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.triton_helpers import libdevice
from torch import nn
import torch.utils.data
import torch.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_per_fused__native_batch_norm_legit_0(in_out_ptr0, in_ptr0,
    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
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_mul_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.7071067811865476
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex // 256
    x4 = xindex % 16
    x0 = xindex % 4
    x5 = xindex % 256
    x2 = xindex // 16 % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 16 * x3), None, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x5, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr5 + (x4 + 16 * x3), None, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr6 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 - tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp2 * tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tl.store(out_ptr0 + x6, tmp12, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf3 = reinterpret_tensor(buf1, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_0[grid(16)](buf3,
            primals_1, buf0, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_1[grid(256)](primals_2, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_2
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf5)
        del primals_3
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf6)
        del primals_5
        buf7 = empty_strided_cuda((4, 4, 4, 4, 4, 4), (1024, 256, 64, 16, 4,
            1), torch.float32)
        triton_poi_fused_add_mul_2[grid(4096)](buf5, primals_4, primals_1,
            buf0, buf3, buf6, primals_6, buf7, 4096, XBLOCK=128, num_warps=
            4, num_stages=1)
        del buf5
        del buf6
        del primals_4
        del primals_6
    return buf7, primals_1, buf0, buf3, reinterpret_tensor(buf4, (64, 4), (
        4, 1), 0)


class WSLinear(nn.Module):

    def __init__(self, in_features, out_features, gain=2):
        super(WSLinear, self).__init__()
        self.linear = nn.Linear(in_features, out_features)
        self.scale = (gain / in_features) ** 0.5
        self.bias = self.linear.bias
        self.linear.bias = None
        nn.init.normal_(self.linear.weight)
        nn.init.zeros_(self.bias)

    def forward(self, x):
        return self.linear(x * self.scale) + self.bias


class AdaINNew(nn.Module):

    def __init__(self, channels, w_dim):
        super().__init__()
        self.instance_norm = nn.InstanceNorm2d(channels)
        self.style_scale = WSLinear(w_dim, channels)
        self.style_bias = WSLinear(w_dim, channels)

    def forward(self, input_0, input_1):
        primals_4 = self.style_scale.bias
        primals_3 = self.style_scale.linear.weight
        primals_6 = self.style_bias.bias
        primals_5 = self.style_bias.linear.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.024384