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
import logging
import torch.nn.functional as F
from torch import nn
from torch.nn import ReLU
from torch.nn import Conv2d
from torch.nn.modules.utils import _pair
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, (8, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 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_3, primals_1, 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_2, buf9, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_2
        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_3, 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, num_splits=1, **kwargs):
    """
    Args:
        norm (str or callable):
    Returns:
        nn.Module or None: the normalization layer
    """
    if isinstance(norm, str):
        if len(norm) == 0:
            return None
        norm = {'BN': BatchNorm(out_channels, **kwargs), 'GhostBN':
            GhostBatchNorm(out_channels, num_splits, **kwargs), 'FrozenBN':
            FrozenBatchNorm(out_channels), 'GN': nn.GroupNorm(32,
            out_channels), 'syncBN': SyncBatchNorm(out_channels, **kwargs)}[
            norm]
    return norm


class BatchNorm(nn.BatchNorm2d):

    def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze
        =False, bias_freeze=False, weight_init=1.0, bias_init=0.0):
        super().__init__(num_features, eps=eps, momentum=momentum)
        if weight_init is not None:
            self.weight.data.fill_(weight_init)
        if bias_init is not None:
            self.bias.data.fill_(bias_init)
        self.weight.requires_grad_(not weight_freeze)
        self.bias.requires_grad_(not bias_freeze)


class SyncBatchNorm(nn.SyncBatchNorm):

    def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze
        =False, bias_freeze=False, weight_init=1.0, bias_init=0.0):
        super().__init__(num_features, eps=eps, momentum=momentum)
        if weight_init is not None:
            self.weight.data.fill_(weight_init)
        if bias_init is not None:
            self.bias.data.fill_(bias_init)
        self.weight.requires_grad_(not weight_freeze)
        self.bias.requires_grad_(not bias_freeze)


class FrozenBatchNorm(BatchNorm):
    """
    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__(num_features, weight_freeze=True, bias_freeze=True)
        self.num_features = num_features
        self.eps = 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 GhostBatchNorm(BatchNorm):

    def __init__(self, num_features, num_splits=1, **kwargs):
        super().__init__(num_features, **kwargs)
        self.num_splits = num_splits
        self.register_buffer('running_mean', torch.zeros(num_features))
        self.register_buffer('running_var', torch.ones(num_features))

    def forward(self, input):
        N, C, H, W = input.shape
        if self.training or not self.track_running_stats:
            self.running_mean = self.running_mean.repeat(self.num_splits)
            self.running_var = self.running_var.repeat(self.num_splits)
            outputs = F.batch_norm(input.view(-1, C * self.num_splits, H, W
                ), self.running_mean, self.running_var, self.weight.repeat(
                self.num_splits), self.bias.repeat(self.num_splits), True,
                self.momentum, self.eps).view(N, C, H, W)
            self.running_mean = torch.mean(self.running_mean.view(self.
                num_splits, self.num_features), dim=0)
            self.running_var = torch.mean(self.running_var.view(self.
                num_splits, self.num_features), dim=0)
            return outputs
        else:
            return F.batch_norm(input, self.running_mean, self.running_var,
                self.weight, self.bias, False, self.momentum, self.eps)


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(nn.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_layer=
        None, num_splits=1, 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_layer is not None
        if self.use_bn:
            self.bn0 = get_norm(norm_layer, channels * radix, num_splits)
        self.relu = ReLU(inplace=True)
        self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
        if self.use_bn:
            self.bn1 = get_norm(norm_layer, inter_channels, num_splits)
        self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.
            cardinality)
        self.rsoftmax = rSoftMax(radix, groups)

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


Measured latency_ms: 0.027036