import torch
import torch.nn as nn
import torch.nn.functional as F


class FCTestNN(nn.Module):

    def __init__(self, class_size):
        super(FCTestNN, self).__init__()
        self.name = 'FCTestNN'
        self.fc1 = nn.Linear(3 * 224 * 224, 256)
        self.fc2 = nn.Linear(256, class_size)

    def forward(self, x):
        x = x.view(-1, 3 * 224 * 224)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = x.squeeze(1)
        return x


def get_inputs():
    return [torch.rand([4, 150528])]


def get_init_inputs():
    return [[], {'class_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
from torch._inductor.runtime import triton_helpers
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 150528), (150528, 1))
    assert_size_stride(primals_2, (256, 150528), (150528, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (4, 256), (256, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (150528,
            256), (1, 150528), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1024)](buf1, primals_3, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf2)
        del primals_5
    return buf2, primals_1, buf1, primals_4


class FCTestNNNew(nn.Module):

    def __init__(self, class_size):
        super(FCTestNNNew, self).__init__()
        self.name = 'FCTestNN'
        self.fc1 = nn.Linear(3 * 224 * 224, 256)
        self.fc2 = nn.Linear(256, class_size)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
