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, math as tl_math
from torch import nn
from torch.nn.functional import interpolate
from typing import cast
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_threshold_backward_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3,
    out_ptr4, out_ptr5, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 25
    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, :]
    rmask = rindex < rnumel
    x0 = xindex
    r3 = rindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 25 * x0), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.full([XBLOCK, 1], 25, tl.int32)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp10 / tmp12
    tmp14 = tmp4 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp3 - tmp13
    tmp21 = 25.0
    tmp22 = tmp19 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 * tmp0
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp32 = 0.0
    tmp33 = tmp31 <= tmp32
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(in_out_ptr0 + (r3 + 25 * x0), tmp3, rmask & xmask)
    tl.store(out_ptr3 + (r3 + 25 * x0), tmp31, rmask & xmask)
    tl.store(out_ptr4 + (r3 + 25 * x0), tmp33, rmask & xmask)
    tl.store(out_ptr5 + x0, tmp25, xmask)
    tl.store(out_ptr1 + x0, 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, (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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(1024)](primals_1, buf0, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
        buf3 = empty_strided_cuda((16,), (1,), torch.float32)
        buf2 = buf1
        del buf1
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf8 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_threshold_backward_1[
            grid(16)](buf2, primals_4, primals_3, primals_5, buf3, buf4,
            buf8, buf9, buf7, 16, 25, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_3
        del primals_4
        del primals_5
    return buf8, primals_2, buf0, buf2, buf3, reinterpret_tensor(buf7, (16,
        ), (1,), 0), buf9, reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 
        1, 1), 0)


class Interpolate(nn.Module):

    def __init__(self, scale_factor: 'float'=1.0, mode: 'str'='nearest'
        ) ->None:
        super().__init__()
        self.scale_factor = scale_factor
        self.mode = mode

    def forward(self, input: 'torch.Tensor') ->torch.Tensor:
        return cast(torch.Tensor, interpolate(input, scale_factor=self.
            scale_factor, mode=self.mode))

    def extra_repr(self) ->str:
        extras = [f'scale_factor={self.scale_factor}']
        if self.mode != 'nearest':
            extras.append(f'mode={self.mode}')
        return ', '.join(extras)


class ConvNew(nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
        'int', *, stride: int=1, upsample: bool=False, norm: bool=True,
        activation: bool=True):
        super().__init__()
        self.upsample = Interpolate(scale_factor=stride) if upsample else None
        self.pad = nn.ReflectionPad2d(kernel_size // 2)
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride=1 if upsample else stride)
        self.norm = nn.InstanceNorm2d(out_channels, affine=True
            ) if norm else None
        self.activation = nn.ReLU() if activation else None

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


Measured latency_ms: 0.046629