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.autograd import Variable
import torch.nn as nn
import torch.autograd
import torch.optim as optim
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_sigmoid_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(256)](buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_sigmoid_0[grid(256)](buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf3, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf1, buf3, primals_3


class NaiveTorchNetNew(nn.Module):
    """A reimplementation of from-scratch NaiveNet using PyTorch"""

    def __init__(self, input_nodes, hidden_nodes, output_nodes, learn_rate=0.1
        ):
        super().__init__()
        self.hidden = nn.Linear(input_nodes, hidden_nodes, bias=False)
        self.output = nn.Linear(hidden_nodes, output_nodes, bias=False)
        self.lr = learn_rate
        self.activation_function = nn.Sigmoid()
        self.optimizer = optim.SGD(self.parameters(), lr=learn_rate)
        self.loss_function = nn.MSELoss()

    def query(self, inputs):
        """Takes an input to the net and returns an output via forward computation"""
        if type(inputs) != torch.autograd.variable.Variable:
            inputs = Variable(torch.Tensor(inputs))
        return {'i': inputs, 'fo': self.forward(inputs)}

    def learn(self, targets, input_layers):
        if type(targets) != torch.autograd.variable.Variable:
            targets = Variable(torch.Tensor(targets))
        final_outputs = input_layers['fo']
        output_errors = self.loss_function(final_outputs, targets)
        self.optimizer.zero_grad()
        output_errors.backward()
        self.optimizer.step()
        return output_errors, final_outputs

    def forward(self, input_0):
        primals_1 = self.hidden.weight
        primals_3 = self.output.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.039094