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 math
import numpy as np
from torch import nn as nn
from torch import optim as optim
from math import *
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_relu_0(in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_add_convolution_native_batch_norm_backward_1(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0,
    out_ptr1, 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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x1, 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')
    tmp16 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp11 = tl.full([1], 1, tl.int32)
    tmp12 = tmp11 / tmp10
    tmp13 = 1.0
    tmp14 = tmp12 * tmp13
    tmp15 = tmp6 * tmp14
    tmp17 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tl.store(out_ptr0 + x3, tmp19, xmask)
    tl.store(out_ptr1 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), 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 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](buf1, 256, XBLOCK=128, num_warps
            =4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = extern_kernels.convolution(primals_2, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_add_convolution_native_batch_norm_backward_1[
            grid(256)](buf2, buf3, primals_5, primals_6, primals_7,
            primals_8, primals_9, buf4, buf5, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf2
        del buf3
        del primals_5
        del primals_6
        del primals_9
    return (buf4, primals_1, primals_2, primals_3, primals_4, primals_7,
        primals_8, buf1, buf5)


class MaybeBatchNorm2d(nn.Module):

    def __init__(self, n_ftr, affine, use_bn):
        super(MaybeBatchNorm2d, self).__init__()
        self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
        self.use_bn = use_bn

    def forward(self, x):
        if self.use_bn:
            x = self.bn(x)
        return x


class FakeRKHSConvNetNew(nn.Module):

    def __init__(self, n_input, n_output, use_bn=False):
        super(FakeRKHSConvNetNew, self).__init__()
        self.conv1 = nn.Conv2d(n_input, n_output, kernel_size=1, stride=1,
            padding=0, bias=False)
        self.bn1 = MaybeBatchNorm2d(n_output, True, use_bn)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(n_output, n_output, kernel_size=1, stride=1,
            padding=0, bias=False)
        self.bn_out = MaybeBatchNorm2d(n_output, True, True)
        self.shortcut = nn.Conv2d(n_input, n_output, kernel_size=1, stride=
            1, padding=0, bias=True)
        if n_output >= n_input:
            eye_mask = np.zeros((n_output, n_input, 1, 1), dtype=np.bool)
            for i in range(n_input):
                eye_mask[i, i, 0, 0] = 1
            self.shortcut.weight.data.uniform_(-0.01, 0.01)
            self.shortcut.weight.data.masked_fill_(torch.tensor(eye_mask), 1.0)

    def init_weights(self, init_scale=1.0):
        nn.init.kaiming_uniform_(self.conv1.weight, a=math.sqrt(5))
        self.conv1.weight.data.mul_(init_scale)
        nn.init.constant_(self.conv2.weight, 0.0)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_5 = self.bn1.bn.weight
        primals_6 = self.bn1.bn.bias
        primals_3 = self.conv2.weight
        primals_7 = self.bn_out.bn.weight
        primals_8 = self.bn_out.bn.bias
        primals_4 = self.shortcut.weight
        primals_9 = self.shortcut.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.034067