# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // Licensed under the Apache License, Version 2.0 (the "License");
# // you may not use this file except in compliance with the License.
# // You may obtain a copy of the License at
# //
# //     http://www.apache.org/licenses/LICENSE-2.0
# //
# // Unless required by applicable law or agreed to in writing, software
# // distributed under the License is distributed on an "AS IS" BASIS,
# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# // See the License for the specific language governing permissions and
# // limitations under the License.

import torch
import torch.nn as nn


class ScalingLayer(nn.Module):
    def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
        super().__init__()
        self.register_buffer('shift', torch.Tensor(mean)[None, :, None, None])
        self.register_buffer('scale', torch.Tensor(std)[None, :, None, None])

    def forward(self, inp):
        return (inp - self.shift) / self.scale
    
    def inv(self, inp):
        return inp * self.scale + self.shift