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
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp30 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp32 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + x2, tmp36, xmask)


@triton.jit
def triton_per_fused_div_mean_std_sub_1(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, 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
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp11, 0)
    tmp14 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tmp18 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp19 = tmp18.to(tl.float32)
    tmp20 = tmp17 / tmp19
    tmp21 = tmp11 - tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp25 = tl.where(xmask, tmp23, 0)
    tmp26 = tl.sum(tmp25, 1)[:, None]
    tmp27 = 63.0
    tmp28 = tmp26 / tmp27
    tmp29 = libdevice.sqrt(tmp28)
    tmp30 = 1e-05
    tmp31 = tmp29 + tmp30
    tmp32 = tmp10 / tmp31
    tl.store(out_ptr0 + (r1 + 64 * x0), tmp10, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr1 + (r1 + 64 * x0), tmp32, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 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, 4), (4, 16, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        buf5 = buf3
        del buf3
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_div_mean_std_sub_1[grid(4)](buf5, primals_1, buf0,
            buf1, buf6, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        buf7 = extern_kernels.convolution(primals_3, buf6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 1, 1), (4, 1, 1, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_2[grid(16)](buf8, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf8, primals_1, primals_3, buf5, buf6


class Conv2dNew(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True):
        super(Conv2dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, dilation, groups, bias)

    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: 0.052665