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 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_per_fused_add_clamp_mean_mul_rsqrt_sub_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp20 = tl.load(in_ptr1 + (x2 + 8 * x3), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (4 + x2 + 8 * x3), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr2 + (4 + x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = tmp0 * tmp0
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 16.0
    tmp11 = tmp4 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tmp11 * tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = 0.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = 1e-08
    tmp18 = tmp16 + tmp17
    tmp19 = libdevice.rsqrt(tmp18)
    tmp22 = tmp20 + tmp21
    tmp23 = 0.7071067811865476
    tmp24 = tmp22 * tmp23
    tmp25 = tmp0 - tmp11
    tmp26 = tmp25 * tmp19
    tmp27 = tmp24 * tmp26
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 * tmp23
    tmp32 = tmp27 + tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp11, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp19, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 4), (4, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_2, (4, 8),
            (1, 4), 0), out=buf4)
        del primals_2
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_clamp_mean_mul_rsqrt_sub_0[grid(16)](buf1,
            buf3, primals_1, buf4, primals_3, buf5, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf4
        del primals_3
    return buf5, primals_1, primals_4, reinterpret_tensor(buf1, (4, 4, 1, 1
        ), (4, 1, 1, 1), 0), buf3


def getLayerNormalizationFactor(x, gain, fromTF):
    """
    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:])
    if not fromTF:
        return math.sqrt(2.0 / fan_in)
    else:
        return gain / math.sqrt(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, gain=math.sqrt(2.0), fromTF=False):
        """
        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
        self.fromTF = fromTF
        if initBiasToZero:
            self.module.bias.data.fill_(0)
        if self.equalized:
            self.module.weight.data.normal_(0, 1)
            self.module.weight.data /= lrMul
            self.weight = getLayerNormalizationFactor(self.module, gain,
                self.fromTF) * lrMul

    def forward(self, x):
        if not self.fromTF:
            x = self.module(x)
            if self.equalized:
                x *= self.weight
        else:
            size = self.module.weight.size()
            if len(size) <= 2:
                x = self.module(x)
                if self.equalized:
                    x -= self.module.bias.data
                    x *= self.weight
                    x += self.module.bias.data
            else:
                x = self.module(x)
                if self.equalized:
                    x -= self.module.bias.view(-1, 1, 1)
                    x *= self.weight
                    x += self.module.bias.view(-1, 1, 1)
        return x


class EqualizedLinear(ConstrainedLayer):

    def __init__(self, nChannelsPrevious, nChannels, bias=True, **kwargs):
        """
        A nn.Linear module with specific constraints
        Args:
            nChannelsPrevious (int): number of channels in the previous layer
            nChannels (int): number of channels of the current layer
            bias (bool): with bias ?
        """
        ConstrainedLayer.__init__(self, nn.Linear(nChannelsPrevious,
            nChannels, bias=bias), **kwargs)


class AdaINNew(nn.Module):

    def __init__(self, dimIn, dimOut, epsilon=1e-08):
        super(AdaINNew, self).__init__()
        self.epsilon = epsilon
        self.styleModulator = EqualizedLinear(dimIn, 2 * dimOut, equalized=
            True, initBiasToZero=True)
        self.dimOut = dimOut

    def forward(self, input_0, input_1):
        primals_2 = self.styleModulator.module.weight
        primals_3 = self.styleModulator.module.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.016323