propcache

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### Changelog fragment examples

Source: https://propcache.aio-libs.org/en/latest/contributing/guidelines

Examples of Towncrier changelog files formatted in reStructuredText, placed in the CHANGES/ directory.

```rst
Dropped Python 3.5 support; Python 3.6 is the minimal supported Python
version -- by :user:`webknjaz`.
```

```rst
Started shipping Windows wheels for the x86 architecture
-- by :user:`Dreamsorcerer`.
```

```rst
Added support for ``GenericAliases`` (``MultiDict[str]``) under Python 3.9
and higher -- by :user:`mjpieters`.
```

--------------------------------

### Build documentation locally

Source: https://propcache.aio-libs.org/en/latest/_sources/contributing/guidelines.rst.txt

Execute this command in the terminal to trigger the local documentation build process.

```shell-session
$ make doc
```

--------------------------------

### Prepare Version and Changelog

Source: https://propcache.aio-libs.org/en/latest/_sources/contributing/release_guide.rst.txt

Update the version string and generate the changelog using towncrier before committing the changes.

```shell-session
[dir:propcache] $ propcache/__init__.py
[dir:propcache] $ python -m towncrier build \
               -- --version 'VERSION_WITHOUT_LEADING_V'
[dir:propcache] $ git commit -v CHANGES{.rst,/} propcache/__init__.py
```

--------------------------------

### Tag the Release

Source: https://propcache.aio-libs.org/en/latest/_sources/contributing/release_guide.rst.txt

Create a GPG-signed Git tag for the release with a descriptive message.

```shell-session
[dir:propcache] $ git tag \
               -s 'VERSION_WITH_LEADING_V' \
               -m 'VERSION_WITH_LEADING_V' \
               -m 'This release does X and Y.'
```

--------------------------------

### Configure build-inplace setting

Source: https://propcache.aio-libs.org/en/latest/changes

Use this command to control whether the project is built in-tree or in a temporary directory during the build process.

```bash
$ python -m build --config-setting=build-inplace=true
```

--------------------------------

### Importing C-based LRU Cache

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Attempt to import the optimized C implementation of the LRU cache wrapper.

```python
try:
    from _functools import _lru_cache_wrapper
except ImportError:
    pass
```

--------------------------------

### Push Release Tag

Source: https://propcache.aio-libs.org/en/latest/_sources/contributing/release_guide.rst.txt

Push the newly created version tag to the upstream repository.

```shell-session
[dir:propcache] $ git push origin 'VERSION_WITH_LEADING_V'
```

--------------------------------

### Find implementation in registry

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Retrieves the best matching implementation from a registry for a given class type.

```python
def _find_impl(cls, registry):
    """Returns the best matching implementation from *registry* for type *cls*.

    Where there is no registered implementation for a specific type, its method
    resolution order is used to find a more generic implementation.

    Note: if *registry* does not contain an implementation for the base
    *object* type, this function may return None.

    """
    mro = _compose_mro(cls, registry.keys())
    match = None
    for t in mro:
        if match is not None:
            # If *match* is an implicit ABC but there is another unrelated,
            # equally matching implicit ABC, refuse the temptation to guess.
            if (t in registry and t not in cls.__mro__
                              and match not in cls.__mro__
                              and not issubclass(match, t)):
                raise RuntimeError("Ambiguous dispatch: {} or {}".format(
                    match, t))
            break
        if t in registry:
            match = t
    return registry.get(match)
```

--------------------------------

### Partial Method Implementation

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Internal methods for handling partial function binding and descriptor protocol integration.

```python
def _make_unbound_method(self):
    def _method(cls_or_self, /, *args, **keywords):
        keywords = {**self.keywords, **keywords}
        return self.func(cls_or_self, *self.args, *args, **keywords)
    _method.__isabstractmethod__ = self.__isabstractmethod__
    _method._partialmethod = self
    return _method

def __get__(self, obj, cls=None):
    get = getattr(self.func, "__get__", None)
    result = None
    if get is not None:
        new_func = get(obj, cls)
        if new_func is not self.func:
            # Assume __get__ returning something new indicates the
            # creation of an appropriate callable
            result = partial(new_func, *self.args, **self.keywords)
            try:
                result.__self__ = new_func.__self__
            except AttributeError:
                pass
    if result is None:
        # If the underlying descriptor didn't do anything, treat this
        # like an instance method
        result = self._make_unbound_method().__get__(obj, cls)
    return result

@property
def __isabstractmethod__(self):
    return getattr(self.func, "__isabstractmethod__", False)

__class_getitem__ = classmethod(GenericAlias)
```

--------------------------------

### Comparison to key converter

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Converts a legacy cmp function into a key function for sorting.

```python
def cmp_to_key(mycmp):
    """Convert a cmp= function into a key= function"""
    class K(object):
        __slots__ = ['obj']
        def __init__(self, obj):
            self.obj = obj
        def __lt__(self, other):
            return mycmp(self.obj, other.obj) < 0
        def __gt__(self, other):
            return mycmp(self.obj, other.obj) > 0
        def __eq__(self, other):
            return mycmp(self.obj, other.obj) == 0
        def __le__(self, other):
            return mycmp(self.obj, other.obj) <= 0
        def __ge__(self, other):
            return mycmp(self.obj, other.obj) >= 0
        __hash__ = None
    return K

try:
    from _functools import cmp_to_key
except ImportError:
    pass
```

--------------------------------

### Using under_cached_property

Source: https://propcache.aio-libs.org/en/latest/_sources/api.rst.txt

Demonstrates defining a class with a cached property that stores results in a private _cache dictionary.

```python
from propcache.api import under_cached_property

class MyClass:

    def __init__(self, data: List[float]):
        self._data = data
        self._cache = {}

    @under_cached_property
    def calculated_data(self):
        return expensive_operation(self._data)

    def clear_cache(self):
        self._cache.clear()

instance = MyClass([1.0, 2.0, 3.0])
print(instance.calculated_data)  # expensive operation

instance.clear_cache()
print(instance.calculated_data)  # expensive operation
```

--------------------------------

### Implement reduce logic

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Core logic for the reduce function, handling empty iterables and initial values.

```python
    if initial is _initial_missing:
        try:
            value = next(it)
        except StopIteration:
            raise TypeError(
                "reduce() of empty iterable with no initial value") from None
    else:
        value = initial

    for element in it:
        value = function(value, element)

    return value

try:
    from _functools import reduce
except ImportError:
    pass
```

--------------------------------

### Compute MRO with extended C3 linearization

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Calculates the method resolution order for a class, optionally inserting abstract base classes.

```python
def _c3_mro(cls, abcs=None):
    """Computes the method resolution order using extended C3 linearization.

    If no *abcs* are given, the algorithm works exactly like the built-in C3
    linearization used for method resolution.

    If given, *abcs* is a list of abstract base classes that should be inserted
    into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
    result. The algorithm inserts ABCs where their functionality is introduced,
    i.e. issubclass(cls, abc) returns True for the class itself but returns
    False for all its direct base classes. Implicit ABCs for a given class
    (either registered or inferred from the presence of a special method like
    __len__) are inserted directly after the last ABC explicitly listed in the
    MRO of said class. If two implicit ABCs end up next to each other in the
    resulting MRO, their ordering depends on the order of types in *abcs*.

    """
    for i, base in enumerate(reversed(cls.__bases__)):
        if hasattr(base, '__abstractmethods__'):
            boundary = len(cls.__bases__) - i
            break   # Bases up to the last explicit ABC are considered first.
    else:
        boundary = 0
    abcs = list(abcs) if abcs else []
    explicit_bases = list(cls.__bases__[:boundary])
    abstract_bases = []
    other_bases = list(cls.__bases__[boundary:])
    for base in abcs:
        if issubclass(cls, base) and not any(
                issubclass(b, base) for b in cls.__bases__
            ):
            # If *cls* is the class that introduces behaviour described by
            # an ABC *base*, insert said ABC to its MRO.
            abstract_bases.append(base)
    for base in abstract_bases:
        abcs.remove(base)
    explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
    abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
    other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
    return _c3_merge(
        [[cls]] +
        explicit_c3_mros + abstract_c3_mros + other_c3_mros +
        [explicit_bases] + [abstract_bases] + [other_bases]
    )
```

--------------------------------

### LRU Cache Decorator Logic

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Handles the initialization of the LRU cache decorator, including argument validation and the creation of the decorating function.

```python
    if isinstance(maxsize, int):
        # Negative maxsize is treated as 0
        if maxsize < 0:
            maxsize = 0
    elif callable(maxsize) and isinstance(typed, bool):
        # The user_function was passed in directly via the maxsize argument
        user_function, maxsize = maxsize, 128
        wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
        wrapper.cache_parameters = lambda : {'maxsize': maxsize, 'typed': typed}
        return update_wrapper(wrapper, user_function)
    elif maxsize is not None:
        raise TypeError(
            'Expected first argument to be an integer, a callable, or None')

    def decorating_function(user_function):
        wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)
        wrapper.cache_parameters = lambda : {'maxsize': maxsize, 'typed': typed}
        return update_wrapper(wrapper, user_function)

    return decorating_function
```

--------------------------------

### Ordering method conversion helpers

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Helper functions used by total_ordering to derive missing comparison methods from existing ones.

```python
def _lt_from_le(self, other):
    'Return a < b.  Computed by @total_ordering from (a <= b) and (a != b).'
    op_result = type(self).__le__(self, other)
    if op_result is NotImplemented:
        return op_result
    return op_result and self != other

def _gt_from_le(self, other):
    'Return a > b.  Computed by @total_ordering from (not a <= b).'
    op_result = type(self).__le__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result

def _lt_from_gt(self, other):
    'Return a < b.  Computed by @total_ordering from (not a > b) and (a != b).'
    op_result = type(self).__gt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result and self != other

def _ge_from_gt(self, other):
    'Return a >= b.  Computed by @total_ordering from (a > b) or (a == b).'
    op_result = type(self).__gt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return op_result or self == other

def _le_from_gt(self, other):
    'Return a <= b.  Computed by @total_ordering from (not a > b).'
    op_result = type(self).__gt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result

def _le_from_ge(self, other):
    'Return a <= b.  Computed by @total_ordering from (not a >= b) or (a == b).'
    op_result = type(self).__ge__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result or self == other

def _gt_from_ge(self, other):
    'Return a > b.  Computed by @total_ordering from (a >= b) and (a != b).'
    op_result = type(self).__ge__(self, other)
    if op_result is NotImplemented:
        return op_result
    return op_result and self != other

def _lt_from_ge(self, other):
    'Return a < b.  Computed by @total_ordering from (not a >= b).'
    op_result = type(self).__ge__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result
```

--------------------------------

### Single Dispatch Method Descriptor

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Implementation of the singledispatchmethod descriptor for class-based generic dispatching.

```python
class singledispatchmethod:
    """Single-dispatch generic method descriptor.

    Supports wrapping existing descriptors and handles non-descriptor
    callables as instance methods.
    """

    def __init__(self, func):
        if not callable(func) and not hasattr(func, "__get__"):
            raise TypeError(f"{func!r} is not callable or a descriptor")

        self.dispatcher = singledispatch(func)
        self.func = func

    def register(self, cls, method=None):
        """generic_method.register(cls, func) -> func

        Registers a new implementation for the given *cls* on a *generic_method*.
        """
        return self.dispatcher.register(cls, func=method)
```

--------------------------------

### Generic Function Dispatch Implementation

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Internal logic for managing function registries and dispatching based on the type of the first argument.

```python
    import types, weakref

    registry = {}
    dispatch_cache = weakref.WeakKeyDictionary()
    cache_token = None

    def dispatch(cls):
        """generic_func.dispatch(cls) -> <function implementation>

        Runs the dispatch algorithm to return the best available implementation
        for the given *cls* registered on *generic_func*.

        """
        nonlocal cache_token
        if cache_token is not None:
            current_token = get_cache_token()
            if cache_token != current_token:
                dispatch_cache.clear()
                cache_token = current_token
        try:
            impl = dispatch_cache[cls]
        except KeyError:
            try:
                impl = registry[cls]
            except KeyError:
                impl = _find_impl(cls, registry)
            dispatch_cache[cls] = impl
        return impl

    def _is_union_type(cls):
        from typing import get_origin, Union
        return get_origin(cls) in {Union, types.UnionType}

    def _is_valid_dispatch_type(cls):
        if isinstance(cls, type):
            return True
        from typing import get_args
        return (_is_union_type(cls) and
                all(isinstance(arg, type) for arg in get_args(cls)))

    def register(cls, func=None):
        """generic_func.register(cls, func) -> func

        Registers a new implementation for the given *cls* on a *generic_func*.

        """
        nonlocal cache_token
        if _is_valid_dispatch_type(cls):
            if func is None:
                return lambda f: register(cls, f)
        else:
            if func is not None:
                raise TypeError(
                    f"Invalid first argument to `register()`. "
                    f"{cls!r} is not a class or union type."
                )
            ann = getattr(cls, '__annotations__', {})
            if not ann:
                raise TypeError(
                    f"Invalid first argument to `register()`: {cls!r}. "
                    f"Use either `@register(some_class)` or plain `@register` "
                    f"on an annotated function."
                )
            func = cls

            # only import typing if annotation parsing is necessary
            from typing import get_type_hints
            argname, cls = next(iter(get_type_hints(func).items()))
            if not _is_valid_dispatch_type(cls):
                if _is_union_type(cls):
                    raise TypeError(
                        f"Invalid annotation for {argname!r}. "
                        f"{cls!r} not all arguments are classes."
                    )
                else:
                    raise TypeError(
                        f"Invalid annotation for {argname!r}. "
                        f"{cls!r} is not a class."
                    )

        if _is_union_type(cls):
            from typing import get_args

            for arg in get_args(cls):
                registry[arg] = func
        else:
            registry[cls] = func
        if cache_token is None and hasattr(cls, '__abstractmethods__'):
            cache_token = get_cache_token()
        dispatch_cache.clear()
        return func

    def wrapper(*args, **kw):
        if not args:
            raise TypeError(f'{funcname} requires at least '
                            '1 positional argument')

        return dispatch(args[0].__class__)(*args, **kw)

    funcname = getattr(func, '__name__', 'singledispatch function')
    registry[object] = func
    wrapper.register = register
    wrapper.dispatch = dispatch
    wrapper.registry = types.MappingProxyType(registry)
    wrapper._clear_cache = dispatch_cache.clear
    update_wrapper(wrapper, func)
    return wrapper
```

--------------------------------

### Define partialmethod class

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

A method descriptor class for partial application, handling descriptors and non-descriptor callables.

```python
class partialmethod(object):
    """Method descriptor with partial application of the given arguments
    and keywords.

    Supports wrapping existing descriptors and handles non-descriptor
    callables as instance methods.
    """

    def __init__(self, func, /, *args, **keywords):
        if not callable(func) and not hasattr(func, "__get__"):
            raise TypeError("{!r} is not callable or a descriptor"
                                 .format(func))

        # func could be a descriptor like classmethod which isn't callable,
        # so we can't inherit from partial (it verifies func is callable)
        if isinstance(func, partialmethod):
            # flattening is mandatory in order to place cls/self before all
            # other arguments
            # it's also more efficient since only one function will be called
            self.func = func.func
            self.args = func.args + args
            self.keywords = {**func.keywords, **keywords}
        else:
            self.func = func
            self.args = args
            self.keywords = keywords

    def __repr__(self):
        args = ", ".join(map(repr, self.args))
        keywords = ", ".join("{}={!r}".format(k, v)
                                 for k, v in self.keywords.items())
        format_string = "{module}.{cls}({func}, {args}, {keywords})"
        return format_string.format(module=self.__class__.__module__,
                                    cls=self.__class__.__qualname__,
                                    func=self.func,
                                    args=args,
                                    keywords=keywords)
```

--------------------------------

### LRU Cache Wrapper Implementation

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Internal wrapper function for size-limited caching that tracks access recency using a circular queue.

```python
def wrapper(*args, **kwds):
    # Size limited caching that tracks accesses by recency
    nonlocal root, hits, misses, full
    key = make_key(args, kwds, typed)
    with lock:
        link = cache_get(key)
        if link is not None:
            # Move the link to the front of the circular queue
            link_prev, link_next, _key, result = link
            link_prev[NEXT] = link_next
            link_next[PREV] = link_prev
            last = root[PREV]
            last[NEXT] = root[PREV] = link
            link[PREV] = last
            link[NEXT] = root
            hits += 1
            return result
        misses += 1
    result = user_function(*args, **kwds)
    with lock:
        if key in cache:
            # Getting here means that this same key was added to the
            # cache while the lock was released.  Since the link
            # update is already done, we need only return the
            # computed result and update the count of misses.
            pass
        elif full:
            # Use the old root to store the new key and result.
            oldroot = root
            oldroot[KEY] = key
            oldroot[RESULT] = result
            # Empty the oldest link and make it the new root.
            # Keep a reference to the old key and old result to
            # prevent their ref counts from going to zero during the
            # update. That will prevent potentially arbitrary object
            # clean-up code (i.e. __del__) from running while we're
            # still adjusting the links.
            root = oldroot[NEXT]
            oldkey = root[KEY]
            oldresult = root[RESULT]
            root[KEY] = root[RESULT] = None
            # Now update the cache dictionary.
            del cache[oldkey]
            # Save the potentially reentrant cache[key] assignment
            # for last, after the root and links have been put in
            # a consistent state.
            cache[key] = oldroot
        else:
            # Put result in a new link at the front of the queue.
            last = root[PREV]
            link = [last, root, key, result]
            last[NEXT] = root[PREV] = cache[key] = link
            # Use the cache_len bound method instead of the len() function
            # which could potentially be wrapped in an lru_cache itself.
            full = (cache_len() >= maxsize)
    return result

def cache_info():
    """Report cache statistics"""
    with lock:
        return _CacheInfo(hits, misses, maxsize, cache_len())

def cache_clear():
    """Clear the cache and cache statistics"""
    nonlocal hits, misses, full
    with lock:
        cache.clear()
        root[:] = [root, root, None, None]
        hits = misses = 0
        full = False

wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return wrapper
```

--------------------------------

### C3 MRO Merge Algorithm

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Merges multiple Method Resolution Orders into a single MRO using the C3 linearization algorithm.

```python
def _c3_merge(sequences):
    """Merges MROs in *sequences* to a single MRO using the C3 algorithm.

    Adapted from https://www.python.org/download/releases/2.3/mro/.

    """
    result = []
    while True:
        sequences = [s for s in sequences if s]   # purge empty sequences
        if not sequences:
            return result
        for s1 in sequences:   # find merge candidates among seq heads
            candidate = s1[0]
            for s2 in sequences:
                if candidate in s2[1:]:
                    candidate = None
                    break      # reject the current head, it appears later
            else:
                break
        if candidate is None:
            raise RuntimeError("Inconsistent hierarchy")
        result.append(candidate)
        # remove the chosen candidate
        for seq in sequences:
            if seq[0] == candidate:
                del seq[0]
```

--------------------------------

### Total ordering helper functions

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Internal helper functions used by the total_ordering decorator to derive comparison operators from existing ones.

```python
def _gt_from_lt(self, other):
    'Return a > b.  Computed by @total_ordering from (not a < b) and (a != b).'
    op_result = type(self).__lt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result and self != other

def _le_from_lt(self, other):
    'Return a <= b.  Computed by @total_ordering from (a < b) or (a == b).'
    op_result = type(self).__lt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return op_result or self == other

def _ge_from_lt(self, other):
    'Return a >= b.  Computed by @total_ordering from (not a < b).'
    op_result = type(self).__lt__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result

def _ge_from_le(self, other):
    'Return a >= b.  Computed by @total_ordering from (not a <= b) or (a == b).'
    op_result = type(self).__le__(self, other)
    if op_result is NotImplemented:
        return op_result
    return not op_result or self == other
```

--------------------------------

### Define partial class

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

A class for partial function application, supporting slots and custom state reduction.

```python
class partial:
    """New function with partial application of the given arguments
    and keywords.
    """

    __slots__ = "func", "args", "keywords", "__dict__", "__weakref__"

    def __new__(cls, func, /, *args, **keywords):
        if not callable(func):
            raise TypeError("the first argument must be callable")

        if hasattr(func, "func"):
            args = func.args + args
            keywords = {**func.keywords, **keywords}
            func = func.func

        self = super(partial, cls).__new__(cls)

        self.func = func
        self.args = args
        self.keywords = keywords
        return self

    def __call__(self, /, *args, **keywords):
        keywords = {**self.keywords, **keywords}
        return self.func(*self.args, *args, **keywords)

    @recursive_repr()
    def __repr__(self):
        qualname = type(self).__qualname__
        args = [repr(self.func)]
        args.extend(repr(x) for x in self.args)
        args.extend(f"{k}={v!r}" for (k, v) in self.keywords.items())
        if type(self).__module__ == "functools":
            return f"functools.{qualname}({', '.join(args)})"
        return f"{qualname}({', '.join(args)})"

    def __reduce__(self):
        return type(self), (self.func,), (self.func, self.args,
               self.keywords or None, self.__dict__ or None)

    def __setstate__(self, state):
        if not isinstance(state, tuple):
            raise TypeError("argument to __setstate__ must be a tuple")
        if len(state) != 4:
            raise TypeError(f"expected 4 items in state, got {len(state)}")
        func, args, kwds, namespace = state
        if (not callable(func) or not isinstance(args, tuple) or
           (kwds is not None and not isinstance(kwds, dict)) or
           (namespace is not None and not isinstance(namespace, dict))):
            raise TypeError("invalid partial state")

        args = tuple(args) # just in case it's a subclass
        if kwds is None:
            kwds = {}
        elif type(kwds) is not dict: # XXX does it need to be *exactly* dict?
            kwds = dict(kwds)
        if namespace is None:
            namespace = {}

        self.__dict__ = namespace
        self.func = func
        self.args = args
        self.keywords = kwds

try:
    from _functools import partial
except ImportError:
    pass
```

--------------------------------

### Partial Unwrapping Helper

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Utility function to recursively unwrap nested partial objects.

```python
def _unwrap_partial(func):
    while isinstance(func, partial):
        func = func.func
    return func
```

--------------------------------

### LRU Cache Wrapper Implementation

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Defines the internal wrapper function that manages cache state, including the linked list structure for tracking cache entries.

```python
def _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):
    # Constants shared by all lru cache instances:
    sentinel = object()          # unique object used to signal cache misses
    make_key = _make_key         # build a key from the function arguments
    PREV, NEXT, KEY, RESULT = 0, 1, 2, 3   # names for the link fields

    cache = {}
    hits = misses = 0
    full = False
    cache_get = cache.get    # bound method to lookup a key or return None
    cache_len = cache.__len__  # get cache size without calling len()
    lock = RLock()           # because linkedlist updates aren't threadsafe
    root = []                # root of the circular doubly linked list
    root[:] = [root, root, None, None]     # initialize by pointing to self

    if maxsize == 0:

        def wrapper(*args, **kwds):
            # No caching -- just a statistics update
            nonlocal misses
            misses += 1
            result = user_function(*args, **kwds)
            return result

    elif maxsize is None:

        def wrapper(*args, **kwds):
            # Simple caching without ordering or size limit
            nonlocal hits, misses
            key = make_key(args, kwds, typed)
            result = cache_get(key, sentinel)
            if result is not sentinel:
                hits += 1
                return result
            misses += 1
            result = user_function(*args, **kwds)
            cache[key] = result
            return result

    else:
```

--------------------------------

### Wraps decorator factory

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

A convenience decorator factory that applies update_wrapper to a wrapper function.

```python
def wraps(wrapped,
          assigned = WRAPPER_ASSIGNMENTS,
          updated = WRAPPER_UPDATES):
    """Decorator factory to apply update_wrapper() to a wrapper function

       Returns a decorator that invokes update_wrapper() with the decorated
       function as the wrapper argument and the arguments to wraps() as the
       remaining arguments. Default arguments are as for update_wrapper().
       This is a convenience function to simplify applying partial() to
       update_wrapper().
    """
    return partial(update_wrapper, wrapped=wrapped,
                   assigned=assigned, updated=updated)
```

--------------------------------

### Single-dispatch decorator

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Placeholder for the single-dispatch generic function decorator.

```python
def singledispatch(func):
    """Single-dispatch generic function decorator.
```

--------------------------------

### LRU Cache Internal Structures

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Supporting classes and key generation logic for the lru_cache decorator.

```python
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])

class _HashedSeq(list):
    """ This class guarantees that hash() will be called no more than once
        per element.  This is important because the lru_cache() will hash
        the key multiple times on a cache miss.

    """

    __slots__ = 'hashvalue'

    def __init__(self, tup, hash=hash):
        self[:] = tup
        self.hashvalue = hash(tup)

    def __hash__(self):
        return self.hashvalue

def _make_key(args, kwds, typed,
             kwd_mark = (object(),),
             fasttypes = {int, str},
             tuple=tuple, type=type, len=len):
    """Make a cache key from optionally typed positional and keyword arguments

    The key is constructed in a way that is flat as possible rather than
    as a nested structure that would take more memory.

    If there is only a single argument and its data type is known to cache
    its hash value, then that argument is returned without a wrapper.  This
    saves space and improves lookup speed.

    """
    # All of code below relies on kwds preserving the order input by the user.
    # Formerly, we sorted() the kwds before looping.  The new way is *much*
    # faster; however, it means that f(x=1, y=2) will now be treated as a
    # distinct call from f(y=2, x=1) which will be cached separately.
    key = args
    if kwds:
        key += kwd_mark
        for item in kwds.items():
            key += item
    if typed:
        key += tuple(type(v) for v in args)
        if kwds:
            key += tuple(type(v) for v in kwds.values())
    elif len(key) == 1 and type(key[0]) in fasttypes:
        return key[0]
    return _HashedSeq(key)
```

--------------------------------

### Unbounded Cache Decorator

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

A lightweight decorator providing an unbounded cache, often referred to as memoization.

```python
def cache(user_function, /):
    'Simple lightweight unbounded cache.  Sometimes called "memoize".'
    return lru_cache(maxsize=None)(user_function)
```

--------------------------------

### Compose MRO for a class

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Calculates the MRO for a class including relevant abstract base classes from a provided iterable.

```python
def _compose_mro(cls, types):
    """Calculates the method resolution order for a given class *cls*.

    Includes relevant abstract base classes (with their respective bases) from
    the *types* iterable. Uses a modified C3 linearization algorithm.

    """
    bases = set(cls.__mro__)
    # Remove entries which are already present in the __mro__ or unrelated.
    def is_related(typ):
        return (typ not in bases and hasattr(typ, '__mro__')
                                 and not isinstance(typ, GenericAlias)
                                 and issubclass(cls, typ))
    types = [n for n in types if is_related(n)]
    # Remove entries which are strict bases of other entries (they will end up
    # in the MRO anyway.
    def is_strict_base(typ):
        for other in types:
            if typ != other and typ in other.__mro__:
                return True
        return False
    types = [n for n in types if not is_strict_base(n)]
    # Subclasses of the ABCs in *types* which are also implemented by
    # *cls* can be used to stabilize ABC ordering.
    type_set = set(types)
    mro = []
    for typ in types:
        found = []
        for sub in typ.__subclasses__():
            if sub not in bases and issubclass(cls, sub):
                found.append([s for s in sub.__mro__ if s in type_set])
        if not found:
            mro.append(typ)
            continue
        # Favor subclasses with the biggest number of useful bases
        found.sort(key=len, reverse=True)
        for sub in found:
            for subcls in sub:
                if subcls not in mro:
                    mro.append(subcls)
    return _c3_mro(cls, abcs=mro)
```

--------------------------------

### Update wrapper function metadata

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Updates a wrapper function to inherit attributes from the wrapped function, ensuring proper introspection.

```python
WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
                       '__annotations__')
WRAPPER_UPDATES = ('__dict__',)
def update_wrapper(wrapper,
                   wrapped,
                   assigned = WRAPPER_ASSIGNMENTS,
                   updated = WRAPPER_UPDATES):
    """Update a wrapper function to look like the wrapped function

       wrapper is the function to be updated
       wrapped is the original function
       assigned is a tuple naming the attributes assigned directly
       from the wrapped function to the wrapper function (defaults to
       functools.WRAPPER_ASSIGNMENTS)
       updated is a tuple naming the attributes of the wrapper that
       are updated with the corresponding attribute from the wrapped
       function (defaults to functools.WRAPPER_UPDATES)
    """
    for attr in assigned:
        try:
            value = getattr(wrapped, attr)
        except AttributeError:
            pass
        else:
            setattr(wrapper, attr, value)
    for attr in updated:
        getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
    # Issue #17482: set __wrapped__ last so we don't inadvertently copy it
    # from the wrapped function when updating __dict__
    wrapper.__wrapped__ = wrapped
    # Return the wrapper so this can be used as a decorator via partial()
    return wrapper
```

--------------------------------

### Total ordering decorator

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Class decorator that fills in missing ordering methods based on defined comparison operations.

```python
_convert = {
    '__lt__': [('__gt__', _gt_from_lt),
               ('__le__', _le_from_lt),
               ('__ge__', _ge_from_lt)],
    '__le__': [('__ge__', _ge_from_le),
               ('__lt__', _lt_from_le),
               ('__gt__', _gt_from_le)],
    '__gt__': [('__lt__', _lt_from_gt),
               ('__ge__', _ge_from_gt),
               ('__le__', _le_from_gt)],
    '__ge__': [('__le__', _le_from_ge),
               ('__gt__', _gt_from_ge),
               ('__lt__', _lt_from_ge)]
}

def total_ordering(cls):
    """Class decorator that fills in missing ordering methods"""
    # Find user-defined comparisons (not those inherited from object).
    roots = {op for op in _convert if getattr(cls, op, None) is not getattr(object, op, None)}
    if not roots:
        raise ValueError('must define at least one ordering operation: < > <= >=')
    root = max(roots)       # prefer __lt__ to __le__ to __gt__ to __ge__
    for opname, opfunc in _convert[root]:
        if opname not in roots:
            opfunc.__name__ = opname
            setattr(cls, opname, opfunc)
    return cls
```

--------------------------------

### Implement cached_property class

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Defines the cached_property class for computing and caching properties on an instance.

```python
class cached_property:
    def __init__(self, func):
        self.func = func
        self.attrname = None
        self.__doc__ = func.__doc__
        self.lock = RLock()

    def __set_name__(self, owner, name):
        if self.attrname is None:
            self.attrname = name
        elif name != self.attrname:
            raise TypeError(
                "Cannot assign the same cached_property to two different names "
                f"({self.attrname!r} and {name!r})."
            )

    def __get__(self, instance, owner=None):
        if instance is None:
            return self
        if self.attrname is None:
            raise TypeError(
                "Cannot use cached_property instance without calling __set_name__ on it.")
        try:
            cache = instance.__dict__
        except AttributeError:  # not all objects have __dict__ (e.g. class defines slots)
            msg = (
                f"No '__dict__' attribute on {type(instance).__name__!r} "
                f"instance to cache {self.attrname!r} property."
            )
            raise TypeError(msg) from None
        val = cache.get(self.attrname, _NOT_FOUND)
        if val is _NOT_FOUND:
            with self.lock:
                # check if another thread filled cache while we awaited lock
                val = cache.get(self.attrname, _NOT_FOUND)
                if val is _NOT_FOUND:
                    val = self.func(instance)
                    try:
                        cache[self.attrname] = val
                    except TypeError:
                        msg = (
                            f"The '__dict__' attribute on {type(instance).__name__!r} instance "
                            f"does not support item assignment for caching {self.attrname!r} property."
                        )
                        raise TypeError(msg) from None
        return val

    __class_getitem__ = classmethod(GenericAlias)
```

--------------------------------

### Define under_cached_property descriptor

Source: https://propcache.aio-libs.org/en/latest/_modules/propcache/_helpers_py

The under_cached_property class acts as a data descriptor that caches the result of a method call in the instance's _cache dictionary.

```python
"""Various helper functions."""

from __future__ import annotations

import sys
from collections.abc import Callable, Mapping
from functools import cached_property
from typing import Any, Generic, Protocol, TypeVar, overload

__all__ = ("under_cached_property", "cached_property")


if sys.version_info >= (3, 11):
    from typing import Self
else:
    Self = Any

_T = TypeVar("_T")
# We use Mapping to make it possible to use TypedDict, but this isn't
# technically type safe as we need to assign into the dict.
_Cache = TypeVar("_Cache", bound=Mapping[str, Any])


class _CacheImpl(Protocol[_Cache]):
    _cache: _Cache



[docs]
class under_cached_property(Generic[_T]):
    """Use as a class method decorator.

    It operates almost exactly like
    the Python `@property` decorator, but it puts the result of the
    method it decorates into the instance dict after the first call,
    effectively replacing the function it decorates with an instance
    variable.  It is, in Python parlance, a data descriptor.
    """

    def __init__(self, wrapped: Callable[[Any], _T]) -> None:
        self.wrapped = wrapped
        self.__doc__ = wrapped.__doc__
        self.name = wrapped.__name__

    @overload
    def __get__(self, inst: None, owner: type[object] | None = None) -> Self: ...

    @overload
    def __get__(
        self, inst: _CacheImpl[Any], owner: type[object] | None = None
    ) -> _T: ...

    def __get__(
        self, inst: _CacheImpl[Any] | None, owner: type[object] | None = None
    ) -> _T | Self:
        if inst is None:
            return self
        try:
            return inst._cache[self.name]  # type: ignore[no-any-return]
        except KeyError:
            val = self.wrapped(inst)
            inst._cache[self.name] = val
            return val

    def __set__(self, inst: _CacheImpl[Any], value: _T) -> None:
        raise AttributeError("cached property is read-only")
```

--------------------------------

### LRU Cache Decorator Definition

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Public interface for the least-recently-used cache decorator.

```python
def lru_cache(maxsize=128, typed=False):
    """Least-recently-used cache decorator.

    If *maxsize* is set to None, the LRU features are disabled and the cache
    can grow without bound.

    If *typed* is True, arguments of different types will be cached separately.
    For example, f(3.0) and f(3) will be treated as distinct calls with
    distinct results.

    Arguments to the cached function must be hashable.

    View the cache statistics named tuple (hits, misses, maxsize, currsize)
    with f.cache_info().  Clear the cache and statistics with f.cache_clear().
    Access the underlying function with f.__wrapped__.

    See:  https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)

    """

    # Users should only access the lru_cache through its public API:
    #       cache_info, cache_clear, and f.__wrapped__
    # The internals of the lru_cache are encapsulated for thread safety and
    # to allow the implementation to change (including a possible C version).
```

--------------------------------

### Reduce sequence function

Source: https://propcache.aio-libs.org/en/latest/_modules/functools

Applies a function of two arguments cumulatively to items of an iterable.

```python
_initial_missing = object()

def reduce(function, sequence, initial=_initial_missing):
    """
    reduce(function, iterable[, initial]) -> value

    Apply a function of two arguments cumulatively to the items of a sequence
    or iterable, from left to right, so as to reduce the iterable to a single
    value.  For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates
    ((((1+2)+3)+4)+5).  If initial is present, it is placed before the items
    of the iterable in the calculation, and serves as a default when the
    iterable is empty.
    """

    it = iter(sequence)
```

--------------------------------

### under_cached_property(func)

Source: https://propcache.aio-libs.org/en/latest/_sources/api.rst.txt

A decorator that caches the result of a method call in the instance's _cache dictionary as a private attribute.

```APIDOC
## under_cached_property(func)

### Description
Transforms a method into a property whose value is computed once and cached in the instance's _cache dictionary. This is useful for keeping the main __dict__ clean.

### Parameters
- **func** (callable) - The method to be decorated.

### Usage
```python
from propcache.api import under_cached_property

class MyClass:
    def __init__(self):
        self._cache = {}

    @under_cached_property
    def calculated_data(self):
        return expensive_operation()
```
```

--------------------------------

### under_cached_property

Source: https://propcache.aio-libs.org/en/latest/_modules/propcache/_helpers_py

A class method decorator that acts as a data descriptor. It caches the result of the decorated method in the instance dictionary after the first call, effectively replacing the function with an instance variable.

```APIDOC
## class under_cached_property(Generic[_T])

### Description
Use as a class method decorator. It operates almost exactly like the Python `@property` decorator, but it puts the result of the method it decorates into the instance dict after the first call, effectively replacing the function it decorates with an instance variable.

### Usage
Decorate a method within a class that implements a `_cache` mapping (e.g., `_CacheImpl`).

### Parameters
- **wrapped** (Callable[[Any], _T]) - Required - The method to be decorated and cached.

### Behavior
- **__get__**: Returns the cached value from `inst._cache` if it exists; otherwise, calls the wrapped method, stores the result in `inst._cache`, and returns it.
- **__set__**: Raises an `AttributeError` as the property is read-only.
```

--------------------------------

### cached_property(func)

Source: https://propcache.aio-libs.org/en/latest/_sources/api.rst.txt

A decorator that caches the result of a method call in the instance's __dict__ dictionary, similar to the standard library implementation.

```APIDOC
## cached_property(func)

### Description
This decorator transforms a method into a property whose value is computed once and cached in the instance's __dict__. It is functionally equivalent to the standard library's cached_property.

### Parameters
- **func** (callable) - The method to be decorated.

### Usage
```python
from propcache.api import cached_property

class MyClass:
    @cached_property
    def value(self):
        return expensive_calculation()
```
```
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