import torch
import torch.nn as nn
import torch.nn.functional as F


class Autoencoder(nn.Module):

    def __init__(self, input_length, output_length=None, neuron_multiplier=
        1, sigmoid=False, drop=False, drop_pct=0.3):
        """
        Dense autoencoder. 
        
        Args:
            input_length (int): Length (i.e., size) of input sample.
            output_length (int): Length of output sample. Defaults to None, leave as default if 
                                 input and output are equal size.
            neuron_multiplier (int): Number to augment the set number of neurons. Defaults to 1.
            sigmoid (boolean): Defaults to False. Leave as default if output is nn.Linear (not sigmoid).
            drop (boolean): Defaults to False. True activates dropout for each layer except final.
            drop_pct (float): Amount of dropout to use (if drop is True). Defaults to 0.3.
            
        """
        super(Autoencoder, self).__init__()
        self.input_length = input_length
        if not output_length:
            self.output_length = input_length
        if output_length:
            self.output_length = output_length
        self.neuron_multiplier = neuron_multiplier
        self.sigmoid = sigmoid
        self.drop = drop
        self.drop_pct = drop_pct
        self.layer1 = nn.Linear(in_features=self.input_length, out_features
            =256 * self.neuron_multiplier)
        self.layer2 = nn.Linear(in_features=256 * self.neuron_multiplier,
            out_features=128 * self.neuron_multiplier)
        self.layer3 = nn.Linear(in_features=128 * self.neuron_multiplier,
            out_features=64 * self.neuron_multiplier)
        self.layer4 = nn.Linear(in_features=64 * self.neuron_multiplier,
            out_features=128 * self.neuron_multiplier)
        self.layer5 = nn.Linear(in_features=128 * self.neuron_multiplier,
            out_features=256 * self.neuron_multiplier)
        self.layer6 = nn.Linear(in_features=256 * self.neuron_multiplier,
            out_features=self.output_length)
        self.out = nn.Linear(in_features=self.output_length, out_features=
            self.output_length)

    def forward(self, x):
        if self.drop:
            drp = nn.Dropout(p=self.drop_pct)
        x = F.relu(self.layer1(x))
        if self.drop:
            x = drp(x)
        x = F.relu(self.layer2(x))
        if self.drop:
            x = drp(x)
        x = F.relu(self.layer3(x))
        if self.drop:
            x = drp(x)
        x = F.relu(self.layer4(x))
        if self.drop:
            x = drp(x)
        x = F.relu(self.layer5(x))
        if self.drop:
            x = drp(x)
        x = F.relu(self.layer6(x))
        if self.drop:
            x = drp(x)
        if self.sigmoid:
            x = torch.sigmoid(self.out(x))
        if not self.sigmoid:
            x = self.out(x)
        return x


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


def get_init_inputs():
    return [[], {'input_length': 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
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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, 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 % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, 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 % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, 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_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, 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
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (128, 256), (256, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (64, 128), (128, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (128, 64), (64, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128), (128, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (4, 256), (256, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 256), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf0
        buf18 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf1,
            primals_2, buf18, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_4, (256, 128), (1, 256), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf2
        buf17 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(8192)](buf3,
            primals_5, buf17, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_6, (128, 64), (1, 128), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf4
        buf16 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_2[grid(4096)](buf5,
            primals_7, buf16, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_8, (64, 128), (1, 64), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf6
        buf15 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(8192)](buf7,
            primals_9, buf15, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_10, (128, 256), (1, 128), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf8
        buf14 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf9,
            primals_11, buf14, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_12, (256, 4), (1, 256), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf10
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(256)](buf11,
            primals_13, buf13, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf11, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf12)
        del primals_15
    return (reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 256), (256, 1), 0),
        reinterpret_tensor(buf3, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf5, (64, 64), (64, 1), 0), reinterpret_tensor(
        buf7, (64, 128), (128, 1), 0), reinterpret_tensor(buf9, (64, 256),
        (256, 1), 0), reinterpret_tensor(buf11, (64, 4), (4, 1), 0),
        primals_14, buf13, primals_12, buf14, primals_10, buf15, primals_8,
        buf16, primals_6, buf17, primals_4, buf18)


class AutoencoderNew(nn.Module):

    def __init__(self, input_length, output_length=None, neuron_multiplier=
        1, sigmoid=False, drop=False, drop_pct=0.3):
        """
        Dense autoencoder. 
        
        Args:
            input_length (int): Length (i.e., size) of input sample.
            output_length (int): Length of output sample. Defaults to None, leave as default if 
                                 input and output are equal size.
            neuron_multiplier (int): Number to augment the set number of neurons. Defaults to 1.
            sigmoid (boolean): Defaults to False. Leave as default if output is nn.Linear (not sigmoid).
            drop (boolean): Defaults to False. True activates dropout for each layer except final.
            drop_pct (float): Amount of dropout to use (if drop is True). Defaults to 0.3.
            
        """
        super(AutoencoderNew, self).__init__()
        self.input_length = input_length
        if not output_length:
            self.output_length = input_length
        if output_length:
            self.output_length = output_length
        self.neuron_multiplier = neuron_multiplier
        self.sigmoid = sigmoid
        self.drop = drop
        self.drop_pct = drop_pct
        self.layer1 = nn.Linear(in_features=self.input_length, out_features
            =256 * self.neuron_multiplier)
        self.layer2 = nn.Linear(in_features=256 * self.neuron_multiplier,
            out_features=128 * self.neuron_multiplier)
        self.layer3 = nn.Linear(in_features=128 * self.neuron_multiplier,
            out_features=64 * self.neuron_multiplier)
        self.layer4 = nn.Linear(in_features=64 * self.neuron_multiplier,
            out_features=128 * self.neuron_multiplier)
        self.layer5 = nn.Linear(in_features=128 * self.neuron_multiplier,
            out_features=256 * self.neuron_multiplier)
        self.layer6 = nn.Linear(in_features=256 * self.neuron_multiplier,
            out_features=self.output_length)
        self.out = nn.Linear(in_features=self.output_length, out_features=
            self.output_length)

    def forward(self, input_0):
        primals_1 = self.layer1.weight
        primals_2 = self.layer1.bias
        primals_4 = self.layer2.weight
        primals_5 = self.layer2.bias
        primals_6 = self.layer3.weight
        primals_7 = self.layer3.bias
        primals_8 = self.layer4.weight
        primals_9 = self.layer4.bias
        primals_10 = self.layer5.weight
        primals_11 = self.layer5.bias
        primals_12 = self.layer6.weight
        primals_13 = self.layer6.bias
        primals_14 = self.out.weight
        primals_15 = self.out.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]
