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.nn as 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_group_norm_0(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    RBLOCK: tl.constexpr = 32
    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 + 32 * 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], 32, 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 = 32.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 16
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4 // 2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4 // 2, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 32.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_per_fused_native_group_norm_2(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    RBLOCK: tl.constexpr = 32
    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 + 32 * 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], 32, 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 = 32.0
    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_hardtanh_native_group_norm_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 16
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4 // 2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4 // 2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = 6.0
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        buf2 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        buf4 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_0[grid(8)](buf0, buf1, buf2,
            buf4, 8, 32, XBLOCK=8, num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_group_norm_relu_1[grid(256)](buf0, buf1,
            buf2, primals_3, primals_4, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_4
        buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = reinterpret_tensor(buf2, (4, 2, 1, 1), (2, 1, 1, 1), 0)
        del buf2
        buf8 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 8, 8), torch.float32)
        buf10 = reinterpret_tensor(buf8, (4, 2, 1, 1), (2, 1, 1, 1), 0)
        del buf8
        triton_per_fused_native_group_norm_2[grid(8)](buf10, buf6, buf7, 8,
            32, XBLOCK=8, num_warps=2, num_stages=1)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_hardtanh_native_group_norm_3[grid(256)](buf6, buf7,
            buf10, primals_6, primals_7, buf11, 256, XBLOCK=128, num_warps=
            4, num_stages=1)
    return (buf11, primals_1, primals_2, primals_3, primals_5, primals_6,
        primals_7, buf0, reinterpret_tensor(buf1, (4, 2), (2, 1), 0),
        reinterpret_tensor(buf4, (4, 2), (2, 1), 0), buf5, buf6, buf7, buf10)


def conv3x3(in_planes, out_planes, stride=1, groups=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
        padding=1, bias=False, groups=groups)


class ResBlockNew(nn.Module):
    expansion = 1

    def __init__(self, inp, oup, stride=1):
        super(ResBlockNew, self).__init__()
        self.conv1 = conv3x3(inp, oup, stride)
        self.norm1 = nn.GroupNorm(2, oup)
        self.conv2 = conv3x3(oup, oup)
        self.norm2 = nn.GroupNorm(2, oup)
        self.relu = nn.ReLU6(inplace=True)
        self.lat = 0
        self.flops = 0
        self.params = 0

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.norm1.weight
        primals_4 = self.norm1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.norm2.weight
        primals_7 = self.norm2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.013793