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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023
from __future__ import annotations

from typing import Any, Union


def val2list(x: Union[list[Any], tuple[Any, ...], Any], repeat_time: int = 1) -> list[Any]:
    """Convert value to list."""
    if isinstance(x, list):
        return x
    elif isinstance(x, tuple):
        return list(x)
    else:
        return [x] * repeat_time


def val2tuple(x: Union[list[Any], tuple[Any, ...], Any], min_len: int = 1, idx_repeat: int = -1) -> tuple[Any, ...]:
    """Convert value to tuple."""
    xlist = list(x) if isinstance(x, (list, tuple)) else [x]
    cur_len = len(xlist)
    if cur_len < min_len and cur_len > 0:
        v = xlist[idx_repeat]
        # Only append as many values as needed:
        xlist[idx_repeat:idx_repeat] = [v] * (min_len - cur_len)
    return tuple(xlist)
