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
import torch.utils.data
import torch.utils
from matplotlib import cm as cm
from torch.nn.parallel import *
from torchvision.models import *
from torchvision.datasets import *
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_poi_fused_relu_0(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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 8 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-2 + x1) + 8 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_2(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 % 4
    r2 = rindex // 4
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 4 * x0 + 16 * r2), 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.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_batch_norm_legit_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, 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 = 16.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x3, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 2, 2), (8, 4, 2, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 4, 3,
            3), (64, 16, 4, 1), 5), primals_3, stride=(2, 2), padding=(0, 0
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 2, 2), (8, 4, 2, 1))
        buf3 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_cat_1[grid(64)](buf1, buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf1
        del buf2
        buf4 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf7 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_2[grid(4)](buf3, buf4,
            buf5, buf7, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_3[grid(64)](buf3, buf4,
            buf5, primals_4, primals_5, buf8, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf5
        del primals_5
    return (buf8, primals_2, primals_3, primals_4, buf0, buf3,
        reinterpret_tensor(buf7, (4,), (1,), 0), reinterpret_tensor(buf4, (
        1, 4, 1, 1), (4, 1, 1, 1), 0))


def get_norm_layer(norm, C):
    if norm in [None, '', 'none']:
        norm_layer = nn.Identity()
    elif norm.startswith('bn'):
        norm_layer = nn.BatchNorm2d(C, track_running_stats=norm.find(
            'track') >= 0)
    else:
        raise NotImplementedError(norm)
    return norm_layer


class FactorizedReduceNew(nn.Module):

    def __init__(self, C_in, C_out, norm='bn', stride=2):
        super(FactorizedReduceNew, self).__init__()
        assert C_out % 2 == 0
        self.stride = stride
        self.relu = nn.ReLU(inplace=False)
        self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=stride, padding
            =0, bias=False)
        self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=stride, padding
            =0, bias=False)
        self.bn = get_norm_layer(norm, C_out)

    def forward(self, input_0):
        primals_2 = self.conv_1.weight
        primals_3 = self.conv_2.weight
        primals_4 = self.bn.weight
        primals_5 = self.bn.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.103328