import math
import torch
from torch import nn
from numpy import int64 as int64
import torch.nn.functional as F
from torch.autograd import Variable


class ShakeShake(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x1, x2, training=True):
        if training:
            alpha = torch.FloatTensor(x1.size(0)).uniform_()
            alpha = alpha.view(alpha.size(0), 1, 1, 1).expand_as(x1)
        else:
            alpha = 0.5
        return alpha * x1 + (1 - alpha) * x2

    @staticmethod
    def backward(ctx, grad_output):
        beta = torch.FloatTensor(grad_output.size(0)).uniform_()
        beta = beta.view(beta.size(0), 1, 1, 1).expand_as(grad_output)
        beta = Variable(beta)
        return beta * grad_output, (1 - beta) * grad_output, None


class Shortcut(nn.Module):

    def __init__(self, in_ch, out_ch, stride):
        super(Shortcut, self).__init__()
        self.stride = stride
        self.conv1 = nn.Conv2d(in_ch, out_ch // 2, 1, stride=1, padding=0,
            bias=False)
        self.conv2 = nn.Conv2d(in_ch, out_ch // 2, 1, stride=1, padding=0,
            bias=False)
        self.bn = nn.BatchNorm2d(out_ch)

    def forward(self, x):
        h = F.relu(x)
        h1 = F.avg_pool2d(h, 1, self.stride)
        h1 = self.conv1(h1)
        h2 = F.avg_pool2d(F.pad(h, (-1, 1, -1, 1)), 1, self.stride)
        h2 = self.conv2(h2)
        h = torch.cat((h1, h2), 1)
        return self.bn(h)


class ShakeBlock(nn.Module):

    def __init__(self, in_ch, out_ch, stride=1):
        super(ShakeBlock, self).__init__()
        self.equal_io = in_ch == out_ch
        self.shortcut = self.equal_io and None or Shortcut(in_ch, out_ch,
            stride=stride)
        self.branch1 = self._make_branch(in_ch, out_ch, stride)
        self.branch2 = self._make_branch(in_ch, out_ch, stride)

    def forward(self, x):
        h1 = self.branch1(x)
        h2 = self.branch2(x)
        h = ShakeShake.apply(h1, h2, self.training)
        h0 = x if self.equal_io else self.shortcut(x)
        return h + h0

    def _make_branch(self, in_ch, out_ch, stride=1):
        return nn.Sequential(nn.ReLU(inplace=False), nn.Conv2d(in_ch,
            out_ch, 3, padding=1, stride=stride, bias=False), nn.
            BatchNorm2d(out_ch), nn.ReLU(inplace=False), nn.Conv2d(out_ch,
            out_ch, 3, padding=1, stride=1, bias=False), nn.BatchNorm2d(out_ch)
            )


class ShakeResNet(nn.Module):

    def __init__(self, depth, w_base, label):
        super(ShakeResNet, self).__init__()
        n_units = (depth - 2) / 6
        in_chs = [16, w_base, w_base * 2, w_base * 4]
        self.in_chs = in_chs
        self.c_in = nn.Conv2d(3, in_chs[0], 3, padding=1)
        self.layer1 = self._make_layer(n_units, in_chs[0], in_chs[1])
        self.layer2 = self._make_layer(n_units, in_chs[1], in_chs[2], 2)
        self.layer3 = self._make_layer(n_units, in_chs[2], in_chs[3], 2)
        self.fc_out = nn.Linear(in_chs[3], label)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        h = self.c_in(x)
        h = self.layer1(h)
        h = self.layer2(h)
        h = self.layer3(h)
        h = F.relu(h)
        h = F.avg_pool2d(h, 8)
        h = h.view(-1, self.in_chs[3])
        h = self.fc_out(h)
        return h

    def _make_layer(self, n_units, in_ch, out_ch, stride=1):
        layers = []
        for i in range(int(n_units)):
            layers.append(ShakeBlock(in_ch, out_ch, stride=stride))
            in_ch, stride = out_ch, 1
        return nn.Sequential(*layers)


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {'depth': 1, 'w_base': 4, 'label': 4}]

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
import math
from torch import nn
from numpy import int64 as int64
import torch.nn.functional as F
from torch.autograd import Variable
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (16, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(262144)](buf1, primals_2, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = torch.ops.aten.avg_pool2d.default(buf1, [8, 8], [8, 8], [0, 
            0], False, True, None)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf3, (256, 16),
            (16, 1), 0), reinterpret_tensor(primals_4, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf4)
        del primals_5
    return buf4, primals_1, primals_3, buf1, reinterpret_tensor(buf3, (256,
        16), (16, 1), 0), primals_4


class ShakeShake(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x1, x2, training=True):
        if training:
            alpha = torch.FloatTensor(x1.size(0)).uniform_()
            alpha = alpha.view(alpha.size(0), 1, 1, 1).expand_as(x1)
        else:
            alpha = 0.5
        return alpha * x1 + (1 - alpha) * x2

    @staticmethod
    def backward(ctx, grad_output):
        beta = torch.FloatTensor(grad_output.size(0)).uniform_()
        beta = beta.view(beta.size(0), 1, 1, 1).expand_as(grad_output)
        beta = Variable(beta)
        return beta * grad_output, (1 - beta) * grad_output, None


class Shortcut(nn.Module):

    def __init__(self, in_ch, out_ch, stride):
        super(Shortcut, self).__init__()
        self.stride = stride
        self.conv1 = nn.Conv2d(in_ch, out_ch // 2, 1, stride=1, padding=0,
            bias=False)
        self.conv2 = nn.Conv2d(in_ch, out_ch // 2, 1, stride=1, padding=0,
            bias=False)
        self.bn = nn.BatchNorm2d(out_ch)

    def forward(self, x):
        h = F.relu(x)
        h1 = F.avg_pool2d(h, 1, self.stride)
        h1 = self.conv1(h1)
        h2 = F.avg_pool2d(F.pad(h, (-1, 1, -1, 1)), 1, self.stride)
        h2 = self.conv2(h2)
        h = torch.cat((h1, h2), 1)
        return self.bn(h)


class ShakeBlock(nn.Module):

    def __init__(self, in_ch, out_ch, stride=1):
        super(ShakeBlock, self).__init__()
        self.equal_io = in_ch == out_ch
        self.shortcut = self.equal_io and None or Shortcut(in_ch, out_ch,
            stride=stride)
        self.branch1 = self._make_branch(in_ch, out_ch, stride)
        self.branch2 = self._make_branch(in_ch, out_ch, stride)

    def forward(self, x):
        h1 = self.branch1(x)
        h2 = self.branch2(x)
        h = ShakeShake.apply(h1, h2, self.training)
        h0 = x if self.equal_io else self.shortcut(x)
        return h + h0

    def _make_branch(self, in_ch, out_ch, stride=1):
        return nn.Sequential(nn.ReLU(inplace=False), nn.Conv2d(in_ch,
            out_ch, 3, padding=1, stride=stride, bias=False), nn.
            BatchNorm2d(out_ch), nn.ReLU(inplace=False), nn.Conv2d(out_ch,
            out_ch, 3, padding=1, stride=1, bias=False), nn.BatchNorm2d(out_ch)
            )


class ShakeResNetNew(nn.Module):

    def __init__(self, depth, w_base, label):
        super(ShakeResNetNew, self).__init__()
        n_units = (depth - 2) / 6
        in_chs = [16, w_base, w_base * 2, w_base * 4]
        self.in_chs = in_chs
        self.c_in = nn.Conv2d(3, in_chs[0], 3, padding=1)
        self.layer1 = self._make_layer(n_units, in_chs[0], in_chs[1])
        self.layer2 = self._make_layer(n_units, in_chs[1], in_chs[2], 2)
        self.layer3 = self._make_layer(n_units, in_chs[2], in_chs[3], 2)
        self.fc_out = nn.Linear(in_chs[3], label)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def _make_layer(self, n_units, in_ch, out_ch, stride=1):
        layers = []
        for i in range(int(n_units)):
            layers.append(ShakeBlock(in_ch, out_ch, stride=stride))
            in_ch, stride = out_ch, 1
        return nn.Sequential(*layers)

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