import torch
import torch.nn as nn


class Denoise_NormalizeLayer(nn.Module):

    def __init__(self):
        super(Denoise_NormalizeLayer, self).__init__()

    def forward(self, inputs: 'torch.tensor'):
        permute_RGBtoBGR = [2, 1, 0]
        inputs = inputs[:, permute_RGBtoBGR, :, :]
        out = inputs / 0.5 - 1
        return out


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


def get_init_inputs():
    return [[], {}]

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
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


@triton.jit
def triton_poi_fused_div_index_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 3
    x0 = xindex % 16
    x2 = xindex // 48
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp1, tmp5)
    tmp7 = tl.where(tmp2, tmp3, tmp6)
    tmp8 = tl.load(in_ptr0 + (x0 + 16 * tmp7 + 64 * x2), xmask)
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = 1.0
    tmp12 = tmp10 - tmp11
    tl.store(out_ptr0 + x3, tmp12, 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, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_index_sub_0[grid(192)](arg0_1, buf0, 192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class Denoise_NormalizeLayerNew(nn.Module):

    def __init__(self):
        super(Denoise_NormalizeLayerNew, self).__init__()

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