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


class SmallAdversarialNetwork(nn.Module):

    def __init__(self, in_feature):
        super(SmallAdversarialNetwork, self).__init__()
        self.ad_layer1 = nn.Linear(in_feature, 64)
        self.ad_layer2 = nn.Linear(64, 1)
        self.relu1 = nn.LeakyReLU()
        self.dropout1 = nn.Dropout(0.5)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.ad_layer1(x)
        x = self.relu1(x)
        x = self.dropout1(x)
        x = self.ad_layer2(x)
        x = self.sigmoid(x)
        return x

    def output_num(self):
        return 1


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


def get_init_inputs():
    return [[], {'in_feature': 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.utils.data
import torch
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4), (4, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 64), (64, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf2 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(4096)](buf0, primals_2, buf1,
            buf2, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_4, (64, 1), (1, 64), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf3
        triton_poi_fused_sigmoid_1[grid(64)](buf4, primals_5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
    return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 64), (64, 1), 0
        ), buf4, primals_4


class SmallAdversarialNetworkNew(nn.Module):

    def __init__(self, in_feature):
        super(SmallAdversarialNetworkNew, self).__init__()
        self.ad_layer1 = nn.Linear(in_feature, 64)
        self.ad_layer2 = nn.Linear(64, 1)
        self.relu1 = nn.LeakyReLU()
        self.dropout1 = nn.Dropout(0.5)
        self.sigmoid = nn.Sigmoid()

    def output_num(self):
        return 1

    def forward(self, input_0):
        primals_1 = self.ad_layer1.weight
        primals_2 = self.ad_layer1.bias
        primals_4 = self.ad_layer2.weight
        primals_5 = self.ad_layer2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
