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
import math
import torch.nn as nn
from numpy import prod
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 112
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7 % 4
    x2 = xindex // 28
    x3 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 0.0
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp0 >= tmp3
    tl.full([1], 7, tl.int64)
    tmp11 = tl.load(in_ptr0 + (x1 + 4 * (-3 + x0) + 16 * x2), tmp8 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tl.where(tmp4, tmp7, tmp11)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.3535533905932738
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(112)](primals_1, buf0, 112, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_mul_1[grid(64)](buf2, primals_3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0), primals_2, buf0


def getLayerNormalizationFactor(x):
    """
    Get He's constant for the given layer
    https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
    """
    size = x.weight.size()
    fan_in = prod(size[1:])
    return math.sqrt(2.0 / fan_in)


class ConstrainedLayer(nn.Module):
    """
    A handy refactor that allows the user to:
    - initialize one layer's bias to zero
    - apply He's initialization at runtime
    """

    def __init__(self, module, equalized=True, lrMul=1.0, initBiasToZero=True):
        """
        equalized (bool): if true, the layer's weight should evolve within
                         the range (-1, 1)
        initBiasToZero (bool): if true, bias will be initialized to zero
        """
        super(ConstrainedLayer, self).__init__()
        self.module = module
        self.equalized = equalized
        if initBiasToZero and module.bias is not None:
            self.module.bias.data.fill_(0)
        if self.equalized:
            self.module.weight.data.normal_(0, 1)
            self.weight = getLayerNormalizationFactor(self.module) * lrMul

    def forward(self, x):
        x = self.module(x)
        if self.equalized:
            x *= self.weight
        return x


class EqualizedConv1d(ConstrainedLayer):

    def __init__(self, nChannelsPrevious, nChannels, kernelSize, padding=0,
        bias=True, stride=1, **kwargs):
        """
        A nn.Conv2d module with specific constraints
        Args:
            nChannelsPrevious (int): number of channels in the previous layer
            nChannels (int): number of channels of the current layer
            kernelSize (int): size of the convolutional kernel
            padding (int): convolution's padding
            bias (bool): with bias ?
        """
        ConstrainedLayer.__init__(self, nn.Conv1d(nChannelsPrevious,
            nChannels, kernelSize, padding=padding, bias=bias, stride=
            stride), **kwargs)


class ShiftedConvNew(nn.Module):

    def __init__(self, dimOutputAR, dimOutputEncoder, kernelSize):
        super(ShiftedConvNew, self).__init__()
        self.module = EqualizedConv1d(dimOutputAR, dimOutputEncoder,
            kernelSize, equalized=True, padding=0)
        self.kernelSize = kernelSize

    def forward(self, input_0):
        primals_1 = self.module.module.weight
        primals_3 = self.module.module.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.015824