import torch
import torch.nn as nn


class Net(nn.Module):

    def __init__(self, input_dim, output_dim, hidden_dim=None, barcode_dim=0):
        super().__init__()
        if hidden_dim is None:
            hidden_dim = [250, 100]
        self.fc1 = nn.Linear(input_dim, hidden_dim[0])
        self.act = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim[0], hidden_dim[1])
        self.fc3 = nn.Linear(hidden_dim[1], output_dim)
        self.barcode_dim = barcode_dim

    def forward(self, x, bar):
        x = x.view((x.size(0), -1))
        bar = bar.view((bar.size(0), -1))
        if self.barcode_dim > 0:
            x = torch.cat((x, bar), dim=1)
        out = self.fc1(x)
        out = self.act(out)
        out = self.fc2(out)
        out = self.act(out)
        out = self.fc3(out)
        return out


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 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 = 1000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 250
    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)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    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, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (250, 4), (4, 1))
    assert_size_stride(primals_4, (250,), (1,))
    assert_size_stride(primals_5, (100, 250), (250, 1))
    assert_size_stride(primals_6, (100,), (1,))
    assert_size_stride(primals_7, (4, 100), (100, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 250), (250, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_3, (4, 250),
            (1, 4), 0), out=buf0)
        del primals_3
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1000)](buf1, primals_4, 1000, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 100), (100, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (250, 100), (
            1, 250), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(400)](buf3, primals_6, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7,
            (100, 4), (1, 100), 0), alpha=1, beta=1, out=buf4)
        del primals_8
    return buf4, primals_1, buf1, buf3, primals_7, primals_5


class NetNew(nn.Module):

    def __init__(self, input_dim, output_dim, hidden_dim=None, barcode_dim=0):
        super().__init__()
        if hidden_dim is None:
            hidden_dim = [250, 100]
        self.fc1 = nn.Linear(input_dim, hidden_dim[0])
        self.act = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim[0], hidden_dim[1])
        self.fc3 = nn.Linear(hidden_dim[1], output_dim)
        self.barcode_dim = barcode_dim

    def forward(self, input_0, input_1):
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_7 = self.fc3.weight
        primals_8 = self.fc3.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
