import torch
from torch import nn


class IWConv2d(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
        stride=1, bias=True):
        super(IWConv2d, self).__init__()
        self.he_init = he_init
        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
            padding=self.padding, bias=bias)

    def forward(self, input):
        output = self.conv(input)
        return output


class DepthToSpace(nn.Module):

    def __init__(self, block_size):
        super(DepthToSpace, self).__init__()
        self.block_size = block_size
        self.block_size_sq = block_size * block_size

    def forward(self, input):
        output = input.permute(0, 2, 3, 1)
        batch_size, input_height, input_width, input_depth = output.size()
        output_depth = int(input_depth / self.block_size_sq)
        output_width = int(input_width * self.block_size)
        output_height = int(input_height * self.block_size)
        t_1 = output.reshape(batch_size, input_height, input_width, self.
            block_size_sq, output_depth)
        spl = t_1.split(self.block_size, 3)
        stacks = [t_t.reshape(batch_size, input_height, output_width,
            output_depth) for t_t in spl]
        output = torch.stack(stacks, 0).transpose(0, 1).permute(0, 2, 1, 3, 4
            ).reshape(batch_size, output_height, output_width, output_depth)
        output = output.permute(0, 3, 1, 2)
        return output


class UpSampleConv(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
        bias=True):
        super(UpSampleConv, self).__init__()
        self.he_init = he_init
        self.conv = IWConv2d(input_dim, output_dim, kernel_size, he_init=
            self.he_init, bias=bias)
        self.depth_to_space = DepthToSpace(2)

    def forward(self, input):
        output = input
        output = torch.cat((output, output, output, output), 1)
        output = self.depth_to_space(output)
        output = self.conv(output)
        return output


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 4, 'kernel_size': 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 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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 32 % 2
    x4 = xindex // 256
    x0 = xindex % 4
    x1 = xindex // 4 % 8
    x3 = xindex // 64 % 4
    x6 = xindex
    tmp0 = x4 + 4 * x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x3 + 16 * x0 + 64 * (x4 + 4 * x2) + x1 //
        2), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (4 * x3 + 16 * x0 + 64 * (-4 + x4 + 4 * x2) + 
        x1 // 2), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x6, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, 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)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 2, 8, 4), (256, 64, 32, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](primals_1, buf0, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 16)](primals_2, buf1, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 4, 8,
            8), (256, 1, 32, 4), 0), buf1, stride=(1, 1), padding=(1, 1),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 7, 7), (196, 1, 28, 4))
        del buf1
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(784)](buf3, primals_3, 784,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf3, primals_2, reinterpret_tensor(buf0, (4, 4, 8, 8), (256, 1,
        32, 4), 0)


class IWConv2d(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
        stride=1, bias=True):
        super(IWConv2d, self).__init__()
        self.he_init = he_init
        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
            padding=self.padding, bias=bias)

    def forward(self, input):
        output = self.conv(input)
        return output


class DepthToSpace(nn.Module):

    def __init__(self, block_size):
        super(DepthToSpace, self).__init__()
        self.block_size = block_size
        self.block_size_sq = block_size * block_size

    def forward(self, input):
        output = input.permute(0, 2, 3, 1)
        batch_size, input_height, input_width, input_depth = output.size()
        output_depth = int(input_depth / self.block_size_sq)
        output_width = int(input_width * self.block_size)
        output_height = int(input_height * self.block_size)
        t_1 = output.reshape(batch_size, input_height, input_width, self.
            block_size_sq, output_depth)
        spl = t_1.split(self.block_size, 3)
        stacks = [t_t.reshape(batch_size, input_height, output_width,
            output_depth) for t_t in spl]
        output = torch.stack(stacks, 0).transpose(0, 1).permute(0, 2, 1, 3, 4
            ).reshape(batch_size, output_height, output_width, output_depth)
        output = output.permute(0, 3, 1, 2)
        return output


class UpSampleConvNew(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
        bias=True):
        super(UpSampleConvNew, self).__init__()
        self.he_init = he_init
        self.conv = IWConv2d(input_dim, output_dim, kernel_size, he_init=
            self.he_init, bias=bias)
        self.depth_to_space = DepthToSpace(2)

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