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 numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 49
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 49 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 196 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_2(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 361
    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 + 1444 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 361 * y3), tmp0, xmask & ymask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 16)](arg1_1, buf0, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 7, 7), (196, 1, 28, 4), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 49)](arg0_1, buf1, 16, 49,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(4, 4),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 19, 19), (1444, 1, 76, 4))
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4, 19, 19), (1444, 361, 19, 1), torch
            .float32)
        triton_poi_fused_convolution_2[grid(16, 361)](buf2, buf3, 16, 361,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf2
    return buf3,


def bilinear_kernel(size, normalize=False):
    """
    Make a 2D bilinear kernel suitable for upsampling/downsampling with
    normalize=False/True. The kernel is size x size square.

    Take
        size: kernel size (square)
        normalize: whether kernel sums to 1 (True) or not

    Give
        kernel: np.array with bilinear kernel coefficient
    """
    factor = (size + 1) // 2
    if size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = np.ogrid[:size, :size]
    kernel = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) /
        factor)
    if normalize:
        kernel /= kernel.sum()
    return kernel


class InterpolatorNew(nn.Module):
    """
    Interpolate by de/up/backward convolution with a bilinear kernel.

    Take
        channel_dim: the input channel dimension
        rate: upsampling rate, that is 4 -> 4x upsampling
        odd: the kernel parity, which is too much to explain here for now, but
             will be handled automagically in the future, promise.
        normalize: whether kernel sums to 1
    """

    def __init__(self, channel_dim, rate, odd=True, normalize=False):
        super().__init__()
        self.rate = rate
        ksize = rate * 2
        if odd:
            ksize -= 1
        kernel = torch.from_numpy(bilinear_kernel(ksize, normalize))
        weight = torch.zeros(channel_dim, channel_dim, ksize, ksize)
        for k in range(channel_dim):
            weight[k, k] = kernel
        self.weight = nn.Parameter(weight, requires_grad=False)

    def forward(self, input_0):
        arg0_1 = self.weight
        arg1_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.050219