import torch
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.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as 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_native_group_norm_relu_threshold_backward_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    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], 64, 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 = tmp0 - tmp10
    tmp18 = 64.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = 0.0
    tmp31 = tmp29 <= tmp30
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp29, xmask)
    tl.store(out_ptr3 + (r1 + 64 * x0), tmp31, xmask)
    tl.store(out_ptr4 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_relu_threshold_backward_0[grid(4)](
            primals_3, primals_1, primals_2, buf0, buf3, buf4, buf5, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        del primals_1
        del primals_2
    return buf3, primals_3, buf4, reinterpret_tensor(buf0, (4, 1, 1), (1, 1,
        1), 0), reinterpret_tensor(buf5, (4, 1, 1), (1, 1, 1), 0)


def swish(x, inplace: 'bool'=False):
    """Swish - Described in: https://arxiv.org/abs/1710.05941
    """
    return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())


def is_exportable():
    return _EXPORTABLE


def is_no_jit():
    return _NO_JIT


def is_scriptable():
    return _SCRIPTABLE


def get_act_layer(name='relu'):
    """ Activation Layer Factory
    Fetching activation layers by name with this function allows export or torch script friendly
    functions to be returned dynamically based on current config.
    """
    if not name:
        return None
    if not (is_no_jit() or is_exportable() or is_scriptable()):
        if name in _ACT_LAYER_ME:
            return _ACT_LAYER_ME[name]
    if is_exportable() and name in ('silu', 'swish'):
        return Swish
    if not (is_no_jit() or is_exportable()):
        if name in _ACT_LAYER_JIT:
            return _ACT_LAYER_JIT[name]
    return _ACT_LAYER_DEFAULT[name]


class Swish(nn.Module):

    def __init__(self, inplace: 'bool'=False):
        super(Swish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return swish(x, self.inplace)


class GroupNormActNew(nn.GroupNorm):

    def __init__(self, num_channels, num_groups, eps=1e-05, affine=True,
        apply_act=True, act_layer=nn.ReLU, inplace=True, drop_block=None):
        super(GroupNormActNew, self).__init__(num_groups, num_channels, eps
            =eps, affine=affine)
        if isinstance(act_layer, str):
            act_layer = get_act_layer(act_layer)
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()

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


Measured latency_ms: 0.022037