import torch
import torch.nn as nn


class RefineModelReLU(torch.nn.Module):

    def __init__(self, in_channels):
        super(RefineModelReLU, self).__init__()
        self.layer1 = nn.Linear(in_channels, 128)
        self.relu1 = nn.ReLU()
        self.layer2 = nn.Linear(128, 64)
        self.relu2 = nn.ReLU()
        self.layer3 = nn.Linear(64, 4)
        self.relu3 = nn.ReLU()
        self.layer4 = nn.Linear(4, 64)
        self.relu4 = nn.ReLU()
        self.layer5 = nn.Linear(64, 128)
        self.relu5 = nn.ReLU()
        self.layer6 = nn.Linear(128, in_channels)

    def forward(self, x):
        x = self.relu1(self.layer1(x))
        x = self.relu2(self.layer2(x))
        latent = self.relu3(self.layer3(x))
        x = self.relu4(self.layer4(latent))
        x = self.relu5(self.layer5(x))
        x = self.layer6(x)
        return x, latent


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


def get_init_inputs():
    return [[], {'in_channels': 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 % 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_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 % 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_2(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)


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) = args
    args.clear()
    assert_size_stride(primals_1, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 128), (128, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 64), (64, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (64, 4), (4, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (128, 64), (64, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (4, 128), (128, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        buf14 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
            primals_2, buf14, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_4, (128, 64), (1, 128), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf2
        buf13 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf3,
            primals_5, buf13, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_relu_2[grid(256)](buf5, primals_7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 64), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf6
        buf12 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf7,
            primals_9, buf12, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_10, (64, 128), (1, 64), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf8
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf9,
            primals_11, buf11, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf9, (64, 128),
            (128, 1), 0), reinterpret_tensor(primals_12, (128, 4), (1, 128),
            0), alpha=1, beta=1, out=buf10)
        del primals_13
    return (reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf3, (64, 64), (64, 1), 0), buf5,
        reinterpret_tensor(buf7, (64, 64), (64, 1), 0), reinterpret_tensor(
        buf9, (64, 128), (128, 1), 0), primals_12, buf11, primals_10, buf12,
        primals_8, primals_6, buf13, primals_4, buf14)


class RefineModelReLUNew(torch.nn.Module):

    def __init__(self, in_channels):
        super(RefineModelReLUNew, self).__init__()
        self.layer1 = nn.Linear(in_channels, 128)
        self.relu1 = nn.ReLU()
        self.layer2 = nn.Linear(128, 64)
        self.relu2 = nn.ReLU()
        self.layer3 = nn.Linear(64, 4)
        self.relu3 = nn.ReLU()
        self.layer4 = nn.Linear(4, 64)
        self.relu4 = nn.ReLU()
        self.layer5 = nn.Linear(64, 128)
        self.relu5 = nn.ReLU()
        self.layer6 = nn.Linear(128, in_channels)

    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_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])
        return output[0], output[1]
