import torch
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 torch.nn as nn
import torch.utils.data
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_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + 2 * x1, tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (1 + 2 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1), torch.
        complex64)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0.copy_(reinterpret_tensor(arg0_1, (4, 1, 4, 4, 4), (64, 64, 16,
            4, 1), 0))
        del arg0_1
        buf2 = torch.ops.aten._fft_c2c.default(buf0, [3, 4], 1, True)
        del buf0
        buf3 = buf2
        del buf2
        buf4 = torch.ops.aten.view_as_real.default(buf3)
        buf5 = buf4
        buf6 = torch.ops.aten.view_as_real.default(buf3)
        buf7 = buf6
        buf8 = empty_strided_cuda((4, 1, 4, 4, 4, 2), (128, 128, 32, 8, 2, 
            1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(512)](buf5, buf7, buf8, 512, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf4
        del buf5
        del buf6
        del buf7
        buf9 = buf3
        del buf3
        buf9.copy_(reinterpret_tensor(arg1_1, (4, 1, 4, 4, 4), (64, 64, 16,
            4, 1), 0))
        del arg1_1
        buf11 = torch.ops.aten._fft_c2c.default(buf9, [3, 4], 1, True)
        del buf9
        buf12 = buf11
        del buf11
        buf13 = torch.ops.aten.view_as_real.default(buf12)
        buf14 = buf13
        buf15 = torch.ops.aten.view_as_real.default(buf12)
        buf16 = buf15
        buf17 = empty_strided_cuda((4, 1, 4, 4, 4, 2), (128, 128, 32, 8, 2,
            1), torch.float32)
        triton_poi_fused_stack_0[grid(512)](buf14, buf16, buf17, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf12
        del buf13
        del buf14
        del buf15
        del buf16
    return buf8, buf17


class FocalFrequencyLossNew(nn.Module):
    """The torch.nn.Module class that implements focal frequency loss - a
    frequency domain loss function for optimizing generative models.

    Ref:
    Focal Frequency Loss for Image Reconstruction and Synthesis. In ICCV 2021.
    <https://arxiv.org/pdf/2012.12821.pdf>

    Args:
        loss_weight (float): weight for focal frequency loss. Default: 1.0
        alpha (float): the scaling factor alpha of the spectrum weight matrix for flexibility. Default: 1.0
        patch_factor (int): the factor to crop image patches for patch-based focal frequency loss. Default: 1
        ave_spectrum (bool): whether to use minibatch average spectrum. Default: False
        log_matrix (bool): whether to adjust the spectrum weight matrix by logarithm. Default: False
        batch_matrix (bool): whether to calculate the spectrum weight matrix using batch-based statistics. Default: False
    """

    def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1,
        ave_spectrum=False, log_matrix=False, batch_matrix=False):
        super(FocalFrequencyLossNew, self).__init__()
        self.loss_weight = loss_weight
        self.alpha = alpha
        self.patch_factor = patch_factor
        self.ave_spectrum = ave_spectrum
        self.log_matrix = log_matrix
        self.batch_matrix = batch_matrix

    def tensor2freq(self, x):
        patch_factor = self.patch_factor
        _, _, h, w = x.shape
        assert h % patch_factor == 0 and w % patch_factor == 0, 'Patch factor should be divisible by image height and width'
        patch_list = []
        patch_h = h // patch_factor
        patch_w = w // patch_factor
        for i in range(patch_factor):
            for j in range(patch_factor):
                patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j *
                    patch_w:(j + 1) * patch_w])
        y = torch.stack(patch_list, 1)
        if torch.__version__.split('+')[0] > '1.7.1':
            freq = torch.fft.fft2(y, norm='ortho')
            freq = torch.stack([freq.real, freq.imag], -1)
        else:
            freq = torch.rfft(y, 2, onesided=False, normalized=True)
        return freq

    def loss_formulation(self, recon_freq, real_freq, matrix=None):
        if matrix is not None:
            weight_matrix = matrix.detach()
        else:
            matrix_tmp = (recon_freq - real_freq) ** 2
            matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]
                ) ** self.alpha
            if self.log_matrix:
                matrix_tmp = torch.log(matrix_tmp + 1.0)
            if self.batch_matrix:
                matrix_tmp = matrix_tmp / matrix_tmp.max()
            else:
                matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1
                    ).values[:, :, :, None, None]
            matrix_tmp[torch.isnan(matrix_tmp)] = 0.0
            matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0)
            weight_matrix = matrix_tmp.clone().detach()
        assert weight_matrix.min().item() >= 0 and weight_matrix.max().item(
            ) <= 1, 'The values of spectrum weight matrix should be in the range [0, 1], but got Min: %.10f Max: %.10f' % (
            weight_matrix.min().item(), weight_matrix.max().item())
        tmp = (recon_freq - real_freq) ** 2
        freq_distance = tmp[..., 0] + tmp[..., 1]
        loss = weight_matrix * freq_distance
        return torch.mean(loss)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.073429