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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable
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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](buf1, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused__softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf3
    return buf4, buf1, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf4


class ReactiveAutoencoder(nn.Module):
    """The RAE a.k.a. SRAE a.k.a. the autoencoder with the strict supervised sparsity matrix.
    This module provides a framework for training an encoder to maximize information throughput,
    while converging on an error_signal. Works currently only for single samples/online learning.
    Planned are batch mode as well as multiple layers."""
    __constants__ = ['input_size', 'output_size']

    def __init__(self, input_size, output_size, reconstruction_loss:
        'nn.Module', hidden_activation:
        'Callable[[torch.Tensor], torch.Tensor]'=None,
        reconstruction_activation: 'Callable[[torch.Tensor], torch.Tensor]'
        =None, bias=True, reconstruction_bias: 'str'='zeros',
        activation_scaling=True):
        super(ReactiveAutoencoder, self).__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_activation = hidden_activation
        self.activation_scaling = activation_scaling
        if activation_scaling:
            self.scaling = None
        self.encoder = nn.Linear(input_size, output_size, bias=bias)
        self.h = torch.zeros(output_size, requires_grad=True)
        self.predict = torch.zeros(output_size)
        self.reconstruction_activation = reconstruction_activation
        self.reconstruction_loss = reconstruction_loss
        self.reconstructed_input = torch.zeros(input_size, requires_grad=True)
        self.reconstruction_bias_type = reconstruction_bias
        self.reconstruction_bias = self.fresh_reconstruction_bias(self.
            reconstruction_bias_type)

    def fresh_reconstruction_bias(self, type: 'str'):
        if type == 'none':
            return None
        elif type == 'zeros':
            return torch.zeros(self.input_size, requires_grad=True)
        elif type == 'ones':
            return torch.ones(self.input_size, requires_grad=True),
        elif type == 'rand':
            return torch.rand(self.input_size, requires_grad=True),
        elif type == 'randn':
            return torch.randn(self.input_size, requires_grad=True),

    def forward(self, x: 'torch.Tensor', error_signal: 'torch.Tensor'=None):
        """The forward pass calculates only the h if no error_signal is provided.
        If an error_signal is provided, then assume same x and use the last h for sparsity and
        reconstruction calculation.
        """
        if error_signal is None:
            with torch.no_grad():
                self.h = self.encoder(x)
                if self.hidden_activation is not None:
                    self.h = self.hidden_activation(self.h)
            return self.h, None
        self.h.requires_grad_()
        self.reconstructed_input = F.linear(self.h, self.encoder.weight.t(),
            self.reconstruction_bias)
        if self.reconstruction_activation is not None:
            self.reconstructed_input = self.reconstruction_activation(self.
                reconstructed_input)
        rec_loss = self.reconstruction_loss(self.reconstructed_input, x)
        rec_loss.backward()
        self.predict = F.linear(x, self.encoder.weight + self.encoder.
            weight.grad, self.encoder.bias)
        delta = self.h - self.predict
        if self.activation_scaling:
            self.scaling = torch.max(torch.abs(error_signal)).item(
                ) / torch.max(delta).item()
            adjusted_delta = delta * self.scaling
            mask = torch.where((error_signal - adjusted_delta).abs() <
                error_signal.abs(), 1, 0)
        else:
            mask = torch.where((error_signal - delta).abs() < error_signal.
                abs(), 1, 0)
        self.encoder.zero_grad()
        masked_encoding = self.h * mask
        self.reconstructed_input = F.linear(masked_encoding, self.encoder.
            weight.t(), self.reconstruction_bias)
        return self.h, self.reconstructed_input

    def backward(self):
        super(ReactiveAutoencoder, self).backward()
        if self.activation_scaling:
            self.encoder.weight.grad *= self.scaling
            self.encoder.bias.grad *= self.scaling
            self.reconstruction_bias.grad += self.scaling

    def reset_parameters(self) ->None:
        super(ReactiveAutoencoder, self).reset_parameters()
        self.reconstruction_bias = self.fresh_reconstruction_bias(self.
            reconstruction_bias_type)


class RAEClassifierNew(nn.Module):
    __constants__ = ['input_size', 'hidden_size', 'output_size']

    def __init__(self, input_size, hidden_size, output_size,
        reconstruction_activation: 'Callable[[torch.Tensor], torch.Tensor]'
        =nn.ReLU(), hidden_activation:
        'Callable[[torch.Tensor], torch.Tensor]'=nn.ReLU(),
        output_activation: 'Callable[[torch.Tensor], torch.Tensor]'=nn.
        Softmax(), reconstruction_loss: 'nn.Module'=nn.MSELoss()):
        super(RAEClassifierNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.input = torch.zeros(input_size)
        self.output_activation = output_activation
        self.reconstruction_loss = reconstruction_loss
        self.autoencoder = ReactiveAutoencoder(input_size, hidden_size,
            self.reconstruction_loss, hidden_activation,
            reconstruction_activation)
        self.classifier = nn.Linear(hidden_size, output_size)
        self.classifier.weight.register_hook(self.backward_classifier_hook)

    def backward_classifier_hook(self, grad):
        """Triggers autoencoder sparsification with classifier, after backward on this classifier."""
        with torch.enable_grad():
            _encoding, reconstruction = self.autoencoder(self.input, torch.
                sum(grad, 0))
            rec_loss = self.reconstruction_loss(reconstruction, self.input)
            rec_loss.backward()

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


Measured latency_ms: 0.056076