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
import numpy as np
from torch import 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, out_ptr1, out_ptr2,
    out_ptr3, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr3 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(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)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg3_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg4_1, (4, 1, 2, 2), (4, 4, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 16)](arg1_1, buf0, buf3,
            buf6, buf9, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1
            )
        del arg1_1
        buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 4)](buf1, buf2, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, arg2_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg2_1
        del buf3
        buf5 = reinterpret_tensor(buf1, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf1
        triton_poi_fused_convolution_1[grid(16, 4)](buf4, buf5, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, arg3_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg3_1
        del buf6
        buf8 = reinterpret_tensor(buf4, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf4
        triton_poi_fused_convolution_1[grid(16, 4)](buf7, buf8, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, arg4_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf10, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg4_1
        del buf9
        buf11 = reinterpret_tensor(buf7, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf7
        triton_poi_fused_convolution_1[grid(16, 4)](buf10, buf11, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf10
    return buf2, buf5, buf8, buf11


def getWavelet(in_channels, pool=True):
    """wavelet decomposition using conv2d"""
    harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]
    harr_wav_LL = np.transpose(harr_wav_L) * harr_wav_L
    harr_wav_LH = np.transpose(harr_wav_L) * harr_wav_H
    harr_wav_HL = np.transpose(harr_wav_H) * harr_wav_L
    harr_wav_HH = np.transpose(harr_wav_H) * harr_wav_H
    filter_LL = torch.from_numpy(harr_wav_LL).unsqueeze(0)
    filter_LH = torch.from_numpy(harr_wav_LH).unsqueeze(0)
    filter_HL = torch.from_numpy(harr_wav_HL).unsqueeze(0)
    filter_HH = torch.from_numpy(harr_wav_HH).unsqueeze(0)
    if pool:
        net = nn.Conv2d
    else:
        net = nn.ConvTranspose2d
    LL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LL.weight.requires_grad = False
    LH.weight.requires_grad = False
    HL.weight.requires_grad = False
    HH.weight.requires_grad = False
    LL.weight.data = filter_LL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    LH.weight.data = filter_LH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HL.weight.data = filter_HL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HH.weight.data = filter_HH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    return LL, LH, HL, HH


class WavePoolNew(nn.Module):

    def __init__(self, in_channels):
        super(WavePoolNew, self).__init__()
        self.LL, self.LH, self.HL, self.HH = getWavelet(in_channels)

    def forward(self, input_0):
        arg0_1 = self.LL.weight
        arg2_1 = self.LH.weight
        arg3_1 = self.HL.weight
        arg4_1 = self.HH.weight
        arg1_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0], output[1], output[2], output[3]


Measured latency_ms: 0.104559