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


class conv_head_pooling(nn.Module):

    def __init__(self, in_feature, out_feature, stride, conv_type,
        padding_mode='zeros', dilation=1):
        super(conv_head_pooling, self).__init__()
        if conv_type == 'depthwise':
            _groups = in_feature
        else:
            _groups = 1
        None
        self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=3,
            padding=dilation, dilation=dilation, stride=stride,
            padding_mode=padding_mode, groups=_groups)
        self.fc = nn.Linear(in_feature, out_feature)

    def forward(self, x, cls_token):
        x = self.conv(x)
        cls_token = self.fc(cls_token)
        return x, cls_token


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


def get_init_inputs():
    return [[], {'in_feature': 4, 'out_feature': 4, 'stride': 1,
        'conv_type': 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_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
    return buf1, reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_3, reinterpret_tensor(primals_6, (64, 4), (4,
        1), 0)


class conv_head_poolingNew(nn.Module):

    def __init__(self, in_feature, out_feature, stride, conv_type,
        padding_mode='zeros', dilation=1):
        super(conv_head_poolingNew, self).__init__()
        if conv_type == 'depthwise':
            _groups = in_feature
        else:
            _groups = 1
        None
        self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=3,
            padding=dilation, dilation=dilation, stride=stride,
            padding_mode=padding_mode, groups=_groups)
        self.fc = nn.Linear(in_feature, out_feature)

    def forward(self, input_0, input_1):
        primals_1 = self.conv.weight
        primals_2 = self.conv.bias
        primals_4 = self.fc.weight
        primals_5 = self.fc.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
