ÿØÿàJFIFÿþ ÿÛC       ÿÛC ÿÀÿÄÿÄ"#QrÿÄÿÄ&1!A"2qQaáÿÚ ?Øy,æ/3JæݹÈ߲؋5êXw²±ÉyˆR”¾I0ó2—PI¾IÌÚiMö¯–þrìN&"KgX:Šíµ•nTJnLK„…@!‰-ý ùúmë;ºgµŒ&ó±hw’¯Õ@”Ü— 9ñ-ë.²1<yà‚¹ïQÐU„ہ?.’¦èûbß±©Ö«Âw*VŒ) `$‰bØԟ’ëXÖ-ËTÜíGÚ3ð«g Ÿ§¯—Jx„–’U/ÂÅv_s(Hÿ@TñJÑãõçn­‚!ÈgfbÓc­:él[ðQe 9ÀPLbÃãCµm[5¿ç'ªjglå‡Ûí_§Úõl-;"PkÞÞÁQâ¼_Ñ^¢SŸx?"¸¦ùY騐ÒOÈ q’`~~ÚtËU¹CڒêV  I1Áß_ÿÙ"""High-level support for working with threads in asyncio""" import functools import contextvars from . import events __all__ = "to_thread", async def to_thread(func, /, *args, **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 = events.get_running_loop() ctx = contextvars.copy_context() func_call = functools.partial(ctx.run, func, *args, **kwargs) return await loop.run_in_executor(None, func_call)