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
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.functional as F
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_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
        del buf0
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 5), (20, 5, 1))
    return reinterpret_tensor(buf2, (4, 5, 4), (20, 1, 5), 0
        ), primals_2, primals_3, reinterpret_tensor(primals_1, (4, 4, 4), (
        16, 1, 4), 0), buf1


def gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
        torch.pow(x, 3))))


class DWConv(nn.Module):
    """
    Depthwise separable 1d convolution
    """

    def __init__(self, nin, nout, kernel_size, bias=True, act='relu'):
        super(DWConv, self).__init__()
        self.depthwise = nn.Conv1d(nin, nin, kernel_size=kernel_size,
            padding=kernel_size // 2, groups=nin, bias=bias)
        self.pointwise = nn.Conv1d(nin, nout, kernel_size=1, bias=bias)
        self.act = act

    def forward(self, x):
        out = self.depthwise(x.permute(0, 2, 1))
        out = self.pointwise(out)
        out = out.permute(0, 2, 1)
        if self.act == 'relu':
            out = F.relu(out)
        elif self.act == 'gelu':
            out = gelu(out)
        return out


class ResizerNew(nn.Module):

    def __init__(self, input_size, output_size, kernel_size, drop_prob=0,
        bias=False, act=None):
        super(ResizerNew, self).__init__()
        self.conv = DWConv(input_size, output_size, kernel_size, bias=bias,
            act=act)
        self.drop = nn.Dropout(drop_prob)

    def forward(self, input_0):
        primals_2 = self.conv.depthwise.weight
        primals_3 = self.conv.pointwise.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.034839