0, arg0_1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class ResidualBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, activation='relu'):
        super().__init__()
        self.in_channels, self.out_channels, self.activation = (in_channels,
            out_channels, activation)
        self.blocks = nn.Identity()
        self.activate = nn.ReLU()
        self.shortcut = nn.Identity()

    @property
    def should_apply_shortcut(self):
        return self.in_channels != self.out_channels

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
