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.nn import Module
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d
from torch.nn import ReLU
from torch.nn.modules.utils import _single
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_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), (8, 4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (32, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (8, 32, 1), (32, 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,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=2, bias=None)
        assert_size_stride(buf0, (4, 8, 1), (8, 1, 1))
        buf1 = buf0
        del buf0
        buf9 = empty_strided_cuda((4, 8, 1), (8, 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, 16, 16), torch.float32)
        triton_poi_fused_mean_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf2, (4, 4, 1
            ), (4, 1, 0), 0), primals_4, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (4, 32, 1), (32, 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,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 8, 1), (8, 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), (4, 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), (8, 1, 1), 0),
        reinterpret_tensor(buf1, (4, 4, 1), (8, 1, 1), 4),
        reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 1), 0), buf4, buf6,
        reinterpret_tensor(buf7, (4, 4, 1), (8, 1, 1), 0),
        reinterpret_tensor(buf7, (4, 4, 1), (8, 1, 1), 4), buf9)


class DropBlock1d(object):

    def __init__(self, *args, **kwargs):
        raise NotImplementedError


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 SplAtConv1dNew(Module):
    """Split-Attention Conv1d
    """

    def __init__(self, in_channels, channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True, radix=2,
        reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=
        None, dropblock_prob=0.0, **kwargs):
        super(SplAtConv1dNew, self).__init__()
        padding = _single(padding)
        self.rectify = rectify and padding[0] > 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 = RFConv1d(in_channels, channels * radix, kernel_size,
                stride, padding, dilation, groups=groups * radix, bias=bias,
                average_mode=rectify_avg, **kwargs)
        else:
            None
            self.conv = Conv1d(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 = norm_layer(channels * radix)
        self.relu = ReLU(inplace=True)
        self.fc1 = Conv1d(channels, inter_channels, 1, groups=self.cardinality)
        if self.use_bn:
            self.bn1 = norm_layer(inter_channels)
        self.fc2 = Conv1d(inter_channels, channels * radix, 1, groups=self.
            cardinality)
        if dropblock_prob > 0.0:
            self.dropblock = DropBlock1d(dropblock_prob, 3)
        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: 0.014748