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.utils.data
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, 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.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(64)](buf1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_3, (1, 4), (1, 1
            ), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_sigmoid_1[grid(256)](buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf1, buf3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, buf3, primals_3


class ContractiveAutoencoderNew(nn.Module):
    """
    Simple contractive autoencoder with a single hidden layer.

    Constructor parameters:
        - num_inputs: Number of input features
        - num_hidden_layer_inputs: Number of input features for the single hidden layer
    """

    def __init__(self, num_inputs, num_hidden_layer_inputs):
        super(ContractiveAutoencoderNew, self).__init__()
        self.num_inputs = num_inputs
        self.num_hidden_layer_inputs = num_hidden_layer_inputs
        self.fc1 = nn.Linear(num_inputs, num_hidden_layer_inputs, bias=False)
        self.fc2 = nn.Linear(num_hidden_layer_inputs, num_inputs, bias=False)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def encoder(self, x):
        h1 = self.relu(self.fc1(x.view(-1, self.num_inputs)))
        return h1

    def decoder(self, z):
        h2 = self.sigmoid(self.fc2(z))
        return h2

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc2.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]


Measured latency_ms: 0.037547