source_code/SegMamba/monai/utils/misc.py

# Copyright (c) MONAI Consortium
# 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.

from __future__ import annotations

import inspect
import itertools
import math
import os
import pprint
import random
import shutil
import subprocess
import tempfile
import types
import warnings
from ast import literal_eval
from collections.abc import Callable, Iterable, Sequence
from distutils.util import strtobool
from math import log10
from pathlib import Path
from typing import TYPE_CHECKING, Any, TypeVar, cast, overload

import numpy as np
import torch

from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
from monai.utils.module import optional_import, version_leq

if TYPE_CHECKING:
    from yaml import SafeLoader
else:
    SafeLoader, _ = optional_import("yaml", name="SafeLoader", as_type="base")

__all__ = [
    "zip_with",
    "star_zip_with",
    "first",
    "issequenceiterable",
    "is_immutable",
    "ensure_tuple",
    "ensure_tuple_size",
    "ensure_tuple_rep",
    "to_tuple_of_dictionaries",
    "fall_back_tuple",
    "is_scalar_tensor",
    "is_scalar",
    "progress_bar",
    "get_seed",
    "set_determinism",
    "list_to_dict",
    "MAX_SEED",
    "copy_to_device",
    "str2bool",
    "str2list",
    "MONAIEnvVars",
    "ImageMetaKey",
    "is_module_ver_at_least",
    "has_option",
    "sample_slices",
    "check_parent_dir",
    "save_obj",
    "label_union",
    "path_to_uri",
    "pprint_edges",
    "check_key_duplicates",
    "CheckKeyDuplicatesYamlLoader",
    "ConvertUnits",
    "check_kwargs_exist_in_class_init",
    "run_cmd",
]

_seed = None
_flag_deterministic = torch.backends.cudnn.deterministic
_flag_cudnn_benchmark = torch.backends.cudnn.benchmark
NP_MAX = np.iinfo(np.uint32).max
MAX_SEED = NP_MAX + 1  # 2**32, the actual seed should be in [0, MAX_SEED - 1] for uint32


def zip_with(op, *vals, mapfunc=map):
    """
    Map `op`, using `mapfunc`, to each tuple derived from zipping the iterables in `vals`.
    """
    return mapfunc(op, zip(*vals))


def star_zip_with(op, *vals):
    """
    Use starmap as the mapping function in zipWith.
    """
    return zip_with(op, *vals, mapfunc=itertools.starmap)


T = TypeVar("T")


@overload
def first(iterable: Iterable[T], default: T) -> T: ...


@overload
def first(iterable: Iterable[T]) -> T | None: ...


def first(iterable: Iterable[T], default: T | None = None) -> T | None:
    """
    Returns the first item in the given iterable or `default` if empty, meaningful mostly with 'for' expressions.
    """
    for i in iterable:
        return i
    return default


def issequenceiterable(obj: Any) -> bool:
    """
    Determine if the object is an iterable sequence and is not a string.
    """
    try:
        if hasattr(obj, "ndim") and obj.ndim == 0:
            return False  # a 0-d tensor is not iterable
    except Exception:
        return False
    return isinstance(obj, Iterable) and not isinstance(obj, (str, bytes))


def is_immutable(obj: Any) -> bool:
    """
    Determine if the object is an immutable object.

    see also https://github.com/python/cpython/blob/3.11/Lib/copy.py#L109
    """
    return isinstance(obj, (type(None), int, float, bool, complex, str, tuple, bytes, type, range, slice))


def ensure_tuple(vals: Any, wrap_array: bool = False) -> tuple:
    """
    Returns a tuple of `vals`.

    Args:
        vals: input data to convert to a tuple.
        wrap_array: if `True`, treat the input numerical array (ndarray/tensor) as one item of the tuple.
            if `False`, try to convert the array with `tuple(vals)`, default to `False`.

    """
    if wrap_array and isinstance(vals, (np.ndarray, torch.Tensor)):
        return (vals,)
    return tuple(vals) if issequenceiterable(vals) else (vals,)


def ensure_tuple_size(vals: Any, dim: int, pad_val: Any = 0, pad_from_start: bool = False) -> tuple:
    """
    Returns a copy of `tup` with `dim` values by either shortened or padded with `pad_val` as necessary.
    """
    tup = ensure_tuple(vals)
    pad_dim = dim - len(tup)
    if pad_dim <= 0:
        return tup[:dim]
    if pad_from_start:
        return (pad_val,) * pad_dim + tup
    return tup + (pad_val,) * pad_dim


def ensure_tuple_rep(tup: Any, dim: int) -> tuple[Any, ...]:
    """
    Returns a copy of `tup` with `dim` values by either shortened or duplicated input.

    Raises:
        ValueError: When ``tup`` is a sequence and ``tup`` length is not ``dim``.

    Examples::

        >>> ensure_tuple_rep(1, 3)
        (1, 1, 1)
        >>> ensure_tuple_rep(None, 3)
        (None, None, None)
        >>> ensure_tuple_rep('test', 3)
        ('test', 'test', 'test')
        >>> ensure_tuple_rep([1, 2, 3], 3)
        (1, 2, 3)
        >>> ensure_tuple_rep(range(3), 3)
        (0, 1, 2)
        >>> ensure_tuple_rep([1, 2], 3)
        ValueError: Sequence must have length 3, got length 2.

    """
    if isinstance(tup, torch.Tensor):
        tup = tup.detach().cpu().numpy()
    if isinstance(tup, np.ndarray):
        tup = tup.tolist()
    if not issequenceiterable(tup):
        return (tup,) * dim
    if len(tup) == dim:
        return tuple(tup)

    raise ValueError(f"Sequence must have length {dim}, got {len(tup)}.")


def to_tuple_of_dictionaries(dictionary_of_tuples: dict, keys: Any) -> tuple[dict[Any, Any], ...]:
    """
    Given a dictionary whose values contain scalars or tuples (with the same length as ``keys``),
    Create a dictionary for each key containing the scalar values mapping to that key.

    Args:
        dictionary_of_tuples: a dictionary whose values are scalars or tuples whose length is
            the length of ``keys``
        keys: a tuple of string values representing the keys in question

    Returns:
        a tuple of dictionaries that contain scalar values, one dictionary for each key

    Raises:
        ValueError: when values in the dictionary are tuples but not the same length as the length
        of ``keys``

    Examples:
        >>> to_tuple_of_dictionaries({'a': 1 'b': (2, 3), 'c': (4, 4)}, ("x", "y"))
        ({'a':1, 'b':2, 'c':4}, {'a':1, 'b':3, 'c':4})

    """

    keys = ensure_tuple(keys)
    if len(keys) == 0:
        return tuple({})

    dict_overrides = {k: ensure_tuple_rep(v, len(keys)) for k, v in dictionary_of_tuples.items()}
    return tuple({k: v[ik] for (k, v) in dict_overrides.items()} for ik in range(len(keys)))


def fall_back_tuple(
    user_provided: Any, default: Sequence | NdarrayTensor, func: Callable = lambda x: x and x > 0
) -> tuple[Any, ...]:
    """
    Refine `user_provided` according to the `default`, and returns as a validated tuple.

    The validation is done for each element in `user_provided` using `func`.
    If `func(user_provided[idx])` returns False, the corresponding `default[idx]` will be used
    as the fallback.

    Typically used when `user_provided` is a tuple of window size provided by the user,
    `default` is defined by data, this function returns an updated `user_provided` with its non-positive
    components replaced by the corresponding components from `default`.

    Args:
        user_provided: item to be validated.
        default: a sequence used to provided the fallbacks.
        func: a Callable to validate every components of `user_provided`.

    Examples::

        >>> fall_back_tuple((1, 2), (32, 32))
        (1, 2)
        >>> fall_back_tuple(None, (32, 32))
        (32, 32)
        >>> fall_back_tuple((-1, 10), (32, 32))
        (32, 10)
        >>> fall_back_tuple((-1, None), (32, 32))
        (32, 32)
        >>> fall_back_tuple((1, None), (32, 32))
        (1, 32)
        >>> fall_back_tuple(0, (32, 32))
        (32, 32)
        >>> fall_back_tuple(range(3), (32, 64, 48))
        (32, 1, 2)
        >>> fall_back_tuple([0], (32, 32))
        ValueError: Sequence must have length 2, got length 1.

    """
    ndim = len(default)
    user = ensure_tuple_rep(user_provided, ndim)
    return tuple(  # use the default values if user provided is not valid
        user_c if func(user_c) else default_c for default_c, user_c in zip(default, user)
    )


def is_scalar_tensor(val: Any) -> bool:
    return isinstance(val, torch.Tensor) and val.ndim == 0


def is_scalar(val: Any) -> bool:
    if isinstance(val, torch.Tensor) and val.ndim == 0:
        return True
    return bool(np.isscalar(val))


def progress_bar(index: int, count: int, desc: str | None = None, bar_len: int = 30, newline: bool = False) -> None:
    """print a progress bar to track some time consuming task.

    Args:
        index: current status in progress.
        count: total steps of the progress.
        desc: description of the progress bar, if not None, show before the progress bar.
        bar_len: the total length of the bar on screen, default is 30 char.
        newline: whether to print in a new line for every index.
    """
    end = "\r" if not newline else "\r\n"
    filled_len = int(bar_len * index // count)
    bar = f"{desc} " if desc is not None else ""
    bar += "[" + "=" * filled_len + " " * (bar_len - filled_len) + "]"
    print(f"{index}/{count} {bar}", end=end)
    if index == count:
        print("")


def get_seed() -> int | None:
    return _seed


def set_determinism(
    seed: int | None = NP_MAX,
    use_deterministic_algorithms: bool | None = None,
    additional_settings: Sequence[Callable[[int], Any]] | Callable[[int], Any] | None = None,
) -> None:
    """
    Set random seed for modules to enable or disable deterministic training.

    Args:
        seed: the random seed to use, default is np.iinfo(np.int32).max.
            It is recommended to set a large seed, i.e. a number that has a good balance
            of 0 and 1 bits. Avoid having many 0 bits in the seed.
            if set to None, will disable deterministic training.
        use_deterministic_algorithms: Set whether PyTorch operations must use "deterministic" algorithms.
        additional_settings: additional settings that need to set random seed.

    Note:

        This function will not affect the randomizable objects in :py:class:`monai.transforms.Randomizable`, which
        have independent random states. For those objects, the ``set_random_state()`` method should be used to
        ensure the deterministic behavior (alternatively, :py:class:`monai.data.DataLoader` by default sets the seeds
        according to the global random state, please see also: :py:class:`monai.data.utils.worker_init_fn` and
        :py:class:`monai.data.utils.set_rnd`).
    """
    if seed is None:
        # cast to 32 bit seed for CUDA
        seed_ = torch.default_generator.seed() % MAX_SEED
        torch.manual_seed(seed_)
    else:
        seed = int(seed) % MAX_SEED
        torch.manual_seed(seed)

    global _seed
    _seed = seed
    random.seed(seed)
    np.random.seed(seed)

    if additional_settings is not None:
        additional_settings = ensure_tuple(additional_settings)
        for func in additional_settings:
            func(seed)

    if torch.backends.flags_frozen():
        warnings.warn("PyTorch global flag support of backends is disabled, enable it to set global `cudnn` flags.")
        torch.backends.__allow_nonbracketed_mutation_flag = True

    if seed is not None:
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False
    else:  # restore the original flags
        torch.backends.cudnn.deterministic = _flag_deterministic
        torch.backends.cudnn.benchmark = _flag_cudnn_benchmark
    if use_deterministic_algorithms is not None:
        if hasattr(torch, "use_deterministic_algorithms"):  # `use_deterministic_algorithms` is new in torch 1.8.0
            torch.use_deterministic_algorithms(use_deterministic_algorithms)
        elif hasattr(torch, "set_deterministic"):  # `set_deterministic` is new in torch 1.7.0
            torch.set_deterministic(use_deterministic_algorithms)
        else:
            warnings.warn("use_deterministic_algorithms=True, but PyTorch version is too old to set the mode.")


def list_to_dict(items):
    """
    To convert a list of "key=value" pairs into a dictionary.
    For examples: items: `["a=1", "b=2", "c=3"]`, return: {"a": "1", "b": "2", "c": "3"}.
    If no "=" in the pair, use None as the value, for example: ["a"], return: {"a": None}.
    Note that it will remove the blanks around keys and values.

    """

    def _parse_var(s):
        items = s.split("=", maxsplit=1)
        key = items[0].strip(" \n\r\t'")
        value = items[1].strip(" \n\r\t'") if len(items) > 1 else None
        return key, value

    d = {}
    if items:
        for item in items:
            key, value = _parse_var(item)

            try:
                if key in d:
                    raise KeyError(f"encounter duplicated key {key}.")
                d[key] = literal_eval(value)
            except ValueError:
                try:
                    d[key] = bool(strtobool(str(value)))
                except ValueError:
                    d[key] = value
    return d


def copy_to_device(
    obj: Any, device: str | torch.device | None, non_blocking: bool = True, verbose: bool = False
) -> Any:
    """
    Copy object or tuple/list/dictionary of objects to ``device``.

    Args:
        obj: object or tuple/list/dictionary of objects to move to ``device``.
        device: move ``obj`` to this device. Can be a string (e.g., ``cpu``, ``cuda``,
            ``cuda:0``, etc.) or of type ``torch.device``.
        non_blocking: when `True`, moves data to device asynchronously if
            possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.
        verbose: when `True`, will print a warning for any elements of incompatible type
            not copied to ``device``.
    Returns:
        Same as input, copied to ``device`` where possible. Original input will be
            unchanged.
    """

    if hasattr(obj, "to"):
        return obj.to(device, non_blocking=non_blocking)
    if isinstance(obj, tuple):
        return tuple(copy_to_device(o, device, non_blocking) for o in obj)
    if isinstance(obj, list):
        return [copy_to_device(o, device, non_blocking) for o in obj]
    if isinstance(obj, dict):
        return {k: copy_to_device(o, device, non_blocking) for k, o in obj.items()}
    if verbose:
        fn_name = cast(types.FrameType, inspect.currentframe()).f_code.co_name
        warnings.warn(f"{fn_name} called with incompatible type: " + f"{type(obj)}. Data will be returned unchanged.")

    return obj


def str2bool(value: str | bool, default: bool = False, raise_exc: bool = True) -> bool:
    """
    Convert a string to a boolean. Case insensitive.
    True: yes, true, t, y, 1. False: no, false, f, n, 0.

    Args:
        value: string to be converted to a boolean. If value is a bool already, simply return it.
        raise_exc: if value not in tuples of expected true or false inputs,
            should we raise an exception? If not, return `default`.
    Raises
        ValueError: value not in tuples of expected true or false inputs and
            `raise_exc` is `True`.
    Useful with argparse, for example:
        parser.add_argument("--convert", default=False, type=str2bool)
        python mycode.py --convert=True
    """

    if isinstance(value, bool):
        return value

    true_set = ("yes", "true", "t", "y", "1")
    false_set = ("no", "false", "f", "n", "0")

    if isinstance(value, str):
        value = value.lower()
        if value in true_set:
            return True
        if value in false_set:
            return False

    if raise_exc:
        raise ValueError(f"Got \"{value}\", expected a value from: {', '.join(true_set + false_set)}")
    return default


def str2list(value: str | list | None, raise_exc: bool = True) -> list | None:
    """
    Convert a string to a list.  Useful with argparse commandline arguments:
        parser.add_argument("--blocks", default=[1,2,3], type=str2list)
        python mycode.py --blocks=1,2,2,4

    Args:
        value: string (comma separated) to be converted to a list
        raise_exc: if not possible to convert to a list, raise an exception
    Raises
        ValueError: value not a string or list or not possible to convert
    """

    if value is None:
        return None
    elif isinstance(value, list):
        return value
    elif isinstance(value, str):
        v = value.split(",")
        for i in range(len(v)):
            try:
                a = literal_eval(v[i].strip())  # attempt to convert
                v[i] = a
            except Exception:
                pass
        return v
    elif raise_exc:
        raise ValueError(f'Unable to convert "{value}", expected a comma-separated str, e.g. 1,2,3')

    return None


class MONAIEnvVars:
    """
    Environment variables used by MONAI.
    """

    @staticmethod
    def data_dir() -> str | None:
        return os.environ.get("MONAI_DATA_DIRECTORY")

    @staticmethod
    def debug() -> bool:
        val = os.environ.get("MONAI_DEBUG", False)
        return val if isinstance(val, bool) else str2bool(val)

    @staticmethod
    def doc_images() -> str | None:
        return os.environ.get("MONAI_DOC_IMAGES")

    @staticmethod
    def algo_hash() -> str | None:
        return os.environ.get("MONAI_ALGO_HASH", "e4cf5a1")

    @staticmethod
    def trace_transform() -> str | None:
        return os.environ.get("MONAI_TRACE_TRANSFORM", "1")

    @staticmethod
    def eval_expr() -> str | None:
        return os.environ.get("MONAI_EVAL_EXPR", "1")

    @staticmethod
    def allow_missing_reference() -> str | None:
        return os.environ.get("MONAI_ALLOW_MISSING_REFERENCE", "1")

    @staticmethod
    def extra_test_data() -> str | None:
        return os.environ.get("MONAI_EXTRA_TEST_DATA", "1")

    @staticmethod
    def testing_algo_template() -> str | None:
        return os.environ.get("MONAI_TESTING_ALGO_TEMPLATE", None)


class ImageMetaKey:
    """
    Common key names in the metadata header of images
    """

    FILENAME_OR_OBJ = "filename_or_obj"
    PATCH_INDEX = "patch_index"
    SPATIAL_SHAPE = "spatial_shape"


def has_option(obj: Callable, keywords: str | Sequence[str]) -> bool:
    """
    Return a boolean indicating whether the given callable `obj` has the `keywords` in its signature.
    """
    if not callable(obj):
        return False
    sig = inspect.signature(obj)
    return all(key in sig.parameters for key in ensure_tuple(keywords))


def is_module_ver_at_least(module, version):
    """Determine if a module's version is at least equal to the given value.

    Args:
        module: imported module's name, e.g., `np` or `torch`.
        version: required version, given as a tuple, e.g., `(1, 8, 0)`.
    Returns:
        `True` if module is the given version or newer.
    """
    test_ver = ".".join(map(str, version))
    return module.__version__ != test_ver and version_leq(test_ver, module.__version__)


def sample_slices(data: NdarrayOrTensor, dim: int = 1, as_indices: bool = True, *slicevals: int) -> NdarrayOrTensor:
    """sample several slices of input numpy array or Tensor on specified `dim`.

    Args:
        data: input data to sample slices, can be numpy array or PyTorch Tensor.
        dim: expected dimension index to sample slices, default to `1`.
        as_indices: if `True`, `slicevals` arg will be treated as the expected indices of slice, like: `1, 3, 5`
            means `data[..., [1, 3, 5], ...]`, if `False`, `slicevals` arg will be treated as args for `slice` func,
            like: `1, None` means `data[..., [1:], ...]`, `1, 5` means `data[..., [1: 5], ...]`.
        slicevals: indices of slices or start and end indices of expected slices, depends on `as_indices` flag.

    """
    slices = [slice(None)] * len(data.shape)
    slices[dim] = slicevals if as_indices else slice(*slicevals)  # type: ignore

    return data[tuple(slices)]


def check_parent_dir(path: PathLike, create_dir: bool = True) -> None:
    """
    Utility to check whether the parent directory of the `path` exists.

    Args:
        path: input path to check the parent directory.
        create_dir: if True, when the parent directory doesn't exist, create the directory,
            otherwise, raise exception.

    """
    path = Path(path)
    path_dir = path.parent
    if not path_dir.exists():
        if create_dir:
            path_dir.mkdir(parents=True)
        else:
            raise ValueError(f"the directory of specified path does not exist: `{path_dir}`.")


def save_obj(
    obj: object,
    path: PathLike,
    create_dir: bool = True,
    atomic: bool = True,
    func: Callable | None = None,
    **kwargs: Any,
) -> None:
    """
    Save an object to file with specified path.
    Support to serialize to a temporary file first, then move to final destination,
    so that files are guaranteed to not be damaged if exception occurs.

    Args:
        obj: input object data to save.
        path: target file path to save the input object.
        create_dir: whether to create dictionary of the path if not existing, default to `True`.
        atomic: if `True`, state is serialized to a temporary file first, then move to final destination.
            so that files are guaranteed to not be damaged if exception occurs. default to `True`.
        func: the function to save file, if None, default to `torch.save`.
        kwargs: other args for the save `func` except for the checkpoint and filename.
            default `func` is `torch.save()`, details of other args:
            https://pytorch.org/docs/stable/generated/torch.save.html.

    """
    path = Path(path)
    check_parent_dir(path=path, create_dir=create_dir)
    if path.exists():
        # remove the existing file
        os.remove(path)

    if func is None:
        func = torch.save

    if not atomic:
        func(obj=obj, f=path, **kwargs)
        return
    try:
        # writing to a temporary directory and then using a nearly atomic rename operation
        with tempfile.TemporaryDirectory() as tempdir:
            temp_path: Path = Path(tempdir) / path.name
            func(obj=obj, f=temp_path, **kwargs)
            if temp_path.is_file():
                shutil.move(str(temp_path), path)
    except PermissionError:  # project-monai/monai issue #3613
        pass


def label_union(x: list | np.ndarray) -> list:
    """
    Compute the union of class IDs in label and generate a list to include all class IDs
    Args:
        x: a list of numbers (for example, class_IDs)

    Returns
        a list showing the union (the union the class IDs)
    """
    return list(set.union(set(np.array(x).tolist())))


def prob2class(x: torch.Tensor, sigmoid: bool = False, threshold: float = 0.5, **kwargs: Any) -> torch.Tensor:
    """
    Compute the lab from the probability of predicted feature maps

    Args:
        sigmoid: If the sigmoid function should be used.
        threshold: threshold value to activate the sigmoid function.
    """
    return torch.argmax(x, **kwargs) if not sigmoid else (x > threshold).int()


def path_to_uri(path: PathLike) -> str:
    """
    Convert a file path to URI. if not absolute path, will convert to absolute path first.

    Args:
        path: input file path to convert, can be a string or `Path` object.

    """
    return Path(path).absolute().as_uri()


def pprint_edges(val: Any, n_lines: int = 20) -> str:
    """
    Pretty print the head and tail ``n_lines`` of ``val``, and omit the middle part if the part has more than 3 lines.

    Returns: the formatted string.
    """
    val_str = pprint.pformat(val).splitlines(True)
    n_lines = max(n_lines, 1)
    if len(val_str) > n_lines * 2 + 3:
        hidden_n = len(val_str) - n_lines * 2
        val_str = val_str[:n_lines] + [f"\n ... omitted {hidden_n} line(s)\n\n"] + val_str[-n_lines:]
    return "".join(val_str)


def check_key_duplicates(ordered_pairs: Sequence[tuple[Any, Any]]) -> dict[Any, Any]:
    """
    Checks if there is a duplicated key in the sequence of `ordered_pairs`.
    If there is - it will log a warning or raise ValueError
    (if configured by environmental var `MONAI_FAIL_ON_DUPLICATE_CONFIG==1`)

    Otherwise, it returns the dict made from this sequence.

    Satisfies a format for an `object_pairs_hook` in `json.load`

    Args:
        ordered_pairs: sequence of (key, value)
    """
    keys = set()
    for k, _ in ordered_pairs:
        if k in keys:
            if os.environ.get("MONAI_FAIL_ON_DUPLICATE_CONFIG", "0") == "1":
                raise ValueError(f"Duplicate key: `{k}`")
            else:
                warnings.warn(f"Duplicate key: `{k}`")
        else:
            keys.add(k)
    return dict(ordered_pairs)


class CheckKeyDuplicatesYamlLoader(SafeLoader):

    def construct_mapping(self, node, deep=False):
        mapping = set()
        for key_node, _ in node.value:
            key = self.construct_object(key_node, deep=deep)
            if key in mapping:
                if os.environ.get("MONAI_FAIL_ON_DUPLICATE_CONFIG", "0") == "1":
                    raise ValueError(f"Duplicate key: `{key}`")
                else:
                    warnings.warn(f"Duplicate key: `{key}`")
            mapping.add(key)
        return super().construct_mapping(node, deep)


class ConvertUnits:
    """
    Convert the values from input unit to the target unit

    Args:
        input_unit: the unit of the input quantity
        target_unit: the unit of the target quantity

    """

    imperial_unit_of_length = {"inch": 0.0254, "foot": 0.3048, "yard": 0.9144, "mile": 1609.344}

    unit_prefix = {
        "peta": 15,
        "tera": 12,
        "giga": 9,
        "mega": 6,
        "kilo": 3,
        "hecto": 2,
        "deca": 1,
        "deci": -1,
        "centi": -2,
        "milli": -3,
        "micro": -6,
        "nano": -9,
        "pico": -12,
        "femto": -15,
    }
    base_units = ["meter", "byte", "bit"]

    def __init__(self, input_unit: str, target_unit: str) -> None:
        self.input_unit, input_base = self._get_valid_unit_and_base(input_unit)
        self.target_unit, target_base = self._get_valid_unit_and_base(target_unit)
        if input_base == target_base:
            self.unit_base = input_base
        else:
            raise ValueError(
                "Both input and target units should be from the same quantity. "
                f"Input quantity is {input_base} while target quantity is {target_base}"
            )
        self._calculate_conversion_factor()

    def _get_valid_unit_and_base(self, unit):
        unit = str(unit).lower()
        if unit in self.imperial_unit_of_length:
            return unit, "meter"
        for base_unit in self.base_units:
            if unit.endswith(base_unit):
                return unit, base_unit
        raise ValueError(f"Currently, it only supports length conversion but `{unit}` is given.")

    def _get_unit_power(self, unit):
        """Calculate the power of the unit factor with respect to the base unit"""
        if unit in self.imperial_unit_of_length:
            return log10(self.imperial_unit_of_length[unit])

        prefix = unit[: len(self.unit_base)]
        if prefix == "":
            return 1.0
        return self.unit_prefix[prefix]

    def _calculate_conversion_factor(self):
        """Calculate unit conversion factor with respect to the input unit"""
        if self.input_unit == self.target_unit:
            return 1.0
        input_power = self._get_unit_power(self.input_unit)
        target_power = self._get_unit_power(self.target_unit)
        self.conversion_factor = 10 ** (input_power - target_power)

    def __call__(self, value: int | float) -> Any:
        return float(value) * self.conversion_factor


def check_kwargs_exist_in_class_init(cls, kwargs):
    """
    Check if the all keys in kwargs exist in the __init__ method of the class.

    Args:
        cls: the class to check.
        kwargs: kwargs to examine.

    Returns:
        a boolean indicating if all keys exist.
        a set of extra keys that are not used in the __init__.
    """
    init_signature = inspect.signature(cls.__init__)
    init_params = set(init_signature.parameters) - {"self"}  # Exclude 'self' from the parameter list
    input_kwargs = set(kwargs)
    extra_kwargs = input_kwargs - init_params

    return extra_kwargs == set(), extra_kwargs


def run_cmd(cmd_list: list[str], **kwargs: Any) -> subprocess.CompletedProcess:
    """
    Run a command by using ``subprocess.run`` with capture_output=True and stderr=subprocess.STDOUT
    so that the raise exception will have that information. The argument `capture_output` can be set explicitly
    if desired, but will be overriden with the debug status from the variable.

    Args:
        cmd_list: a list of strings describing the command to run.
        kwargs: keyword arguments supported by the ``subprocess.run`` method.

    Returns:
        a CompletedProcess instance after the command completes.
    """
    debug = MONAIEnvVars.debug()
    kwargs["capture_output"] = kwargs.get("capture_output", debug)

    if kwargs.pop("run_cmd_verbose", False):
        import monai

        monai.apps.utils.get_logger("run_cmd").info(f"{cmd_list}")
    try:
        return subprocess.run(cmd_list, **kwargs)
    except subprocess.CalledProcessError as e:
        if not debug:
            raise
        output = str(e.stdout.decode(errors="replace"))
        errors = str(e.stderr.decode(errors="replace"))
        raise RuntimeError(f"subprocess call error {e.returncode}: {errors}, {output}.") from e


def is_sqrt(num: Sequence[int] | int) -> bool:
    """
    Determine if the input is a square number or a squence of square numbers.
    """
    num = ensure_tuple(num)
    sqrt_num = [int(math.sqrt(_num)) for _num in num]
    ret = [_i * _j for _i, _j in zip(sqrt_num, sqrt_num)]
    return ensure_tuple(ret) == num


def unsqueeze_right(arr: NdarrayOrTensor, ndim: int) -> NdarrayOrTensor:
    """Append 1-sized dimensions to `arr` to create a result with `ndim` dimensions."""
    return arr[(...,) + (None,) * (ndim - arr.ndim)]


def unsqueeze_left(arr: NdarrayOrTensor, ndim: int) -> NdarrayOrTensor:
    """Prepend 1-sized dimensions to `arr` to create a result with `ndim` dimensions."""
    return arr[(None,) * (ndim - arr.ndim)]