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 libdevice
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):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x4, 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 + x4, tmp4, xmask)
    tl.store(out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_view_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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * (x1 % 4 // 4) + 64 * ((4 *
        (x1 // 4 % 4) + x1 % 4) // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused__weight_norm_interface_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__weight_norm_interface_3(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 / tmp2
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = 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, 1), (1, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (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 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf10, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_view_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused__weight_norm_interface_2[grid(4)](primals_7, buf4,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__weight_norm_interface_3[grid(16)](primals_7,
            primals_6, buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf6,
            primals_5, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_view_1[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (64, 4), (4, 1), 0)
        del buf6
        extern_kernels.addmm(primals_8, buf7, reinterpret_tensor(buf5, (4, 
            4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_8
    return reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf5, primals_6, primals_7, reinterpret_tensor(primals_3, (64, 4
        ), (4, 1), 0), buf2, buf4, buf7, buf5, buf9, primals_4, buf10


class LinearWeightNorm(nn.Module):

    def __init__(self, in_features, out_features, bias=True):
        super(LinearWeightNorm, self).__init__()
        self.linear = nn.Linear(in_features, out_features, bias=bias)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.normal_(self.linear.weight, mean=0.0, std=0.05)
        if self.linear.bias is not None:
            nn.init.constant_(self.linear.bias, 0)
        self.linear = nn.utils.weight_norm(self.linear)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)

    def init(self, x, init_scale=1.0):
        with torch.no_grad():
            out = self(x).view(-1, self.linear.out_features)
            mean = out.mean(dim=0)
            std = out.std(dim=0)
            inv_stdv = init_scale / (std + 1e-06)
            self.linear.weight_g.mul_(inv_stdv.unsqueeze(1))
            if self.linear.bias is not None:
                self.linear.bias.add_(-mean).mul_(inv_stdv)
            return self(x)

    def forward(self, input):
        return self.linear(input)


class NICEMLPBlockNew(nn.Module):

    def __init__(self, in_features, out_features, hidden_features, activation):
        super(NICEMLPBlockNew, self).__init__()
        assert activation in ['relu', 'elu', 'leaky_relu']
        self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
        self.fc2 = nn.Linear(hidden_features, hidden_features, bias=True)
        self.fc3 = LinearWeightNorm(hidden_features, out_features, bias=True)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'elu':
            self.activation = nn.ELU(inplace=True)
        else:
            self.activation = nn.LeakyReLU(inplace=True, negative_slope=0.1)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.constant_(self.fc1.bias, 0.0)
        nn.init.constant_(self.fc2.bias, 0.0)

    def init(self, x, init_scale=1.0):
        with torch.no_grad():
            out = self.activation(self.fc1(x))
            out = self.activation(self.fc2(out))
            out = self.fc3.init(out, init_scale=0.0 * init_scale)
            return out

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_8 = self.fc3.linear.bias
        primals_6 = self.fc3.linear.weight_g
        primals_7 = self.fc3.linear.weight_v
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.060493