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 math as tl_math
from torch.autograd import Function
from torch.nn import Module
import logging
import torch.utils.data
import torch.distributed as dist
from torch import nn
import torch.nn.functional as F
from torch.autograd.function import Function
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from torch.nn import BatchNorm2d
from torch.nn import ReLU
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_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 1.0
    tmp6 = tmp4 / tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 8
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 8 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 8 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tl.store(out_ptr0 + x3, tmp11, xmask)


@triton.jit
def triton_poi_fused_add_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 8 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp6 = tl.load(in_ptr1 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (32, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (8, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_7, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=2, bias=None)
        assert_size_stride(buf0, (4, 8, 1, 1), (8, 1, 1, 1))
        buf1 = reinterpret_tensor(buf0, (4, 8, 1, 1), (8, 1, 32, 32), 0)
        del buf0
        buf9 = empty_strided_cuda((4, 8, 1, 1), (8, 1, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(32)](buf1,
            primals_3, buf9, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_add_mean_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, 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, 32, 1, 1), (32, 1, 1, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_2[grid(128)](buf4, primals_5, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 8, 1, 1), (8, 1, 1, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_3[grid(32)](buf6, primals_7, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((4, 2, 1, 4), (8, 4, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(32)](buf6, buf7, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_add_mul_5[grid(16)](buf7, buf1, buf8, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
    return (buf8, primals_1, primals_2, primals_4, primals_6,
        reinterpret_tensor(buf1, (4, 4, 1, 1), (8, 1, 1, 1), 0),
        reinterpret_tensor(buf1, (4, 4, 1, 1), (8, 1, 1, 1), 4), buf2, buf4,
        buf6, reinterpret_tensor(buf7, (4, 4, 1, 1), (8, 1, 1, 1), 0),
        reinterpret_tensor(buf7, (4, 4, 1, 1), (8, 1, 1, 1), 4), buf9)


def get_norm(norm, out_channels):
    """
    Args:
        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
            or a callable that takes a channel number and returns
            the normalization layer as a nn.Module.

    Returns:
        nn.Module or None: the normalization layer
    """
    if isinstance(norm, str):
        if len(norm) == 0:
            return None
        norm = {'BN': BatchNorm2d, 'SyncBN': NaiveSyncBatchNorm, 'FrozenBN':
            FrozenBatchNorm2d, 'GN': lambda channels: nn.GroupNorm(32,
            channels), 'nnSyncBN': nn.SyncBatchNorm, 'noNorm': NoNorm}[norm]
    return norm(out_channels)


class FrozenBatchNorm2d(nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    It contains non-trainable buffers called
    "weight" and "bias", "running_mean", "running_var",
    initialized to perform identity transformation.

    The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
    which are computed from the original four parameters of BN.
    The affine transform `x * weight + bias` will perform the equivalent
    computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
    When loading a backbone model from Caffe2, "running_mean" and "running_var"
    will be left unchanged as identity transformation.

    Other pre-trained backbone models may contain all 4 parameters.

    The forward is implemented by `F.batch_norm(..., training=False)`.
    """
    _version = 3

    def __init__(self, num_features, eps=1e-05):
        super().__init__()
        self.num_features = num_features
        self.eps = eps
        self.register_buffer('weight', torch.ones(num_features))
        self.register_buffer('bias', torch.zeros(num_features))
        self.register_buffer('running_mean', torch.zeros(num_features))
        self.register_buffer('running_var', torch.ones(num_features) - eps)

    def forward(self, x):
        if x.requires_grad:
            scale = self.weight * (self.running_var + self.eps).rsqrt()
            bias = self.bias - self.running_mean * scale
            scale = scale.reshape(1, -1, 1, 1)
            bias = bias.reshape(1, -1, 1, 1)
            return x * scale + bias
        else:
            return F.batch_norm(x, self.running_mean, self.running_var,
                self.weight, self.bias, training=False, eps=self.eps)

    def _load_from_state_dict(self, state_dict, prefix, local_metadata,
        strict, missing_keys, unexpected_keys, error_msgs):
        version = local_metadata.get('version', None)
        if version is None or version < 2:
            if prefix + 'running_mean' not in state_dict:
                state_dict[prefix + 'running_mean'] = torch.zeros_like(self
                    .running_mean)
            if prefix + 'running_var' not in state_dict:
                state_dict[prefix + 'running_var'] = torch.ones_like(self.
                    running_var)
        if version is not None and version < 3:
            logger = logging.getLogger(__name__)
            logger.info('FrozenBatchNorm {} is upgraded to version 3.'.
                format(prefix.rstrip('.')))
            state_dict[prefix + 'running_var'] -= self.eps
        super()._load_from_state_dict(state_dict, prefix, local_metadata,
            strict, missing_keys, unexpected_keys, error_msgs)

    def __repr__(self):
        return 'FrozenBatchNorm2d(num_features={}, eps={})'.format(self.
            num_features, self.eps)

    @classmethod
    def convert_frozen_batchnorm(cls, module):
        """
        Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.

        Args:
            module (torch.nn.Module):

        Returns:
            If module is BatchNorm/SyncBatchNorm, returns a new module.
            Otherwise, in-place convert module and return it.

        Similar to convert_sync_batchnorm in
        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
        """
        bn_module = nn.modules.batchnorm
        bn_module = bn_module.BatchNorm2d, bn_module.SyncBatchNorm
        res = module
        if isinstance(module, bn_module):
            res = cls(module.num_features)
            if module.affine:
                res.weight.data = module.weight.data.clone().detach()
                res.bias.data = module.bias.data.clone().detach()
            res.running_mean.data = module.running_mean.data
            res.running_var.data = module.running_var.data
            res.eps = module.eps
        else:
            for name, child in module.named_children():
                new_child = cls.convert_frozen_batchnorm(child)
                if new_child is not child:
                    res.add_module(name, new_child)
        return res


class NoNorm(nn.Module):

    def __init__(self, *args, **kwargs):
        super().__init__()

    def forward(self, x):
        return x


class _NewEmptyTensorOp(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x, new_shape):
        ctx.shape = x.shape
        return x.new_empty(new_shape)

    @staticmethod
    def backward(ctx, grad):
        shape = ctx.shape
        return _NewEmptyTensorOp.apply(grad, shape), None


class Conv2d(torch.nn.Conv2d):
    """
    A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
    """

    def __init__(self, *args, **kwargs):
        """
        Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:

        Args:
            norm (nn.Module, optional): a normalization layer
            activation (callable(Tensor) -> Tensor): a callable activation function

        It assumes that norm layer is used before activation.
        """
        norm = kwargs.pop('norm', None)
        activation = kwargs.pop('activation', None)
        super().__init__(*args, **kwargs)
        self.norm = norm
        self.activation = activation

    def forward(self, x):
        if x.numel() == 0 and self.training:
            assert not isinstance(self.norm, torch.nn.SyncBatchNorm
                ), 'SyncBatchNorm does not support empty inputs!'
        if x.numel() == 0 and TORCH_VERSION <= (1, 4):
            assert not isinstance(self.norm, torch.nn.GroupNorm
                ), 'GroupNorm does not support empty inputs in PyTorch <=1.4!'
            output_shape = [((i + 2 * p - (di * (k - 1) + 1)) // s + 1) for
                i, p, di, k, s in zip(x.shape[-2:], self.padding, self.
                dilation, self.kernel_size, self.stride)]
            output_shape = [x.shape[0], self.weight.shape[0]] + output_shape
            empty = _NewEmptyTensorOp.apply(x, output_shape)
            if self.training:
                _dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
                return empty + _dummy
            else:
                return empty
        x = super().forward(x)
        if self.norm is not None:
            x = self.norm(x)
        if self.activation is not None:
            x = self.activation(x)
        return x


class AllReduce(Function):

    @staticmethod
    def forward(ctx, input):
        input_list = [torch.zeros_like(input) for k in range(dist.
            get_world_size())]
        dist.all_gather(input_list, input, async_op=False)
        inputs = torch.stack(input_list, dim=0)
        return torch.sum(inputs, dim=0)

    @staticmethod
    def backward(ctx, grad_output):
        dist.all_reduce(grad_output, async_op=False)
        return grad_output


class NaiveSyncBatchNorm(BatchNorm2d):
    """
    `torch.nn.SyncBatchNorm` has known unknown bugs.
    It produces significantly worse AP (and sometimes goes NaN)
    when the batch size on each worker is quite different
    (e.g., when scale augmentation is used, or when it is applied to mask head).

    Use this implementation before `nn.SyncBatchNorm` is fixed.
    It is slower than `nn.SyncBatchNorm`.

    Note:
        There isn't a single definition of Sync BatchNorm.

        When ``stats_mode==""``, this module computes overall statistics by using
        statistics of each worker with equal weight.  The result is true statistics
        of all samples (as if they are all on one worker) only when all workers
        have the same (N, H, W). This mode does not support inputs with zero batch size.

        When ``stats_mode=="N"``, this module computes overall statistics by weighting
        the statistics of each worker by their ``N``. The result is true statistics
        of all samples (as if they are all on one worker) only when all workers
        have the same (H, W). It is slower than ``stats_mode==""``.

        Even though the result of this module may not be the true statistics of all samples,
        it may still be reasonable because it might be preferrable to assign equal weights
        to all workers, regardless of their (H, W) dimension, instead of putting larger weight
        on larger images. From preliminary experiments, little difference is found between such
        a simplified implementation and an accurate computation of overall mean & variance.
    """

    def __init__(self, *args, stats_mode='', **kwargs):
        super().__init__(*args, **kwargs)
        assert stats_mode in ['', 'N']
        self._stats_mode = stats_mode

    def forward(self, input):
        if comm.get_world_size() == 1 or not self.training:
            return super().forward(input)
        B, C = input.shape[0], input.shape[1]
        mean = torch.mean(input, dim=[0, 2, 3])
        meansqr = torch.mean(input * input, dim=[0, 2, 3])
        if self._stats_mode == '':
            assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
            vec = torch.cat([mean, meansqr], dim=0)
            vec = AllReduce.apply(vec) * (1.0 / dist.get_world_size())
            mean, meansqr = torch.split(vec, C)
            momentum = self.momentum
        else:
            if B == 0:
                vec = torch.zeros([2 * C + 1], device=mean.device, dtype=
                    mean.dtype)
                vec = vec + input.sum()
            else:
                vec = torch.cat([mean, meansqr, torch.ones([1], device=mean
                    .device, dtype=mean.dtype)], dim=0)
            vec = AllReduce.apply(vec * B)
            total_batch = vec[-1].detach()
            momentum = total_batch.clamp(max=1) * self.momentum
            total_batch = torch.max(total_batch, torch.ones_like(total_batch))
            mean, meansqr, _ = torch.split(vec / total_batch, C)
        var = meansqr - mean * mean
        invstd = torch.rsqrt(var + self.eps)
        scale = self.weight * invstd
        bias = self.bias - mean * scale
        scale = scale.reshape(1, -1, 1, 1)
        bias = bias.reshape(1, -1, 1, 1)
        self.running_mean += momentum * (mean.detach() - self.running_mean)
        self.running_var += momentum * (var.detach() - self.running_var)
        return input * scale + bias


class rSoftMax(nn.Module):

    def __init__(self, radix, cardinality):
        super().__init__()
        self.radix = radix
        self.cardinality = cardinality

    def forward(self, x):
        batch = x.size(0)
        if self.radix > 1:
            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
            x = F.softmax(x, dim=1)
            x = x.reshape(batch, -1)
        else:
            x = torch.sigmoid(x)
        return x


class SplAtConv2dNew(Module):
    """Split-Attention Conv2d
    """

    def __init__(self, in_channels, channels, kernel_size, stride=(1, 1),
        padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2,
        reduction_factor=4, rectify=False, rectify_avg=False, norm=None,
        dropblock_prob=0.0, **kwargs):
        super(SplAtConv2dNew, self).__init__()
        padding = _pair(padding)
        self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
        self.rectify_avg = rectify_avg
        inter_channels = max(in_channels * radix // reduction_factor, 32)
        self.radix = radix
        self.cardinality = groups
        self.channels = channels
        self.dropblock_prob = dropblock_prob
        if self.rectify:
            self.conv = RFConv2d(in_channels, channels * radix, kernel_size,
                stride, padding, dilation, groups=groups * radix, bias=bias,
                average_mode=rectify_avg, **kwargs)
        else:
            self.conv = Conv2d(in_channels, channels * radix, kernel_size,
                stride, padding, dilation, groups=groups * radix, bias=bias,
                **kwargs)
        self.use_bn = norm is not None
        if self.use_bn:
            self.bn0 = get_norm(norm, channels * radix)
        self.relu = ReLU(inplace=True)
        self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
        if self.use_bn:
            self.bn1 = get_norm(norm, inter_channels)
        self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.
            cardinality)
        if dropblock_prob > 0.0:
            self.dropblock = DropBlock2D(dropblock_prob, 3)
        self.rsoftmax = rSoftMax(radix, groups)

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.020569