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
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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_neg_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = -tmp0
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_neg_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_2, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(
            primals_1, (4, 64), (64, 1), 0), buf0, 1.0, None)
        buf2 = buf1
        del buf1
        buf3 = buf2
        del buf2
        triton_poi_fused_neg_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf4 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(
            primals_1, (4, 64), (64, 1), 0), buf0, 2.0, None)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_neg_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf3, buf6, reinterpret_tensor(primals_1, (4, 64), (64, 1), 0
        ), buf0, buf5


def adder2d_function(X, W, stride=1, padding=0, groups=1):
    n_filters, _d_filter, h_filter, w_filter = W.size()
    n_x, _d_x, h_x, w_x = X.size()
    h_out = (h_x - h_filter + 2 * padding) / stride + 1
    w_out = (w_x - w_filter + 2 * padding) / stride + 1
    h_out, w_out = int(h_out), int(w_out)
    if groups == 1:
        X_col = torch.nn.functional.unfold(X.view(1, -1, h_x, w_x),
            h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
            -1, h_out * w_out)
        X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
        W_col = W.view(n_filters, -1)
        out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
        out_2 = out_2.detach()
        out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
        out.data = out_2.data
        out = out.view(n_filters, h_out, w_out, n_x)
        out = out.permute(3, 0, 1, 2).contiguous()
        return out
    else:
        torch.Tensor(n_x, n_filters, h_out, w_out)
        outs = []
        for g in range(groups):
            X_part = X[:, g, ...].unsqueeze(1)
            W_part = W[g, ...].unsqueeze(0)
            X_col = torch.nn.functional.unfold(X_part.view(1, -1, h_x, w_x),
                h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
                -1, h_out * w_out)
            X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
            W_col = W_part.view(1, -1)
            out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
            out_2 = out_2.detach()
            out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
            out.data = out_2.data
            out = out.view(1, h_out, w_out, n_x)
            out = out.permute(3, 0, 1, 2).contiguous()
            outs.append(out)
        return torch.cat(outs, dim=1)


class adder2dNew(nn.Module):

    def __init__(self, input_channel, output_channel, kernel_size, stride=1,
        padding=0, groups=1, bias=False):
        super(adder2dNew, self).__init__()
        self.stride = stride
        self.padding = padding
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel_size = kernel_size
        self.groups = groups
        self.adder = torch.nn.Parameter(nn.init.kaiming_normal_(torch.randn
            (output_channel, input_channel // groups, kernel_size,
            kernel_size)))
        self.bias = bias
        if bias:
            self.b = torch.nn.Parameter(nn.init.kaiming_uniform_(torch.
                zeros(output_channel)))

    def extra_repr(self):
        return (
            f"{'v2'.upper()}, {self.input_channel}, {self.output_channel}, " +
            f'kenrel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}, groups={self.groups}, bias={self.bias}'
            )

    def forward(self, input_0):
        primals_1 = self.adder
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.054145