import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F


class XConv2d(nn.Conv2d):
    """
    X-Convolution layer.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    groups : int, default 1
        Number of groups.
    expand_ratio : int, default 2
        Ratio of expansion.
    """

    def __init__(self, in_channels, out_channels, kernel_size, groups=1,
        expand_ratio=2, **kwargs):
        super(XConv2d, self).__init__(in_channels=in_channels, out_channels
            =out_channels, kernel_size=kernel_size, groups=groups, **kwargs)
        self.expand_ratio = expand_ratio
        if isinstance(kernel_size, int):
            kernel_size = kernel_size, kernel_size
        grouped_in_channels = in_channels // groups
        self.mask = torch.nn.Parameter(data=torch.Tensor(out_channels,
            grouped_in_channels, *kernel_size), requires_grad=False)
        self.init_parameters()

    def init_parameters(self):
        shape = self.mask.shape
        expand_size = max(shape[1] // self.expand_ratio, 1)
        self.mask[:] = 0
        for i in range(shape[0]):
            jj = torch.randperm(shape[1], device=self.mask.device)[:expand_size
                ]
            self.mask[i, jj, :, :] = 1

    def forward(self, input):
        masked_weight = self.weight.mul(self.mask)
        return F.conv2d(input=input, weight=masked_weight, bias=self.bias,
            stride=self.stride, padding=self.padding, dilation=self.
            dilation, groups=self.groups)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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
import torch.utils.data
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_mul_0(in_ptr0, in_ptr1, 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_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    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, primals_4 = 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,))
    assert_size_stride(primals_4, (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_mul_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(primals_4, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf2, primals_2, primals_4, buf0


class XConv2dNew(nn.Conv2d):
    """
    X-Convolution layer.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    groups : int, default 1
        Number of groups.
    expand_ratio : int, default 2
        Ratio of expansion.
    """

    def __init__(self, in_channels, out_channels, kernel_size, groups=1,
        expand_ratio=2, **kwargs):
        super(XConv2dNew, self).__init__(in_channels=in_channels,
            out_channels=out_channels, kernel_size=kernel_size, groups=
            groups, **kwargs)
        self.expand_ratio = expand_ratio
        if isinstance(kernel_size, int):
            kernel_size = kernel_size, kernel_size
        grouped_in_channels = in_channels // groups
        self.mask = torch.nn.Parameter(data=torch.Tensor(out_channels,
            grouped_in_channels, *kernel_size), requires_grad=False)
        self.init_parameters()

    def init_parameters(self):
        shape = self.mask.shape
        expand_size = max(shape[1] // self.expand_ratio, 1)
        self.mask[:] = 0
        for i in range(shape[0]):
            jj = torch.randperm(shape[1], device=self.mask.device)[:expand_size
                ]
            self.mask[i, jj, :, :] = 1

    def forward(self, input_0):
        primals_1 = self.weight
        primals_3 = self.bias
        primals_2 = self.mask
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
