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 math as tl_math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
from itertools import product as product
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp4 * tmp3
    tl.store(in_out_ptr0 + x3, tmp5, 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
            XBLOCK=256, 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, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_mul_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class NormLayer(nn.Module):
    """Normalization Layers.

    Args:
        channels: input channels, for batch norm and instance norm.
        input_size: input shape without batch size, for layer norm.
    """

    def __init__(self, channels, normalize_shape=None, norm_type='bn'):
        super(NormLayer, self).__init__()
        norm_type = norm_type.lower()
        self.norm_type = norm_type
        if norm_type == 'bn':
            self.norm = nn.BatchNorm2d(channels, affine=True)
        elif norm_type == 'in':
            self.norm = nn.InstanceNorm2d(channels, affine=False)
        elif norm_type == 'gn':
            self.norm = nn.GroupNorm(32, channels, affine=True)
        elif norm_type == 'pixel':
            self.norm = lambda x: F.normalize(x, p=2, dim=1)
        elif norm_type == 'layer':
            self.norm = nn.LayerNorm(normalize_shape)
        elif norm_type == 'none':
            self.norm = lambda x: x * 1.0
        else:
            assert 1 == 0, f'Norm type {norm_type} not support.'

    def forward(self, x, ref=None):
        if self.norm_type == 'spade':
            return self.norm(x, ref)
        else:
            return self.norm(x)


class ReluLayer(nn.Module):
    """Relu Layer.

    Args:
        relu type: type of relu layer, candidates are
            - ReLU
            - LeakyReLU: default relu slope 0.2
            - PRelu
            - SELU
            - none: direct pass
    """

    def __init__(self, channels, relu_type='relu'):
        super(ReluLayer, self).__init__()
        relu_type = relu_type.lower()
        if relu_type == 'relu':
            self.func = nn.ReLU(True)
        elif relu_type == 'leakyrelu':
            self.func = nn.LeakyReLU(0.2, inplace=True)
        elif relu_type == 'prelu':
            self.func = nn.PReLU(channels)
        elif relu_type == 'selu':
            self.func = nn.SELU(True)
        elif relu_type == 'none':
            self.func = lambda x: x * 1.0
        else:
            assert 1 == 0, f'Relu type {relu_type} not support.'

    def forward(self, x):
        return self.func(x)


class ConvLayerNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, scale=
        'none', norm_type='none', relu_type='none', use_pad=True, bias=True):
        super(ConvLayerNew, self).__init__()
        self.use_pad = use_pad
        self.norm_type = norm_type
        if norm_type in ['bn']:
            bias = False
        stride = 2 if scale == 'down' else 1
        self.scale_func = lambda x: x
        if scale == 'up':
            self.scale_func = lambda x: nn.functional.interpolate(x,
                scale_factor=2, mode='nearest')
        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size -
            1.0) / 2)))
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, bias=bias)
        self.relu = ReluLayer(out_channels, relu_type)
        self.norm = NormLayer(out_channels, norm_type=norm_type)

    def forward(self, input_0):
        primals_2 = self.conv2d.weight
        primals_3 = self.conv2d.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.033382