import torch
import torch.nn as nn
import torch.utils.data


class SplitAndConcat(nn.Module):
    """Split the data from split_dim and concatenate in concat_dim.

    @param split_dim from which axis the data will be chunk
    @param concat_dim to which axis the data will be concatenated
    @param chunk size of the data to be chunk/concatenated
    """

    def __init__(self, split_dim: 'int'=1, concat_dim: 'int'=0, chunk: 'int'=2
        ):
        super(SplitAndConcat, self).__init__()
        self.split_dim = split_dim
        self.concat_dim = concat_dim
        self.chunk = chunk

    def forward(self, x):
        x = torch.chunk(x, self.chunk, dim=self.split_dim)
        x = torch.cat(x, dim=self.concat_dim)
        return x

    def extra_repr(self):
        return (
            f'split_dim={self.split_dim}, concat_dim={self.concat_dim}, chunk={self.chunk}'
            )


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
import torch.utils.data
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_cat_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
    x1 = xindex // 32
    x0 = xindex % 32
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * (-4 + x1)), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, 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((8, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SplitAndConcatNew(nn.Module):
    """Split the data from split_dim and concatenate in concat_dim.

    @param split_dim from which axis the data will be chunk
    @param concat_dim to which axis the data will be concatenated
    @param chunk size of the data to be chunk/concatenated
    """

    def __init__(self, split_dim: 'int'=1, concat_dim: 'int'=0, chunk: 'int'=2
        ):
        super(SplitAndConcatNew, self).__init__()
        self.split_dim = split_dim
        self.concat_dim = concat_dim
        self.chunk = chunk

    def extra_repr(self):
        return (
            f'split_dim={self.split_dim}, concat_dim={self.concat_dim}, chunk={self.chunk}'
            )

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