submodule/sapiens/engine/mmengine/utils/misc.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import collections.abc
import functools
import itertools
import logging
import re
import subprocess
import textwrap
import warnings
from collections import abc
from importlib import import_module
from inspect import getfullargspec, ismodule
from itertools import repeat
from typing import Any, Callable, Optional, Type, Union


# From PyTorch internals
def _ntuple(n):

    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple


def is_str(x):
    """Whether the input is an string instance.

    Note: This method is deprecated since python 2 is no longer supported.
    """
    return isinstance(x, str)


def import_modules_from_strings(imports, allow_failed_imports=False):
    """Import modules from the given list of strings.

    Args:
        imports (list | str | None): The given module names to be imported.
        allow_failed_imports (bool): If True, the failed imports will return
            None. Otherwise, an ImportError is raise. Defaults to False.

    Returns:
        list[module] | module | None: The imported modules.

    Examples:
        >>> osp, sys = import_modules_from_strings(
        ...     ['os.path', 'sys'])
        >>> import os.path as osp_
        >>> import sys as sys_
        >>> assert osp == osp_
        >>> assert sys == sys_
    """
    if not imports:
        return
    single_import = False
    if isinstance(imports, str):
        single_import = True
        imports = [imports]
    if not isinstance(imports, list):
        raise TypeError(
            f'custom_imports must be a list but got type {type(imports)}')
    imported = []
    for imp in imports:
        if not isinstance(imp, str):
            raise TypeError(
                f'{imp} is of type {type(imp)} and cannot be imported.')
        try:
            imported_tmp = import_module(imp)
        except ImportError:
            if allow_failed_imports:
                warnings.warn(f'{imp} failed to import and is ignored.',
                              UserWarning)
                imported_tmp = None
            else:
                raise ImportError(f'Failed to import {imp}')
        imported.append(imported_tmp)
    if single_import:
        imported = imported[0]
    return imported


def iter_cast(inputs, dst_type, return_type=None):
    """Cast elements of an iterable object into some type.

    Args:
        inputs (Iterable): The input object.
        dst_type (type): Destination type.
        return_type (type, optional): If specified, the output object will be
            converted to this type, otherwise an iterator.

    Returns:
        iterator or specified type: The converted object.
    """
    if not isinstance(inputs, abc.Iterable):
        raise TypeError('inputs must be an iterable object')
    if not isinstance(dst_type, type):
        raise TypeError('"dst_type" must be a valid type')

    out_iterable = map(dst_type, inputs)

    if return_type is None:
        return out_iterable
    else:
        return return_type(out_iterable)


def list_cast(inputs, dst_type):
    """Cast elements of an iterable object into a list of some type.

    A partial method of :func:`iter_cast`.
    """
    return iter_cast(inputs, dst_type, return_type=list)


def tuple_cast(inputs, dst_type):
    """Cast elements of an iterable object into a tuple of some type.

    A partial method of :func:`iter_cast`.
    """
    return iter_cast(inputs, dst_type, return_type=tuple)


def is_seq_of(seq: Any,
              expected_type: Union[Type, tuple],
              seq_type: Type = None) -> bool:
    """Check whether it is a sequence of some type.

    Args:
        seq (Sequence): The sequence to be checked.
        expected_type (type or tuple): Expected type of sequence items.
        seq_type (type, optional): Expected sequence type. Defaults to None.

    Returns:
        bool: Return True if ``seq`` is valid else False.

    Examples:
        >>> from mmengine.utils import is_seq_of
        >>> seq = ['a', 'b', 'c']
        >>> is_seq_of(seq, str)
        True
        >>> is_seq_of(seq, int)
        False
    """
    if seq_type is None:
        exp_seq_type = abc.Sequence
    else:
        assert isinstance(seq_type, type)
        exp_seq_type = seq_type
    if not isinstance(seq, exp_seq_type):
        return False
    for item in seq:
        if not isinstance(item, expected_type):
            return False
    return True


def is_list_of(seq, expected_type):
    """Check whether it is a list of some type.

    A partial method of :func:`is_seq_of`.
    """
    return is_seq_of(seq, expected_type, seq_type=list)


def is_tuple_of(seq, expected_type):
    """Check whether it is a tuple of some type.

    A partial method of :func:`is_seq_of`.
    """
    return is_seq_of(seq, expected_type, seq_type=tuple)


def slice_list(in_list, lens):
    """Slice a list into several sub lists by a list of given length.

    Args:
        in_list (list): The list to be sliced.
        lens(int or list): The expected length of each out list.

    Returns:
        list: A list of sliced list.
    """
    if isinstance(lens, int):
        assert len(in_list) % lens == 0
        lens = [lens] * int(len(in_list) / lens)
    if not isinstance(lens, list):
        raise TypeError('"indices" must be an integer or a list of integers')
    elif sum(lens) != len(in_list):
        raise ValueError('sum of lens and list length does not '
                         f'match: {sum(lens)} != {len(in_list)}')
    out_list = []
    idx = 0
    for i in range(len(lens)):
        out_list.append(in_list[idx:idx + lens[i]])
        idx += lens[i]
    return out_list


def concat_list(in_list):
    """Concatenate a list of list into a single list.

    Args:
        in_list (list): The list of list to be merged.

    Returns:
        list: The concatenated flat list.
    """
    return list(itertools.chain(*in_list))


def apply_to(data: Any, expr: Callable, apply_func: Callable):
    """Apply function to each element in dict, list or tuple that matches with
    the expression.

    For examples, if you want to convert each element in a list of dict from
    `np.ndarray` to `Tensor`. You can use the following code:

    Examples:
        >>> from mmengine.utils import apply_to
        >>> import numpy as np
        >>> import torch
        >>> data = dict(array=[np.array(1)]) # {'array': [array(1)]}
        >>> result = apply_to(data, lambda x: isinstance(x, np.ndarray), lambda x: torch.from_numpy(x))
        >>> print(result) # {'array': [tensor(1)]}

    Args:
        data (Any): Data to be applied.
        expr (Callable): Expression to tell which data should be applied with
            the function. It should return a boolean.
        apply_func (Callable): Function applied to data.

    Returns:
        Any: The data after applying.
    """  # noqa: E501
    if isinstance(data, dict):
        # Keep the original dict type
        res = type(data)()
        for key, value in data.items():
            res[key] = apply_to(value, expr, apply_func)
        return res
    elif isinstance(data, tuple) and hasattr(data, '_fields'):
        # namedtuple
        return type(data)(*(apply_to(sample, expr, apply_func) for sample in data))  # type: ignore  # noqa: E501  # yapf:disable
    elif isinstance(data, (tuple, list)):
        return type(data)(apply_to(sample, expr, apply_func) for sample in data)  # type: ignore  # noqa: E501  # yapf:disable
    elif expr(data):
        return apply_func(data)
    else:
        return data


def check_prerequisites(
        prerequisites,
        checker,
        msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
                 'found, please install them first.'):  # yapf: disable
    """A decorator factory to check if prerequisites are satisfied.

    Args:
        prerequisites (str of list[str]): Prerequisites to be checked.
        checker (callable): The checker method that returns True if a
            prerequisite is meet, False otherwise.
        msg_tmpl (str): The message template with two variables.

    Returns:
        decorator: A specific decorator.
    """

    def wrap(func):

        @functools.wraps(func)
        def wrapped_func(*args, **kwargs):
            requirements = [prerequisites] if isinstance(
                prerequisites, str) else prerequisites
            missing = []
            for item in requirements:
                if not checker(item):
                    missing.append(item)
            if missing:
                print(msg_tmpl.format(', '.join(missing), func.__name__))
                raise RuntimeError('Prerequisites not meet.')
            else:
                return func(*args, **kwargs)

        return wrapped_func

    return wrap


def _check_py_package(package):
    try:
        import_module(package)
    except ImportError:
        return False
    else:
        return True


def _check_executable(cmd):
    if subprocess.call(f'which {cmd}', shell=True) != 0:
        return False
    else:
        return True


def requires_package(prerequisites):
    """A decorator to check if some python packages are installed.

    Example:
        >>> @requires_package('numpy')
        >>> func(arg1, args):
        >>>     return numpy.zeros(1)
        array([0.])
        >>> @requires_package(['numpy', 'non_package'])
        >>> func(arg1, args):
        >>>     return numpy.zeros(1)
        ImportError
    """
    return check_prerequisites(prerequisites, checker=_check_py_package)


def requires_executable(prerequisites):
    """A decorator to check if some executable files are installed.

    Example:
        >>> @requires_executable('ffmpeg')
        >>> func(arg1, args):
        >>>     print(1)
        1
    """
    return check_prerequisites(prerequisites, checker=_check_executable)


def deprecated_api_warning(name_dict: dict,
                           cls_name: Optional[str] = None) -> Callable:
    """A decorator to check if some arguments are deprecate and try to replace
    deprecate src_arg_name to dst_arg_name.

    Args:
        name_dict(dict):
            key (str): Deprecate argument names.
            val (str): Expected argument names.

    Returns:
        func: New function.
    """

    def api_warning_wrapper(old_func):

        @functools.wraps(old_func)
        def new_func(*args, **kwargs):
            # get the arg spec of the decorated method
            args_info = getfullargspec(old_func)
            # get name of the function
            func_name = old_func.__name__
            if cls_name is not None:
                func_name = f'{cls_name}.{func_name}'
            if args:
                arg_names = args_info.args[:len(args)]
                for src_arg_name, dst_arg_name in name_dict.items():
                    if src_arg_name in arg_names:
                        warnings.warn(
                            f'"{src_arg_name}" is deprecated in '
                            f'`{func_name}`, please use "{dst_arg_name}" '
                            'instead', DeprecationWarning)
                        arg_names[arg_names.index(src_arg_name)] = dst_arg_name
            if kwargs:
                for src_arg_name, dst_arg_name in name_dict.items():
                    if src_arg_name in kwargs:
                        assert dst_arg_name not in kwargs, (
                            f'The expected behavior is to replace '
                            f'the deprecated key `{src_arg_name}` to '
                            f'new key `{dst_arg_name}`, but got them '
                            f'in the arguments at the same time, which '
                            f'is confusing. `{src_arg_name} will be '
                            f'deprecated in the future, please '
                            f'use `{dst_arg_name}` instead.')

                        warnings.warn(
                            f'"{src_arg_name}" is deprecated in '
                            f'`{func_name}`, please use "{dst_arg_name}" '
                            'instead', DeprecationWarning)
                        kwargs[dst_arg_name] = kwargs.pop(src_arg_name)

            # apply converted arguments to the decorated method
            output = old_func(*args, **kwargs)
            return output

        return new_func

    return api_warning_wrapper


def is_method_overridden(method: str, base_class: type,
                         derived_class: Union[type, Any]) -> bool:
    """Check if a method of base class is overridden in derived class.

    Args:
        method (str): the method name to check.
        base_class (type): the class of the base class.
        derived_class (type | Any): the class or instance of the derived class.
    """
    assert isinstance(base_class, type), \
        "base_class doesn't accept instance, Please pass class instead."

    if not isinstance(derived_class, type):
        derived_class = derived_class.__class__

    base_method = getattr(base_class, method)
    derived_method = getattr(derived_class, method)
    return derived_method != base_method


def has_method(obj: object, method: str) -> bool:
    """Check whether the object has a method.

    Args:
        method (str): The method name to check.
        obj (object): The object to check.

    Returns:
        bool: True if the object has the method else False.
    """
    return hasattr(obj, method) and callable(getattr(obj, method))


def deprecated_function(since: str, removed_in: str,
                        instructions: str) -> Callable:
    """Marks functions as deprecated.

    Throw a warning when a deprecated function is called, and add a note in the
    docstring. Modified from https://github.com/pytorch/pytorch/blob/master/torch/onnx/_deprecation.py

    Args:
        since (str): The version when the function was first deprecated.
        removed_in (str): The version when the function will be removed.
        instructions (str): The action users should take.

    Returns:
        Callable: A new function, which will be deprecated soon.
    """  # noqa: E501
    from mmengine import print_log

    def decorator(function):

        @functools.wraps(function)
        def wrapper(*args, **kwargs):
            print_log(
                f"'{function.__module__}.{function.__name__}' "
                f'is deprecated in version {since} and will be '
                f'removed in version {removed_in}. Please {instructions}.',
                logger='current',
                level=logging.WARNING,
            )
            return function(*args, **kwargs)

        indent = '    '
        # Add a deprecation note to the docstring.
        docstring = function.__doc__ or ''
        # Add a note to the docstring.
        deprecation_note = textwrap.dedent(f"""\
            .. deprecated:: {since}
                Deprecated and will be removed in version {removed_in}.
                Please {instructions}.
            """)
        # Split docstring at first occurrence of newline
        pattern = '\n\n'
        summary_and_body = re.split(pattern, docstring, 1)

        if len(summary_and_body) > 1:
            summary, body = summary_and_body
            body = textwrap.indent(textwrap.dedent(body), indent)
            summary = '\n'.join(
                [textwrap.dedent(string) for string in summary.split('\n')])
            summary = textwrap.indent(summary, prefix=indent)
            # Dedent the body. We cannot do this with the presence of the
            # summary because the body contains leading whitespaces when the
            # summary does not.
            new_docstring_parts = [
                deprecation_note, '\n\n', summary, '\n\n', body
            ]
        else:
            summary = summary_and_body[0]
            summary = '\n'.join(
                [textwrap.dedent(string) for string in summary.split('\n')])
            summary = textwrap.indent(summary, prefix=indent)
            new_docstring_parts = [deprecation_note, '\n\n', summary]

        wrapper.__doc__ = ''.join(new_docstring_parts)

        return wrapper

    return decorator


def get_object_from_string(obj_name: str):
    """Get object from name.

    Args:
        obj_name (str): The name of the object.

    Examples:
        >>> get_object_from_string('torch.optim.sgd.SGD')
        >>> torch.optim.sgd.SGD
    """
    parts = iter(obj_name.split('.'))
    module_name = next(parts)
    # import module
    while True:
        try:
            module = import_module(module_name)
            part = next(parts)
            # mmcv.ops has nms.py and nms function at the same time. So the
            # function will have a higher priority
            obj = getattr(module, part, None)
            if obj is not None and not ismodule(obj):
                break
            module_name = f'{module_name}.{part}'
        except StopIteration:
            # if obj is a module
            return module
        except ImportError:
            return None

    # get class or attribute from module
    obj = module
    while True:
        try:
            obj = getattr(obj, part)
            part = next(parts)
        except StopIteration:
            return obj
        except AttributeError:
            return None