import torch


class Feedforward(torch.nn.Module):

    def __init__(self, input_size, hidden_size=100):
        super(Feedforward, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(self.hidden_size, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x, y):
        inp = torch.vstack([x, y])
        hidden = self.fc1(inp)
        relu = self.relu(hidden)
        output = self.fc2(relu)
        output = self.sigmoid(output)
        return output


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


def get_init_inputs():
    return [[], {'input_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
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * (-4 + x1)), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 100
    x2 = xindex % 1600
    x3 = xindex // 1600
    tmp0 = tl.load(in_out_ptr0 + x4, 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    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, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (100, 4), (4, 1))
    assert_size_stride(primals_4, (100,), (1,))
    assert_size_stride(primals_5, (1, 100), (100, 1))
    assert_size_stride(primals_6, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((8, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((128, 100), (100, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (128, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 100), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (8, 4, 4, 100), (1600, 400, 100, 1), 0)
        del buf1
        buf5 = empty_strided_cuda((8, 4, 4, 100), (1664, 400, 100, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(12800)](buf2,
            primals_4, buf5, 12800, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((128, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (128, 100), (100, 1), 0),
            reinterpret_tensor(primals_5, (100, 1), (1, 100), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (8, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf3
        triton_poi_fused_sigmoid_2[grid(128)](buf4, primals_6, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_6
    return buf4, reinterpret_tensor(buf0, (128, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (128, 100), (100, 1), 0
        ), buf4, primals_5, buf5


class FeedforwardNew(torch.nn.Module):

    def __init__(self, input_size, hidden_size=100):
        super(FeedforwardNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(self.hidden_size, 1)
        self.sigmoid = torch.nn.Sigmoid()

    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_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
