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


class VideoBoringModel(nn.Module):

    def __init__(self, in_channel):
        super().__init__()
        self.avg_pool3d = nn.AdaptiveAvgPool3d(1)
        self.fc = nn.Linear(in_channel, 1024)

    def forward(self, x):
        x = self.avg_pool3d(x).squeeze()
        x = self.fc(x)
        return x

    def output_size(self):
        return self.fc.in_features


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


def get_init_inputs():
    return [[], {'in_channel': 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.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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, 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, (1024, 4), (4, 1))
    assert_size_stride(primals_3, (1024,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(4)](buf1, primals_1, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((1, 1024), (1024, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (1, 4), (0,
            1), 0), reinterpret_tensor(primals_2, (4, 1024), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_2
        del primals_3
    return reinterpret_tensor(buf2, (1024,), (1,), 0), reinterpret_tensor(buf1,
        (1, 4), (4, 1), 0)


class VideoBoringModelNew(nn.Module):

    def __init__(self, in_channel):
        super().__init__()
        self.avg_pool3d = nn.AdaptiveAvgPool3d(1)
        self.fc = nn.Linear(in_channel, 1024)

    def output_size(self):
        return self.fc.in_features

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