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.cuda
from torch import nn
import torch.distributed
from torch.cuda.amp import autocast as autocast
import torch.utils.data
import torch.optim
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_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + 0)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp9 = tmp8 / tmp6
    tmp10 = tmp0 * tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp10, rmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, 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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 1), (1, 1, 1))
    assert_size_stride(primals_2, (1, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 1), (1, 1, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 4, 3), (12, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0[grid(1)](buf1, primals_2,
            primals_1, buf2, 1, 12, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_4, (1,
            4, 4), (16, 4, 1), 0), buf2, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 1, 4), (4, 4, 1))
        buf4 = reinterpret_tensor(buf3, (1, 4), (4, 1), 0)
        del buf3
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4)](buf4, primals_3,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_3
    return buf4, buf2, primals_1, primals_2, buf1, buf2, reinterpret_tensor(
        primals_4, (1, 4, 4), (16, 4, 1), 0), buf5


class ConvNorm(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super().__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            bias=bias)
        torch.nn.init.xavier_uniform_(self.conv.weight, gain=torch.nn.init.
            calculate_gain(w_init_gain))

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal


class BottleneckLayerLayerNew(nn.Module):

    def __init__(self, in_dim, reduction_factor, norm='weightnorm',
        non_linearity='relu', use_pconv=False):
        super(BottleneckLayerLayerNew, self).__init__()
        self.reduction_factor = reduction_factor
        reduced_dim = int(in_dim / reduction_factor)
        self.out_dim = reduced_dim
        if self.reduction_factor > 1:
            fn = ConvNorm(in_dim, reduced_dim, kernel_size=3)
            if norm == 'weightnorm':
                fn = torch.nn.utils.weight_norm(fn.conv, name='weight')
            elif norm == 'instancenorm':
                fn = nn.Sequential(fn, nn.InstanceNorm1d(reduced_dim,
                    affine=True))
            self.projection_fn = fn
            self.non_linearity = non_linearity

    def forward(self, input_0):
        primals_3 = self.projection_fn.bias
        primals_1 = self.projection_fn.weight_g
        primals_2 = self.projection_fn.weight_v
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.013938