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# -*- coding: utf-8 -*-

"""Memoization of generators.

Memoize generator functions (generator definitions), iterators,

or iterators created by factory functions."""

__all__ = ["gmemoize", "imemoize", "fimemoize"]

from collections.abc import Callable, Generator, Iterable, Iterator

from functools import wraps

from threading import RLock

from typing import Any, TypeVar

from .arity import resolve_bindings, tuplify_bindings

from .regutil import register_decorator

from .symbol import sym

F = TypeVar('F', bound=Callable)

def gmemoize(gfunc: F) -> F:

"""Decorator: produce memoized generator instances.

Similar to ``itertools.tee``, but the whole sequence is kept in memory

indefinitely, so more instances of the memoized generator can be created

at any time (instead of having to specify how many copies at ``tee`` time).

Decorate the **generator function** (i.e. generator definition) with this.

- If the gfunc takes arguments, they must be hashable. A separate memoized

sequence is created for each unique set of argument values seen.

- For simplicity, the generator itself may use ``yield`` for output only;

``send`` is not supported.

- Any exceptions raised by the generator (except StopIteration) are also

memoized, like in ``memoize``.

- Thread-safe. Calls to ``next`` on the memoized generator from different

threads are serialized via a lock. Each memoized sequence has its own

lock. This uses ``threading.RLock``, so re-entering from the same

thread (e.g. in recursively defined sequences) is fine.

- For infinite sequences, use this only if you can guarantee only a

reasonable number of terms will ever be evaluated (w.r.t. available RAM).

- Typically, this should be the outermost decorator if several are used

on the same gfunc.

Usage::

evals = 0

@gmemoize

def one_two_three():

global evals

for j in range(3):

evals += 1

yield j

g1 = one_two_three()

assert tuple(x for x in g1) == (0, 1, 2)

g2 = one_two_three()

assert tuple(x for x in g2) == (0, 1, 2)

assert evals == 3

It doesn't matter when the generator instances are created, or when which

of them is advanced. All instances created with the same arguments (here none)

to the gfunc share the same memoized sequence.

**Recipe**: *Memoizing only a part of a sequence*.

Build a chain of generators, then memoize only the last one::

def orig():

yield from range(100)

def evens():

yield from (x for x in orig() if x % 2 == 0)

evaluations = collections.Counter()

@gmemoize

def some_evens(n): # drop n first terms

evaluations[n] += 1

yield from drop(n, evens())

last(some_evens(25))

last(some_evens(20))

assert all(v == 1 for k, v in evaluations.items())

Or with ``lambda`` for a more compact presentation::

orig = lambda: (yield from range(100))

evens = lambda: (yield from (x for x in orig() if x % 2 == 0))

some_evens = gmemoize(lambda n: (yield from drop(n, evens())))

last(some_evens(25))

See also ``imemoize``, ``fimemoize``.

"""

memos: dict[tuple, tuple] = {}

@wraps(gfunc)

def gmemoized(*args: Any, **kwargs: Any) -> "_MemoizedGenerator":

k = tuplify_bindings(resolve_bindings(gfunc, *args, **kwargs))

if k not in memos:

# underlying generator instance, memo instance, lock instance

memos[k] = (gfunc(*args, **kwargs), [], RLock())

return _MemoizedGenerator(*memos[k])

return gmemoized

_success = sym("_success")

_fail = sym("_fail")

class _MemoizedGenerator:

"""Wrapper that manages one memoized sequence. Co-operates with gmemoize."""

def __init__(self, g: Generator, memo: list, lock: RLock) -> None:

self.g = g

self.memo = memo # each instance for the same g gets the same memo

self.lock = lock

self.j: int = 0 # current position in memo

def __repr__(self) -> str:

return f"<_MemoizedGenerator object {self.g.__name__} at 0x{id(self):x}>"

# Support the `collections.abc.Iterable` API

def __iter__(self) -> "_MemoizedGenerator":

return self

def __next__(self) -> Any:

j = self.j

memo = self.memo

with self.lock:

if j < len(memo):

result = memo[j]

else:

try:

result = (_success, next(self.g))

except BaseException as err: # StopIteration propagates, not a BaseException

result = (_fail, err)

memo.append(result)

kind, value = result

self.j += 1

if kind is _fail:

raise value

return value

# Support a subset of the `collections.abc.Sequence` API for already-computed items

def __len__(self) -> int:

return len(self.memo)

def __getitem__(self, k: int | slice) -> Any:

if not isinstance(k, (int, slice)):

raise TypeError(f"Expected an int or slice index, got {type(k)} with value {repr(k)}")

length = len(self.memo)

if isinstance(k, slice):

# For slices where at least one item raises an exception, we raise the

# exception that is encountered first when walking the slice.

lst = []

for kind, value in self.memo[k]:

if kind is _fail:

raise value

lst.append(value)

return lst

if k >= length or k < -length:

raise IndexError(f"memoized generator index out of range; got {k}, with {len(self.memo)} items currently available")

kind, value = self.memo[k]

if kind is _fail:

raise value

return value

def imemoize(iterable: Iterable) -> Callable:

"""Memoize an iterable.

Return a gfunc with no parameters which, when called, returns a generator

that yields items from the memoized iterable. The original iterable is

used to retrieve more terms when needed.

``imemoize`` essentially makes the iterable restartable (arbitrarily "tee-able"),

at the cost of keeping the whole history in memory.

Unlike ``itertools.tee``, there is no need to specify how many copies

are needed at ``tee`` time; a new copy can be created at any time,

by calling the returned gfunc.

Like ``itertools.tee``, after memoizing, the original iterator should not

be used. The danger is that if something outside the memoization mechanism

advances it, some values will be lost before they reach the memo.

Example::

evens = (x for x in range(100) if x % 2 == 0)

some_evens = imemoize(drop(25, evens))

assert last(some_evens()) == last(some_evens()) # iterating twice!

In the example, whenever ``some_evens`` is called, it returns a new

memoized generator instance that yields items from the memoized iterable.

If you need to take arguments to create the iterable, see ``fimemoize``.

"""

@gmemoize

def iterable_as_gfunc() -> Iterator:

yield from iterable

return iterable_as_gfunc

@register_decorator(priority=10)

def fimemoize(ifactory: F) -> F:

"""Like imemoize, but for cases where creating the iterable needs arguments.

``ifactory`` is a function, which takes any number of positional or keyword

arguments, and returns an iterable.

This is similar to ``gmemoize``, but for a regular function that returns

an iterable (instead of for a gfunc).

This works by defining a gfunc based on ``ifactory``, and gmemoizing that.

Hence arguments of ``ifactory`` must be hashable, and each unique set of

argument values produces its own memoized sequence.

The return value is a gfunc, which takes the same arguments as ``ifactory``.

Example::

def evens():

yield from (x for x in range(100) if x % 2 == 0)

@fimemoize

def some_evens(n): # regular function!

return drop(n, evens())

assert last(some_evens(25)) == last(some_evens(25))

Compare to::

def evens():

yield from (x for x in range(100) if x % 2 == 0)

@gmemoize

def some_evens(n): # gfunc!

yield from drop(n, evens())

assert last(some_evens(25)) == last(some_evens(25))

Using lambda, the original example is equivalent to::

evens = lambda: (yield from (x for x in range(100) if x % 2 == 0))

some_evens = fimemoize(lambda n: drop(n, evens()))

assert last(some_evens(25)) == last(some_evens(25))

"""

@wraps(ifactory)

def gfunc(*args: Any, **kwargs: Any) -> Iterator:

yield from ifactory(*args, **kwargs)

return gmemoize(gfunc)

# return gmemoize(lambda *a, **kw: (yield from ifactory(*a, **kw)))