import torch


class LayerCake(torch.nn.Module):

    def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
        """
        In the constructor we instantiate two nn.Linear modules and assign them as
        member variables.
        """
        super(LayerCake, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, H1)
        self.linear2 = torch.nn.Linear(H1, H2)
        self.linear3 = torch.nn.Linear(H2, H3)
        self.linear4 = torch.nn.Linear(H3, H4)
        self.linear5 = torch.nn.Linear(H4, H5)
        self.linear6 = torch.nn.Linear(H5, D_out)

    def forward(self, x):
        """
        In the forward function we accept a Tensor of input data and we must return
        a Tensor of output data. We can use Modules defined in the constructor as
        well as arbitrary (differentiable) operations on Tensors.
        """
        x = self.linear1(x).clamp(min=0)
        x = self.linear2(x).clamp(min=0)
        x = self.linear3(x).clamp(min=0)
        x = self.linear4(x).clamp(min=0)
        x = self.linear5(x).clamp(min=0)
        y_pred = self.linear6(x)
        return y_pred


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


def get_init_inputs():
    return [[], {'D_in': 4, 'H1': 4, 'H2': 4, 'H3': 4, 'H4': 4, 'H5': 4,
        'D_out': 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
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_clamp_ge_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp2 >= tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp5, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (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_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_0[grid(256)](buf0, primals_2, buf1, buf15,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clamp_ge_0[grid(256)](buf2, primals_5, buf3, buf14,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clamp_ge_0[grid(256)](buf4, primals_7, buf5, buf13,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = buf4
        del buf4
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clamp_ge_0[grid(256)](buf6, primals_9, buf7, buf12,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = buf6
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clamp_ge_0[grid(256)](buf8, primals_11, buf9,
            buf11, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = buf8
        del buf8
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf9, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_13
    return (reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1),
        0), reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf9, (64, 4), (4, 1), 0), primals_12, buf11,
        primals_10, buf12, primals_8, buf13, primals_6, buf14, primals_4, buf15
        )


class LayerCakeNew(torch.nn.Module):

    def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
        """
        In the constructor we instantiate two nn.Linear modules and assign them as
        member variables.
        """
        super(LayerCakeNew, self).__init__()
        self.linear1 = torch.nn.Linear(D_in, H1)
        self.linear2 = torch.nn.Linear(H1, H2)
        self.linear3 = torch.nn.Linear(H2, H3)
        self.linear4 = torch.nn.Linear(H3, H4)
        self.linear5 = torch.nn.Linear(H4, H5)
        self.linear6 = torch.nn.Linear(H5, D_out)

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_6 = self.linear3.weight
        primals_7 = self.linear3.bias
        primals_8 = self.linear4.weight
        primals_9 = self.linear4.bias
        primals_10 = self.linear5.weight
        primals_11 = self.linear5.bias
        primals_12 = self.linear6.weight
        primals_13 = self.linear6.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]
