"""Adapted. Original source: https://github.com/maxfischer2781/asyncstdlib/blob/master/asyncstdlib/itertools.py MIT License """ import asyncio import contextvars import functools import inspect from collections import deque from typing import ( Any, AsyncContextManager, AsyncGenerator, AsyncIterable, AsyncIterator, Awaitable, Callable, Coroutine, Deque, Generic, Iterable, Iterator, List, Optional, Tuple, TypeVar, Union, cast, overload, ) T = TypeVar("T") _no_default = object() # https://github.com/python/cpython/blob/main/Lib/test/test_asyncgen.py#L54 # before 3.10, the builtin anext() was not available def py_anext( iterator: AsyncIterator[T], default: Union[T, Any] = _no_default ) -> Awaitable[Union[T, None, Any]]: """Pure-Python implementation of anext() for testing purposes. Closely matches the builtin anext() C implementation. Can be used to compare the built-in implementation of the inner coroutines machinery to C-implementation of __anext__() and send() or throw() on the returned generator. """ try: __anext__ = cast( Callable[[AsyncIterator[T]], Awaitable[T]], type(iterator).__anext__ ) except AttributeError: raise TypeError(f"{iterator!r} is not an async iterator") if default is _no_default: return __anext__(iterator) async def anext_impl() -> Union[T, Any]: try: # The C code is way more low-level than this, as it implements # all methods of the iterator protocol. In this implementation # we're relying on higher-level coroutine concepts, but that's # exactly what we want -- crosstest pure-Python high-level # implementation and low-level C anext() iterators. return await __anext__(iterator) except StopAsyncIteration: return default return anext_impl() class NoLock: """Dummy lock that provides the proper interface but no protection.""" async def __aenter__(self) -> None: pass async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> bool: return False async def tee_peer( iterator: AsyncIterator[T], # the buffer specific to this peer buffer: Deque[T], # the buffers of all peers, including our own peers: List[Deque[T]], lock: AsyncContextManager[Any], ) -> AsyncGenerator[T, None]: """Iterate over :py:func:`~.tee`.""" try: while True: if not buffer: async with lock: # Another peer produced an item while we were waiting for the lock. # Proceed with the next loop iteration to yield the item. if buffer: continue try: item = await iterator.__anext__() except StopAsyncIteration: break else: # Append to all buffers, including our own. We'll fetch our # item from the buffer again, instead of yielding it directly. # This ensures the proper item ordering if any of our peers # are fetching items concurrently. They may have buffered their # item already. for peer_buffer in peers: peer_buffer.append(item) yield buffer.popleft() finally: async with lock: # this peer is done – remove its buffer for idx, peer_buffer in enumerate(peers): # pragma: no branch if peer_buffer is buffer: peers.pop(idx) break # if we are the last peer, try and close the iterator if not peers and hasattr(iterator, "aclose"): await iterator.aclose() class Tee(Generic[T]): """Create ``n`` separate asynchronous iterators over ``iterable``. This splits a single ``iterable`` into multiple iterators, each providing the same items in the same order. All child iterators may advance separately but pare the same items from ``iterable`` -- when the most advanced iterator retrieves an item, it is buffered until the least advanced iterator has yielded it as well. A ``tee`` works lazily and can handle an infinite ``iterable``, provided that all iterators advance. .. code-block:: python3 async def derivative(sensor_data): previous, current = a.tee(sensor_data, n=2) await a.anext(previous) # advance one iterator return a.map(operator.sub, previous, current) Unlike :py:func:`itertools.tee`, :py:func:`~.tee` returns a custom type instead of a :py:class:`tuple`. Like a tuple, it can be indexed, iterated and unpacked to get the child iterators. In addition, its :py:meth:`~.tee.aclose` method immediately closes all children, and it can be used in an ``async with`` context for the same effect. If ``iterable`` is an iterator and read elsewhere, ``tee`` will *not* provide these items. Also, ``tee`` must internally buffer each item until the last iterator has yielded it; if the most and least advanced iterator differ by most data, using a :py:class:`list` is more efficient (but not lazy). If the underlying iterable is concurrency safe (``anext`` may be awaited concurrently) the resulting iterators are concurrency safe as well. Otherwise, the iterators are safe if there is only ever one single "most advanced" iterator. To enforce sequential use of ``anext``, provide a ``lock`` - e.g. an :py:class:`asyncio.Lock` instance in an :py:mod:`asyncio` application - and access is automatically synchronised. """ def __init__( self, iterable: AsyncIterator[T], n: int = 2, *, lock: Optional[AsyncContextManager[Any]] = None, ): self._iterator = iterable.__aiter__() # before 3.10 aiter() doesn't exist self._buffers: List[Deque[T]] = [deque() for _ in range(n)] self._children = tuple( tee_peer( iterator=self._iterator, buffer=buffer, peers=self._buffers, lock=lock if lock is not None else NoLock(), ) for buffer in self._buffers ) def __len__(self) -> int: return len(self._children) @overload def __getitem__(self, item: int) -> AsyncIterator[T]: ... @overload def __getitem__(self, item: slice) -> Tuple[AsyncIterator[T], ...]: ... def __getitem__( self, item: Union[int, slice] ) -> Union[AsyncIterator[T], Tuple[AsyncIterator[T], ...]]: return self._children[item] def __iter__(self) -> Iterator[AsyncIterator[T]]: yield from self._children async def __aenter__(self) -> "Tee[T]": return self async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> bool: await self.aclose() return False async def aclose(self) -> None: for child in self._children: await child.aclose() atee = Tee async def async_zip(*async_iterables): """Async version of zip.""" # Before Python 3.10, aiter() was not available iterators = [iterable.__aiter__() for iterable in async_iterables] while True: try: items = await asyncio.gather( *(py_anext(iterator) for iterator in iterators) ) yield tuple(items) except StopAsyncIteration: break def ensure_async_iterator( iterable: Union[Iterable, AsyncIterable], ) -> AsyncIterator: if hasattr(iterable, "__anext__"): return cast(AsyncIterator, iterable) elif hasattr(iterable, "__aiter__"): return cast(AsyncIterator, iterable.__aiter__()) else: class AsyncIteratorWrapper: def __init__(self, iterable: Iterable): self._iterator = iter(iterable) async def __anext__(self): try: return next(self._iterator) except StopIteration: raise StopAsyncIteration def __aiter__(self): return self return AsyncIteratorWrapper(iterable) def aiter_with_concurrency( n: Optional[int], generator: AsyncIterator[Coroutine[None, None, T]], *, _eager_consumption_timeout: float = 0, ) -> AsyncGenerator[T, None]: """Process async generator with max parallelism. Args: n: The number of tasks to run concurrently. generator: The async generator to process. _eager_consumption_timeout: If set, check for completed tasks after each iteration and yield their results. This can be used to consume the generator eagerly while still respecting the concurrency limit. Yields: The processed items yielded by the async generator. """ if n == 0: async def consume(): async for item in generator: yield await item return consume() semaphore = cast( asyncio.Semaphore, asyncio.Semaphore(n) if n is not None else NoLock() ) async def process_item(ix: int, item): async with semaphore: res = await item return (ix, res) async def process_generator(): tasks = {} accepts_context = asyncio_accepts_context() ix = 0 async for item in generator: if accepts_context: context = contextvars.copy_context() task = asyncio.create_task(process_item(ix, item), context=context) else: task = asyncio.create_task(process_item(ix, item)) tasks[ix] = task ix += 1 if _eager_consumption_timeout > 0: try: for _fut in asyncio.as_completed( tasks.values(), timeout=_eager_consumption_timeout, ): task_idx, res = await _fut yield res del tasks[task_idx] except asyncio.TimeoutError: pass if n is not None and len(tasks) >= n: done, _ = await asyncio.wait( tasks.values(), return_when=asyncio.FIRST_COMPLETED ) for task in done: task_idx, res = task.result() yield res del tasks[task_idx] for task in asyncio.as_completed(tasks.values()): _, res = await task yield res return process_generator() def accepts_context(callable: Callable[..., Any]) -> bool: """Check if a callable accepts a context argument.""" try: return inspect.signature(callable).parameters.get("context") is not None except ValueError: return False # Ported from Python 3.9+ to support Python 3.8 async def aio_to_thread( func, /, *args, __ctx: Optional[contextvars.Context] = None, **kwargs ): """Asynchronously run function *func* in a separate thread. Any *args and **kwargs supplied for this function are directly passed to *func*. Also, the current :class:`contextvars.Context` is propagated, allowing context variables from the main thread to be accessed in the separate thread. Return a coroutine that can be awaited to get the eventual result of *func*. """ loop = asyncio.get_running_loop() ctx = __ctx or contextvars.copy_context() func_call = functools.partial(ctx.run, func, *args, **kwargs) return await loop.run_in_executor(None, func_call) @functools.lru_cache(maxsize=1) def asyncio_accepts_context(): """Check if the current asyncio event loop accepts a context argument.""" return accepts_context(asyncio.create_task)