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parrot/lib/python3.10/asyncio/__init__.py

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+"""The asyncio package, tracking PEP 3156."""
+
+# flake8: noqa
+
+import sys
+
+# This relies on each of the submodules having an __all__ variable.
+from .base_events import *
+from .coroutines import *
+from .events import *
+from .exceptions import *
+from .futures import *
+from .locks import *
+from .protocols import *
+from .runners import *
+from .queues import *
+from .streams import *
+from .subprocess import *
+from .tasks import *
+from .threads import *
+from .transports import *
+
+__all__ = (base_events.__all__ +
+           coroutines.__all__ +
+           events.__all__ +
+           exceptions.__all__ +
+           futures.__all__ +
+           locks.__all__ +
+           protocols.__all__ +
+           runners.__all__ +
+           queues.__all__ +
+           streams.__all__ +
+           subprocess.__all__ +
+           tasks.__all__ +
+           threads.__all__ +
+           transports.__all__)
+
+if sys.platform == 'win32':  # pragma: no cover
+    from .windows_events import *
+    __all__ += windows_events.__all__
+else:
+    from .unix_events import *  # pragma: no cover
+    __all__ += unix_events.__all__

parrot/lib/python3.10/asyncio/__main__.py

@@ -0,0 +1,127 @@
+import ast
+import asyncio
+import code
+import concurrent.futures
+import inspect
+import sys
+import threading
+import types
+import warnings
+
+from . import futures
+
+
+class AsyncIOInteractiveConsole(code.InteractiveConsole):
+
+    def __init__(self, locals, loop):
+        super().__init__(locals)
+        self.compile.compiler.flags |= ast.PyCF_ALLOW_TOP_LEVEL_AWAIT
+
+        self.loop = loop
+
+    def runcode(self, code):
+        future = concurrent.futures.Future()
+
+        def callback():
+            global repl_future
+            global repl_future_interrupted
+
+            repl_future = None
+            repl_future_interrupted = False
+
+            func = types.FunctionType(code, self.locals)
+            try:
+                coro = func()
+            except SystemExit:
+                raise
+            except KeyboardInterrupt as ex:
+                repl_future_interrupted = True
+                future.set_exception(ex)
+                return
+            except BaseException as ex:
+                future.set_exception(ex)
+                return
+
+            if not inspect.iscoroutine(coro):
+                future.set_result(coro)
+                return
+
+            try:
+                repl_future = self.loop.create_task(coro)
+                futures._chain_future(repl_future, future)
+            except BaseException as exc:
+                future.set_exception(exc)
+
+        loop.call_soon_threadsafe(callback)
+
+        try:
+            return future.result()
+        except SystemExit:
+            raise
+        except BaseException:
+            if repl_future_interrupted:
+                self.write("\nKeyboardInterrupt\n")
+            else:
+                self.showtraceback()
+
+
+class REPLThread(threading.Thread):
+
+    def run(self):
+        try:
+            banner = (
+                f'asyncio REPL {sys.version} on {sys.platform}\n'
+                f'Use "await" directly instead of "asyncio.run()".\n'
+                f'Type "help", "copyright", "credits" or "license" '
+                f'for more information.\n'
+                f'{getattr(sys, "ps1", ">>> ")}import asyncio'
+            )
+
+            console.interact(
+                banner=banner,
+                exitmsg='exiting asyncio REPL...')
+        finally:
+            warnings.filterwarnings(
+                'ignore',
+                message=r'^coroutine .* was never awaited$',
+                category=RuntimeWarning)
+
+            loop.call_soon_threadsafe(loop.stop)
+
+
+if __name__ == '__main__':
+    sys.audit("cpython.run_stdin")
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    repl_locals = {'asyncio': asyncio}
+    for key in {'__name__', '__package__',
+                '__loader__', '__spec__',
+                '__builtins__', '__file__'}:
+        repl_locals[key] = locals()[key]
+
+    console = AsyncIOInteractiveConsole(repl_locals, loop)
+
+    repl_future = None
+    repl_future_interrupted = False
+
+    try:
+        import readline  # NoQA
+    except ImportError:
+        pass
+
+    repl_thread = REPLThread()
+    repl_thread.daemon = True
+    repl_thread.start()
+
+    while True:
+        try:
+            loop.run_forever()
+        except KeyboardInterrupt:
+            if repl_future and not repl_future.done():
+                repl_future.cancel()
+                repl_future_interrupted = True
+            continue
+        else:
+            break

parrot/lib/python3.10/asyncio/base_futures.py

@@ -0,0 +1,80 @@
+__all__ = ()
+
+import reprlib
+from _thread import get_ident
+
+from . import format_helpers
+
+# States for Future.
+_PENDING = 'PENDING'
+_CANCELLED = 'CANCELLED'
+_FINISHED = 'FINISHED'
+
+
+def isfuture(obj):
+    """Check for a Future.
+
+    This returns True when obj is a Future instance or is advertising
+    itself as duck-type compatible by setting _asyncio_future_blocking.
+    See comment in Future for more details.
+    """
+    return (hasattr(obj.__class__, '_asyncio_future_blocking') and
+            obj._asyncio_future_blocking is not None)
+
+
+def _format_callbacks(cb):
+    """helper function for Future.__repr__"""
+    size = len(cb)
+    if not size:
+        cb = ''
+
+    def format_cb(callback):
+        return format_helpers._format_callback_source(callback, ())
+
+    if size == 1:
+        cb = format_cb(cb[0][0])
+    elif size == 2:
+        cb = '{}, {}'.format(format_cb(cb[0][0]), format_cb(cb[1][0]))
+    elif size > 2:
+        cb = '{}, <{} more>, {}'.format(format_cb(cb[0][0]),
+                                        size - 2,
+                                        format_cb(cb[-1][0]))
+    return f'cb=[{cb}]'
+
+
+# bpo-42183: _repr_running is needed for repr protection
+# when a Future or Task result contains itself directly or indirectly.
+# The logic is borrowed from @reprlib.recursive_repr decorator.
+# Unfortunately, the direct decorator usage is impossible because of
+# AttributeError: '_asyncio.Task' object has no attribute '__module__' error.
+#
+# After fixing this thing we can return to the decorator based approach.
+_repr_running = set()
+
+
+def _future_repr_info(future):
+    # (Future) -> str
+    """helper function for Future.__repr__"""
+    info = [future._state.lower()]
+    if future._state == _FINISHED:
+        if future._exception is not None:
+            info.append(f'exception={future._exception!r}')
+        else:
+            key = id(future), get_ident()
+            if key in _repr_running:
+                result = '...'
+            else:
+                _repr_running.add(key)
+                try:
+                    # use reprlib to limit the length of the output, especially
+                    # for very long strings
+                    result = reprlib.repr(future._result)
+                finally:
+                    _repr_running.discard(key)
+            info.append(f'result={result}')
+    if future._callbacks:
+        info.append(_format_callbacks(future._callbacks))
+    if future._source_traceback:
+        frame = future._source_traceback[-1]
+        info.append(f'created at {frame[0]}:{frame[1]}')
+    return info

parrot/lib/python3.10/asyncio/base_tasks.py

@@ -0,0 +1,85 @@
+import linecache
+import traceback
+
+from . import base_futures
+from . import coroutines
+
+
+def _task_repr_info(task):
+    info = base_futures._future_repr_info(task)
+
+    if task._must_cancel:
+        # replace status
+        info[0] = 'cancelling'
+
+    info.insert(1, 'name=%r' % task.get_name())
+
+    coro = coroutines._format_coroutine(task._coro)
+    info.insert(2, f'coro=<{coro}>')
+
+    if task._fut_waiter is not None:
+        info.insert(3, f'wait_for={task._fut_waiter!r}')
+    return info
+
+
+def _task_get_stack(task, limit):
+    frames = []
+    if hasattr(task._coro, 'cr_frame'):
+        # case 1: 'async def' coroutines
+        f = task._coro.cr_frame
+    elif hasattr(task._coro, 'gi_frame'):
+        # case 2: legacy coroutines
+        f = task._coro.gi_frame
+    elif hasattr(task._coro, 'ag_frame'):
+        # case 3: async generators
+        f = task._coro.ag_frame
+    else:
+        # case 4: unknown objects
+        f = None
+    if f is not None:
+        while f is not None:
+            if limit is not None:
+                if limit <= 0:
+                    break
+                limit -= 1
+            frames.append(f)
+            f = f.f_back
+        frames.reverse()
+    elif task._exception is not None:
+        tb = task._exception.__traceback__
+        while tb is not None:
+            if limit is not None:
+                if limit <= 0:
+                    break
+                limit -= 1
+            frames.append(tb.tb_frame)
+            tb = tb.tb_next
+    return frames
+
+
+def _task_print_stack(task, limit, file):
+    extracted_list = []
+    checked = set()
+    for f in task.get_stack(limit=limit):
+        lineno = f.f_lineno
+        co = f.f_code
+        filename = co.co_filename
+        name = co.co_name
+        if filename not in checked:
+            checked.add(filename)
+            linecache.checkcache(filename)
+        line = linecache.getline(filename, lineno, f.f_globals)
+        extracted_list.append((filename, lineno, name, line))
+
+    exc = task._exception
+    if not extracted_list:
+        print(f'No stack for {task!r}', file=file)
+    elif exc is not None:
+        print(f'Traceback for {task!r} (most recent call last):', file=file)
+    else:
+        print(f'Stack for {task!r} (most recent call last):', file=file)
+
+    traceback.print_list(extracted_list, file=file)
+    if exc is not None:
+        for line in traceback.format_exception_only(exc.__class__, exc):
+            print(line, file=file, end='')

parrot/lib/python3.10/asyncio/coroutines.py

@@ -0,0 +1,269 @@
+__all__ = 'coroutine', 'iscoroutinefunction', 'iscoroutine'
+
+import collections.abc
+import functools
+import inspect
+import os
+import sys
+import traceback
+import types
+import warnings
+
+from . import base_futures
+from . import constants
+from . import format_helpers
+from .log import logger
+
+
+def _is_debug_mode():
+    # If you set _DEBUG to true, @coroutine will wrap the resulting
+    # generator objects in a CoroWrapper instance (defined below).  That
+    # instance will log a message when the generator is never iterated
+    # over, which may happen when you forget to use "await" or "yield from"
+    # with a coroutine call.
+    # Note that the value of the _DEBUG flag is taken
+    # when the decorator is used, so to be of any use it must be set
+    # before you define your coroutines.  A downside of using this feature
+    # is that tracebacks show entries for the CoroWrapper.__next__ method
+    # when _DEBUG is true.
+    return sys.flags.dev_mode or (not sys.flags.ignore_environment and
+                                  bool(os.environ.get('PYTHONASYNCIODEBUG')))
+
+
+_DEBUG = _is_debug_mode()
+
+
+class CoroWrapper:
+    # Wrapper for coroutine object in _DEBUG mode.
+
+    def __init__(self, gen, func=None):
+        assert inspect.isgenerator(gen) or inspect.iscoroutine(gen), gen
+        self.gen = gen
+        self.func = func  # Used to unwrap @coroutine decorator
+        self._source_traceback = format_helpers.extract_stack(sys._getframe(1))
+        self.__name__ = getattr(gen, '__name__', None)
+        self.__qualname__ = getattr(gen, '__qualname__', None)
+
+    def __repr__(self):
+        coro_repr = _format_coroutine(self)
+        if self._source_traceback:
+            frame = self._source_traceback[-1]
+            coro_repr += f', created at {frame[0]}:{frame[1]}'
+
+        return f'<{self.__class__.__name__} {coro_repr}>'
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return self.gen.send(None)
+
+    def send(self, value):
+        return self.gen.send(value)
+
+    def throw(self, type, value=None, traceback=None):
+        return self.gen.throw(type, value, traceback)
+
+    def close(self):
+        return self.gen.close()
+
+    @property
+    def gi_frame(self):
+        return self.gen.gi_frame
+
+    @property
+    def gi_running(self):
+        return self.gen.gi_running
+
+    @property
+    def gi_code(self):
+        return self.gen.gi_code
+
+    def __await__(self):
+        return self
+
+    @property
+    def gi_yieldfrom(self):
+        return self.gen.gi_yieldfrom
+
+    def __del__(self):
+        # Be careful accessing self.gen.frame -- self.gen might not exist.
+        gen = getattr(self, 'gen', None)
+        frame = getattr(gen, 'gi_frame', None)
+        if frame is not None and frame.f_lasti == -1:
+            msg = f'{self!r} was never yielded from'
+            tb = getattr(self, '_source_traceback', ())
+            if tb:
+                tb = ''.join(traceback.format_list(tb))
+                msg += (f'\nCoroutine object created at '
+                        f'(most recent call last, truncated to '
+                        f'{constants.DEBUG_STACK_DEPTH} last lines):\n')
+                msg += tb.rstrip()
+            logger.error(msg)
+
+
+def coroutine(func):
+    """Decorator to mark coroutines.
+
+    If the coroutine is not yielded from before it is destroyed,
+    an error message is logged.
+    """
+    warnings.warn('"@coroutine" decorator is deprecated since Python 3.8, use "async def" instead',
+                  DeprecationWarning,
+                  stacklevel=2)
+    if inspect.iscoroutinefunction(func):
+        # In Python 3.5 that's all we need to do for coroutines
+        # defined with "async def".
+        return func
+
+    if inspect.isgeneratorfunction(func):
+        coro = func
+    else:
+        @functools.wraps(func)
+        def coro(*args, **kw):
+            res = func(*args, **kw)
+            if (base_futures.isfuture(res) or inspect.isgenerator(res) or
+                    isinstance(res, CoroWrapper)):
+                res = yield from res
+            else:
+                # If 'res' is an awaitable, run it.
+                try:
+                    await_meth = res.__await__
+                except AttributeError:
+                    pass
+                else:
+                    if isinstance(res, collections.abc.Awaitable):
+                        res = yield from await_meth()
+            return res
+
+    coro = types.coroutine(coro)
+    if not _DEBUG:
+        wrapper = coro
+    else:
+        @functools.wraps(func)
+        def wrapper(*args, **kwds):
+            w = CoroWrapper(coro(*args, **kwds), func=func)
+            if w._source_traceback:
+                del w._source_traceback[-1]
+            # Python < 3.5 does not implement __qualname__
+            # on generator objects, so we set it manually.
+            # We use getattr as some callables (such as
+            # functools.partial may lack __qualname__).
+            w.__name__ = getattr(func, '__name__', None)
+            w.__qualname__ = getattr(func, '__qualname__', None)
+            return w
+
+    wrapper._is_coroutine = _is_coroutine  # For iscoroutinefunction().
+    return wrapper
+
+
+# A marker for iscoroutinefunction.
+_is_coroutine = object()
+
+
+def iscoroutinefunction(func):
+    """Return True if func is a decorated coroutine function."""
+    return (inspect.iscoroutinefunction(func) or
+            getattr(func, '_is_coroutine', None) is _is_coroutine)
+
+
+# Prioritize native coroutine check to speed-up
+# asyncio.iscoroutine.
+_COROUTINE_TYPES = (types.CoroutineType, types.GeneratorType,
+                    collections.abc.Coroutine, CoroWrapper)
+_iscoroutine_typecache = set()
+
+
+def iscoroutine(obj):
+    """Return True if obj is a coroutine object."""
+    if type(obj) in _iscoroutine_typecache:
+        return True
+
+    if isinstance(obj, _COROUTINE_TYPES):
+        # Just in case we don't want to cache more than 100
+        # positive types.  That shouldn't ever happen, unless
+        # someone stressing the system on purpose.
+        if len(_iscoroutine_typecache) < 100:
+            _iscoroutine_typecache.add(type(obj))
+        return True
+    else:
+        return False
+
+
+def _format_coroutine(coro):
+    assert iscoroutine(coro)
+
+    is_corowrapper = isinstance(coro, CoroWrapper)
+
+    def get_name(coro):
+        # Coroutines compiled with Cython sometimes don't have
+        # proper __qualname__ or __name__.  While that is a bug
+        # in Cython, asyncio shouldn't crash with an AttributeError
+        # in its __repr__ functions.
+        if is_corowrapper:
+            return format_helpers._format_callback(coro.func, (), {})
+
+        if hasattr(coro, '__qualname__') and coro.__qualname__:
+            coro_name = coro.__qualname__
+        elif hasattr(coro, '__name__') and coro.__name__:
+            coro_name = coro.__name__
+        else:
+            # Stop masking Cython bugs, expose them in a friendly way.
+            coro_name = f'<{type(coro).__name__} without __name__>'
+        return f'{coro_name}()'
+
+    def is_running(coro):
+        try:
+            return coro.cr_running
+        except AttributeError:
+            try:
+                return coro.gi_running
+            except AttributeError:
+                return False
+
+    coro_code = None
+    if hasattr(coro, 'cr_code') and coro.cr_code:
+        coro_code = coro.cr_code
+    elif hasattr(coro, 'gi_code') and coro.gi_code:
+        coro_code = coro.gi_code
+
+    coro_name = get_name(coro)
+
+    if not coro_code:
+        # Built-in types might not have __qualname__ or __name__.
+        if is_running(coro):
+            return f'{coro_name} running'
+        else:
+            return coro_name
+
+    coro_frame = None
+    if hasattr(coro, 'gi_frame') and coro.gi_frame:
+        coro_frame = coro.gi_frame
+    elif hasattr(coro, 'cr_frame') and coro.cr_frame:
+        coro_frame = coro.cr_frame
+
+    # If Cython's coroutine has a fake code object without proper
+    # co_filename -- expose that.
+    filename = coro_code.co_filename or '<empty co_filename>'
+
+    lineno = 0
+    if (is_corowrapper and
+            coro.func is not None and
+            not inspect.isgeneratorfunction(coro.func)):
+        source = format_helpers._get_function_source(coro.func)
+        if source is not None:
+            filename, lineno = source
+        if coro_frame is None:
+            coro_repr = f'{coro_name} done, defined at {filename}:{lineno}'
+        else:
+            coro_repr = f'{coro_name} running, defined at {filename}:{lineno}'
+
+    elif coro_frame is not None:
+        lineno = coro_frame.f_lineno
+        coro_repr = f'{coro_name} running at {filename}:{lineno}'
+
+    else:
+        lineno = coro_code.co_firstlineno
+        coro_repr = f'{coro_name} done, defined at {filename}:{lineno}'
+
+    return coro_repr

parrot/lib/python3.10/asyncio/exceptions.py

@@ -0,0 +1,58 @@
+"""asyncio exceptions."""
+
+
+__all__ = ('CancelledError', 'InvalidStateError', 'TimeoutError',
+           'IncompleteReadError', 'LimitOverrunError',
+           'SendfileNotAvailableError')
+
+
+class CancelledError(BaseException):
+    """The Future or Task was cancelled."""
+
+
+class TimeoutError(Exception):
+    """The operation exceeded the given deadline."""
+
+
+class InvalidStateError(Exception):
+    """The operation is not allowed in this state."""
+
+
+class SendfileNotAvailableError(RuntimeError):
+    """Sendfile syscall is not available.
+
+    Raised if OS does not support sendfile syscall for given socket or
+    file type.
+    """
+
+
+class IncompleteReadError(EOFError):
+    """
+    Incomplete read error. Attributes:
+
+    - partial: read bytes string before the end of stream was reached
+    - expected: total number of expected bytes (or None if unknown)
+    """
+    def __init__(self, partial, expected):
+        r_expected = 'undefined' if expected is None else repr(expected)
+        super().__init__(f'{len(partial)} bytes read on a total of '
+                         f'{r_expected} expected bytes')
+        self.partial = partial
+        self.expected = expected
+
+    def __reduce__(self):
+        return type(self), (self.partial, self.expected)
+
+
+class LimitOverrunError(Exception):
+    """Reached the buffer limit while looking for a separator.
+
+    Attributes:
+    - consumed: total number of to be consumed bytes.
+    """
+    def __init__(self, message, consumed):
+        super().__init__(message)
+        self.consumed = consumed
+
+    def __reduce__(self):
+        return type(self), (self.args[0], self.consumed)

parrot/lib/python3.10/asyncio/format_helpers.py

@@ -0,0 +1,76 @@
+import functools
+import inspect
+import reprlib
+import sys
+import traceback
+
+from . import constants
+
+
+def _get_function_source(func):
+    func = inspect.unwrap(func)
+    if inspect.isfunction(func):
+        code = func.__code__
+        return (code.co_filename, code.co_firstlineno)
+    if isinstance(func, functools.partial):
+        return _get_function_source(func.func)
+    if isinstance(func, functools.partialmethod):
+        return _get_function_source(func.func)
+    return None
+
+
+def _format_callback_source(func, args):
+    func_repr = _format_callback(func, args, None)
+    source = _get_function_source(func)
+    if source:
+        func_repr += f' at {source[0]}:{source[1]}'
+    return func_repr
+
+
+def _format_args_and_kwargs(args, kwargs):
+    """Format function arguments and keyword arguments.
+
+    Special case for a single parameter: ('hello',) is formatted as ('hello').
+    """
+    # use reprlib to limit the length of the output
+    items = []
+    if args:
+        items.extend(reprlib.repr(arg) for arg in args)
+    if kwargs:
+        items.extend(f'{k}={reprlib.repr(v)}' for k, v in kwargs.items())
+    return '({})'.format(', '.join(items))
+
+
+def _format_callback(func, args, kwargs, suffix=''):
+    if isinstance(func, functools.partial):
+        suffix = _format_args_and_kwargs(args, kwargs) + suffix
+        return _format_callback(func.func, func.args, func.keywords, suffix)
+
+    if hasattr(func, '__qualname__') and func.__qualname__:
+        func_repr = func.__qualname__
+    elif hasattr(func, '__name__') and func.__name__:
+        func_repr = func.__name__
+    else:
+        func_repr = repr(func)
+
+    func_repr += _format_args_and_kwargs(args, kwargs)
+    if suffix:
+        func_repr += suffix
+    return func_repr
+
+
+def extract_stack(f=None, limit=None):
+    """Replacement for traceback.extract_stack() that only does the
+    necessary work for asyncio debug mode.
+    """
+    if f is None:
+        f = sys._getframe().f_back
+    if limit is None:
+        # Limit the amount of work to a reasonable amount, as extract_stack()
+        # can be called for each coroutine and future in debug mode.
+        limit = constants.DEBUG_STACK_DEPTH
+    stack = traceback.StackSummary.extract(traceback.walk_stack(f),
+                                           limit=limit,
+                                           lookup_lines=False)
+    stack.reverse()
+    return stack

parrot/lib/python3.10/asyncio/futures.py

@@ -0,0 +1,426 @@
+"""A Future class similar to the one in PEP 3148."""
+
+__all__ = (
+    'Future', 'wrap_future', 'isfuture',
+)
+
+import concurrent.futures
+import contextvars
+import logging
+import sys
+from types import GenericAlias
+
+from . import base_futures
+from . import events
+from . import exceptions
+from . import format_helpers
+
+
+isfuture = base_futures.isfuture
+
+
+_PENDING = base_futures._PENDING
+_CANCELLED = base_futures._CANCELLED
+_FINISHED = base_futures._FINISHED
+
+
+STACK_DEBUG = logging.DEBUG - 1  # heavy-duty debugging
+
+
+class Future:
+    """This class is *almost* compatible with concurrent.futures.Future.
+
+    Differences:
+
+    - This class is not thread-safe.
+
+    - result() and exception() do not take a timeout argument and
+      raise an exception when the future isn't done yet.
+
+    - Callbacks registered with add_done_callback() are always called
+      via the event loop's call_soon().
+
+    - This class is not compatible with the wait() and as_completed()
+      methods in the concurrent.futures package.
+
+    (In Python 3.4 or later we may be able to unify the implementations.)
+    """
+
+    # Class variables serving as defaults for instance variables.
+    _state = _PENDING
+    _result = None
+    _exception = None
+    _loop = None
+    _source_traceback = None
+    _cancel_message = None
+    # A saved CancelledError for later chaining as an exception context.
+    _cancelled_exc = None
+
+    # This field is used for a dual purpose:
+    # - Its presence is a marker to declare that a class implements
+    #   the Future protocol (i.e. is intended to be duck-type compatible).
+    #   The value must also be not-None, to enable a subclass to declare
+    #   that it is not compatible by setting this to None.
+    # - It is set by __iter__() below so that Task._step() can tell
+    #   the difference between
+    #   `await Future()` or`yield from Future()` (correct) vs.
+    #   `yield Future()` (incorrect).
+    _asyncio_future_blocking = False
+
+    __log_traceback = False
+
+    def __init__(self, *, loop=None):
+        """Initialize the future.
+
+        The optional event_loop argument allows explicitly setting the event
+        loop object used by the future. If it's not provided, the future uses
+        the default event loop.
+        """
+        if loop is None:
+            self._loop = events._get_event_loop()
+        else:
+            self._loop = loop
+        self._callbacks = []
+        if self._loop.get_debug():
+            self._source_traceback = format_helpers.extract_stack(
+                sys._getframe(1))
+
+    _repr_info = base_futures._future_repr_info
+
+    def __repr__(self):
+        return '<{} {}>'.format(self.__class__.__name__,
+                                ' '.join(self._repr_info()))
+
+    def __del__(self):
+        if not self.__log_traceback:
+            # set_exception() was not called, or result() or exception()
+            # has consumed the exception
+            return
+        exc = self._exception
+        context = {
+            'message':
+                f'{self.__class__.__name__} exception was never retrieved',
+            'exception': exc,
+            'future': self,
+        }
+        if self._source_traceback:
+            context['source_traceback'] = self._source_traceback
+        self._loop.call_exception_handler(context)
+
+    __class_getitem__ = classmethod(GenericAlias)
+
+    @property
+    def _log_traceback(self):
+        return self.__log_traceback
+
+    @_log_traceback.setter
+    def _log_traceback(self, val):
+        if val:
+            raise ValueError('_log_traceback can only be set to False')
+        self.__log_traceback = False
+
+    def get_loop(self):
+        """Return the event loop the Future is bound to."""
+        loop = self._loop
+        if loop is None:
+            raise RuntimeError("Future object is not initialized.")
+        return loop
+
+    def _make_cancelled_error(self):
+        """Create the CancelledError to raise if the Future is cancelled.
+
+        This should only be called once when handling a cancellation since
+        it erases the saved context exception value.
+        """
+        if self._cancel_message is None:
+            exc = exceptions.CancelledError()
+        else:
+            exc = exceptions.CancelledError(self._cancel_message)
+        exc.__context__ = self._cancelled_exc
+        # Remove the reference since we don't need this anymore.
+        self._cancelled_exc = None
+        return exc
+
+    def cancel(self, msg=None):
+        """Cancel the future and schedule callbacks.
+
+        If the future is already done or cancelled, return False.  Otherwise,
+        change the future's state to cancelled, schedule the callbacks and
+        return True.
+        """
+        self.__log_traceback = False
+        if self._state != _PENDING:
+            return False
+        self._state = _CANCELLED
+        self._cancel_message = msg
+        self.__schedule_callbacks()
+        return True
+
+    def __schedule_callbacks(self):
+        """Internal: Ask the event loop to call all callbacks.
+
+        The callbacks are scheduled to be called as soon as possible. Also
+        clears the callback list.
+        """
+        callbacks = self._callbacks[:]
+        if not callbacks:
+            return
+
+        self._callbacks[:] = []
+        for callback, ctx in callbacks:
+            self._loop.call_soon(callback, self, context=ctx)
+
+    def cancelled(self):
+        """Return True if the future was cancelled."""
+        return self._state == _CANCELLED
+
+    # Don't implement running(); see http://bugs.python.org/issue18699
+
+    def done(self):
+        """Return True if the future is done.
+
+        Done means either that a result / exception are available, or that the
+        future was cancelled.
+        """
+        return self._state != _PENDING
+
+    def result(self):
+        """Return the result this future represents.
+
+        If the future has been cancelled, raises CancelledError.  If the
+        future's result isn't yet available, raises InvalidStateError.  If
+        the future is done and has an exception set, this exception is raised.
+        """
+        if self._state == _CANCELLED:
+            exc = self._make_cancelled_error()
+            raise exc
+        if self._state != _FINISHED:
+            raise exceptions.InvalidStateError('Result is not ready.')
+        self.__log_traceback = False
+        if self._exception is not None:
+            raise self._exception.with_traceback(self._exception_tb)
+        return self._result
+
+    def exception(self):
+        """Return the exception that was set on this future.
+
+        The exception (or None if no exception was set) is returned only if
+        the future is done.  If the future has been cancelled, raises
+        CancelledError.  If the future isn't done yet, raises
+        InvalidStateError.
+        """
+        if self._state == _CANCELLED:
+            exc = self._make_cancelled_error()
+            raise exc
+        if self._state != _FINISHED:
+            raise exceptions.InvalidStateError('Exception is not set.')
+        self.__log_traceback = False
+        return self._exception
+
+    def add_done_callback(self, fn, *, context=None):
+        """Add a callback to be run when the future becomes done.
+
+        The callback is called with a single argument - the future object. If
+        the future is already done when this is called, the callback is
+        scheduled with call_soon.
+        """
+        if self._state != _PENDING:
+            self._loop.call_soon(fn, self, context=context)
+        else:
+            if context is None:
+                context = contextvars.copy_context()
+            self._callbacks.append((fn, context))
+
+    # New method not in PEP 3148.
+
+    def remove_done_callback(self, fn):
+        """Remove all instances of a callback from the "call when done" list.
+
+        Returns the number of callbacks removed.
+        """
+        filtered_callbacks = [(f, ctx)
+                              for (f, ctx) in self._callbacks
+                              if f != fn]
+        removed_count = len(self._callbacks) - len(filtered_callbacks)
+        if removed_count:
+            self._callbacks[:] = filtered_callbacks
+        return removed_count
+
+    # So-called internal methods (note: no set_running_or_notify_cancel()).
+
+    def set_result(self, result):
+        """Mark the future done and set its result.
+
+        If the future is already done when this method is called, raises
+        InvalidStateError.
+        """
+        if self._state != _PENDING:
+            raise exceptions.InvalidStateError(f'{self._state}: {self!r}')
+        self._result = result
+        self._state = _FINISHED
+        self.__schedule_callbacks()
+
+    def set_exception(self, exception):
+        """Mark the future done and set an exception.
+
+        If the future is already done when this method is called, raises
+        InvalidStateError.
+        """
+        if self._state != _PENDING:
+            raise exceptions.InvalidStateError(f'{self._state}: {self!r}')
+        if isinstance(exception, type):
+            exception = exception()
+        if type(exception) is StopIteration:
+            raise TypeError("StopIteration interacts badly with generators "
+                            "and cannot be raised into a Future")
+        self._exception = exception
+        self._exception_tb = exception.__traceback__
+        self._state = _FINISHED
+        self.__schedule_callbacks()
+        self.__log_traceback = True
+
+    def __await__(self):
+        if not self.done():
+            self._asyncio_future_blocking = True
+            yield self  # This tells Task to wait for completion.
+        if not self.done():
+            raise RuntimeError("await wasn't used with future")
+        return self.result()  # May raise too.
+
+    __iter__ = __await__  # make compatible with 'yield from'.
+
+
+# Needed for testing purposes.
+_PyFuture = Future
+
+
+def _get_loop(fut):
+    # Tries to call Future.get_loop() if it's available.
+    # Otherwise fallbacks to using the old '_loop' property.
+    try:
+        get_loop = fut.get_loop
+    except AttributeError:
+        pass
+    else:
+        return get_loop()
+    return fut._loop
+
+
+def _set_result_unless_cancelled(fut, result):
+    """Helper setting the result only if the future was not cancelled."""
+    if fut.cancelled():
+        return
+    fut.set_result(result)
+
+
+def _convert_future_exc(exc):
+    exc_class = type(exc)
+    if exc_class is concurrent.futures.CancelledError:
+        return exceptions.CancelledError(*exc.args)
+    elif exc_class is concurrent.futures.TimeoutError:
+        return exceptions.TimeoutError(*exc.args)
+    elif exc_class is concurrent.futures.InvalidStateError:
+        return exceptions.InvalidStateError(*exc.args)
+    else:
+        return exc
+
+
+def _set_concurrent_future_state(concurrent, source):
+    """Copy state from a future to a concurrent.futures.Future."""
+    assert source.done()
+    if source.cancelled():
+        concurrent.cancel()
+    if not concurrent.set_running_or_notify_cancel():
+        return
+    exception = source.exception()
+    if exception is not None:
+        concurrent.set_exception(_convert_future_exc(exception))
+    else:
+        result = source.result()
+        concurrent.set_result(result)
+
+
+def _copy_future_state(source, dest):
+    """Internal helper to copy state from another Future.
+
+    The other Future may be a concurrent.futures.Future.
+    """
+    assert source.done()
+    if dest.cancelled():
+        return
+    assert not dest.done()
+    if source.cancelled():
+        dest.cancel()
+    else:
+        exception = source.exception()
+        if exception is not None:
+            dest.set_exception(_convert_future_exc(exception))
+        else:
+            result = source.result()
+            dest.set_result(result)
+
+
+def _chain_future(source, destination):
+    """Chain two futures so that when one completes, so does the other.
+
+    The result (or exception) of source will be copied to destination.
+    If destination is cancelled, source gets cancelled too.
+    Compatible with both asyncio.Future and concurrent.futures.Future.
+    """
+    if not isfuture(source) and not isinstance(source,
+                                               concurrent.futures.Future):
+        raise TypeError('A future is required for source argument')
+    if not isfuture(destination) and not isinstance(destination,
+                                                    concurrent.futures.Future):
+        raise TypeError('A future is required for destination argument')
+    source_loop = _get_loop(source) if isfuture(source) else None
+    dest_loop = _get_loop(destination) if isfuture(destination) else None
+
+    def _set_state(future, other):
+        if isfuture(future):
+            _copy_future_state(other, future)
+        else:
+            _set_concurrent_future_state(future, other)
+
+    def _call_check_cancel(destination):
+        if destination.cancelled():
+            if source_loop is None or source_loop is dest_loop:
+                source.cancel()
+            else:
+                source_loop.call_soon_threadsafe(source.cancel)
+
+    def _call_set_state(source):
+        if (destination.cancelled() and
+                dest_loop is not None and dest_loop.is_closed()):
+            return
+        if dest_loop is None or dest_loop is source_loop:
+            _set_state(destination, source)
+        else:
+            if dest_loop.is_closed():
+                return
+            dest_loop.call_soon_threadsafe(_set_state, destination, source)
+
+    destination.add_done_callback(_call_check_cancel)
+    source.add_done_callback(_call_set_state)
+
+
+def wrap_future(future, *, loop=None):
+    """Wrap concurrent.futures.Future object."""
+    if isfuture(future):
+        return future
+    assert isinstance(future, concurrent.futures.Future), \
+        f'concurrent.futures.Future is expected, got {future!r}'
+    if loop is None:
+        loop = events._get_event_loop()
+    new_future = loop.create_future()
+    _chain_future(future, new_future)
+    return new_future
+
+
+try:
+    import _asyncio
+except ImportError:
+    pass
+else:
+    # _CFuture is needed for tests.
+    Future = _CFuture = _asyncio.Future

parrot/lib/python3.10/asyncio/locks.py

@@ -0,0 +1,438 @@
+"""Synchronization primitives."""
+
+__all__ = ('Lock', 'Event', 'Condition', 'Semaphore', 'BoundedSemaphore')
+
+import collections
+
+from . import exceptions
+from . import mixins
+from . import tasks
+
+
+class _ContextManagerMixin:
+    async def __aenter__(self):
+        await self.acquire()
+        # We have no use for the "as ..."  clause in the with
+        # statement for locks.
+        return None
+
+    async def __aexit__(self, exc_type, exc, tb):
+        self.release()
+
+
+class Lock(_ContextManagerMixin, mixins._LoopBoundMixin):
+    """Primitive lock objects.
+
+    A primitive lock is a synchronization primitive that is not owned
+    by a particular coroutine when locked.  A primitive lock is in one
+    of two states, 'locked' or 'unlocked'.
+
+    It is created in the unlocked state.  It has two basic methods,
+    acquire() and release().  When the state is unlocked, acquire()
+    changes the state to locked and returns immediately.  When the
+    state is locked, acquire() blocks until a call to release() in
+    another coroutine changes it to unlocked, then the acquire() call
+    resets it to locked and returns.  The release() method should only
+    be called in the locked state; it changes the state to unlocked
+    and returns immediately.  If an attempt is made to release an
+    unlocked lock, a RuntimeError will be raised.
+
+    When more than one coroutine is blocked in acquire() waiting for
+    the state to turn to unlocked, only one coroutine proceeds when a
+    release() call resets the state to unlocked; first coroutine which
+    is blocked in acquire() is being processed.
+
+    acquire() is a coroutine and should be called with 'await'.
+
+    Locks also support the asynchronous context management protocol.
+    'async with lock' statement should be used.
+
+    Usage:
+
+        lock = Lock()
+        ...
+        await lock.acquire()
+        try:
+            ...
+        finally:
+            lock.release()
+
+    Context manager usage:
+
+        lock = Lock()
+        ...
+        async with lock:
+             ...
+
+    Lock objects can be tested for locking state:
+
+        if not lock.locked():
+           await lock.acquire()
+        else:
+           # lock is acquired
+           ...
+
+    """
+
+    def __init__(self, *, loop=mixins._marker):
+        super().__init__(loop=loop)
+        self._waiters = None
+        self._locked = False
+
+    def __repr__(self):
+        res = super().__repr__()
+        extra = 'locked' if self._locked else 'unlocked'
+        if self._waiters:
+            extra = f'{extra}, waiters:{len(self._waiters)}'
+        return f'<{res[1:-1]} [{extra}]>'
+
+    def locked(self):
+        """Return True if lock is acquired."""
+        return self._locked
+
+    async def acquire(self):
+        """Acquire a lock.
+
+        This method blocks until the lock is unlocked, then sets it to
+        locked and returns True.
+        """
+        if (not self._locked and (self._waiters is None or
+                all(w.cancelled() for w in self._waiters))):
+            self._locked = True
+            return True
+
+        if self._waiters is None:
+            self._waiters = collections.deque()
+        fut = self._get_loop().create_future()
+        self._waiters.append(fut)
+
+        # Finally block should be called before the CancelledError
+        # handling as we don't want CancelledError to call
+        # _wake_up_first() and attempt to wake up itself.
+        try:
+            try:
+                await fut
+            finally:
+                self._waiters.remove(fut)
+        except exceptions.CancelledError:
+            if not self._locked:
+                self._wake_up_first()
+            raise
+
+        self._locked = True
+        return True
+
+    def release(self):
+        """Release a lock.
+
+        When the lock is locked, reset it to unlocked, and return.
+        If any other coroutines are blocked waiting for the lock to become
+        unlocked, allow exactly one of them to proceed.
+
+        When invoked on an unlocked lock, a RuntimeError is raised.
+
+        There is no return value.
+        """
+        if self._locked:
+            self._locked = False
+            self._wake_up_first()
+        else:
+            raise RuntimeError('Lock is not acquired.')
+
+    def _wake_up_first(self):
+        """Wake up the first waiter if it isn't done."""
+        if not self._waiters:
+            return
+        try:
+            fut = next(iter(self._waiters))
+        except StopIteration:
+            return
+
+        # .done() necessarily means that a waiter will wake up later on and
+        # either take the lock, or, if it was cancelled and lock wasn't
+        # taken already, will hit this again and wake up a new waiter.
+        if not fut.done():
+            fut.set_result(True)
+
+
+class Event(mixins._LoopBoundMixin):
+    """Asynchronous equivalent to threading.Event.
+
+    Class implementing event objects. An event manages a flag that can be set
+    to true with the set() method and reset to false with the clear() method.
+    The wait() method blocks until the flag is true. The flag is initially
+    false.
+    """
+
+    def __init__(self, *, loop=mixins._marker):
+        super().__init__(loop=loop)
+        self._waiters = collections.deque()
+        self._value = False
+
+    def __repr__(self):
+        res = super().__repr__()
+        extra = 'set' if self._value else 'unset'
+        if self._waiters:
+            extra = f'{extra}, waiters:{len(self._waiters)}'
+        return f'<{res[1:-1]} [{extra}]>'
+
+    def is_set(self):
+        """Return True if and only if the internal flag is true."""
+        return self._value
+
+    def set(self):
+        """Set the internal flag to true. All coroutines waiting for it to
+        become true are awakened. Coroutine that call wait() once the flag is
+        true will not block at all.
+        """
+        if not self._value:
+            self._value = True
+
+            for fut in self._waiters:
+                if not fut.done():
+                    fut.set_result(True)
+
+    def clear(self):
+        """Reset the internal flag to false. Subsequently, coroutines calling
+        wait() will block until set() is called to set the internal flag
+        to true again."""
+        self._value = False
+
+    async def wait(self):
+        """Block until the internal flag is true.
+
+        If the internal flag is true on entry, return True
+        immediately.  Otherwise, block until another coroutine calls
+        set() to set the flag to true, then return True.
+        """
+        if self._value:
+            return True
+
+        fut = self._get_loop().create_future()
+        self._waiters.append(fut)
+        try:
+            await fut
+            return True
+        finally:
+            self._waiters.remove(fut)
+
+
+class Condition(_ContextManagerMixin, mixins._LoopBoundMixin):
+    """Asynchronous equivalent to threading.Condition.
+
+    This class implements condition variable objects. A condition variable
+    allows one or more coroutines to wait until they are notified by another
+    coroutine.
+
+    A new Lock object is created and used as the underlying lock.
+    """
+
+    def __init__(self, lock=None, *, loop=mixins._marker):
+        super().__init__(loop=loop)
+        if lock is None:
+            lock = Lock()
+
+        self._lock = lock
+        # Export the lock's locked(), acquire() and release() methods.
+        self.locked = lock.locked
+        self.acquire = lock.acquire
+        self.release = lock.release
+
+        self._waiters = collections.deque()
+
+    def __repr__(self):
+        res = super().__repr__()
+        extra = 'locked' if self.locked() else 'unlocked'
+        if self._waiters:
+            extra = f'{extra}, waiters:{len(self._waiters)}'
+        return f'<{res[1:-1]} [{extra}]>'
+
+    async def wait(self):
+        """Wait until notified.
+
+        If the calling coroutine has not acquired the lock when this
+        method is called, a RuntimeError is raised.
+
+        This method releases the underlying lock, and then blocks
+        until it is awakened by a notify() or notify_all() call for
+        the same condition variable in another coroutine.  Once
+        awakened, it re-acquires the lock and returns True.
+        """
+        if not self.locked():
+            raise RuntimeError('cannot wait on un-acquired lock')
+
+        self.release()
+        try:
+            fut = self._get_loop().create_future()
+            self._waiters.append(fut)
+            try:
+                await fut
+                return True
+            finally:
+                self._waiters.remove(fut)
+
+        finally:
+            # Must reacquire lock even if wait is cancelled
+            cancelled = False
+            while True:
+                try:
+                    await self.acquire()
+                    break
+                except exceptions.CancelledError:
+                    cancelled = True
+
+            if cancelled:
+                raise exceptions.CancelledError
+
+    async def wait_for(self, predicate):
+        """Wait until a predicate becomes true.
+
+        The predicate should be a callable which result will be
+        interpreted as a boolean value.  The final predicate value is
+        the return value.
+        """
+        result = predicate()
+        while not result:
+            await self.wait()
+            result = predicate()
+        return result
+
+    def notify(self, n=1):
+        """By default, wake up one coroutine waiting on this condition, if any.
+        If the calling coroutine has not acquired the lock when this method
+        is called, a RuntimeError is raised.
+
+        This method wakes up at most n of the coroutines waiting for the
+        condition variable; it is a no-op if no coroutines are waiting.
+
+        Note: an awakened coroutine does not actually return from its
+        wait() call until it can reacquire the lock. Since notify() does
+        not release the lock, its caller should.
+        """
+        if not self.locked():
+            raise RuntimeError('cannot notify on un-acquired lock')
+
+        idx = 0
+        for fut in self._waiters:
+            if idx >= n:
+                break
+
+            if not fut.done():
+                idx += 1
+                fut.set_result(False)
+
+    def notify_all(self):
+        """Wake up all threads waiting on this condition. This method acts
+        like notify(), but wakes up all waiting threads instead of one. If the
+        calling thread has not acquired the lock when this method is called,
+        a RuntimeError is raised.
+        """
+        self.notify(len(self._waiters))
+
+
+class Semaphore(_ContextManagerMixin, mixins._LoopBoundMixin):
+    """A Semaphore implementation.
+
+    A semaphore manages an internal counter which is decremented by each
+    acquire() call and incremented by each release() call. The counter
+    can never go below zero; when acquire() finds that it is zero, it blocks,
+    waiting until some other thread calls release().
+
+    Semaphores also support the context management protocol.
+
+    The optional argument gives the initial value for the internal
+    counter; it defaults to 1. If the value given is less than 0,
+    ValueError is raised.
+    """
+
+    def __init__(self, value=1, *, loop=mixins._marker):
+        super().__init__(loop=loop)
+        if value < 0:
+            raise ValueError("Semaphore initial value must be >= 0")
+        self._waiters = None
+        self._value = value
+
+    def __repr__(self):
+        res = super().__repr__()
+        extra = 'locked' if self.locked() else f'unlocked, value:{self._value}'
+        if self._waiters:
+            extra = f'{extra}, waiters:{len(self._waiters)}'
+        return f'<{res[1:-1]} [{extra}]>'
+
+    def locked(self):
+        """Returns True if semaphore cannot be acquired immediately."""
+        return self._value == 0 or (
+            any(not w.cancelled() for w in (self._waiters or ())))
+
+    async def acquire(self):
+        """Acquire a semaphore.
+
+        If the internal counter is larger than zero on entry,
+        decrement it by one and return True immediately.  If it is
+        zero on entry, block, waiting until some other coroutine has
+        called release() to make it larger than 0, and then return
+        True.
+        """
+        if not self.locked():
+            self._value -= 1
+            return True
+
+        if self._waiters is None:
+            self._waiters = collections.deque()
+        fut = self._get_loop().create_future()
+        self._waiters.append(fut)
+
+        # Finally block should be called before the CancelledError
+        # handling as we don't want CancelledError to call
+        # _wake_up_first() and attempt to wake up itself.
+        try:
+            try:
+                await fut
+            finally:
+                self._waiters.remove(fut)
+        except exceptions.CancelledError:
+            if not fut.cancelled():
+                self._value += 1
+                self._wake_up_next()
+            raise
+
+        if self._value > 0:
+            self._wake_up_next()
+        return True
+
+    def release(self):
+        """Release a semaphore, incrementing the internal counter by one.
+
+        When it was zero on entry and another coroutine is waiting for it to
+        become larger than zero again, wake up that coroutine.
+        """
+        self._value += 1
+        self._wake_up_next()
+
+    def _wake_up_next(self):
+        """Wake up the first waiter that isn't done."""
+        if not self._waiters:
+            return
+
+        for fut in self._waiters:
+            if not fut.done():
+                self._value -= 1
+                fut.set_result(True)
+                return
+
+
+class BoundedSemaphore(Semaphore):
+    """A bounded semaphore implementation.
+
+    This raises ValueError in release() if it would increase the value
+    above the initial value.
+    """
+
+    def __init__(self, value=1, *, loop=mixins._marker):
+        self._bound_value = value
+        super().__init__(value, loop=loop)
+
+    def release(self):
+        if self._value >= self._bound_value:
+            raise ValueError('BoundedSemaphore released too many times')
+        super().release()

parrot/lib/python3.10/asyncio/mixins.py

@@ -0,0 +1,31 @@
+"""Event loop mixins."""
+
+import threading
+from . import events
+
+_global_lock = threading.Lock()
+
+# Used as a sentinel for loop parameter
+_marker = object()
+
+
+class _LoopBoundMixin:
+    _loop = None
+
+    def __init__(self, *, loop=_marker):
+        if loop is not _marker:
+            raise TypeError(
+                f'As of 3.10, the *loop* parameter was removed from '
+                f'{type(self).__name__}() since it is no longer necessary'
+            )
+
+    def _get_loop(self):
+        loop = events._get_running_loop()
+
+        if self._loop is None:
+            with _global_lock:
+                if self._loop is None:
+                    self._loop = loop
+        if loop is not self._loop:
+            raise RuntimeError(f'{self!r} is bound to a different event loop')
+        return loop

parrot/lib/python3.10/asyncio/protocols.py

@@ -0,0 +1,216 @@
+"""Abstract Protocol base classes."""
+
+__all__ = (
+    'BaseProtocol', 'Protocol', 'DatagramProtocol',
+    'SubprocessProtocol', 'BufferedProtocol',
+)
+
+
+class BaseProtocol:
+    """Common base class for protocol interfaces.
+
+    Usually user implements protocols that derived from BaseProtocol
+    like Protocol or ProcessProtocol.
+
+    The only case when BaseProtocol should be implemented directly is
+    write-only transport like write pipe
+    """
+
+    __slots__ = ()
+
+    def connection_made(self, transport):
+        """Called when a connection is made.
+
+        The argument is the transport representing the pipe connection.
+        To receive data, wait for data_received() calls.
+        When the connection is closed, connection_lost() is called.
+        """
+
+    def connection_lost(self, exc):
+        """Called when the connection is lost or closed.
+
+        The argument is an exception object or None (the latter
+        meaning a regular EOF is received or the connection was
+        aborted or closed).
+        """
+
+    def pause_writing(self):
+        """Called when the transport's buffer goes over the high-water mark.
+
+        Pause and resume calls are paired -- pause_writing() is called
+        once when the buffer goes strictly over the high-water mark
+        (even if subsequent writes increases the buffer size even
+        more), and eventually resume_writing() is called once when the
+        buffer size reaches the low-water mark.
+
+        Note that if the buffer size equals the high-water mark,
+        pause_writing() is not called -- it must go strictly over.
+        Conversely, resume_writing() is called when the buffer size is
+        equal or lower than the low-water mark.  These end conditions
+        are important to ensure that things go as expected when either
+        mark is zero.
+
+        NOTE: This is the only Protocol callback that is not called
+        through EventLoop.call_soon() -- if it were, it would have no
+        effect when it's most needed (when the app keeps writing
+        without yielding until pause_writing() is called).
+        """
+
+    def resume_writing(self):
+        """Called when the transport's buffer drains below the low-water mark.
+
+        See pause_writing() for details.
+        """
+
+
+class Protocol(BaseProtocol):
+    """Interface for stream protocol.
+
+    The user should implement this interface.  They can inherit from
+    this class but don't need to.  The implementations here do
+    nothing (they don't raise exceptions).
+
+    When the user wants to requests a transport, they pass a protocol
+    factory to a utility function (e.g., EventLoop.create_connection()).
+
+    When the connection is made successfully, connection_made() is
+    called with a suitable transport object.  Then data_received()
+    will be called 0 or more times with data (bytes) received from the
+    transport; finally, connection_lost() will be called exactly once
+    with either an exception object or None as an argument.
+
+    State machine of calls:
+
+      start -> CM [-> DR*] [-> ER?] -> CL -> end
+
+    * CM: connection_made()
+    * DR: data_received()
+    * ER: eof_received()
+    * CL: connection_lost()
+    """
+
+    __slots__ = ()
+
+    def data_received(self, data):
+        """Called when some data is received.
+
+        The argument is a bytes object.
+        """
+
+    def eof_received(self):
+        """Called when the other end calls write_eof() or equivalent.
+
+        If this returns a false value (including None), the transport
+        will close itself.  If it returns a true value, closing the
+        transport is up to the protocol.
+        """
+
+
+class BufferedProtocol(BaseProtocol):
+    """Interface for stream protocol with manual buffer control.
+
+    Event methods, such as `create_server` and `create_connection`,
+    accept factories that return protocols that implement this interface.
+
+    The idea of BufferedProtocol is that it allows to manually allocate
+    and control the receive buffer.  Event loops can then use the buffer
+    provided by the protocol to avoid unnecessary data copies.  This
+    can result in noticeable performance improvement for protocols that
+    receive big amounts of data.  Sophisticated protocols can allocate
+    the buffer only once at creation time.
+
+    State machine of calls:
+
+      start -> CM [-> GB [-> BU?]]* [-> ER?] -> CL -> end
+
+    * CM: connection_made()
+    * GB: get_buffer()
+    * BU: buffer_updated()
+    * ER: eof_received()
+    * CL: connection_lost()
+    """
+
+    __slots__ = ()
+
+    def get_buffer(self, sizehint):
+        """Called to allocate a new receive buffer.
+
+        *sizehint* is a recommended minimal size for the returned
+        buffer.  When set to -1, the buffer size can be arbitrary.
+
+        Must return an object that implements the
+        :ref:`buffer protocol <bufferobjects>`.
+        It is an error to return a zero-sized buffer.
+        """
+
+    def buffer_updated(self, nbytes):
+        """Called when the buffer was updated with the received data.
+
+        *nbytes* is the total number of bytes that were written to
+        the buffer.
+        """
+
+    def eof_received(self):
+        """Called when the other end calls write_eof() or equivalent.
+
+        If this returns a false value (including None), the transport
+        will close itself.  If it returns a true value, closing the
+        transport is up to the protocol.
+        """
+
+
+class DatagramProtocol(BaseProtocol):
+    """Interface for datagram protocol."""
+
+    __slots__ = ()
+
+    def datagram_received(self, data, addr):
+        """Called when some datagram is received."""
+
+    def error_received(self, exc):
+        """Called when a send or receive operation raises an OSError.
+
+        (Other than BlockingIOError or InterruptedError.)
+        """
+
+
+class SubprocessProtocol(BaseProtocol):
+    """Interface for protocol for subprocess calls."""
+
+    __slots__ = ()
+
+    def pipe_data_received(self, fd, data):
+        """Called when the subprocess writes data into stdout/stderr pipe.
+
+        fd is int file descriptor.
+        data is bytes object.
+        """
+
+    def pipe_connection_lost(self, fd, exc):
+        """Called when a file descriptor associated with the child process is
+        closed.
+
+        fd is the int file descriptor that was closed.
+        """
+
+    def process_exited(self):
+        """Called when subprocess has exited."""
+
+
+def _feed_data_to_buffered_proto(proto, data):
+    data_len = len(data)
+    while data_len:
+        buf = proto.get_buffer(data_len)
+        buf_len = len(buf)
+        if not buf_len:
+            raise RuntimeError('get_buffer() returned an empty buffer')
+
+        if buf_len >= data_len:
+            buf[:data_len] = data
+            proto.buffer_updated(data_len)
+            return
+        else:
+            buf[:buf_len] = data[:buf_len]
+            proto.buffer_updated(buf_len)
+            data = data[buf_len:]
+            data_len = len(data)

parrot/lib/python3.10/asyncio/runners.py

@@ -0,0 +1,73 @@
+__all__ = 'run',
+
+from . import coroutines
+from . import events
+from . import tasks
+
+
+def run(main, *, debug=None):
+    """Execute the coroutine and return the result.
+
+    This function runs the passed coroutine, taking care of
+    managing the asyncio event loop and finalizing asynchronous
+    generators.
+
+    This function cannot be called when another asyncio event loop is
+    running in the same thread.
+
+    If debug is True, the event loop will be run in debug mode.
+
+    This function always creates a new event loop and closes it at the end.
+    It should be used as a main entry point for asyncio programs, and should
+    ideally only be called once.
+
+    Example:
+
+        async def main():
+            await asyncio.sleep(1)
+            print('hello')
+
+        asyncio.run(main())
+    """
+    if events._get_running_loop() is not None:
+        raise RuntimeError(
+            "asyncio.run() cannot be called from a running event loop")
+
+    if not coroutines.iscoroutine(main):
+        raise ValueError("a coroutine was expected, got {!r}".format(main))
+
+    loop = events.new_event_loop()
+    try:
+        events.set_event_loop(loop)
+        if debug is not None:
+            loop.set_debug(debug)
+        return loop.run_until_complete(main)
+    finally:
+        try:
+            _cancel_all_tasks(loop)
+            loop.run_until_complete(loop.shutdown_asyncgens())
+            loop.run_until_complete(loop.shutdown_default_executor())
+        finally:
+            events.set_event_loop(None)
+            loop.close()
+
+
+def _cancel_all_tasks(loop):
+    to_cancel = tasks.all_tasks(loop)
+    if not to_cancel:
+        return
+
+    for task in to_cancel:
+        task.cancel()
+
+    loop.run_until_complete(tasks.gather(*to_cancel, return_exceptions=True))
+
+    for task in to_cancel:
+        if task.cancelled():
+            continue
+        if task.exception() is not None:
+            loop.call_exception_handler({
+                'message': 'unhandled exception during asyncio.run() shutdown',
+                'exception': task.exception(),
+                'task': task,
+            })

parrot/lib/python3.10/asyncio/selector_events.py

@@ -0,0 +1,1105 @@
+"""Event loop using a selector and related classes.
+
+A selector is a "notify-when-ready" multiplexer.  For a subclass which
+also includes support for signal handling, see the unix_events sub-module.
+"""
+
+__all__ = 'BaseSelectorEventLoop',
+
+import collections
+import errno
+import functools
+import selectors
+import socket
+import warnings
+import weakref
+try:
+    import ssl
+except ImportError:  # pragma: no cover
+    ssl = None
+
+from . import base_events
+from . import constants
+from . import events
+from . import futures
+from . import protocols
+from . import sslproto
+from . import transports
+from . import trsock
+from .log import logger
+
+
+def _test_selector_event(selector, fd, event):
+    # Test if the selector is monitoring 'event' events
+    # for the file descriptor 'fd'.
+    try:
+        key = selector.get_key(fd)
+    except KeyError:
+        return False
+    else:
+        return bool(key.events & event)
+
+
+class BaseSelectorEventLoop(base_events.BaseEventLoop):
+    """Selector event loop.
+
+    See events.EventLoop for API specification.
+    """
+
+    def __init__(self, selector=None):
+        super().__init__()
+
+        if selector is None:
+            selector = selectors.DefaultSelector()
+        logger.debug('Using selector: %s', selector.__class__.__name__)
+        self._selector = selector
+        self._make_self_pipe()
+        self._transports = weakref.WeakValueDictionary()
+
+    def _make_socket_transport(self, sock, protocol, waiter=None, *,
+                               extra=None, server=None):
+        return _SelectorSocketTransport(self, sock, protocol, waiter,
+                                        extra, server)
+
+    def _make_ssl_transport(
+            self, rawsock, protocol, sslcontext, waiter=None,
+            *, server_side=False, server_hostname=None,
+            extra=None, server=None,
+            ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):
+        ssl_protocol = sslproto.SSLProtocol(
+                self, protocol, sslcontext, waiter,
+                server_side, server_hostname,
+                ssl_handshake_timeout=ssl_handshake_timeout)
+        _SelectorSocketTransport(self, rawsock, ssl_protocol,
+                                 extra=extra, server=server)
+        return ssl_protocol._app_transport
+
+    def _make_datagram_transport(self, sock, protocol,
+                                 address=None, waiter=None, extra=None):
+        return _SelectorDatagramTransport(self, sock, protocol,
+                                          address, waiter, extra)
+
+    def close(self):
+        if self.is_running():
+            raise RuntimeError("Cannot close a running event loop")
+        if self.is_closed():
+            return
+        self._close_self_pipe()
+        super().close()
+        if self._selector is not None:
+            self._selector.close()
+            self._selector = None
+
+    def _close_self_pipe(self):
+        self._remove_reader(self._ssock.fileno())
+        self._ssock.close()
+        self._ssock = None
+        self._csock.close()
+        self._csock = None
+        self._internal_fds -= 1
+
+    def _make_self_pipe(self):
+        # A self-socket, really. :-)
+        self._ssock, self._csock = socket.socketpair()
+        self._ssock.setblocking(False)
+        self._csock.setblocking(False)
+        self._internal_fds += 1
+        self._add_reader(self._ssock.fileno(), self._read_from_self)
+
+    def _process_self_data(self, data):
+        pass
+
+    def _read_from_self(self):
+        while True:
+            try:
+                data = self._ssock.recv(4096)
+                if not data:
+                    break
+                self._process_self_data(data)
+            except InterruptedError:
+                continue
+            except BlockingIOError:
+                break
+
+    def _write_to_self(self):
+        # This may be called from a different thread, possibly after
+        # _close_self_pipe() has been called or even while it is
+        # running.  Guard for self._csock being None or closed.  When
+        # a socket is closed, send() raises OSError (with errno set to
+        # EBADF, but let's not rely on the exact error code).
+        csock = self._csock
+        if csock is None:
+            return
+
+        try:
+            csock.send(b'\0')
+        except OSError:
+            if self._debug:
+                logger.debug("Fail to write a null byte into the "
+                             "self-pipe socket",
+                             exc_info=True)
+
+    def _start_serving(self, protocol_factory, sock,
+                       sslcontext=None, server=None, backlog=100,
+                       ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):
+        self._add_reader(sock.fileno(), self._accept_connection,
+                         protocol_factory, sock, sslcontext, server, backlog,
+                         ssl_handshake_timeout)
+
+    def _accept_connection(
+            self, protocol_factory, sock,
+            sslcontext=None, server=None, backlog=100,
+            ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):
+        # This method is only called once for each event loop tick where the
+        # listening socket has triggered an EVENT_READ. There may be multiple
+        # connections waiting for an .accept() so it is called in a loop.
+        # See https://bugs.python.org/issue27906 for more details.
+        for _ in range(backlog):
+            try:
+                conn, addr = sock.accept()
+                if self._debug:
+                    logger.debug("%r got a new connection from %r: %r",
+                                 server, addr, conn)
+                conn.setblocking(False)
+            except (BlockingIOError, InterruptedError, ConnectionAbortedError):
+                # Early exit because the socket accept buffer is empty.
+                return None
+            except OSError as exc:
+                # There's nowhere to send the error, so just log it.
+                if exc.errno in (errno.EMFILE, errno.ENFILE,
+                                 errno.ENOBUFS, errno.ENOMEM):
+                    # Some platforms (e.g. Linux keep reporting the FD as
+                    # ready, so we remove the read handler temporarily.
+                    # We'll try again in a while.
+                    self.call_exception_handler({
+                        'message': 'socket.accept() out of system resource',
+                        'exception': exc,
+                        'socket': trsock.TransportSocket(sock),
+                    })
+                    self._remove_reader(sock.fileno())
+                    self.call_later(constants.ACCEPT_RETRY_DELAY,
+                                    self._start_serving,
+                                    protocol_factory, sock, sslcontext, server,
+                                    backlog, ssl_handshake_timeout)
+                else:
+                    raise  # The event loop will catch, log and ignore it.
+            else:
+                extra = {'peername': addr}
+                accept = self._accept_connection2(
+                    protocol_factory, conn, extra, sslcontext, server,
+                    ssl_handshake_timeout)
+                self.create_task(accept)
+
+    async def _accept_connection2(
+            self, protocol_factory, conn, extra,
+            sslcontext=None, server=None,
+            ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):
+        protocol = None
+        transport = None
+        try:
+            protocol = protocol_factory()
+            waiter = self.create_future()
+            if sslcontext:
+                transport = self._make_ssl_transport(
+                    conn, protocol, sslcontext, waiter=waiter,
+                    server_side=True, extra=extra, server=server,
+                    ssl_handshake_timeout=ssl_handshake_timeout)
+            else:
+                transport = self._make_socket_transport(
+                    conn, protocol, waiter=waiter, extra=extra,
+                    server=server)
+
+            try:
+                await waiter
+            except BaseException:
+                transport.close()
+                raise
+                # It's now up to the protocol to handle the connection.
+
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            if self._debug:
+                context = {
+                    'message':
+                        'Error on transport creation for incoming connection',
+                    'exception': exc,
+                }
+                if protocol is not None:
+                    context['protocol'] = protocol
+                if transport is not None:
+                    context['transport'] = transport
+                self.call_exception_handler(context)
+
+    def _ensure_fd_no_transport(self, fd):
+        fileno = fd
+        if not isinstance(fileno, int):
+            try:
+                fileno = int(fileno.fileno())
+            except (AttributeError, TypeError, ValueError):
+                # This code matches selectors._fileobj_to_fd function.
+                raise ValueError(f"Invalid file object: {fd!r}") from None
+        try:
+            transport = self._transports[fileno]
+        except KeyError:
+            pass
+        else:
+            if not transport.is_closing():
+                raise RuntimeError(
+                    f'File descriptor {fd!r} is used by transport '
+                    f'{transport!r}')
+
+    def _add_reader(self, fd, callback, *args):
+        self._check_closed()
+        handle = events.Handle(callback, args, self, None)
+        try:
+            key = self._selector.get_key(fd)
+        except KeyError:
+            self._selector.register(fd, selectors.EVENT_READ,
+                                    (handle, None))
+        else:
+            mask, (reader, writer) = key.events, key.data
+            self._selector.modify(fd, mask | selectors.EVENT_READ,
+                                  (handle, writer))
+            if reader is not None:
+                reader.cancel()
+        return handle
+
+    def _remove_reader(self, fd):
+        if self.is_closed():
+            return False
+        try:
+            key = self._selector.get_key(fd)
+        except KeyError:
+            return False
+        else:
+            mask, (reader, writer) = key.events, key.data
+            mask &= ~selectors.EVENT_READ
+            if not mask:
+                self._selector.unregister(fd)
+            else:
+                self._selector.modify(fd, mask, (None, writer))
+
+            if reader is not None:
+                reader.cancel()
+                return True
+            else:
+                return False
+
+    def _add_writer(self, fd, callback, *args):
+        self._check_closed()
+        handle = events.Handle(callback, args, self, None)
+        try:
+            key = self._selector.get_key(fd)
+        except KeyError:
+            self._selector.register(fd, selectors.EVENT_WRITE,
+                                    (None, handle))
+        else:
+            mask, (reader, writer) = key.events, key.data
+            self._selector.modify(fd, mask | selectors.EVENT_WRITE,
+                                  (reader, handle))
+            if writer is not None:
+                writer.cancel()
+        return handle
+
+    def _remove_writer(self, fd):
+        """Remove a writer callback."""
+        if self.is_closed():
+            return False
+        try:
+            key = self._selector.get_key(fd)
+        except KeyError:
+            return False
+        else:
+            mask, (reader, writer) = key.events, key.data
+            # Remove both writer and connector.
+            mask &= ~selectors.EVENT_WRITE
+            if not mask:
+                self._selector.unregister(fd)
+            else:
+                self._selector.modify(fd, mask, (reader, None))
+
+            if writer is not None:
+                writer.cancel()
+                return True
+            else:
+                return False
+
+    def add_reader(self, fd, callback, *args):
+        """Add a reader callback."""
+        self._ensure_fd_no_transport(fd)
+        self._add_reader(fd, callback, *args)
+
+    def remove_reader(self, fd):
+        """Remove a reader callback."""
+        self._ensure_fd_no_transport(fd)
+        return self._remove_reader(fd)
+
+    def add_writer(self, fd, callback, *args):
+        """Add a writer callback.."""
+        self._ensure_fd_no_transport(fd)
+        self._add_writer(fd, callback, *args)
+
+    def remove_writer(self, fd):
+        """Remove a writer callback."""
+        self._ensure_fd_no_transport(fd)
+        return self._remove_writer(fd)
+
+    async def sock_recv(self, sock, n):
+        """Receive data from the socket.
+
+        The return value is a bytes object representing the data received.
+        The maximum amount of data to be received at once is specified by
+        nbytes.
+        """
+        base_events._check_ssl_socket(sock)
+        if self._debug and sock.gettimeout() != 0:
+            raise ValueError("the socket must be non-blocking")
+        try:
+            return sock.recv(n)
+        except (BlockingIOError, InterruptedError):
+            pass
+        fut = self.create_future()
+        fd = sock.fileno()
+        self._ensure_fd_no_transport(fd)
+        handle = self._add_reader(fd, self._sock_recv, fut, sock, n)
+        fut.add_done_callback(
+            functools.partial(self._sock_read_done, fd, handle=handle))
+        return await fut
+
+    def _sock_read_done(self, fd, fut, handle=None):
+        if handle is None or not handle.cancelled():
+            self.remove_reader(fd)
+
+    def _sock_recv(self, fut, sock, n):
+        # _sock_recv() can add itself as an I/O callback if the operation can't
+        # be done immediately. Don't use it directly, call sock_recv().
+        if fut.done():
+            return
+        try:
+            data = sock.recv(n)
+        except (BlockingIOError, InterruptedError):
+            return  # try again next time
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+        else:
+            fut.set_result(data)
+
+    async def sock_recv_into(self, sock, buf):
+        """Receive data from the socket.
+
+        The received data is written into *buf* (a writable buffer).
+        The return value is the number of bytes written.
+        """
+        base_events._check_ssl_socket(sock)
+        if self._debug and sock.gettimeout() != 0:
+            raise ValueError("the socket must be non-blocking")
+        try:
+            return sock.recv_into(buf)
+        except (BlockingIOError, InterruptedError):
+            pass
+        fut = self.create_future()
+        fd = sock.fileno()
+        self._ensure_fd_no_transport(fd)
+        handle = self._add_reader(fd, self._sock_recv_into, fut, sock, buf)
+        fut.add_done_callback(
+            functools.partial(self._sock_read_done, fd, handle=handle))
+        return await fut
+
+    def _sock_recv_into(self, fut, sock, buf):
+        # _sock_recv_into() can add itself as an I/O callback if the operation
+        # can't be done immediately. Don't use it directly, call
+        # sock_recv_into().
+        if fut.done():
+            return
+        try:
+            nbytes = sock.recv_into(buf)
+        except (BlockingIOError, InterruptedError):
+            return  # try again next time
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+        else:
+            fut.set_result(nbytes)
+
+    async def sock_sendall(self, sock, data):
+        """Send data to the socket.
+
+        The socket must be connected to a remote socket. This method continues
+        to send data from data until either all data has been sent or an
+        error occurs. None is returned on success. On error, an exception is
+        raised, and there is no way to determine how much data, if any, was
+        successfully processed by the receiving end of the connection.
+        """
+        base_events._check_ssl_socket(sock)
+        if self._debug and sock.gettimeout() != 0:
+            raise ValueError("the socket must be non-blocking")
+        try:
+            n = sock.send(data)
+        except (BlockingIOError, InterruptedError):
+            n = 0
+
+        if n == len(data):
+            # all data sent
+            return
+
+        fut = self.create_future()
+        fd = sock.fileno()
+        self._ensure_fd_no_transport(fd)
+        # use a trick with a list in closure to store a mutable state
+        handle = self._add_writer(fd, self._sock_sendall, fut, sock,
+                                  memoryview(data), [n])
+        fut.add_done_callback(
+            functools.partial(self._sock_write_done, fd, handle=handle))
+        return await fut
+
+    def _sock_sendall(self, fut, sock, view, pos):
+        if fut.done():
+            # Future cancellation can be scheduled on previous loop iteration
+            return
+        start = pos[0]
+        try:
+            n = sock.send(view[start:])
+        except (BlockingIOError, InterruptedError):
+            return
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+            return
+
+        start += n
+
+        if start == len(view):
+            fut.set_result(None)
+        else:
+            pos[0] = start
+
+    async def sock_connect(self, sock, address):
+        """Connect to a remote socket at address.
+
+        This method is a coroutine.
+        """
+        base_events._check_ssl_socket(sock)
+        if self._debug and sock.gettimeout() != 0:
+            raise ValueError("the socket must be non-blocking")
+
+        if sock.family == socket.AF_INET or (
+                base_events._HAS_IPv6 and sock.family == socket.AF_INET6):
+            resolved = await self._ensure_resolved(
+                address, family=sock.family, type=sock.type, proto=sock.proto,
+                loop=self,
+            )
+            _, _, _, _, address = resolved[0]
+
+        fut = self.create_future()
+        self._sock_connect(fut, sock, address)
+        try:
+            return await fut
+        finally:
+            # Needed to break cycles when an exception occurs.
+            fut = None
+
+    def _sock_connect(self, fut, sock, address):
+        fd = sock.fileno()
+        try:
+            sock.connect(address)
+        except (BlockingIOError, InterruptedError):
+            # Issue #23618: When the C function connect() fails with EINTR, the
+            # connection runs in background. We have to wait until the socket
+            # becomes writable to be notified when the connection succeed or
+            # fails.
+            self._ensure_fd_no_transport(fd)
+            handle = self._add_writer(
+                fd, self._sock_connect_cb, fut, sock, address)
+            fut.add_done_callback(
+                functools.partial(self._sock_write_done, fd, handle=handle))
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+        else:
+            fut.set_result(None)
+        finally:
+            fut = None
+
+    def _sock_write_done(self, fd, fut, handle=None):
+        if handle is None or not handle.cancelled():
+            self.remove_writer(fd)
+
+    def _sock_connect_cb(self, fut, sock, address):
+        if fut.done():
+            return
+
+        try:
+            err = sock.getsockopt(socket.SOL_SOCKET, socket.SO_ERROR)
+            if err != 0:
+                # Jump to any except clause below.
+                raise OSError(err, f'Connect call failed {address}')
+        except (BlockingIOError, InterruptedError):
+            # socket is still registered, the callback will be retried later
+            pass
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+        else:
+            fut.set_result(None)
+        finally:
+            fut = None
+
+    async def sock_accept(self, sock):
+        """Accept a connection.
+
+        The socket must be bound to an address and listening for connections.
+        The return value is a pair (conn, address) where conn is a new socket
+        object usable to send and receive data on the connection, and address
+        is the address bound to the socket on the other end of the connection.
+        """
+        base_events._check_ssl_socket(sock)
+        if self._debug and sock.gettimeout() != 0:
+            raise ValueError("the socket must be non-blocking")
+        fut = self.create_future()
+        self._sock_accept(fut, sock)
+        return await fut
+
+    def _sock_accept(self, fut, sock):
+        fd = sock.fileno()
+        try:
+            conn, address = sock.accept()
+            conn.setblocking(False)
+        except (BlockingIOError, InterruptedError):
+            self._ensure_fd_no_transport(fd)
+            handle = self._add_reader(fd, self._sock_accept, fut, sock)
+            fut.add_done_callback(
+                functools.partial(self._sock_read_done, fd, handle=handle))
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            fut.set_exception(exc)
+        else:
+            fut.set_result((conn, address))
+
+    async def _sendfile_native(self, transp, file, offset, count):
+        del self._transports[transp._sock_fd]
+        resume_reading = transp.is_reading()
+        transp.pause_reading()
+        await transp._make_empty_waiter()
+        try:
+            return await self.sock_sendfile(transp._sock, file, offset, count,
+                                            fallback=False)
+        finally:
+            transp._reset_empty_waiter()
+            if resume_reading:
+                transp.resume_reading()
+            self._transports[transp._sock_fd] = transp
+
+    def _process_events(self, event_list):
+        for key, mask in event_list:
+            fileobj, (reader, writer) = key.fileobj, key.data
+            if mask & selectors.EVENT_READ and reader is not None:
+                if reader._cancelled:
+                    self._remove_reader(fileobj)
+                else:
+                    self._add_callback(reader)
+            if mask & selectors.EVENT_WRITE and writer is not None:
+                if writer._cancelled:
+                    self._remove_writer(fileobj)
+                else:
+                    self._add_callback(writer)
+
+    def _stop_serving(self, sock):
+        self._remove_reader(sock.fileno())
+        sock.close()
+
+
+class _SelectorTransport(transports._FlowControlMixin,
+                         transports.Transport):
+
+    max_size = 256 * 1024  # Buffer size passed to recv().
+
+    _buffer_factory = bytearray  # Constructs initial value for self._buffer.
+
+    # Attribute used in the destructor: it must be set even if the constructor
+    # is not called (see _SelectorSslTransport which may start by raising an
+    # exception)
+    _sock = None
+
+    def __init__(self, loop, sock, protocol, extra=None, server=None):
+        super().__init__(extra, loop)
+        self._extra['socket'] = trsock.TransportSocket(sock)
+        try:
+            self._extra['sockname'] = sock.getsockname()
+        except OSError:
+            self._extra['sockname'] = None
+        if 'peername' not in self._extra:
+            try:
+                self._extra['peername'] = sock.getpeername()
+            except socket.error:
+                self._extra['peername'] = None
+        self._sock = sock
+        self._sock_fd = sock.fileno()
+
+        self._protocol_connected = False
+        self.set_protocol(protocol)
+
+        self._server = server
+        self._buffer = self._buffer_factory()
+        self._conn_lost = 0  # Set when call to connection_lost scheduled.
+        self._closing = False  # Set when close() called.
+        if self._server is not None:
+            self._server._attach()
+        loop._transports[self._sock_fd] = self
+
+    def __repr__(self):
+        info = [self.__class__.__name__]
+        if self._sock is None:
+            info.append('closed')
+        elif self._closing:
+            info.append('closing')
+        info.append(f'fd={self._sock_fd}')
+        # test if the transport was closed
+        if self._loop is not None and not self._loop.is_closed():
+            polling = _test_selector_event(self._loop._selector,
+                                           self._sock_fd, selectors.EVENT_READ)
+            if polling:
+                info.append('read=polling')
+            else:
+                info.append('read=idle')
+
+            polling = _test_selector_event(self._loop._selector,
+                                           self._sock_fd,
+                                           selectors.EVENT_WRITE)
+            if polling:
+                state = 'polling'
+            else:
+                state = 'idle'
+
+            bufsize = self.get_write_buffer_size()
+            info.append(f'write=<{state}, bufsize={bufsize}>')
+        return '<{}>'.format(' '.join(info))
+
+    def abort(self):
+        self._force_close(None)
+
+    def set_protocol(self, protocol):
+        self._protocol = protocol
+        self._protocol_connected = True
+
+    def get_protocol(self):
+        return self._protocol
+
+    def is_closing(self):
+        return self._closing
+
+    def close(self):
+        if self._closing:
+            return
+        self._closing = True
+        self._loop._remove_reader(self._sock_fd)
+        if not self._buffer:
+            self._conn_lost += 1
+            self._loop._remove_writer(self._sock_fd)
+            self._loop.call_soon(self._call_connection_lost, None)
+
+    def __del__(self, _warn=warnings.warn):
+        if self._sock is not None:
+            _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)
+            self._sock.close()
+
+    def _fatal_error(self, exc, message='Fatal error on transport'):
+        # Should be called from exception handler only.
+        if isinstance(exc, OSError):
+            if self._loop.get_debug():
+                logger.debug("%r: %s", self, message, exc_info=True)
+        else:
+            self._loop.call_exception_handler({
+                'message': message,
+                'exception': exc,
+                'transport': self,
+                'protocol': self._protocol,
+            })
+        self._force_close(exc)
+
+    def _force_close(self, exc):
+        if self._conn_lost:
+            return
+        if self._buffer:
+            self._buffer.clear()
+            self._loop._remove_writer(self._sock_fd)
+        if not self._closing:
+            self._closing = True
+            self._loop._remove_reader(self._sock_fd)
+        self._conn_lost += 1
+        self._loop.call_soon(self._call_connection_lost, exc)
+
+    def _call_connection_lost(self, exc):
+        try:
+            if self._protocol_connected:
+                self._protocol.connection_lost(exc)
+        finally:
+            self._sock.close()
+            self._sock = None
+            self._protocol = None
+            self._loop = None
+            server = self._server
+            if server is not None:
+                server._detach()
+                self._server = None
+
+    def get_write_buffer_size(self):
+        return len(self._buffer)
+
+    def _add_reader(self, fd, callback, *args):
+        if self._closing:
+            return
+
+        self._loop._add_reader(fd, callback, *args)
+
+
+class _SelectorSocketTransport(_SelectorTransport):
+
+    _start_tls_compatible = True
+    _sendfile_compatible = constants._SendfileMode.TRY_NATIVE
+
+    def __init__(self, loop, sock, protocol, waiter=None,
+                 extra=None, server=None):
+
+        self._read_ready_cb = None
+        super().__init__(loop, sock, protocol, extra, server)
+        self._eof = False
+        self._paused = False
+        self._empty_waiter = None
+
+        # Disable the Nagle algorithm -- small writes will be
+        # sent without waiting for the TCP ACK.  This generally
+        # decreases the latency (in some cases significantly.)
+        base_events._set_nodelay(self._sock)
+
+        self._loop.call_soon(self._protocol.connection_made, self)
+        # only start reading when connection_made() has been called
+        self._loop.call_soon(self._add_reader,
+                             self._sock_fd, self._read_ready)
+        if waiter is not None:
+            # only wake up the waiter when connection_made() has been called
+            self._loop.call_soon(futures._set_result_unless_cancelled,
+                                 waiter, None)
+
+    def set_protocol(self, protocol):
+        if isinstance(protocol, protocols.BufferedProtocol):
+            self._read_ready_cb = self._read_ready__get_buffer
+        else:
+            self._read_ready_cb = self._read_ready__data_received
+
+        super().set_protocol(protocol)
+
+    def is_reading(self):
+        return not self._paused and not self._closing
+
+    def pause_reading(self):
+        if self._closing or self._paused:
+            return
+        self._paused = True
+        self._loop._remove_reader(self._sock_fd)
+        if self._loop.get_debug():
+            logger.debug("%r pauses reading", self)
+
+    def resume_reading(self):
+        if self._closing or not self._paused:
+            return
+        self._paused = False
+        self._add_reader(self._sock_fd, self._read_ready)
+        if self._loop.get_debug():
+            logger.debug("%r resumes reading", self)
+
+    def _read_ready(self):
+        self._read_ready_cb()
+
+    def _read_ready__get_buffer(self):
+        if self._conn_lost:
+            return
+
+        try:
+            buf = self._protocol.get_buffer(-1)
+            if not len(buf):
+                raise RuntimeError('get_buffer() returned an empty buffer')
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(
+                exc, 'Fatal error: protocol.get_buffer() call failed.')
+            return
+
+        try:
+            nbytes = self._sock.recv_into(buf)
+        except (BlockingIOError, InterruptedError):
+            return
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(exc, 'Fatal read error on socket transport')
+            return
+
+        if not nbytes:
+            self._read_ready__on_eof()
+            return
+
+        try:
+            self._protocol.buffer_updated(nbytes)
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(
+                exc, 'Fatal error: protocol.buffer_updated() call failed.')
+
+    def _read_ready__data_received(self):
+        if self._conn_lost:
+            return
+        try:
+            data = self._sock.recv(self.max_size)
+        except (BlockingIOError, InterruptedError):
+            return
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(exc, 'Fatal read error on socket transport')
+            return
+
+        if not data:
+            self._read_ready__on_eof()
+            return
+
+        try:
+            self._protocol.data_received(data)
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(
+                exc, 'Fatal error: protocol.data_received() call failed.')
+
+    def _read_ready__on_eof(self):
+        if self._loop.get_debug():
+            logger.debug("%r received EOF", self)
+
+        try:
+            keep_open = self._protocol.eof_received()
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(
+                exc, 'Fatal error: protocol.eof_received() call failed.')
+            return
+
+        if keep_open:
+            # We're keeping the connection open so the
+            # protocol can write more, but we still can't
+            # receive more, so remove the reader callback.
+            self._loop._remove_reader(self._sock_fd)
+        else:
+            self.close()
+
+    def write(self, data):
+        if not isinstance(data, (bytes, bytearray, memoryview)):
+            raise TypeError(f'data argument must be a bytes-like object, '
+                            f'not {type(data).__name__!r}')
+        if self._eof:
+            raise RuntimeError('Cannot call write() after write_eof()')
+        if self._empty_waiter is not None:
+            raise RuntimeError('unable to write; sendfile is in progress')
+        if not data:
+            return
+
+        if self._conn_lost:
+            if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:
+                logger.warning('socket.send() raised exception.')
+            self._conn_lost += 1
+            return
+
+        if not self._buffer:
+            # Optimization: try to send now.
+            try:
+                n = self._sock.send(data)
+            except (BlockingIOError, InterruptedError):
+                pass
+            except (SystemExit, KeyboardInterrupt):
+                raise
+            except BaseException as exc:
+                self._fatal_error(exc, 'Fatal write error on socket transport')
+                return
+            else:
+                data = data[n:]
+                if not data:
+                    return
+            # Not all was written; register write handler.
+            self._loop._add_writer(self._sock_fd, self._write_ready)
+
+        # Add it to the buffer.
+        self._buffer.extend(data)
+        self._maybe_pause_protocol()
+
+    def _write_ready(self):
+        assert self._buffer, 'Data should not be empty'
+
+        if self._conn_lost:
+            return
+        try:
+            n = self._sock.send(self._buffer)
+        except (BlockingIOError, InterruptedError):
+            pass
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._loop._remove_writer(self._sock_fd)
+            self._buffer.clear()
+            self._fatal_error(exc, 'Fatal write error on socket transport')
+            if self._empty_waiter is not None:
+                self._empty_waiter.set_exception(exc)
+        else:
+            if n:
+                del self._buffer[:n]
+            self._maybe_resume_protocol()  # May append to buffer.
+            if not self._buffer:
+                self._loop._remove_writer(self._sock_fd)
+                if self._empty_waiter is not None:
+                    self._empty_waiter.set_result(None)
+                if self._closing:
+                    self._call_connection_lost(None)
+                elif self._eof:
+                    self._sock.shutdown(socket.SHUT_WR)
+
+    def write_eof(self):
+        if self._closing or self._eof:
+            return
+        self._eof = True
+        if not self._buffer:
+            self._sock.shutdown(socket.SHUT_WR)
+
+    def can_write_eof(self):
+        return True
+
+    def _call_connection_lost(self, exc):
+        super()._call_connection_lost(exc)
+        if self._empty_waiter is not None:
+            self._empty_waiter.set_exception(
+                ConnectionError("Connection is closed by peer"))
+
+    def _make_empty_waiter(self):
+        if self._empty_waiter is not None:
+            raise RuntimeError("Empty waiter is already set")
+        self._empty_waiter = self._loop.create_future()
+        if not self._buffer:
+            self._empty_waiter.set_result(None)
+        return self._empty_waiter
+
+    def _reset_empty_waiter(self):
+        self._empty_waiter = None
+
+
+class _SelectorDatagramTransport(_SelectorTransport):
+
+    _buffer_factory = collections.deque
+
+    def __init__(self, loop, sock, protocol, address=None,
+                 waiter=None, extra=None):
+        super().__init__(loop, sock, protocol, extra)
+        self._address = address
+        self._loop.call_soon(self._protocol.connection_made, self)
+        # only start reading when connection_made() has been called
+        self._loop.call_soon(self._add_reader,
+                             self._sock_fd, self._read_ready)
+        if waiter is not None:
+            # only wake up the waiter when connection_made() has been called
+            self._loop.call_soon(futures._set_result_unless_cancelled,
+                                 waiter, None)
+
+    def get_write_buffer_size(self):
+        return sum(len(data) for data, _ in self._buffer)
+
+    def _read_ready(self):
+        if self._conn_lost:
+            return
+        try:
+            data, addr = self._sock.recvfrom(self.max_size)
+        except (BlockingIOError, InterruptedError):
+            pass
+        except OSError as exc:
+            self._protocol.error_received(exc)
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            self._fatal_error(exc, 'Fatal read error on datagram transport')
+        else:
+            self._protocol.datagram_received(data, addr)
+
+    def sendto(self, data, addr=None):
+        if not isinstance(data, (bytes, bytearray, memoryview)):
+            raise TypeError(f'data argument must be a bytes-like object, '
+                            f'not {type(data).__name__!r}')
+        if not data:
+            return
+
+        if self._address:
+            if addr not in (None, self._address):
+                raise ValueError(
+                    f'Invalid address: must be None or {self._address}')
+            addr = self._address
+
+        if self._conn_lost and self._address:
+            if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:
+                logger.warning('socket.send() raised exception.')
+            self._conn_lost += 1
+            return
+
+        if not self._buffer:
+            # Attempt to send it right away first.
+            try:
+                if self._extra['peername']:
+                    self._sock.send(data)
+                else:
+                    self._sock.sendto(data, addr)
+                return
+            except (BlockingIOError, InterruptedError):
+                self._loop._add_writer(self._sock_fd, self._sendto_ready)
+            except OSError as exc:
+                self._protocol.error_received(exc)
+                return
+            except (SystemExit, KeyboardInterrupt):
+                raise
+            except BaseException as exc:
+                self._fatal_error(
+                    exc, 'Fatal write error on datagram transport')
+                return
+
+        # Ensure that what we buffer is immutable.
+        self._buffer.append((bytes(data), addr))
+        self._maybe_pause_protocol()
+
+    def _sendto_ready(self):
+        while self._buffer:
+            data, addr = self._buffer.popleft()
+            try:
+                if self._extra['peername']:
+                    self._sock.send(data)
+                else:
+                    self._sock.sendto(data, addr)
+            except (BlockingIOError, InterruptedError):
+                self._buffer.appendleft((data, addr))  # Try again later.
+                break
+            except OSError as exc:
+                self._protocol.error_received(exc)
+                return
+            except (SystemExit, KeyboardInterrupt):
+                raise
+            except BaseException as exc:
+                self._fatal_error(
+                    exc, 'Fatal write error on datagram transport')
+                return
+
+        self._maybe_resume_protocol()  # May append to buffer.
+        if not self._buffer:
+            self._loop._remove_writer(self._sock_fd)
+            if self._closing:
+                self._call_connection_lost(None)

parrot/lib/python3.10/asyncio/subprocess.py

@@ -0,0 +1,223 @@
+__all__ = 'create_subprocess_exec', 'create_subprocess_shell'
+
+import subprocess
+
+from . import events
+from . import protocols
+from . import streams
+from . import tasks
+from .log import logger
+
+
+PIPE = subprocess.PIPE
+STDOUT = subprocess.STDOUT
+DEVNULL = subprocess.DEVNULL
+
+
+class SubprocessStreamProtocol(streams.FlowControlMixin,
+                               protocols.SubprocessProtocol):
+    """Like StreamReaderProtocol, but for a subprocess."""
+
+    def __init__(self, limit, loop):
+        super().__init__(loop=loop)
+        self._limit = limit
+        self.stdin = self.stdout = self.stderr = None
+        self._transport = None
+        self._process_exited = False
+        self._pipe_fds = []
+        self._stdin_closed = self._loop.create_future()
+
+    def __repr__(self):
+        info = [self.__class__.__name__]
+        if self.stdin is not None:
+            info.append(f'stdin={self.stdin!r}')
+        if self.stdout is not None:
+            info.append(f'stdout={self.stdout!r}')
+        if self.stderr is not None:
+            info.append(f'stderr={self.stderr!r}')
+        return '<{}>'.format(' '.join(info))
+
+    def connection_made(self, transport):
+        self._transport = transport
+
+        stdout_transport = transport.get_pipe_transport(1)
+        if stdout_transport is not None:
+            self.stdout = streams.StreamReader(limit=self._limit,
+                                               loop=self._loop)
+            self.stdout.set_transport(stdout_transport)
+            self._pipe_fds.append(1)
+
+        stderr_transport = transport.get_pipe_transport(2)
+        if stderr_transport is not None:
+            self.stderr = streams.StreamReader(limit=self._limit,
+                                               loop=self._loop)
+            self.stderr.set_transport(stderr_transport)
+            self._pipe_fds.append(2)
+
+        stdin_transport = transport.get_pipe_transport(0)
+        if stdin_transport is not None:
+            self.stdin = streams.StreamWriter(stdin_transport,
+                                              protocol=self,
+                                              reader=None,
+                                              loop=self._loop)
+
+    def pipe_data_received(self, fd, data):
+        if fd == 1:
+            reader = self.stdout
+        elif fd == 2:
+            reader = self.stderr
+        else:
+            reader = None
+        if reader is not None:
+            reader.feed_data(data)
+
+    def pipe_connection_lost(self, fd, exc):
+        if fd == 0:
+            pipe = self.stdin
+            if pipe is not None:
+                pipe.close()
+            self.connection_lost(exc)
+            if exc is None:
+                self._stdin_closed.set_result(None)
+            else:
+                self._stdin_closed.set_exception(exc)
+            return
+        if fd == 1:
+            reader = self.stdout
+        elif fd == 2:
+            reader = self.stderr
+        else:
+            reader = None
+        if reader is not None:
+            if exc is None:
+                reader.feed_eof()
+            else:
+                reader.set_exception(exc)
+
+        if fd in self._pipe_fds:
+            self._pipe_fds.remove(fd)
+        self._maybe_close_transport()
+
+    def process_exited(self):
+        self._process_exited = True
+        self._maybe_close_transport()
+
+    def _maybe_close_transport(self):
+        if len(self._pipe_fds) == 0 and self._process_exited:
+            self._transport.close()
+            self._transport = None
+
+    def _get_close_waiter(self, stream):
+        if stream is self.stdin:
+            return self._stdin_closed
+
+
+class Process:
+    def __init__(self, transport, protocol, loop):
+        self._transport = transport
+        self._protocol = protocol
+        self._loop = loop
+        self.stdin = protocol.stdin
+        self.stdout = protocol.stdout
+        self.stderr = protocol.stderr
+        self.pid = transport.get_pid()
+
+    def __repr__(self):
+        return f'<{self.__class__.__name__} {self.pid}>'
+
+    @property
+    def returncode(self):
+        return self._transport.get_returncode()
+
+    async def wait(self):
+        """Wait until the process exit and return the process return code."""
+        return await self._transport._wait()
+
+    def send_signal(self, signal):
+        self._transport.send_signal(signal)
+
+    def terminate(self):
+        self._transport.terminate()
+
+    def kill(self):
+        self._transport.kill()
+
+    async def _feed_stdin(self, input):
+        debug = self._loop.get_debug()
+        self.stdin.write(input)
+        if debug:
+            logger.debug(
+                '%r communicate: feed stdin (%s bytes)', self, len(input))
+        try:
+            await self.stdin.drain()
+        except (BrokenPipeError, ConnectionResetError) as exc:
+            # communicate() ignores BrokenPipeError and ConnectionResetError
+            if debug:
+                logger.debug('%r communicate: stdin got %r', self, exc)
+
+        if debug:
+            logger.debug('%r communicate: close stdin', self)
+        self.stdin.close()
+
+    async def _noop(self):
+        return None
+
+    async def _read_stream(self, fd):
+        transport = self._transport.get_pipe_transport(fd)
+        if fd == 2:
+            stream = self.stderr
+        else:
+            assert fd == 1
+            stream = self.stdout
+        if self._loop.get_debug():
+            name = 'stdout' if fd == 1 else 'stderr'
+            logger.debug('%r communicate: read %s', self, name)
+        output = await stream.read()
+        if self._loop.get_debug():
+            name = 'stdout' if fd == 1 else 'stderr'
+            logger.debug('%r communicate: close %s', self, name)
+        transport.close()
+        return output
+
+    async def communicate(self, input=None):
+        if input is not None:
+            stdin = self._feed_stdin(input)
+        else:
+            stdin = self._noop()
+        if self.stdout is not None:
+            stdout = self._read_stream(1)
+        else:
+            stdout = self._noop()
+        if self.stderr is not None:
+            stderr = self._read_stream(2)
+        else:
+            stderr = self._noop()
+        stdin, stdout, stderr = await tasks.gather(stdin, stdout, stderr)
+        await self.wait()
+        return (stdout, stderr)
+
+
+async def create_subprocess_shell(cmd, stdin=None, stdout=None, stderr=None,
+                                  limit=streams._DEFAULT_LIMIT, **kwds):
+    loop = events.get_running_loop()
+    protocol_factory = lambda: SubprocessStreamProtocol(limit=limit,
+                                                        loop=loop)
+    transport, protocol = await loop.subprocess_shell(
+        protocol_factory,
+        cmd, stdin=stdin, stdout=stdout,
+        stderr=stderr, **kwds)
+    return Process(transport, protocol, loop)
+
+
+async def create_subprocess_exec(program, *args, stdin=None, stdout=None,
+                                 stderr=None, limit=streams._DEFAULT_LIMIT,
+                                 **kwds):
+    loop = events.get_running_loop()
+    protocol_factory = lambda: SubprocessStreamProtocol(limit=limit,
+                                                        loop=loop)
+    transport, protocol = await loop.subprocess_exec(
+        protocol_factory,
+        program, *args,
+        stdin=stdin, stdout=stdout,
+        stderr=stderr, **kwds)
+    return Process(transport, protocol, loop)

parrot/lib/python3.10/asyncio/tasks.py

@@ -0,0 +1,946 @@
+"""Support for tasks, coroutines and the scheduler."""
+
+__all__ = (
+    'Task', 'create_task',
+    'FIRST_COMPLETED', 'FIRST_EXCEPTION', 'ALL_COMPLETED',
+    'wait', 'wait_for', 'as_completed', 'sleep',
+    'gather', 'shield', 'ensure_future', 'run_coroutine_threadsafe',
+    'current_task', 'all_tasks',
+    '_register_task', '_unregister_task', '_enter_task', '_leave_task',
+)
+
+import concurrent.futures
+import contextvars
+import functools
+import inspect
+import itertools
+import types
+import warnings
+import weakref
+from types import GenericAlias
+
+from . import base_tasks
+from . import coroutines
+from . import events
+from . import exceptions
+from . import futures
+from .coroutines import _is_coroutine
+
+# Helper to generate new task names
+# This uses itertools.count() instead of a "+= 1" operation because the latter
+# is not thread safe. See bpo-11866 for a longer explanation.
+_task_name_counter = itertools.count(1).__next__
+
+
+def current_task(loop=None):
+    """Return a currently executed task."""
+    if loop is None:
+        loop = events.get_running_loop()
+    return _current_tasks.get(loop)
+
+
+def all_tasks(loop=None):
+    """Return a set of all tasks for the loop."""
+    if loop is None:
+        loop = events.get_running_loop()
+    # Looping over a WeakSet (_all_tasks) isn't safe as it can be updated from another
+    # thread while we do so. Therefore we cast it to list prior to filtering. The list
+    # cast itself requires iteration, so we repeat it several times ignoring
+    # RuntimeErrors (which are not very likely to occur). See issues 34970 and 36607 for
+    # details.
+    i = 0
+    while True:
+        try:
+            tasks = list(_all_tasks)
+        except RuntimeError:
+            i += 1
+            if i >= 1000:
+                raise
+        else:
+            break
+    return {t for t in tasks
+            if futures._get_loop(t) is loop and not t.done()}
+
+
+def _set_task_name(task, name):
+    if name is not None:
+        try:
+            set_name = task.set_name
+        except AttributeError:
+            pass
+        else:
+            set_name(name)
+
+
+class Task(futures._PyFuture):  # Inherit Python Task implementation
+                                # from a Python Future implementation.
+
+    """A coroutine wrapped in a Future."""
+
+    # An important invariant maintained while a Task not done:
+    #
+    # - Either _fut_waiter is None, and _step() is scheduled;
+    # - or _fut_waiter is some Future, and _step() is *not* scheduled.
+    #
+    # The only transition from the latter to the former is through
+    # _wakeup().  When _fut_waiter is not None, one of its callbacks
+    # must be _wakeup().
+
+    # If False, don't log a message if the task is destroyed whereas its
+    # status is still pending
+    _log_destroy_pending = True
+
+    def __init__(self, coro, *, loop=None, name=None):
+        super().__init__(loop=loop)
+        if self._source_traceback:
+            del self._source_traceback[-1]
+        if not coroutines.iscoroutine(coro):
+            # raise after Future.__init__(), attrs are required for __del__
+            # prevent logging for pending task in __del__
+            self._log_destroy_pending = False
+            raise TypeError(f"a coroutine was expected, got {coro!r}")
+
+        if name is None:
+            self._name = f'Task-{_task_name_counter()}'
+        else:
+            self._name = str(name)
+
+        self._must_cancel = False
+        self._fut_waiter = None
+        self._coro = coro
+        self._context = contextvars.copy_context()
+
+        self._loop.call_soon(self.__step, context=self._context)
+        _register_task(self)
+
+    def __del__(self):
+        if self._state == futures._PENDING and self._log_destroy_pending:
+            context = {
+                'task': self,
+                'message': 'Task was destroyed but it is pending!',
+            }
+            if self._source_traceback:
+                context['source_traceback'] = self._source_traceback
+            self._loop.call_exception_handler(context)
+        super().__del__()
+
+    __class_getitem__ = classmethod(GenericAlias)
+
+    def _repr_info(self):
+        return base_tasks._task_repr_info(self)
+
+    def get_coro(self):
+        return self._coro
+
+    def get_name(self):
+        return self._name
+
+    def set_name(self, value):
+        self._name = str(value)
+
+    def set_result(self, result):
+        raise RuntimeError('Task does not support set_result operation')
+
+    def set_exception(self, exception):
+        raise RuntimeError('Task does not support set_exception operation')
+
+    def get_stack(self, *, limit=None):
+        """Return the list of stack frames for this task's coroutine.
+
+        If the coroutine is not done, this returns the stack where it is
+        suspended.  If the coroutine has completed successfully or was
+        cancelled, this returns an empty list.  If the coroutine was
+        terminated by an exception, this returns the list of traceback
+        frames.
+
+        The frames are always ordered from oldest to newest.
+
+        The optional limit gives the maximum number of frames to
+        return; by default all available frames are returned.  Its
+        meaning differs depending on whether a stack or a traceback is
+        returned: the newest frames of a stack are returned, but the
+        oldest frames of a traceback are returned.  (This matches the
+        behavior of the traceback module.)
+
+        For reasons beyond our control, only one stack frame is
+        returned for a suspended coroutine.
+        """
+        return base_tasks._task_get_stack(self, limit)
+
+    def print_stack(self, *, limit=None, file=None):
+        """Print the stack or traceback for this task's coroutine.
+
+        This produces output similar to that of the traceback module,
+        for the frames retrieved by get_stack().  The limit argument
+        is passed to get_stack().  The file argument is an I/O stream
+        to which the output is written; by default output is written
+        to sys.stderr.
+        """
+        return base_tasks._task_print_stack(self, limit, file)
+
+    def cancel(self, msg=None):
+        """Request that this task cancel itself.
+
+        This arranges for a CancelledError to be thrown into the
+        wrapped coroutine on the next cycle through the event loop.
+        The coroutine then has a chance to clean up or even deny
+        the request using try/except/finally.
+
+        Unlike Future.cancel, this does not guarantee that the
+        task will be cancelled: the exception might be caught and
+        acted upon, delaying cancellation of the task or preventing
+        cancellation completely.  The task may also return a value or
+        raise a different exception.
+
+        Immediately after this method is called, Task.cancelled() will
+        not return True (unless the task was already cancelled).  A
+        task will be marked as cancelled when the wrapped coroutine
+        terminates with a CancelledError exception (even if cancel()
+        was not called).
+        """
+        self._log_traceback = False
+        if self.done():
+            return False
+        if self._fut_waiter is not None:
+            if self._fut_waiter.cancel(msg=msg):
+                # Leave self._fut_waiter; it may be a Task that
+                # catches and ignores the cancellation so we may have
+                # to cancel it again later.
+                return True
+        # It must be the case that self.__step is already scheduled.
+        self._must_cancel = True
+        self._cancel_message = msg
+        return True
+
+    def __step(self, exc=None):
+        if self.done():
+            raise exceptions.InvalidStateError(
+                f'_step(): already done: {self!r}, {exc!r}')
+        if self._must_cancel:
+            if not isinstance(exc, exceptions.CancelledError):
+                exc = self._make_cancelled_error()
+            self._must_cancel = False
+        coro = self._coro
+        self._fut_waiter = None
+
+        _enter_task(self._loop, self)
+        # Call either coro.throw(exc) or coro.send(None).
+        try:
+            if exc is None:
+                # We use the `send` method directly, because coroutines
+                # don't have `__iter__` and `__next__` methods.
+                result = coro.send(None)
+            else:
+                result = coro.throw(exc)
+        except StopIteration as exc:
+            if self._must_cancel:
+                # Task is cancelled right before coro stops.
+                self._must_cancel = False
+                super().cancel(msg=self._cancel_message)
+            else:
+                super().set_result(exc.value)
+        except exceptions.CancelledError as exc:
+            # Save the original exception so we can chain it later.
+            self._cancelled_exc = exc
+            super().cancel()  # I.e., Future.cancel(self).
+        except (KeyboardInterrupt, SystemExit) as exc:
+            super().set_exception(exc)
+            raise
+        except BaseException as exc:
+            super().set_exception(exc)
+        else:
+            blocking = getattr(result, '_asyncio_future_blocking', None)
+            if blocking is not None:
+                # Yielded Future must come from Future.__iter__().
+                if futures._get_loop(result) is not self._loop:
+                    new_exc = RuntimeError(
+                        f'Task {self!r} got Future '
+                        f'{result!r} attached to a different loop')
+                    self._loop.call_soon(
+                        self.__step, new_exc, context=self._context)
+                elif blocking:
+                    if result is self:
+                        new_exc = RuntimeError(
+                            f'Task cannot await on itself: {self!r}')
+                        self._loop.call_soon(
+                            self.__step, new_exc, context=self._context)
+                    else:
+                        result._asyncio_future_blocking = False
+                        result.add_done_callback(
+                            self.__wakeup, context=self._context)
+                        self._fut_waiter = result
+                        if self._must_cancel:
+                            if self._fut_waiter.cancel(
+                                    msg=self._cancel_message):
+                                self._must_cancel = False
+                else:
+                    new_exc = RuntimeError(
+                        f'yield was used instead of yield from '
+                        f'in task {self!r} with {result!r}')
+                    self._loop.call_soon(
+                        self.__step, new_exc, context=self._context)
+
+            elif result is None:
+                # Bare yield relinquishes control for one event loop iteration.
+                self._loop.call_soon(self.__step, context=self._context)
+            elif inspect.isgenerator(result):
+                # Yielding a generator is just wrong.
+                new_exc = RuntimeError(
+                    f'yield was used instead of yield from for '
+                    f'generator in task {self!r} with {result!r}')
+                self._loop.call_soon(
+                    self.__step, new_exc, context=self._context)
+            else:
+                # Yielding something else is an error.
+                new_exc = RuntimeError(f'Task got bad yield: {result!r}')
+                self._loop.call_soon(
+                    self.__step, new_exc, context=self._context)
+        finally:
+            _leave_task(self._loop, self)
+            self = None  # Needed to break cycles when an exception occurs.
+
+    def __wakeup(self, future):
+        try:
+            future.result()
+        except BaseException as exc:
+            # This may also be a cancellation.
+            self.__step(exc)
+        else:
+            # Don't pass the value of `future.result()` explicitly,
+            # as `Future.__iter__` and `Future.__await__` don't need it.
+            # If we call `_step(value, None)` instead of `_step()`,
+            # Python eval loop would use `.send(value)` method call,
+            # instead of `__next__()`, which is slower for futures
+            # that return non-generator iterators from their `__iter__`.
+            self.__step()
+        self = None  # Needed to break cycles when an exception occurs.
+
+
+_PyTask = Task
+
+
+try:
+    import _asyncio
+except ImportError:
+    pass
+else:
+    # _CTask is needed for tests.
+    Task = _CTask = _asyncio.Task
+
+
+def create_task(coro, *, name=None):
+    """Schedule the execution of a coroutine object in a spawn task.
+
+    Return a Task object.
+    """
+    loop = events.get_running_loop()
+    task = loop.create_task(coro)
+    _set_task_name(task, name)
+    return task
+
+
+# wait() and as_completed() similar to those in PEP 3148.
+
+FIRST_COMPLETED = concurrent.futures.FIRST_COMPLETED
+FIRST_EXCEPTION = concurrent.futures.FIRST_EXCEPTION
+ALL_COMPLETED = concurrent.futures.ALL_COMPLETED
+
+
+async def wait(fs, *, timeout=None, return_when=ALL_COMPLETED):
+    """Wait for the Futures and coroutines given by fs to complete.
+
+    The fs iterable must not be empty.
+
+    Coroutines will be wrapped in Tasks.
+
+    Returns two sets of Future: (done, pending).
+
+    Usage:
+
+        done, pending = await asyncio.wait(fs)
+
+    Note: This does not raise TimeoutError! Futures that aren't done
+    when the timeout occurs are returned in the second set.
+    """
+    if futures.isfuture(fs) or coroutines.iscoroutine(fs):
+        raise TypeError(f"expect a list of futures, not {type(fs).__name__}")
+    if not fs:
+        raise ValueError('Set of coroutines/Futures is empty.')
+    if return_when not in (FIRST_COMPLETED, FIRST_EXCEPTION, ALL_COMPLETED):
+        raise ValueError(f'Invalid return_when value: {return_when}')
+
+    loop = events.get_running_loop()
+
+    fs = set(fs)
+
+    if any(coroutines.iscoroutine(f) for f in fs):
+        warnings.warn("The explicit passing of coroutine objects to "
+                      "asyncio.wait() is deprecated since Python 3.8, and "
+                      "scheduled for removal in Python 3.11.",
+                      DeprecationWarning, stacklevel=2)
+
+    fs = {ensure_future(f, loop=loop) for f in fs}
+
+    return await _wait(fs, timeout, return_when, loop)
+
+
+def _release_waiter(waiter, *args):
+    if not waiter.done():
+        waiter.set_result(None)
+
+
+async def wait_for(fut, timeout):
+    """Wait for the single Future or coroutine to complete, with timeout.
+
+    Coroutine will be wrapped in Task.
+
+    Returns result of the Future or coroutine.  When a timeout occurs,
+    it cancels the task and raises TimeoutError.  To avoid the task
+    cancellation, wrap it in shield().
+
+    If the wait is cancelled, the task is also cancelled.
+
+    This function is a coroutine.
+    """
+    loop = events.get_running_loop()
+
+    if timeout is None:
+        return await fut
+
+    if timeout <= 0:
+        fut = ensure_future(fut, loop=loop)
+
+        if fut.done():
+            return fut.result()
+
+        await _cancel_and_wait(fut, loop=loop)
+        try:
+            return fut.result()
+        except exceptions.CancelledError as exc:
+            raise exceptions.TimeoutError() from exc
+
+    waiter = loop.create_future()
+    timeout_handle = loop.call_later(timeout, _release_waiter, waiter)
+    cb = functools.partial(_release_waiter, waiter)
+
+    fut = ensure_future(fut, loop=loop)
+    fut.add_done_callback(cb)
+
+    try:
+        # wait until the future completes or the timeout
+        try:
+            await waiter
+        except exceptions.CancelledError:
+            if fut.done():
+                return fut.result()
+            else:
+                fut.remove_done_callback(cb)
+                # We must ensure that the task is not running
+                # after wait_for() returns.
+                # See https://bugs.python.org/issue32751
+                await _cancel_and_wait(fut, loop=loop)
+                raise
+
+        if fut.done():
+            return fut.result()
+        else:
+            fut.remove_done_callback(cb)
+            # We must ensure that the task is not running
+            # after wait_for() returns.
+            # See https://bugs.python.org/issue32751
+            await _cancel_and_wait(fut, loop=loop)
+            # In case task cancellation failed with some
+            # exception, we should re-raise it
+            # See https://bugs.python.org/issue40607
+            try:
+                return fut.result()
+            except exceptions.CancelledError as exc:
+                raise exceptions.TimeoutError() from exc
+    finally:
+        timeout_handle.cancel()
+
+
+async def _wait(fs, timeout, return_when, loop):
+    """Internal helper for wait().
+
+    The fs argument must be a collection of Futures.
+    """
+    assert fs, 'Set of Futures is empty.'
+    waiter = loop.create_future()
+    timeout_handle = None
+    if timeout is not None:
+        timeout_handle = loop.call_later(timeout, _release_waiter, waiter)
+    counter = len(fs)
+
+    def _on_completion(f):
+        nonlocal counter
+        counter -= 1
+        if (counter <= 0 or
+            return_when == FIRST_COMPLETED or
+            return_when == FIRST_EXCEPTION and (not f.cancelled() and
+                                                f.exception() is not None)):
+            if timeout_handle is not None:
+                timeout_handle.cancel()
+            if not waiter.done():
+                waiter.set_result(None)
+
+    for f in fs:
+        f.add_done_callback(_on_completion)
+
+    try:
+        await waiter
+    finally:
+        if timeout_handle is not None:
+            timeout_handle.cancel()
+        for f in fs:
+            f.remove_done_callback(_on_completion)
+
+    done, pending = set(), set()
+    for f in fs:
+        if f.done():
+            done.add(f)
+        else:
+            pending.add(f)
+    return done, pending
+
+
+async def _cancel_and_wait(fut, loop):
+    """Cancel the *fut* future or task and wait until it completes."""
+
+    waiter = loop.create_future()
+    cb = functools.partial(_release_waiter, waiter)
+    fut.add_done_callback(cb)
+
+    try:
+        fut.cancel()
+        # We cannot wait on *fut* directly to make
+        # sure _cancel_and_wait itself is reliably cancellable.
+        await waiter
+    finally:
+        fut.remove_done_callback(cb)
+
+
+# This is *not* a @coroutine!  It is just an iterator (yielding Futures).
+def as_completed(fs, *, timeout=None):
+    """Return an iterator whose values are coroutines.
+
+    When waiting for the yielded coroutines you'll get the results (or
+    exceptions!) of the original Futures (or coroutines), in the order
+    in which and as soon as they complete.
+
+    This differs from PEP 3148; the proper way to use this is:
+
+        for f in as_completed(fs):
+            result = await f  # The 'await' may raise.
+            # Use result.
+
+    If a timeout is specified, the 'await' will raise
+    TimeoutError when the timeout occurs before all Futures are done.
+
+    Note: The futures 'f' are not necessarily members of fs.
+    """
+    if futures.isfuture(fs) or coroutines.iscoroutine(fs):
+        raise TypeError(f"expect an iterable of futures, not {type(fs).__name__}")
+
+    from .queues import Queue  # Import here to avoid circular import problem.
+    done = Queue()
+
+    loop = events._get_event_loop()
+    todo = {ensure_future(f, loop=loop) for f in set(fs)}
+    timeout_handle = None
+
+    def _on_timeout():
+        for f in todo:
+            f.remove_done_callback(_on_completion)
+            done.put_nowait(None)  # Queue a dummy value for _wait_for_one().
+        todo.clear()  # Can't do todo.remove(f) in the loop.
+
+    def _on_completion(f):
+        if not todo:
+            return  # _on_timeout() was here first.
+        todo.remove(f)
+        done.put_nowait(f)
+        if not todo and timeout_handle is not None:
+            timeout_handle.cancel()
+
+    async def _wait_for_one():
+        f = await done.get()
+        if f is None:
+            # Dummy value from _on_timeout().
+            raise exceptions.TimeoutError
+        return f.result()  # May raise f.exception().
+
+    for f in todo:
+        f.add_done_callback(_on_completion)
+    if todo and timeout is not None:
+        timeout_handle = loop.call_later(timeout, _on_timeout)
+    for _ in range(len(todo)):
+        yield _wait_for_one()
+
+
+@types.coroutine
+def __sleep0():
+    """Skip one event loop run cycle.
+
+    This is a private helper for 'asyncio.sleep()', used
+    when the 'delay' is set to 0.  It uses a bare 'yield'
+    expression (which Task.__step knows how to handle)
+    instead of creating a Future object.
+    """
+    yield
+
+
+async def sleep(delay, result=None):
+    """Coroutine that completes after a given time (in seconds)."""
+    if delay <= 0:
+        await __sleep0()
+        return result
+
+    loop = events.get_running_loop()
+    future = loop.create_future()
+    h = loop.call_later(delay,
+                        futures._set_result_unless_cancelled,
+                        future, result)
+    try:
+        return await future
+    finally:
+        h.cancel()
+
+
+def ensure_future(coro_or_future, *, loop=None):
+    """Wrap a coroutine or an awaitable in a future.
+
+    If the argument is a Future, it is returned directly.
+    """
+    return _ensure_future(coro_or_future, loop=loop)
+
+
+def _ensure_future(coro_or_future, *, loop=None):
+    if futures.isfuture(coro_or_future):
+        if loop is not None and loop is not futures._get_loop(coro_or_future):
+            raise ValueError('The future belongs to a different loop than '
+                            'the one specified as the loop argument')
+        return coro_or_future
+    called_wrap_awaitable = False
+    if not coroutines.iscoroutine(coro_or_future):
+        if inspect.isawaitable(coro_or_future):
+            coro_or_future = _wrap_awaitable(coro_or_future)
+            called_wrap_awaitable = True
+        else:
+            raise TypeError('An asyncio.Future, a coroutine or an awaitable '
+                            'is required')
+
+    if loop is None:
+        loop = events._get_event_loop(stacklevel=4)
+    try:
+        return loop.create_task(coro_or_future)
+    except RuntimeError: 
+        if not called_wrap_awaitable:
+            coro_or_future.close()
+        raise
+
+
+@types.coroutine
+def _wrap_awaitable(awaitable):
+    """Helper for asyncio.ensure_future().
+
+    Wraps awaitable (an object with __await__) into a coroutine
+    that will later be wrapped in a Task by ensure_future().
+    """
+    return (yield from awaitable.__await__())
+
+_wrap_awaitable._is_coroutine = _is_coroutine
+
+
+class _GatheringFuture(futures.Future):
+    """Helper for gather().
+
+    This overrides cancel() to cancel all the children and act more
+    like Task.cancel(), which doesn't immediately mark itself as
+    cancelled.
+    """
+
+    def __init__(self, children, *, loop):
+        assert loop is not None
+        super().__init__(loop=loop)
+        self._children = children
+        self._cancel_requested = False
+
+    def cancel(self, msg=None):
+        if self.done():
+            return False
+        ret = False
+        for child in self._children:
+            if child.cancel(msg=msg):
+                ret = True
+        if ret:
+            # If any child tasks were actually cancelled, we should
+            # propagate the cancellation request regardless of
+            # *return_exceptions* argument.  See issue 32684.
+            self._cancel_requested = True
+        return ret
+
+
+def gather(*coros_or_futures, return_exceptions=False):
+    """Return a future aggregating results from the given coroutines/futures.
+
+    Coroutines will be wrapped in a future and scheduled in the event
+    loop. They will not necessarily be scheduled in the same order as
+    passed in.
+
+    All futures must share the same event loop.  If all the tasks are
+    done successfully, the returned future's result is the list of
+    results (in the order of the original sequence, not necessarily
+    the order of results arrival).  If *return_exceptions* is True,
+    exceptions in the tasks are treated the same as successful
+    results, and gathered in the result list; otherwise, the first
+    raised exception will be immediately propagated to the returned
+    future.
+
+    Cancellation: if the outer Future is cancelled, all children (that
+    have not completed yet) are also cancelled.  If any child is
+    cancelled, this is treated as if it raised CancelledError --
+    the outer Future is *not* cancelled in this case.  (This is to
+    prevent the cancellation of one child to cause other children to
+    be cancelled.)
+
+    If *return_exceptions* is False, cancelling gather() after it
+    has been marked done won't cancel any submitted awaitables.
+    For instance, gather can be marked done after propagating an
+    exception to the caller, therefore, calling ``gather.cancel()``
+    after catching an exception (raised by one of the awaitables) from
+    gather won't cancel any other awaitables.
+    """
+    if not coros_or_futures:
+        loop = events._get_event_loop()
+        outer = loop.create_future()
+        outer.set_result([])
+        return outer
+
+    def _done_callback(fut):
+        nonlocal nfinished
+        nfinished += 1
+
+        if outer is None or outer.done():
+            if not fut.cancelled():
+                # Mark exception retrieved.
+                fut.exception()
+            return
+
+        if not return_exceptions:
+            if fut.cancelled():
+                # Check if 'fut' is cancelled first, as
+                # 'fut.exception()' will *raise* a CancelledError
+                # instead of returning it.
+                exc = fut._make_cancelled_error()
+                outer.set_exception(exc)
+                return
+            else:
+                exc = fut.exception()
+                if exc is not None:
+                    outer.set_exception(exc)
+                    return
+
+        if nfinished == nfuts:
+            # All futures are done; create a list of results
+            # and set it to the 'outer' future.
+            results = []
+
+            for fut in children:
+                if fut.cancelled():
+                    # Check if 'fut' is cancelled first, as 'fut.exception()'
+                    # will *raise* a CancelledError instead of returning it.
+                    # Also, since we're adding the exception return value
+                    # to 'results' instead of raising it, don't bother
+                    # setting __context__.  This also lets us preserve
+                    # calling '_make_cancelled_error()' at most once.
+                    res = exceptions.CancelledError(
+                        '' if fut._cancel_message is None else
+                        fut._cancel_message)
+                else:
+                    res = fut.exception()
+                    if res is None:
+                        res = fut.result()
+                results.append(res)
+
+            if outer._cancel_requested:
+                # If gather is being cancelled we must propagate the
+                # cancellation regardless of *return_exceptions* argument.
+                # See issue 32684.
+                exc = fut._make_cancelled_error()
+                outer.set_exception(exc)
+            else:
+                outer.set_result(results)
+
+    arg_to_fut = {}
+    children = []
+    nfuts = 0
+    nfinished = 0
+    loop = None
+    outer = None  # bpo-46672
+    for arg in coros_or_futures:
+        if arg not in arg_to_fut:
+            fut = _ensure_future(arg, loop=loop)
+            if loop is None:
+                loop = futures._get_loop(fut)
+            if fut is not arg:
+                # 'arg' was not a Future, therefore, 'fut' is a new
+                # Future created specifically for 'arg'.  Since the caller
+                # can't control it, disable the "destroy pending task"
+                # warning.
+                fut._log_destroy_pending = False
+
+            nfuts += 1
+            arg_to_fut[arg] = fut
+            fut.add_done_callback(_done_callback)
+
+        else:
+            # There's a duplicate Future object in coros_or_futures.
+            fut = arg_to_fut[arg]
+
+        children.append(fut)
+
+    outer = _GatheringFuture(children, loop=loop)
+    return outer
+
+
+def shield(arg):
+    """Wait for a future, shielding it from cancellation.
+
+    The statement
+
+        task = asyncio.create_task(something())
+        res = await shield(task)
+
+    is exactly equivalent to the statement
+
+        res = await something()
+
+    *except* that if the coroutine containing it is cancelled, the
+    task running in something() is not cancelled.  From the POV of
+    something(), the cancellation did not happen.  But its caller is
+    still cancelled, so the yield-from expression still raises
+    CancelledError.  Note: If something() is cancelled by other means
+    this will still cancel shield().
+
+    If you want to completely ignore cancellation (not recommended)
+    you can combine shield() with a try/except clause, as follows:
+
+        task = asyncio.create_task(something())
+        try:
+            res = await shield(task)
+        except CancelledError:
+            res = None
+
+    Save a reference to tasks passed to this function, to avoid
+    a task disappearing mid-execution. The event loop only keeps
+    weak references to tasks. A task that isn't referenced elsewhere
+    may get garbage collected at any time, even before it's done.
+    """
+    inner = _ensure_future(arg)
+    if inner.done():
+        # Shortcut.
+        return inner
+    loop = futures._get_loop(inner)
+    outer = loop.create_future()
+
+    def _inner_done_callback(inner):
+        if outer.cancelled():
+            if not inner.cancelled():
+                # Mark inner's result as retrieved.
+                inner.exception()
+            return
+
+        if inner.cancelled():
+            outer.cancel()
+        else:
+            exc = inner.exception()
+            if exc is not None:
+                outer.set_exception(exc)
+            else:
+                outer.set_result(inner.result())
+
+
+    def _outer_done_callback(outer):
+        if not inner.done():
+            inner.remove_done_callback(_inner_done_callback)
+
+    inner.add_done_callback(_inner_done_callback)
+    outer.add_done_callback(_outer_done_callback)
+    return outer
+
+
+def run_coroutine_threadsafe(coro, loop):
+    """Submit a coroutine object to a given event loop.
+
+    Return a concurrent.futures.Future to access the result.
+    """
+    if not coroutines.iscoroutine(coro):
+        raise TypeError('A coroutine object is required')
+    future = concurrent.futures.Future()
+
+    def callback():
+        try:
+            futures._chain_future(ensure_future(coro, loop=loop), future)
+        except (SystemExit, KeyboardInterrupt):
+            raise
+        except BaseException as exc:
+            if future.set_running_or_notify_cancel():
+                future.set_exception(exc)
+            raise
+
+    loop.call_soon_threadsafe(callback)
+    return future
+
+
+# WeakSet containing all alive tasks.
+_all_tasks = weakref.WeakSet()
+
+# Dictionary containing tasks that are currently active in
+# all running event loops.  {EventLoop: Task}
+_current_tasks = {}
+
+
+def _register_task(task):
+    """Register a new task in asyncio as executed by loop."""
+    _all_tasks.add(task)
+
+
+def _enter_task(loop, task):
+    current_task = _current_tasks.get(loop)
+    if current_task is not None:
+        raise RuntimeError(f"Cannot enter into task {task!r} while another "
+                           f"task {current_task!r} is being executed.")
+    _current_tasks[loop] = task
+
+
+def _leave_task(loop, task):
+    current_task = _current_tasks.get(loop)
+    if current_task is not task:
+        raise RuntimeError(f"Leaving task {task!r} does not match "
+                           f"the current task {current_task!r}.")
+    del _current_tasks[loop]
+
+
+def _unregister_task(task):
+    """Unregister a task."""
+    _all_tasks.discard(task)
+
+
+_py_register_task = _register_task
+_py_unregister_task = _unregister_task
+_py_enter_task = _enter_task
+_py_leave_task = _leave_task
+
+
+try:
+    from _asyncio import (_register_task, _unregister_task,
+                          _enter_task, _leave_task,
+                          _all_tasks, _current_tasks)
+except ImportError:
+    pass
+else:
+    _c_register_task = _register_task
+    _c_unregister_task = _unregister_task
+    _c_enter_task = _enter_task
+    _c_leave_task = _leave_task

parrot/lib/python3.10/asyncio/threads.py

@@ -0,0 +1,25 @@
+"""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)

parrot/lib/python3.10/asyncio/windows_utils.py

@@ -0,0 +1,173 @@
+"""Various Windows specific bits and pieces."""
+
+import sys
+
+if sys.platform != 'win32':  # pragma: no cover
+    raise ImportError('win32 only')
+
+import _winapi
+import itertools
+import msvcrt
+import os
+import subprocess
+import tempfile
+import warnings
+
+
+__all__ = 'pipe', 'Popen', 'PIPE', 'PipeHandle'
+
+
+# Constants/globals
+
+
+BUFSIZE = 8192
+PIPE = subprocess.PIPE
+STDOUT = subprocess.STDOUT
+_mmap_counter = itertools.count()
+
+
+# Replacement for os.pipe() using handles instead of fds
+
+
+def pipe(*, duplex=False, overlapped=(True, True), bufsize=BUFSIZE):
+    """Like os.pipe() but with overlapped support and using handles not fds."""
+    address = tempfile.mktemp(
+        prefix=r'\\.\pipe\python-pipe-{:d}-{:d}-'.format(
+            os.getpid(), next(_mmap_counter)))
+
+    if duplex:
+        openmode = _winapi.PIPE_ACCESS_DUPLEX
+        access = _winapi.GENERIC_READ | _winapi.GENERIC_WRITE
+        obsize, ibsize = bufsize, bufsize
+    else:
+        openmode = _winapi.PIPE_ACCESS_INBOUND
+        access = _winapi.GENERIC_WRITE
+        obsize, ibsize = 0, bufsize
+
+    openmode |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE
+
+    if overlapped[0]:
+        openmode |= _winapi.FILE_FLAG_OVERLAPPED
+
+    if overlapped[1]:
+        flags_and_attribs = _winapi.FILE_FLAG_OVERLAPPED
+    else:
+        flags_and_attribs = 0
+
+    h1 = h2 = None
+    try:
+        h1 = _winapi.CreateNamedPipe(
+            address, openmode, _winapi.PIPE_WAIT,
+            1, obsize, ibsize, _winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL)
+
+        h2 = _winapi.CreateFile(
+            address, access, 0, _winapi.NULL, _winapi.OPEN_EXISTING,
+            flags_and_attribs, _winapi.NULL)
+
+        ov = _winapi.ConnectNamedPipe(h1, overlapped=True)
+        ov.GetOverlappedResult(True)
+        return h1, h2
+    except:
+        if h1 is not None:
+            _winapi.CloseHandle(h1)
+        if h2 is not None:
+            _winapi.CloseHandle(h2)
+        raise
+
+
+# Wrapper for a pipe handle
+
+
+class PipeHandle:
+    """Wrapper for an overlapped pipe handle which is vaguely file-object like.
+
+    The IOCP event loop can use these instead of socket objects.
+    """
+    def __init__(self, handle):
+        self._handle = handle
+
+    def __repr__(self):
+        if self._handle is not None:
+            handle = f'handle={self._handle!r}'
+        else:
+            handle = 'closed'
+        return f'<{self.__class__.__name__} {handle}>'
+
+    @property
+    def handle(self):
+        return self._handle
+
+    def fileno(self):
+        if self._handle is None:
+            raise ValueError("I/O operation on closed pipe")
+        return self._handle
+
+    def close(self, *, CloseHandle=_winapi.CloseHandle):
+        if self._handle is not None:
+            CloseHandle(self._handle)
+            self._handle = None
+
+    def __del__(self, _warn=warnings.warn):
+        if self._handle is not None:
+            _warn(f"unclosed {self!r}", ResourceWarning, source=self)
+            self.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, t, v, tb):
+        self.close()
+
+
+# Replacement for subprocess.Popen using overlapped pipe handles
+
+
+class Popen(subprocess.Popen):
+    """Replacement for subprocess.Popen using overlapped pipe handles.
+
+    The stdin, stdout, stderr are None or instances of PipeHandle.
+    """
+    def __init__(self, args, stdin=None, stdout=None, stderr=None, **kwds):
+        assert not kwds.get('universal_newlines')
+        assert kwds.get('bufsize', 0) == 0
+        stdin_rfd = stdout_wfd = stderr_wfd = None
+        stdin_wh = stdout_rh = stderr_rh = None
+        if stdin == PIPE:
+            stdin_rh, stdin_wh = pipe(overlapped=(False, True), duplex=True)
+            stdin_rfd = msvcrt.open_osfhandle(stdin_rh, os.O_RDONLY)
+        else:
+            stdin_rfd = stdin
+        if stdout == PIPE:
+            stdout_rh, stdout_wh = pipe(overlapped=(True, False))
+            stdout_wfd = msvcrt.open_osfhandle(stdout_wh, 0)
+        else:
+            stdout_wfd = stdout
+        if stderr == PIPE:
+            stderr_rh, stderr_wh = pipe(overlapped=(True, False))
+            stderr_wfd = msvcrt.open_osfhandle(stderr_wh, 0)
+        elif stderr == STDOUT:
+            stderr_wfd = stdout_wfd
+        else:
+            stderr_wfd = stderr
+        try:
+            super().__init__(args, stdin=stdin_rfd, stdout=stdout_wfd,
+                             stderr=stderr_wfd, **kwds)
+        except:
+            for h in (stdin_wh, stdout_rh, stderr_rh):
+                if h is not None:
+                    _winapi.CloseHandle(h)
+            raise
+        else:
+            if stdin_wh is not None:
+                self.stdin = PipeHandle(stdin_wh)
+            if stdout_rh is not None:
+                self.stdout = PipeHandle(stdout_rh)
+            if stderr_rh is not None:
+                self.stderr = PipeHandle(stderr_rh)
+        finally:
+            if stdin == PIPE:
+                os.close(stdin_rfd)
+            if stdout == PIPE:
+                os.close(stdout_wfd)
+            if stderr == PIPE:
+                os.close(stderr_wfd)

parrot/lib/python3.10/site-packages/fire-0.6.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

parrot/lib/python3.10/site-packages/fire-0.6.0.dist-info/LICENSE

@@ -0,0 +1,13 @@
+Copyright 2017 Google Inc. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

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parrot/lib/python3.10/site-packages/fire-0.6.0.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
+Root-Is-Purelib: true
+Tag: py2-none-any
+Tag: py3-none-any
+

parrot/lib/python3.10/site-packages/fire-0.6.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+fire

parrot/lib/python3.10/site-packages/jsonschema_specifications-2023.12.1.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

parrot/lib/python3.10/site-packages/jsonschema_specifications-2023.12.1.dist-info/METADATA

@@ -0,0 +1,56 @@
+Metadata-Version: 2.1
+Name: jsonschema-specifications
+Version: 2023.12.1
+Summary: The JSON Schema meta-schemas and vocabularies, exposed as a Registry
+Project-URL: Documentation, https://jsonschema-specifications.readthedocs.io/
+Project-URL: Homepage, https://github.com/python-jsonschema/jsonschema-specifications
+Project-URL: Issues, https://github.com/python-jsonschema/jsonschema-specifications/issues/
+Project-URL: Funding, https://github.com/sponsors/Julian
+Project-URL: Tidelift, https://tidelift.com/subscription/pkg/pypi-jsonschema-specifications?utm_source=pypi-jsonschema-specifications&utm_medium=referral&utm_campaign=pypi-link
+Project-URL: Source, https://github.com/python-jsonschema/jsonschema-specifications
+Author: Julian Berman
+Author-email: Julian+jsonschema-specifications@GrayVines.com
+License: MIT
+License-File: COPYING
+Keywords: data validation,json,json schema,jsonschema,validation
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: File Formats :: JSON :: JSON Schema
+Requires-Python: >=3.8
+Requires-Dist: importlib-resources>=1.4.0; python_version < '3.9'
+Requires-Dist: referencing>=0.31.0
+Description-Content-Type: text/x-rst
+
+=============================
+``jsonschema-specifications``
+=============================
+
+|PyPI| |Pythons| |CI| |ReadTheDocs|
+
+JSON support files from the `JSON Schema Specifications <https://json-schema.org/specification.html>`_ (metaschemas, vocabularies, etc.), packaged for runtime access from Python as a `referencing-based Schema Registry <https://referencing.readthedocs.io/en/stable/api/#referencing.Registry>`_.
+
+.. |PyPI| image:: https://img.shields.io/pypi/v/jsonschema-specifications.svg
+  :alt: PyPI version
+  :target: https://pypi.org/project/jsonschema-specifications/
+
+.. |Pythons| image:: https://img.shields.io/pypi/pyversions/jsonschema-specifications.svg
+  :alt: Supported Python versions
+  :target: https://pypi.org/project/jsonschema-specifications/
+
+.. |CI| image:: https://github.com/python-jsonschema/jsonschema-specifications/workflows/CI/badge.svg
+  :alt: Build status
+  :target: https://github.com/python-jsonschema/jsonschema-specifications/actions?query=workflow%3ACI
+
+.. |ReadTheDocs| image:: https://readthedocs.org/projects/jsonschema-specifications/badge/?version=stable&style=flat
+  :alt: ReadTheDocs status
+  :target: https://jsonschema-specifications.readthedocs.io/en/stable/

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parrot/lib/python3.10/site-packages/jsonschema_specifications-2023.12.1.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: hatchling 1.21.0
+Root-Is-Purelib: true
+Tag: py3-none-any

parrot/lib/python3.10/site-packages/jsonschema_specifications-2023.12.1.dist-info/licenses/COPYING

@@ -0,0 +1,19 @@
+Copyright (c) 2022 Julian Berman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

parrot/lib/python3.10/site-packages/multiprocess-0.70.16.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

parrot/lib/python3.10/site-packages/multiprocess-0.70.16.dist-info/LICENSE

@@ -0,0 +1,38 @@
+Copyright (c) 2008-2016 California Institute of Technology.
+Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
+All rights reserved.
+
+This software forks the python package "multiprocessing". Licence and
+copyright information for multiprocessing can be found in "COPYING".
+
+This software is available subject to the conditions and terms laid
+out below. By downloading and using this software you are agreeing
+to the following conditions.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+    - Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+    - Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+
+    - Neither the names of the copyright holders nor the names of any of
+      the contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+

parrot/lib/python3.10/site-packages/multiprocess-0.70.16.dist-info/METADATA

@@ -0,0 +1,203 @@
+Metadata-Version: 2.1
+Name: multiprocess
+Version: 0.70.16
+Summary: better multiprocessing and multithreading in Python
+Home-page: https://github.com/uqfoundation/multiprocess
+Download-URL: https://pypi.org/project/multiprocess/#files
+Author: Mike McKerns
+Author-email: mmckerns@uqfoundation.org
+Maintainer: Mike McKerns
+Maintainer-email: mmckerns@uqfoundation.org
+License: BSD-3-Clause
+Project-URL: Documentation, http://multiprocess.rtfd.io
+Project-URL: Source Code, https://github.com/uqfoundation/multiprocess
+Project-URL: Bug Tracker, https://github.com/uqfoundation/multiprocess/issues
+Platform: Linux
+Platform: Windows
+Platform: Mac
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Software Development
+Requires-Python: >=3.8
+License-File: LICENSE
+License-File: COPYING
+Requires-Dist: dill (>=0.3.8)
+
+-----------------------------------------------------------------
+multiprocess: better multiprocessing and multithreading in Python
+-----------------------------------------------------------------
+
+About Multiprocess
+==================
+
+``multiprocess`` is a fork of ``multiprocessing``. ``multiprocess`` extends ``multiprocessing`` to provide enhanced serialization, using `dill`. ``multiprocess`` leverages ``multiprocessing`` to support the spawning of processes using the API of the Python standard library's ``threading`` module. ``multiprocessing`` has been distributed as part of the standard library since Python 2.6.
+
+``multiprocess`` is part of ``pathos``,  a Python framework for heterogeneous computing.
+``multiprocess`` is in active development, so any user feedback, bug reports, comments,
+or suggestions are highly appreciated.  A list of issues is located at https://github.com/uqfoundation/multiprocess/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
+
+
+Major Features
+==============
+
+``multiprocess`` enables:
+
+    - objects to be transferred between processes using pipes or multi-producer/multi-consumer queues
+    - objects to be shared between processes using a server process or (for simple data) shared memory
+
+``multiprocess`` provides:
+
+    - equivalents of all the synchronization primitives in ``threading``
+    - a ``Pool`` class to facilitate submitting tasks to worker processes
+    - enhanced serialization, using ``dill``
+
+
+Current Release
+===============
+
+The latest released version of ``multiprocess`` is available from:
+
+    https://pypi.org/project/multiprocess
+
+``multiprocess`` is distributed under a 3-clause BSD license, and is a fork of ``multiprocessing``.
+
+
+Development Version
+===================
+
+You can get the latest development version with all the shiny new features at:
+
+    https://github.com/uqfoundation
+
+If you have a new contribution, please submit a pull request.
+
+
+Installation
+============
+
+``multiprocess`` can be installed with ``pip``::
+
+    $ pip install multiprocess
+
+For Python 2, a C compiler is required to build the included extension module from source. Python 3 and binary installs do not require a C compiler.
+
+
+Requirements
+============
+
+``multiprocess`` requires:
+
+    - ``python`` (or ``pypy``), **>=3.8**
+    - ``setuptools``, **>=42**
+    - ``dill``, **>=0.3.8**
+
+
+Basic Usage
+===========
+
+The ``multiprocess.Process`` class follows the API of ``threading.Thread``.
+For example ::
+
+    from multiprocess import Process, Queue
+
+    def f(q):
+        q.put('hello world')
+
+    if __name__ == '__main__':
+        q = Queue()
+        p = Process(target=f, args=[q])
+        p.start()
+        print (q.get())
+        p.join()
+
+Synchronization primitives like locks, semaphores and conditions are
+available, for example ::
+
+    >>> from multiprocess import Condition
+    >>> c = Condition()
+    >>> print (c)
+    <Condition(<RLock(None, 0)>), 0>
+    >>> c.acquire()
+    True
+    >>> print (c)
+    <Condition(<RLock(MainProcess, 1)>), 0>
+
+One can also use a manager to create shared objects either in shared
+memory or in a server process, for example ::
+
+    >>> from multiprocess import Manager
+    >>> manager = Manager()
+    >>> l = manager.list(range(10))
+    >>> l.reverse()
+    >>> print (l)
+    [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+    >>> print (repr(l))
+    <Proxy[list] object at 0x00E1B3B0>
+
+Tasks can be offloaded to a pool of worker processes in various ways,
+for example ::
+
+    >>> from multiprocess import Pool
+    >>> def f(x): return x*x
+    ...
+    >>> p = Pool(4)
+    >>> result = p.map_async(f, range(10))
+    >>> print (result.get(timeout=1))
+    [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+
+When ``dill`` is installed, serialization is extended to most objects,
+for example ::
+
+    >>> from multiprocess import Pool
+    >>> p = Pool(4)
+    >>> print (p.map(lambda x: (lambda y:y**2)(x) + x, xrange(10)))
+    [0, 2, 6, 12, 20, 30, 42, 56, 72, 90]
+
+
+More Information
+================
+
+Probably the best way to get started is to look at the documentation at
+http://multiprocess.rtfd.io. Also see ``multiprocess.tests`` for scripts that
+demonstrate how ``multiprocess`` can be used to leverge multiple processes
+to execute Python in parallel. You can run the test suite with
+``python -m multiprocess.tests``. As ``multiprocess`` conforms to the
+``multiprocessing`` interface, the examples and documentation found at
+http://docs.python.org/library/multiprocessing.html also apply to
+``multiprocess`` if one will ``import multiprocessing as multiprocess``.
+See https://github.com/uqfoundation/multiprocess/tree/master/py3.12/examples
+for a set of examples that demonstrate some basic use cases and benchmarking
+for running Python code in parallel. Please feel free to submit a ticket on
+github, or ask a question on stackoverflow (**@Mike McKerns**). If you would
+like to share how you use ``multiprocess`` in your work, please send an email
+(to **mmckerns at uqfoundation dot org**).
+
+
+Citation
+========
+
+If you use ``multiprocess`` to do research that leads to publication, we ask that you
+acknowledge use of ``multiprocess`` by citing the following in your publication::
+
+    M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
+    "Building a framework for predictive science", Proceedings of
+    the 10th Python in Science Conference, 2011;
+    http://arxiv.org/pdf/1202.1056
+
+    Michael McKerns and Michael Aivazis,
+    "pathos: a framework for heterogeneous computing", 2010- ;
+    https://uqfoundation.github.io/project/pathos
+
+Please see https://uqfoundation.github.io/project/pathos or
+http://arxiv.org/pdf/1202.1056 for further information.

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+

vllm/lib/python3.10/site-packages/torch/include/ATen/native/BatchLinearAlgebra.h

@@ -0,0 +1,321 @@
+#pragma once
+
+#include <optional>
+#include <c10/util/string_view.h>
+#include <ATen/Config.h>
+#include <ATen/native/DispatchStub.h>
+
+// Forward declare TI
+namespace at {
+class Tensor;
+struct TensorIterator;
+
+namespace native {
+enum class TransposeType;
+}
+
+}
+
+namespace at::native {
+
+enum class LapackLstsqDriverType : int64_t { Gels, Gelsd, Gelsy, Gelss};
+
+#if AT_BUILD_WITH_LAPACK()
+// Define per-batch functions to be used in the implementation of batched
+// linear algebra operations
+
+template <class scalar_t>
+void lapackCholesky(char uplo, int n, scalar_t *a, int lda, int *info);
+
+template <class scalar_t>
+void lapackCholeskyInverse(char uplo, int n, scalar_t *a, int lda, int *info);
+
+template <class scalar_t, class value_t=scalar_t>
+void lapackEig(char jobvl, char jobvr, int n, scalar_t *a, int lda, scalar_t *w, scalar_t* vl, int ldvl, scalar_t *vr, int ldvr, scalar_t *work, int lwork, value_t *rwork, int *info);
+
+template <class scalar_t>
+void lapackGeqrf(int m, int n, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);
+
+template <class scalar_t>
+void lapackOrgqr(int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);
+
+template <class scalar_t>
+void lapackOrmqr(char side, char trans, int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *c, int ldc, scalar_t *work, int lwork, int *info);
+
+template <class scalar_t, class value_t = scalar_t>
+void lapackSyevd(char jobz, char uplo, int n, scalar_t* a, int lda, value_t* w, scalar_t* work, int lwork, value_t* rwork, int lrwork, int* iwork, int liwork, int* info);
+
+template <class scalar_t>
+void lapackGels(char trans, int m, int n, int nrhs,
+    scalar_t *a, int lda, scalar_t *b, int ldb,
+    scalar_t *work, int lwork, int *info);
+
+template <class scalar_t, class value_t = scalar_t>
+void lapackGelsd(int m, int n, int nrhs,
+    scalar_t *a, int lda, scalar_t *b, int ldb,
+    value_t *s, value_t rcond, int *rank,
+    scalar_t* work, int lwork,
+    value_t *rwork, int* iwork, int *info);
+
+template <class scalar_t, class value_t = scalar_t>
+void lapackGelsy(int m, int n, int nrhs,
+    scalar_t *a, int lda, scalar_t *b, int ldb,
+    int *jpvt, value_t rcond, int *rank,
+    scalar_t *work, int lwork, value_t* rwork, int *info);
+
+template <class scalar_t, class value_t = scalar_t>
+void lapackGelss(int m, int n, int nrhs,
+    scalar_t *a, int lda, scalar_t *b, int ldb,
+    value_t *s, value_t rcond, int *rank,
+    scalar_t *work, int lwork,
+    value_t *rwork, int *info);
+
+template <LapackLstsqDriverType, class scalar_t, class value_t = scalar_t>
+struct lapackLstsq_impl;
+
+template <class scalar_t, class value_t>
+struct lapackLstsq_impl<LapackLstsqDriverType::Gels, scalar_t, value_t> {
+  static void call(
+      char trans, int m, int n, int nrhs,
+      scalar_t *a, int lda, scalar_t *b, int ldb,
+      scalar_t *work, int lwork, int *info, // Gels flavor
+      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
+      value_t *s, // Gelss flavor
+      int *iwork // Gelsd flavor
+      ) {
+    lapackGels<scalar_t>(
+        trans, m, n, nrhs,
+        a, lda, b, ldb,
+        work, lwork, info);
+  }
+};
+
+template <class scalar_t, class value_t>
+struct lapackLstsq_impl<LapackLstsqDriverType::Gelsy, scalar_t, value_t> {
+  static void call(
+      char trans, int m, int n, int nrhs,
+      scalar_t *a, int lda, scalar_t *b, int ldb,
+      scalar_t *work, int lwork, int *info, // Gels flavor
+      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
+      value_t *s, // Gelss flavor
+      int *iwork // Gelsd flavor
+      ) {
+    lapackGelsy<scalar_t, value_t>(
+        m, n, nrhs,
+        a, lda, b, ldb,
+        jpvt, rcond, rank,
+        work, lwork, rwork, info);
+  }
+};
+
+template <class scalar_t, class value_t>
+struct lapackLstsq_impl<LapackLstsqDriverType::Gelsd, scalar_t, value_t> {
+  static void call(
+      char trans, int m, int n, int nrhs,
+      scalar_t *a, int lda, scalar_t *b, int ldb,
+      scalar_t *work, int lwork, int *info, // Gels flavor
+      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
+      value_t *s, // Gelss flavor
+      int *iwork // Gelsd flavor
+      ) {
+    lapackGelsd<scalar_t, value_t>(
+        m, n, nrhs,
+        a, lda, b, ldb,
+        s, rcond, rank,
+        work, lwork,
+        rwork, iwork, info);
+  }
+};
+
+template <class scalar_t, class value_t>
+struct lapackLstsq_impl<LapackLstsqDriverType::Gelss, scalar_t, value_t> {
+  static void call(
+      char trans, int m, int n, int nrhs,
+      scalar_t *a, int lda, scalar_t *b, int ldb,
+      scalar_t *work, int lwork, int *info, // Gels flavor
+      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
+      value_t *s, // Gelss flavor
+      int *iwork // Gelsd flavor
+      ) {
+    lapackGelss<scalar_t, value_t>(
+        m, n, nrhs,
+        a, lda, b, ldb,
+        s, rcond, rank,
+        work, lwork,
+        rwork, info);
+  }
+};
+
+template <LapackLstsqDriverType driver_type, class scalar_t, class value_t = scalar_t>
+void lapackLstsq(
+    char trans, int m, int n, int nrhs,
+    scalar_t *a, int lda, scalar_t *b, int ldb,
+    scalar_t *work, int lwork, int *info, // Gels flavor
+    int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
+    value_t *s, // Gelss flavor
+    int *iwork // Gelsd flavor
+    ) {
+  lapackLstsq_impl<driver_type, scalar_t, value_t>::call(
+      trans, m, n, nrhs,
+      a, lda, b, ldb,
+      work, lwork, info,
+      jpvt, rcond, rank, rwork,
+      s,
+      iwork);
+}
+
+template <class scalar_t>
+void lapackLuSolve(char trans, int n, int nrhs, scalar_t *a, int lda, int *ipiv, scalar_t *b, int ldb, int *info);
+
+template <class scalar_t>
+void lapackLu(int m, int n, scalar_t *a, int lda, int *ipiv, int *info);
+
+template <class scalar_t>
+void lapackLdlHermitian(
+    char uplo,
+    int n,
+    scalar_t* a,
+    int lda,
+    int* ipiv,
+    scalar_t* work,
+    int lwork,
+    int* info);
+
+template <class scalar_t>
+void lapackLdlSymmetric(
+    char uplo,
+    int n,
+    scalar_t* a,
+    int lda,
+    int* ipiv,
+    scalar_t* work,
+    int lwork,
+    int* info);
+
+template <class scalar_t>
+void lapackLdlSolveHermitian(
+    char uplo,
+    int n,
+    int nrhs,
+    scalar_t* a,
+    int lda,
+    int* ipiv,
+    scalar_t* b,
+    int ldb,
+    int* info);
+
+template <class scalar_t>
+void lapackLdlSolveSymmetric(
+    char uplo,
+    int n,
+    int nrhs,
+    scalar_t* a,
+    int lda,
+    int* ipiv,
+    scalar_t* b,
+    int ldb,
+    int* info);
+
+template<class scalar_t, class value_t=scalar_t>
+void lapackSvd(char jobz, int m, int n, scalar_t *a, int lda, value_t *s, scalar_t *u, int ldu, scalar_t *vt, int ldvt, scalar_t *work, int lwork, value_t *rwork, int *iwork, int *info);
+#endif
+
+#if AT_BUILD_WITH_BLAS()
+template <class scalar_t>
+void blasTriangularSolve(char side, char uplo, char trans, char diag, int n, int nrhs, scalar_t* a, int lda, scalar_t* b, int ldb);
+#endif
+
+using cholesky_fn = void (*)(const Tensor& /*input*/, const Tensor& /*info*/, bool /*upper*/);
+DECLARE_DISPATCH(cholesky_fn, cholesky_stub);
+
+using cholesky_inverse_fn = Tensor& (*)(Tensor& /*result*/, Tensor& /*infos*/, bool /*upper*/);
+
+DECLARE_DISPATCH(cholesky_inverse_fn, cholesky_inverse_stub);
+
+using linalg_eig_fn = void (*)(Tensor& /*eigenvalues*/, Tensor& /*eigenvectors*/, Tensor& /*infos*/, const Tensor& /*input*/, bool /*compute_eigenvectors*/);
+
+DECLARE_DISPATCH(linalg_eig_fn, linalg_eig_stub);
+
+using geqrf_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/);
+DECLARE_DISPATCH(geqrf_fn, geqrf_stub);
+
+using orgqr_fn = Tensor& (*)(Tensor& /*result*/, const Tensor& /*tau*/);
+DECLARE_DISPATCH(orgqr_fn, orgqr_stub);
+
+using ormqr_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/, const Tensor& /*other*/, bool /*left*/, bool /*transpose*/);
+DECLARE_DISPATCH(ormqr_fn, ormqr_stub);
+
+using linalg_eigh_fn = void (*)(
+    const Tensor& /*eigenvalues*/,
+    const Tensor& /*eigenvectors*/,
+    const Tensor& /*infos*/,
+    bool /*upper*/,
+    bool /*compute_eigenvectors*/);
+DECLARE_DISPATCH(linalg_eigh_fn, linalg_eigh_stub);
+
+using lstsq_fn = void (*)(
+    const Tensor& /*a*/,
+    Tensor& /*b*/,
+    Tensor& /*rank*/,
+    Tensor& /*singular_values*/,
+    Tensor& /*infos*/,
+    double /*rcond*/,
+    std::string /*driver_name*/);
+DECLARE_DISPATCH(lstsq_fn, lstsq_stub);
+
+using triangular_solve_fn = void (*)(
+    const Tensor& /*A*/,
+    const Tensor& /*B*/,
+    bool /*left*/,
+    bool /*upper*/,
+    TransposeType /*transpose*/,
+    bool /*unitriangular*/);
+DECLARE_DISPATCH(triangular_solve_fn, triangular_solve_stub);
+
+using lu_factor_fn = void (*)(
+    const Tensor& /*input*/,
+    const Tensor& /*pivots*/,
+    const Tensor& /*infos*/,
+    bool /*compute_pivots*/);
+DECLARE_DISPATCH(lu_factor_fn, lu_factor_stub);
+
+using unpack_pivots_fn = void(*)(
+  TensorIterator& iter,
+  const int64_t dim_size,
+  const int64_t max_pivot);
+DECLARE_DISPATCH(unpack_pivots_fn, unpack_pivots_stub);
+
+using lu_solve_fn = void (*)(
+    const Tensor& /*LU*/,
+    const Tensor& /*pivots*/,
+    const Tensor& /*B*/,
+    TransposeType /*trans*/);
+DECLARE_DISPATCH(lu_solve_fn, lu_solve_stub);
+
+using ldl_factor_fn = void (*)(
+    const Tensor& /*LD*/,
+    const Tensor& /*pivots*/,
+    const Tensor& /*info*/,
+    bool /*upper*/,
+    bool /*hermitian*/);
+DECLARE_DISPATCH(ldl_factor_fn, ldl_factor_stub);
+
+using svd_fn = void (*)(
+    const Tensor& /*A*/,
+    const bool /*full_matrices*/,
+    const bool /*compute_uv*/,
+    const std::optional<c10::string_view>& /*driver*/,
+    const Tensor& /*U*/,
+    const Tensor& /*S*/,
+    const Tensor& /*Vh*/,
+    const Tensor& /*info*/);
+DECLARE_DISPATCH(svd_fn, svd_stub);
+
+using ldl_solve_fn = void (*)(
+    const Tensor& /*LD*/,
+    const Tensor& /*pivots*/,
+    const Tensor& /*result*/,
+    bool /*upper*/,
+    bool /*hermitian*/);
+DECLARE_DISPATCH(ldl_solve_fn, ldl_solve_stub);
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/BinaryOps.h

@@ -0,0 +1,119 @@
+#pragma once
+
+#include <ATen/core/TensorBase.h>
+#include <ATen/native/DispatchStub.h>
+#include <c10/core/Scalar.h>
+#include <c10/util/TypeSafeSignMath.h>
+
+
+namespace at {
+struct TensorIterator;
+struct TensorIteratorBase;
+}
+
+namespace at::native {
+
+inline void alpha_check(const ScalarType dtype, const Scalar& alpha) {
+  TORCH_CHECK(! alpha.isBoolean() || dtype == ScalarType::Bool,
+              "Boolean alpha only supported for Boolean results.");
+  TORCH_CHECK(isFloatingType(dtype) || isComplexType(dtype)
+              || alpha.isIntegral(true),
+              "For integral input tensors, argument alpha must not be a floating point number.");
+  TORCH_CHECK(isComplexType(dtype) || !alpha.isComplex(),
+              "For non-complex input tensors, argument alpha must not be a complex number.")
+}
+
+// Basic checking for all sub functions.
+inline void sub_check(const TensorBase& self, const TensorBase& other) {
+  TORCH_CHECK(self.scalar_type() != kBool || other.scalar_type() != kBool,
+              "Subtraction, the `-` operator, with two bool tensors is not supported. "
+              "Use the `^` or `logical_xor()` operator instead.")
+  TORCH_CHECK(self.scalar_type() != kBool && other.scalar_type() != kBool,
+              "Subtraction, the `-` operator, with a bool tensor is not supported. "
+              "If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.");
+}
+
+inline void sub_check(const TensorBase& self, const Scalar& scalar) {
+  TORCH_CHECK(self.scalar_type() != kBool || !scalar.isBoolean(),
+              "Subtraction, the `-` operator, with two bool tensors is not supported. "
+              "Use the `^` or `logical_xor()` operator instead.")
+  TORCH_CHECK(self.scalar_type() != kBool && !scalar.isBoolean(),
+              "Subtraction, the `-` operator, with a bool tensor is not supported. "
+              "If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.");
+}
+
+using structured_binary_fn_alpha = void(*)(TensorIteratorBase&, const Scalar& alpha);
+using structured_binary_fn_double = void(*)(TensorIteratorBase&, double);
+using structured_binary_fn = void(*)(TensorIteratorBase&);
+
+using binary_fn_alpha = void(*)(TensorIteratorBase&, const Scalar& alpha);
+using binary_fn_double = void(*)(TensorIterator&, double);
+using binary_fn = void(*)(TensorIterator&);
+using binary_clamp_fn_alpha =
+    void(*)(TensorIterator&, const Scalar& alpha, const Scalar& min_val, const Scalar& max_val);
+
+// NB: codegenned
+DECLARE_DISPATCH(structured_binary_fn_alpha, add_stub);
+
+DECLARE_DISPATCH(binary_clamp_fn_alpha, add_clamp_stub);
+DECLARE_DISPATCH(structured_binary_fn_alpha, sub_stub);
+DECLARE_DISPATCH(structured_binary_fn, mul_stub);
+DECLARE_DISPATCH(structured_binary_fn, div_true_stub);
+DECLARE_DISPATCH(structured_binary_fn, div_floor_stub);
+DECLARE_DISPATCH(structured_binary_fn, div_trunc_stub);
+DECLARE_DISPATCH(structured_binary_fn, atan2_stub);
+DECLARE_DISPATCH(structured_binary_fn, remainder_stub);
+DECLARE_DISPATCH(structured_binary_fn, bitwise_and_stub);
+DECLARE_DISPATCH(structured_binary_fn, bitwise_or_stub);
+DECLARE_DISPATCH(structured_binary_fn, bitwise_xor_stub);
+DECLARE_DISPATCH(structured_binary_fn, lshift_stub);
+DECLARE_DISPATCH(structured_binary_fn, rshift_stub);
+DECLARE_DISPATCH(binary_fn, logical_xor_stub);
+DECLARE_DISPATCH(binary_fn, logical_and_stub);
+DECLARE_DISPATCH(binary_fn, logical_or_stub);
+DECLARE_DISPATCH(structured_binary_fn, lt_stub);
+DECLARE_DISPATCH(structured_binary_fn, le_stub);
+DECLARE_DISPATCH(structured_binary_fn, gt_stub);
+DECLARE_DISPATCH(structured_binary_fn, ge_stub);
+DECLARE_DISPATCH(structured_binary_fn, eq_stub);
+DECLARE_DISPATCH(structured_binary_fn, ne_stub);
+DECLARE_DISPATCH(binary_fn, max_elementwise_stub);
+DECLARE_DISPATCH(binary_fn, min_elementwise_stub);
+DECLARE_DISPATCH(structured_binary_fn, maximum_stub);
+DECLARE_DISPATCH(structured_binary_fn, minimum_stub);
+DECLARE_DISPATCH(structured_binary_fn, fmax_stub);
+DECLARE_DISPATCH(structured_binary_fn, fmin_stub);
+DECLARE_DISPATCH(structured_binary_fn_double, smooth_l1_stub);
+DECLARE_DISPATCH(binary_fn_double, huber_stub);
+DECLARE_DISPATCH(structured_binary_fn, sigmoid_backward_stub);
+DECLARE_DISPATCH(binary_fn_alpha, logit_backward_stub);
+DECLARE_DISPATCH(structured_binary_fn, tanh_backward_stub);
+DECLARE_DISPATCH(structured_binary_fn, mse_stub);
+DECLARE_DISPATCH(structured_binary_fn, fmod_stub);
+DECLARE_DISPATCH(structured_binary_fn, logaddexp_stub);
+DECLARE_DISPATCH(structured_binary_fn, logaddexp2_stub);
+DECLARE_DISPATCH(structured_binary_fn, gcd_stub);
+DECLARE_DISPATCH(structured_binary_fn, lcm_stub);
+DECLARE_DISPATCH(structured_binary_fn, hypot_stub);
+DECLARE_DISPATCH(structured_binary_fn, igamma_stub);
+DECLARE_DISPATCH(structured_binary_fn, igammac_stub);
+DECLARE_DISPATCH(structured_binary_fn, nextafter_stub);
+DECLARE_DISPATCH(structured_binary_fn, heaviside_stub);
+DECLARE_DISPATCH(structured_binary_fn, copysign_stub);
+DECLARE_DISPATCH(structured_binary_fn, xlogy_stub);
+DECLARE_DISPATCH(structured_binary_fn, xlog1py_stub);
+DECLARE_DISPATCH(structured_binary_fn, zeta_stub);
+DECLARE_DISPATCH(structured_binary_fn, chebyshev_polynomial_t_stub);
+DECLARE_DISPATCH(structured_binary_fn, chebyshev_polynomial_u_stub);
+DECLARE_DISPATCH(structured_binary_fn, chebyshev_polynomial_v_stub);
+DECLARE_DISPATCH(structured_binary_fn, chebyshev_polynomial_w_stub);
+DECLARE_DISPATCH(structured_binary_fn, hermite_polynomial_h_stub);
+DECLARE_DISPATCH(structured_binary_fn, hermite_polynomial_he_stub);
+DECLARE_DISPATCH(structured_binary_fn, laguerre_polynomial_l_stub);
+DECLARE_DISPATCH(structured_binary_fn, legendre_polynomial_p_stub);
+DECLARE_DISPATCH(structured_binary_fn, shifted_chebyshev_polynomial_t_stub);
+DECLARE_DISPATCH(structured_binary_fn, shifted_chebyshev_polynomial_u_stub);
+DECLARE_DISPATCH(structured_binary_fn, shifted_chebyshev_polynomial_v_stub);
+DECLARE_DISPATCH(structured_binary_fn, shifted_chebyshev_polynomial_w_stub);
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/ComplexHelper.h

@@ -0,0 +1,97 @@
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <c10/util/irange.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/NativeFunctions.h>
+#else
+#include <ATen/ops/view_as_real_native.h>
+#include <ATen/ops/view_as_complex_native.h>
+
+#include <utility>
+#endif
+
+// WARNING: this header contains non-inline functions and should be only
+// included from ONE cpp file
+
+namespace at::native {
+
+// View tensor with new dtype, storage offset, sizes and strides
+inline Tensor view_tensor(
+    const Tensor &tensor, ScalarType dtype,
+    c10::SymInt offset, SymIntArrayRef sizes, SymIntArrayRef strides) {
+  Storage storage = tensor.storage();
+  auto key_set = tensor.key_set().remove(DispatchKey::Conjugate);
+  auto new_tensor = detail::make_tensor<TensorImpl>(
+      c10::TensorImpl::VIEW, std::move(storage), key_set, scalarTypeToTypeMeta(dtype));
+  auto * impl = new_tensor.unsafeGetTensorImpl();
+  impl->set_sizes_and_strides(sizes, strides, offset);
+  return new_tensor;
+}
+
+inline SymDimVector computeStrideForViewAsReal(SymIntArrayRef oldstride) {
+  SymDimVector res(oldstride.size() + 1);
+  for (const auto i : c10::irange(oldstride.size())) {
+    res[i] = oldstride[i] * 2;
+  }
+  res.back() = 1;
+  return res;
+}
+
+inline Tensor _view_as_real_physical(const Tensor& self) {
+  TORCH_CHECK(self.is_complex(), "view_as_real is only supported for complex tensors");
+  auto old_sizes = self.sym_sizes();
+  SymDimVector new_sizes(old_sizes.size() + 1);
+  std::copy(old_sizes.begin(), old_sizes.end(), new_sizes.begin());
+  // last dimension will always have two elements containing the real and imag vals
+  new_sizes.back() = 2;
+  auto new_strides = computeStrideForViewAsReal(self.sym_strides());
+  auto new_storage_offset = self.sym_storage_offset() * 2;
+  const auto float_type = c10::toRealValueType(self.scalar_type());
+  auto real_tensor = view_tensor(self, float_type, std::move(new_storage_offset), new_sizes, new_strides);
+  return real_tensor;
+}
+
+// expects as input a complex tensor and returns back a tensor
+// with corresponding real dtype containing the complex values
+// in the last two dimensions
+Tensor view_as_real(const Tensor& self) {
+  TORCH_CHECK(!self.is_conj(), "view_as_real doesn't work on unresolved conjugated tensors.  To resolve the conjugate tensor so you can view it as real, use self.resolve_conj(); however, be warned that the resulting tensor will NOT alias the original.");
+  return _view_as_real_physical(self);
+}
+
+inline SymDimVector computeStrideForViewAsComplex(SymIntArrayRef oldstride) {
+  const auto dim = oldstride.size();
+  TORCH_CHECK(dim > 0 && oldstride[dim - 1] == 1, "Tensor must have a last dimension with stride 1");
+
+  SymDimVector res(dim - 1);
+  for (const auto i : c10::irange(res.size())) {
+    TORCH_CHECK(oldstride[i] % 2 == 0, "Tensor must have a stride divisible by 2 for all but last dimension");
+    res[i] = oldstride[i] / 2;
+  }
+  return res;
+}
+
+// expects as input a float or double tensor with last dimension of size 2
+// and returns back a tensor with corresponding complex dtype
+Tensor view_as_complex(const Tensor& self) {
+  TORCH_CHECK(
+    self.scalar_type() == kFloat || self.scalar_type() == kDouble || self.scalar_type() == kHalf,
+    "view_as_complex is only supported for half, float and double tensors, but got a tensor of scalar type: ", self.scalar_type());
+
+  auto old_sizes = self.sym_sizes();
+  TORCH_CHECK(!old_sizes.empty(), "Input tensor must have one or more dimensions");
+  TORCH_CHECK(old_sizes[old_sizes.size()-1] == 2, "Tensor must have a last dimension of size 2");
+  SymDimVector new_sizes(old_sizes.begin(), old_sizes.end() - 1);
+
+  const auto new_strides = computeStrideForViewAsComplex(self.sym_strides());
+  const auto complex_type = c10::toComplexType(self.scalar_type());
+
+  TORCH_CHECK(self.sym_storage_offset() % 2 == 0, "Tensor must have a storage_offset divisible by 2");
+  const auto new_storage_offset = self.sym_storage_offset() / 2;
+
+  return view_tensor(self, complex_type, new_storage_offset, new_sizes, new_strides);
+}
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/CompositeRandomAccessorCommon.h

@@ -0,0 +1,263 @@
+#include <utility>
+
+#pragma once
+
+namespace at::native {
+
+namespace {
+
+// operator_brackets_proxy is used in
+// CompositeRandomAccessor in place of operator[].
+// For some iterators, references returned by operator[]
+// could become invalid, operator_brackets_proxy tries to
+// resolve that by making accessor[n] to be equivalent to
+// *(accessor + n).
+template <typename Accessor>
+class operator_brackets_proxy {
+  using reference = typename std::iterator_traits<Accessor>::reference;
+  using value_type = typename std::iterator_traits<Accessor>::value_type;
+
+public:
+  C10_HOST_DEVICE
+  operator_brackets_proxy(Accessor const& accessor)
+    : accessor(accessor)
+  {}
+
+  C10_HOST_DEVICE
+  operator reference() {
+    return *accessor;
+  }
+
+  C10_HOST_DEVICE
+  reference operator*() {
+    return *accessor;
+  }
+
+  C10_HOST_DEVICE
+  operator_brackets_proxy& operator=(value_type const& val) {
+    *accessor = val;
+    return *this;
+  }
+
+private:
+  Accessor accessor;
+};
+
+}
+
+// references_holder is used as a surrogate for the
+// references type from std::iterator_traits in CompositeRandomAccessor.
+// It is assumed in CompositeRandomAccessor that
+// References = tuple<Types&...>,
+// Values = tuple<Types...> by default,
+// but they could be anything as long as References could be
+// cast to Values.
+// If you plan to use it with STL, for example, you will need to
+// define 'swap` and `get`(aka std::get) methods.
+template <typename Values, typename References>
+class references_holder {
+public:
+  using values = Values;
+  using references = References;
+
+  C10_HOST_DEVICE
+  references_holder(references refs)
+    : refs{std::move(refs)}
+  {}
+
+  C10_HOST_DEVICE
+  operator references() {
+    return refs;
+  }
+
+  C10_HOST_DEVICE
+  operator values() {
+    return refs;
+  }
+
+  C10_HOST_DEVICE
+  references_holder& operator=(values vals) {
+    refs = vals;
+    return *this;
+  }
+
+  C10_HOST_DEVICE
+  references& data() {
+    return refs;
+  }
+
+protected:
+  references refs;
+};
+
+// CompositeRandomAccessor is essentially a simplified version of
+// a random access iterator over two random access iterators.
+// TupleInfo should contain a variadic type `tuple`, and a method `tie`,
+// which constructs a tuple of references from a variadic list of arguments.
+template <typename KeyAccessor, typename ValueAccessor, typename TupleInfo>
+class CompositeRandomAccessor {
+  using self_type = CompositeRandomAccessor<KeyAccessor, ValueAccessor, TupleInfo>;
+
+  using key_accessor_value_type =
+    typename std::iterator_traits<KeyAccessor>::value_type;
+  using value_accessor_value_type =
+    typename std::iterator_traits<ValueAccessor>::value_type;
+  using key_accessor_reference_type =
+    typename std::iterator_traits<KeyAccessor>::reference;
+  using value_accessor_reference_type =
+    typename std::iterator_traits<ValueAccessor>::reference;
+
+  using composite_value_type = typename TupleInfo::template tuple<
+    key_accessor_value_type,
+    value_accessor_value_type>;
+  using composite_reference = typename TupleInfo::template tuple<
+    key_accessor_reference_type,
+    value_accessor_reference_type>;
+
+public:
+  using value_type = composite_value_type;
+  using reference = references_holder<composite_value_type, composite_reference>;
+  // Note that CompositeRandomAccessor does not hold key and values
+  // in a specific datastructure, which means that a pointer to a (key, value)
+  // is not defined. Hence we just use a pointer type of the KeyAccessor.
+  using pointer = typename std::iterator_traits<KeyAccessor>::pointer;
+  using difference_type = typename std::iterator_traits<KeyAccessor>::difference_type;
+  using iterator_category = std::random_access_iterator_tag;
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor() = default;
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor(KeyAccessor keys, ValueAccessor values)
+    : keys(keys), values(values)
+  {}
+
+  // Pointer-like operations {
+  C10_HOST_DEVICE
+  reference operator*() const {
+    return TupleInfo::tie(*keys, *values);
+  }
+
+  // operator->() is supposed to return a pointer type.
+  // Since CompositeRandomAccessor does not hold pointers to pairs,
+  // we just return a pointer to a key.
+  C10_HOST_DEVICE
+  auto* operator->() const {
+    return keys.operator->();
+  }
+
+  C10_HOST_DEVICE
+  reference operator[](difference_type idx) {
+    return operator_brackets_proxy<self_type>(
+      CompositeRandomAccessor(keys + idx, values + idx)
+    );
+  }
+  // }
+
+  // Prefix/postfix increment/decrement {
+  C10_HOST_DEVICE
+  CompositeRandomAccessor& operator++() {
+    ++keys;
+    ++values;
+    return *this;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor operator++(int) {
+    CompositeRandomAccessor copy(*this);
+    ++*this;
+    return copy;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor& operator--() {
+    --keys;
+    --values;
+    return *this;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor operator--(int) {
+    CompositeRandomAccessor copy(*this);
+    --*this;
+    return copy;
+  }
+  // }
+
+  // Arithmetic operations {
+  C10_HOST_DEVICE
+  CompositeRandomAccessor& operator+=(difference_type offset) {
+    keys += offset;
+    values += offset;
+    return *this;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor operator+(difference_type offset) const {
+    return CompositeRandomAccessor(keys + offset, values + offset);
+  }
+
+  C10_HOST_DEVICE
+  friend CompositeRandomAccessor operator+(
+    difference_type offset,
+    const CompositeRandomAccessor& accessor
+  ) {
+    return accessor + offset;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor& operator-=(difference_type offset) {
+    keys -= offset;
+    values -= offset;
+    return *this;
+  }
+
+  C10_HOST_DEVICE
+  CompositeRandomAccessor operator-(difference_type offset) const {
+    return CompositeRandomAccessor(keys - offset, values - offset);
+  }
+
+  C10_HOST_DEVICE
+  difference_type operator-(const CompositeRandomAccessor& other) const {
+    return keys - other.keys;
+  }
+  // }
+
+  // Comparison operators {
+  C10_HOST_DEVICE
+  bool operator==(const CompositeRandomAccessor& other) const {
+    return keys == other.keys;
+  }
+
+  C10_HOST_DEVICE
+  bool operator!=(const CompositeRandomAccessor& other) const {
+    return keys != other.keys;
+  }
+
+  C10_HOST_DEVICE
+  bool operator<(const CompositeRandomAccessor& other) const {
+    return keys < other.keys;
+  }
+
+  C10_HOST_DEVICE
+  bool operator<=(const CompositeRandomAccessor& other) const {
+    return keys <= other.keys;
+  }
+
+  C10_HOST_DEVICE
+  bool operator>(const CompositeRandomAccessor& other) const {
+    return keys > other.keys;
+  }
+
+  C10_HOST_DEVICE
+  bool operator>=(const CompositeRandomAccessor& other) const {
+    return keys >= other.keys;
+  }
+  // }
+
+protected:
+  KeyAccessor keys;
+  ValueAccessor values;
+};
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/Cross.h

@@ -0,0 +1,14 @@
+#pragma once
+
+#include <ATen/native/DispatchStub.h>
+
+namespace at {
+class Tensor;
+
+namespace native {
+
+using cross_fn = void(*)(const Tensor&, const Tensor&, const Tensor&, const int64_t d);
+
+DECLARE_DISPATCH(cross_fn, cross_stub);
+
+}} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/DilatedConvolutionUtils.h

@@ -0,0 +1,229 @@
+#pragma once
+
+#include <algorithm>
+#include <vector>
+
+#include <ATen/div_rtn.h>
+#include <ATen/core/Tensor.h>
+#include <c10/util/irange.h>
+
+#define TORCH_CHECK_DIM_SIZE(T, DIM, DIM_SIZE, SIZE) \
+  TORCH_CHECK(                                       \
+      T.dim() == DIM && T.size(DIM_SIZE) == SIZE,    \
+      "Need " #T " of dimension ",                   \
+      DIM,                                           \
+      " and " #T ".size[",                           \
+      DIM_SIZE,                                      \
+      "] == ",                                       \
+      SIZE,                                          \
+      " but got input to be of shape ",              \
+      T.sizes())
+
+namespace at::native::internal {
+namespace {
+inline bool all_positive(IntArrayRef& arr) {
+  return std::all_of(
+      arr.begin(), arr.end(), [](int64_t item) { return item > 0; });
+}
+
+inline bool all_nonnegative(std::vector<int64_t>& arr) {
+  return std::all_of(
+      arr.begin(), arr.end(), [](int64_t item) { return item >= 0; });
+}
+
+} // namespace
+
+// calculate the rear part of output tensor sizes
+template <int64_t dim>
+std::vector<int64_t> get_output_size(
+    const Tensor& input,
+    IntArrayRef kernel_size,
+    IntArrayRef stride_size,
+    IntArrayRef pad_size,
+    IntArrayRef dilation_size) {
+  std::vector<int64_t> sizes;
+  for (const auto index : c10::irange(dim)) {
+    sizes.push_back(
+        div_rtn<int64_t>(
+            input.size(index + input.dim() - dim) + 2 * pad_size[index] -
+                (dilation_size[index] * (kernel_size[index] - 1) + 1),
+            stride_size[index]) +
+        1);
+  }
+  return sizes;
+}
+
+// calculate the sizes of output tensor
+template <int64_t dim>
+std::vector<int64_t> get_output_size(
+    const Tensor& input,
+    const Tensor& weight,
+    IntArrayRef kernel_size,
+    IntArrayRef stride_size,
+    IntArrayRef pad_size,
+    IntArrayRef dilation_size) {
+  auto output_size = get_output_size<dim>(
+      input, kernel_size, stride_size, pad_size, dilation_size);
+  output_size.insert(output_size.begin(), weight.size(0));
+  if (input.dim() == dim + 2) {
+    output_size.insert(output_size.begin(), input.size(0));
+  }
+  return output_size;
+}
+/*
+  slow_conv_dilated_shape_check - check user-input to dilated convolution
+  forward and backward functions.
+*/
+template <int64_t dim>
+void slow_conv_dilated_shape_check(
+    const Tensor& input,
+    const Tensor& weight,
+    const Tensor& bias,
+    const Tensor& grad_output,
+    IntArrayRef kernel_size,
+    IntArrayRef stride_size,
+    IntArrayRef pad_size,
+    IntArrayRef dilation_size) {
+  /*
+    When the following tensors are defined:
+
+    bias, grad_weight, grad_output
+
+    then these are assumed to be contiguous without checking
+    because of these tensors are made contiguous by calling
+    .contiguous() method or by resizing of zero-sized tensors in
+    forward/backward functions.
+
+    When grad_weight is defined then it is assumed without
+    checking to have the same shape as weight, see backward
+    functions.
+   */
+  // Check size arguments
+  TORCH_CHECK(
+      kernel_size.size() == dim,
+      "kernel sizes length should be ",
+      dim,
+      ", but got ",
+      kernel_size.size());
+  TORCH_CHECK(
+      stride_size.size() == dim,
+      "strides length should be ",
+      dim,
+      ", but got ",
+      stride_size.size());
+  TORCH_CHECK(
+      dilation_size.size() == dim,
+      "dilations length should be ",
+      dim,
+      ", but got ",
+      dilation_size.size());
+  TORCH_CHECK(
+      pad_size.size() == dim,
+      "pads length should be ",
+      dim,
+      ", but got ",
+      pad_size.size());
+
+  TORCH_CHECK(
+      all_positive(kernel_size),
+      "kernel size should be greater than zero, but got ",
+      kernel_size);
+  TORCH_CHECK(
+      all_positive(stride_size),
+      "stride should be greater than zero, but got ",
+      stride_size);
+  TORCH_CHECK(
+      all_positive(dilation_size),
+      "dilation should be greater than zero, but got ",
+      dilation_size);
+
+  // check input
+  TORCH_CHECK(input.defined(), "input must be defined");
+  bool is_batch = input.dim() == dim + 2;
+  int64_t n = (is_batch ? 2 : 1);
+  int64_t ndim = n + dim;
+  if (!is_batch) {
+    // input dim has to be dim + 1 if not batched
+    TORCH_CHECK(
+        input.dim() == dim + 1,
+        "input must be 4D or 5D tensor but got ",
+        input.dim(),
+        "D tensor");
+  }
+
+  // check output sizes
+  auto output_size = get_output_size<dim>(
+      input, kernel_size, stride_size, pad_size, dilation_size);
+
+  TORCH_CHECK(
+      all_nonnegative(output_size),
+      "calculated output size ",
+      output_size,
+      " is too small (all sizes must be non-negative)");
+
+  // check weight
+  TORCH_CHECK(weight.defined(), "weight must be defined");
+  TORCH_CHECK(
+      weight.dim() == dim + 2,
+      "weight must be ",
+      dim + 2,
+      "D tensor but got ",
+      weight.dim(),
+      "D tensor dim=",
+      dim);
+  TORCH_CHECK(
+      weight.sizes().slice(2) == kernel_size,
+      "weight[2:] shape ",
+      weight.sizes().slice(2),
+      " must be equal to kernel_size ",
+      kernel_size);
+
+  TORCH_CHECK_DIM_SIZE(input, input.dim(), (is_batch ? 1 : 0), weight.size(1));
+
+  // check bias when present
+  if (bias.defined()) {
+    TORCH_CHECK(
+        bias.dim() == 1,
+        "bias must be 1D tensor but got ",
+        bias.dim(),
+        "D tensor");
+    TORCH_CHECK_DIM_SIZE(bias, 1, 0, weight.size(0));
+  }
+
+  // check grad_output when present
+  if (grad_output.defined()) {
+    TORCH_CHECK(
+        grad_output.dim() == ndim,
+        "grad_output must be ",
+        ndim,
+        "D tensor but got ",
+        grad_output.dim(),
+        "D tensor");
+    if (is_batch) {
+      TORCH_CHECK(
+          grad_output.size(0) == input.size(0),
+          "grad_output.size(0)=",
+          grad_output.size(0),
+          " must be input.size(0)=",
+          input.size(0));
+    }
+    TORCH_CHECK(
+        grad_output.size(n - 1) == weight.size(0),
+        "grad_output.size(",
+        n - 1,
+        ")=",
+        grad_output.size(n - 1),
+        " must be weight.size(0)=",
+        weight.size(0));
+    TORCH_CHECK(
+        grad_output.sizes().slice(n) == output_size,
+        "grad_output[",
+        n,
+        ":] shape",
+        grad_output.sizes().slice(n),
+        " must be equal to output size ",
+        output_size);
+  }
+}
+
+} // namespace at::native::internal

vllm/lib/python3.10/site-packages/torch/include/ATen/native/DistributionTemplates.h

@@ -0,0 +1,394 @@
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
+#include <ATen/Generator.h>
+#include <ATen/ExpandUtils.h>
+#include <ATen/Tensor.h>
+#include <ATen/MemoryOverlap.h>
+#include <ATen/NamedTensorUtils.h>
+#include <ATen/native/Resize.h>
+#include <ATen/native/TensorIterator.h>
+#include <cmath>
+#include <limits>
+#include <optional>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/empty_like.h>
+#include <ATen/ops/empty.h>
+#include <ATen/ops/full.h>
+#include <ATen/ops/view_as_real.h>
+#endif
+
+namespace at::native::templates {
+
+// ==================================================== Random ========================================================
+
+// The purpose of `update_from` and `update_to` is to find the closest valid int64_t number that can be used as actual `from`.
+// The current implementation of `random_` uses uint64_t arithmetics and casts the result to the target dtype(scalar_t).
+// This casting can result in generating numbers that happen to be greater or equal to `to` value. For instance:
+//
+//    auto actual = torch::empty({3, 3}, torch::half);
+//    actual.random_(0, 65504);
+//
+// If random's uint64_t arithmetics produces 65503 as a random value after casting to torch::half it becomes 65504
+// and violates the requirement that random value must be less than `to`. To resolve this issue `update_from` and `update_to`
+// moves `from` to the right and `to` to the left to the next closest value that won't go outside [from, to) after casting to
+// the target dtype. For `to` = 65504 it moves left for (1 << (log2(to) - 11 + 1)) = 32 and becomes 65472, which is previous
+// available number for torch::half dtype.
+template<typename scalar_t>
+int64_t update_from(int64_t from) {
+  static_assert(
+    std::is_floating_point<scalar_t>::value ||
+    std::is_same<scalar_t, at::Half>::value ||
+    std::is_same<scalar_t, at::BFloat16>::value, "scalar_t must be floating-point type");
+  const auto from_plus_1 = static_cast<int64_t>(static_cast<scalar_t>(from + 1));
+  if (from_plus_1 < from) {
+    int64_t from_ = std::abs(from + 1);
+    int n = 0;
+    while (from_ >>= 1) ++n;
+    // NOLINTNEXTLINE(clang-analyzer-core.UndefinedBinaryOperatorResult)
+    from = from_plus_1 + (1LL << (n - std::numeric_limits<scalar_t>::digits + 1));
+  }
+  return from;
+}
+
+template<typename scalar_t>
+int64_t update_to(int64_t to) {
+  static_assert(
+    std::is_floating_point<scalar_t>::value ||
+    std::is_same<scalar_t, at::Half>::value ||
+    std::is_same<scalar_t, at::BFloat16>::value, "scalar_t must be floating-point type");
+  const auto to_minus_1 = static_cast<int64_t>(static_cast<scalar_t>(to - 1));
+  if (to_minus_1 >= to) {
+    int64_t to_ = std::abs(to - 1);
+    int n = 0;
+    while (to_ >>= 1) ++n;
+    // NOLINTNEXTLINE(clang-analyzer-core.UndefinedBinaryOperatorResult)
+    to = to_minus_1 - (1LL << (n - std::numeric_limits<scalar_t>::digits + 1));
+  }
+  return to;
+}
+
+// Return earlier for not invoking kernel.
+// See https://github.com/pytorch/pytorch/issues/103418 for more details
+#define CHECK_EMPTY_AND_RETURN(tensor) \
+  if (tensor.numel() == 0) {  \
+    return tensor;  \
+  }
+
+template<template<typename> class random_kernel, typename RNG>
+at::Tensor& random_impl(at::Tensor& self, std::optional<Generator> generator) {
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = at::TensorIterator::borrowing_nullary_op(self);
+  random_kernel<RNG>()(iter, generator);
+  return self;
+}
+
+#define CHECK_OUT_OF_BOUNDS(var, name, min, max, dtype) \
+  TORCH_CHECK(var >= min && var <= max, name , " is out of bounds for ", dtype); \
+
+#define WARN_OUT_OF_BOUNDS(var, name, digits, dtype) \
+  if (var < -(1LL << digits) || var > (1LL << digits)) { \
+    TORCH_WARN(name , " is out of bounds [-(2^", digits, "), 2^", digits, "]. ", \
+      "Due to precision limitations ", dtype, " can support discrete uniform distribution only within this range. ", \
+      "This warning will become an error in version 1.7 release, please fix the code in advance"); \
+  }
+
+inline void check_from_to_in_range(int64_t from, int64_t to_inc, caffe2::TypeMeta dtype) {
+  const auto scalar_type = typeMetaToScalarType(dtype);
+  if (isFloatingType(scalar_type)) {
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, scalar_type, "check_random_fp_bounds", [&] {
+      const auto min = static_cast<double>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<double>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", min, max, dtype);
+
+      constexpr auto digits = std::numeric_limits<scalar_t>::digits;
+      WARN_OUT_OF_BOUNDS(from, "from", digits, dtype);
+      WARN_OUT_OF_BOUNDS(to_inc, "to - 1", digits, dtype);
+    });
+  } else if (scalar_type == kUInt64) {
+    // When you do a comparison between int64_t and uint64_t, the usual
+    // arithmetic conversions say that the int64_t value is promoted to
+    // unsigned. But this conversion wraps around: if I had -1 as my int64_t,
+    // then it will promote to 0xFFFFFFFFFFFFFFFF in uint64_t. This is never
+    // the right thing to do.
+    CHECK_OUT_OF_BOUNDS(from, "from", 0, INT64_MAX, dtype);
+    CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", 0, INT64_MAX, dtype);
+  } else if (isIntegralType(scalar_type, /*includeBool=*/true)) {
+    AT_DISPATCH_V2(scalar_type, "check_random_integral_bounds", AT_WRAP([&]() {
+      const auto min = static_cast<int64_t>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<int64_t>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", min, max, dtype);
+    }), AT_EXPAND(AT_INTEGRAL_TYPES), kUInt16, kUInt32, kBool);
+  } else {
+    TORCH_CHECK(false, "check_random_bounds handles only integral, floating-point and boolean types");
+  }
+}
+
+template<template<typename> class random_from_to_kernel, typename RNG>
+at::Tensor& random_from_to_impl(at::Tensor& self, int64_t from, std::optional<int64_t> to_opt, std::optional<Generator> generator) {
+  uint64_t range = 0;
+  auto iter = at::TensorIterator::borrowing_nullary_op(self);
+  if (to_opt.has_value()) {
+    // [from, to)
+    int64_t to = *to_opt;
+    TORCH_CHECK(from < to, "random_ expects 'from' to be less than 'to', but got from=", from, " >= to=", to);
+    if (isFloatingType(iter.dtype())) {
+      AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "random_update_from_to", [&] {
+        from = update_from<scalar_t>(from);
+        to = update_to<scalar_t>(to);
+        TORCH_CHECK(from < to, "random_ expects 'from' casted to dtype to be less than 'to' casted to dtype, but got from=", from, " >= to=", to);
+      });
+    }
+    check_from_to_in_range(from, to - 1, self.dtype());
+    CHECK_EMPTY_AND_RETURN(self);
+    range = static_cast<uint64_t>(to) - static_cast<uint64_t>(from);
+    random_from_to_kernel<RNG>()(iter, range, from, generator);
+  } else if (from != std::numeric_limits<int64_t>::lowest()) {
+    // [from, std::numeric_limits<int64_t>::max()]
+    int64_t to_inc = 0;
+    if (isFloatingType(iter.dtype())) {
+      AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "random_from_to_range_calc", [&] {
+        constexpr int64_t scalar_t_max = static_cast<int64_t>(1) << std::numeric_limits<scalar_t>::digits;
+        to_inc = scalar_t_max > std::numeric_limits<int64_t>::max() ? std::numeric_limits<int64_t>::max() : static_cast<int64_t>(scalar_t_max);
+        from = update_from<scalar_t>(from);
+        TORCH_CHECK(from < to_inc, "random_ expects 'from' casted to dtype to be less than or equal to 'to_inc' casted to dtype, but got from=", from, " > to_inc=", to_inc);
+      });
+    } else if (isIntegralType(iter.dtype(), /*includeBool=*/true)) {
+      AT_DISPATCH_V2(self.scalar_type(), "random_from_to_range_calc", AT_WRAP([&] {
+        if constexpr (std::is_same_v<scalar_t, bool>) {
+          to_inc = static_cast<int64_t>(true);
+        } else {
+          to_inc = static_cast<int64_t>(std::numeric_limits<scalar_t>::max());
+        }
+      }), AT_EXPAND(AT_INTEGRAL_TYPES_V2), kBool);
+    } else {
+      TORCH_CHECK(false, "random_from_to_impl handles only integral, floating-point and boolean types");
+    }
+    check_from_to_in_range(from, to_inc, self.dtype());
+    CHECK_EMPTY_AND_RETURN(self);
+    range = static_cast<uint64_t>(to_inc) - static_cast<uint64_t>(from) + 1;
+    random_from_to_kernel<RNG>()(iter, range, from, generator);
+  } else {
+    // [std::numeric_limits<int64_t>::lowest(), std::numeric_limits<int64_t>::max()]
+    // range = 2^64
+    CHECK_EMPTY_AND_RETURN(self);
+    random_from_to_kernel<RNG>()(iter, generator);
+  }
+  return self;
+}
+
+// ==================================================== Normal ========================================================
+
+#define CHECK_NORMAL_TENSOR_STD(std) \
+  do { \
+    TORCH_CHECK( \
+      !std.is_complex(), \
+      "normal expects standard deviation to be non-complex"); \
+    TORCH_CHECK( \
+      std.numel() == 0 || std.is_meta() || std.min().ge(0).item<bool>(), \
+      "normal expects all elements of std >= 0.0"); \
+  } while (0)
+
+#define CHECK_NORMAL_STD(std) \
+  TORCH_CHECK(std >= 0.0, "normal expects std >= 0.0, but found std ", std);
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_impl_(Tensor& self, double mean, double std, std::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  CHECK_EMPTY_AND_RETURN(self);
+
+  if (self.is_complex()) {
+    auto float_tensor = at::view_as_real(self);
+    // variance for normal distribution of the real and imaginary values
+    // is half of the input variance
+    normal_kernel<RNG>()(float_tensor, mean, std/(std::sqrt(2)), gen);
+  } else {
+    normal_kernel<RNG>()(self, mean, std, gen);
+  }
+  return self;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, const Tensor& mean, double std, std::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  auto std_tensor = at::empty_like(output, MemoryFormat::Contiguous);
+  auto shape = at::infer_size(mean.sizes(), std_tensor.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, std, gen);
+  output.add_(mean);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, double mean, const Tensor& std, std::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto mean_tensor = at::full({}, mean, output.options());
+  auto shape = at::infer_size(mean_tensor.sizes(), std.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, 1, gen);
+  // CUDA NB: addcmul_out copies the tensor to be added into the output.
+  // The previous function here was addcmul_out(output, mean_tensor, output, std, 1);
+  // The third argument is not a constant reference and hence the samples in output are overwritten.
+  // Consequently, the computation performed is mean_tensor + mean_tensor * std instead of mean_tensor + output * std
+  output.mul_(std).add_(mean_tensor);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor& normal_out_impl(Tensor& output, const Tensor& mean, const Tensor& std, std::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto shape = at::infer_size(mean.sizes(), std.sizes());
+  at::native::resize_output(output, shape);
+  normal_impl_<normal_kernel, RNG>(output, 0, 1, gen);
+  // CUDA NB: addcmul_out copies the tensor to be added into the output.
+  // The previous function here was addcmul_out(output, mean, output, std, 1);
+  // The third argument is not a constant reference and hence the samples in output are overwritten.
+  // Consequently, the computation performed is mean + mean * std instead of mean + output * std
+  output.mul_(std).add_(mean);
+  return output;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(const Tensor& mean, double std, std::optional<Generator> gen) {
+  CHECK_NORMAL_STD(std);
+  Tensor ret = at::empty_like(mean, MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(double mean, const Tensor& std, std::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  Tensor ret = at::empty_like(std, MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+template<template<typename> class normal_kernel, typename RNG>
+Tensor normal_impl(const Tensor& mean, const Tensor& std, std::optional<Generator> gen) {
+  CHECK_NORMAL_TENSOR_STD(std);
+  auto shape = at::infer_size(mean.sizes(), std.sizes());
+  Tensor ret = at::empty(shape, mean.options(), MemoryFormat::Contiguous);
+  normal_out_impl<normal_kernel, RNG>(ret, mean, std, gen);
+  return ret;
+}
+
+// ==================================================== Uniform =======================================================
+
+template<template<typename> class uniform_kernel, typename RNG>
+at::Tensor& uniform_impl_(at::Tensor& self, double from, double to, std::optional<Generator> generator) {
+  if (self.is_complex()) {
+    CHECK_EMPTY_AND_RETURN(self);
+    auto float_tensor = at::view_as_real(self);
+    uniform_impl_<uniform_kernel, RNG>(float_tensor, from, to, generator);
+  } else {
+    AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "check_uniform_bounds", [&] {
+      [[maybe_unused]] const auto dtype = self.dtype();
+      const auto min = static_cast<double>(std::numeric_limits<scalar_t>::lowest());
+      const auto max = static_cast<double>(std::numeric_limits<scalar_t>::max());
+      CHECK_OUT_OF_BOUNDS(from, "from", min, max, dtype);
+      CHECK_OUT_OF_BOUNDS(to, "to", min, max, dtype);
+      TORCH_CHECK(from <= to, "uniform_ expects to return a [from, to) range, but found from=", from, " > to=", to);
+      TORCH_CHECK((to - from) <= std::numeric_limits<scalar_t>::max(),
+            "uniform_ expects to-from <= std::numeric_limits<", toString(self.scalar_type()),
+            ">::max(), but found to=", to, " and from=", from,
+            " which result in to-from to exceed the limit");
+      from = std::min(std::max(from, min), max);
+      to = std::max(std::min(to, max), min);
+    });
+    CHECK_EMPTY_AND_RETURN(self);
+    auto iter = at::TensorIterator::borrowing_nullary_op(self);
+    uniform_kernel<RNG>()(iter, from, to, generator);
+  }
+  return self;
+}
+
+// ================================================== LogNormal =======================================================
+
+template<template<typename> class log_normal_kernel, typename RNG>
+at::Tensor& log_normal_impl_(at::Tensor& self, double mean, double std, std::optional<Generator> gen) {
+  TORCH_CHECK(std > 0.0, "log_normal_ expects std > 0.0, but found std=", std);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  log_normal_kernel<RNG>()(iter, mean, std, gen);
+  return self;
+}
+
+// =================================================== Geometric ======================================================
+
+template<template<typename> class geometric_kernel, typename RNG>
+Tensor& geometric_impl_(Tensor& self, double p, std::optional<Generator> gen) {
+  TORCH_CHECK(0 < p && p < 1, "geometric_ expects p to be in (0, 1), but got p=", p);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  geometric_kernel<RNG>()(iter, p, gen);
+  return self;
+}
+
+// ================================================== Exponential =====================================================
+
+template<template<typename> class exponential_kernel, typename RNG>
+Tensor& exponential_impl_(Tensor& self, double lambda, std::optional<Generator> gen) {
+  TORCH_CHECK(lambda > 0.0, "exponential_ expects lambda > 0.0, but found lambda=", lambda);
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  exponential_kernel<RNG>()(iter, lambda, gen);
+  return self;
+}
+
+// ==================================================== Cauchy ========================================================
+
+template<template<typename> class cauchy_kernel, typename RNG>
+Tensor& cauchy_impl_(Tensor& self, double median, double sigma, std::optional<Generator> gen) {
+  // TODO: instead of variable name 'sigma', use 'gamma' or 'scale'
+  // the variance, squared sigma, is undefined for cauchy distribution
+  TORCH_CHECK(sigma > 0.0, "cauchy_ expects sigma > 0.0, but found sigma=", sigma);
+  TORCH_CHECK(at::isFloatingType(self.scalar_type()), "Cauchy distribution is a continuous probability distribution. dtype must be a floating point but you specified ", self.dtype());
+  CHECK_EMPTY_AND_RETURN(self);
+  auto iter = TensorIterator::borrowing_nullary_op(self);
+  cauchy_kernel<RNG>()(iter, median, sigma, gen);
+  return self;
+}
+
+// ==================================================== Bernoulli =====================================================
+
+template<template<typename> class bernoulli_tensor_kernel, typename RNG>
+Tensor& bernoulli_impl_(Tensor& self, const Tensor& p_, std::optional<Generator> gen) {
+  CHECK_EMPTY_AND_RETURN(self);
+  NoNamesGuard guard;
+  at::assert_no_internal_overlap(self);
+  bernoulli_tensor_kernel<RNG>()(self, p_, gen);
+  return self;
+}
+
+template<template<typename> class bernoulli_scalar_kernel, typename RNG>
+Tensor& bernoulli_impl_(Tensor& self, double p, std::optional<Generator> gen) {
+  TORCH_CHECK(0 <= p && p <= 1, "bernoulli_ expects p to be in [0, 1], but got p=", p);
+  CHECK_EMPTY_AND_RETURN(self);
+  at::assert_no_internal_overlap(self);
+  bernoulli_scalar_kernel<RNG>()(self, p, gen);
+  return self;
+}
+
+template<template<typename> class bernoulli_tensor_kernel, typename RNG>
+Tensor& bernoulli_out_impl(Tensor& result, const Tensor& self, std::optional<Generator> gen) {
+  // result.resize_as_(self) requires self to have same dtype as result, so we
+  // use resize_ instead.
+  // TODO: Fix resize_as_. See pytorch/pytorch#11665.
+  result.resize_(self.sizes());
+  bernoulli_impl_<bernoulli_tensor_kernel, RNG>(result, self, gen);
+  namedinference::propagate_names(result, self);
+  return result;
+}
+
+#undef CHECK_OUT_OF_BOUNDS
+#undef WARN_OUT_OF_BOUNDS
+
+} // namespace at::native::templates

vllm/lib/python3.10/site-packages/torch/include/ATen/native/EmbeddingBag.h

@@ -0,0 +1,153 @@
+#include <ATen/core/Tensor.h>
+#include <ATen/Config.h>
+#include <cstdint>
+
+#ifdef USE_FBGEMM
+#include <fbgemm/FbgemmEmbedding.h>
+#endif
+
+namespace at::native {
+
+enum class EmbeddingBagMode {
+  SUM = 0,
+  MEAN = 1,
+  MAX = 2,
+};
+
+[[maybe_unused]] static bool operator==(int64_t op1, EmbeddingBagMode op2) {
+  return op1 == static_cast<int64_t>(op2);
+}
+
+[[maybe_unused]] static bool operator!=(int64_t op1, EmbeddingBagMode op2) {
+  return !(op1 == op2);
+}
+
+void check_arguments(
+    const Tensor& weight,
+    const Tensor& indices,
+    const Tensor& offsets,
+    const int64_t mode,
+    const std::optional<Tensor>& per_sample_weights,
+    bool include_last_offset);
+
+void make_bag_size_out(
+    Tensor& bag_size_out,
+    const Tensor& offsets,
+    const Tensor& indices,
+    const int64_t mode,
+    const bool include_last_offset,
+    const bool requires_grad);
+
+void make_max_indices_out(
+    Tensor& max_indices_out,
+    const Tensor& weight,
+    const Tensor& indices,
+    const Tensor& offsets,
+    const Tensor& bag_size,
+    const int64_t mode,
+    bool include_last_offset);
+
+void make_offset2bag_out(
+    Tensor& offset2bag,
+    Tensor& output,
+    const Tensor& weight,
+    const Tensor& indices,
+    const Tensor& offsets,
+    const int64_t mode,
+    const std::optional<Tensor>& per_sample_weights,
+    const int64_t padding_idx = -1);
+
+#ifdef USE_FBGEMM
+
+template<bool has_weight, typename TIndex, typename TData>
+struct _CallbackAndBlockSize {
+    using TCallback = typename fbgemm::EmbeddingSpMDMKernelSignature<TData, TIndex, TIndex, TData>::Type;
+
+    int64_t blockSize = -1;
+    TCallback callback = nullptr;
+
+    static TCallback generateCallback(int64_t block_size) {
+        return fbgemm::GenerateEmbeddingSpMDM<TData, TIndex, TIndex, TData>(
+                block_size,
+                has_weight,
+                /* normalize_by_lengths */false,
+                /* prefetch */16,
+                /* is_weight_positional */false,
+                /* use_offsets */true);
+    }
+
+    _CallbackAndBlockSize() = default;
+
+    explicit _CallbackAndBlockSize(std::optional<int64_t> maybe_block_size)
+      : blockSize(maybe_block_size.value_or(-1))
+      , callback(maybe_block_size.has_value() ? generateCallback(maybe_block_size.value()) : nullptr)
+    {}
+};
+
+template<typename... StorageMixins>
+struct _EmbeddingBagKernelCacheImpl : private StorageMixins... {
+
+    _EmbeddingBagKernelCacheImpl() = default;
+    // use each of the mixins to store corresponding kernel and block size
+    explicit _EmbeddingBagKernelCacheImpl(std::optional<int64_t> maybe_block_size)
+      : StorageMixins(maybe_block_size)...
+    {}
+
+    // this method is thread safe (call sites may call from different threads)
+    template<bool has_weight, typename TIndex, typename TData>
+    typename _CallbackAndBlockSize<has_weight, TIndex, TData>::TCallback
+    getCallback(int64_t block_size) const {
+        // if the cache doesn't store the kernel for the incoming block size
+        // (so it is different from the one stored in corresponding mixin)
+        // regenerate the kernel (not writing it into the cache so we avoid locks)
+        if (block_size != _CallbackAndBlockSize<has_weight, TIndex, TData>::blockSize) {
+            return _CallbackAndBlockSize<has_weight, TIndex, TData>::generateCallback(block_size);
+        }
+        // else retrieve the cached kernel from the corresponding mixin
+        return _CallbackAndBlockSize<has_weight, TIndex, TData>::callback;
+    }
+};
+
+// instantiate the cache with the list of storage mixins
+// for each of the 8 _EmbeddingBagKernelCache* usages in the EmbeddingBag.cpp impl file
+using _EmbeddingBagKernelCache = _EmbeddingBagKernelCacheImpl<
+    _CallbackAndBlockSize<true, int32_t, float>,
+    _CallbackAndBlockSize<false, int32_t, float>,
+    _CallbackAndBlockSize<true, int64_t, float>,
+    _CallbackAndBlockSize<false, int64_t, float>,
+    _CallbackAndBlockSize<true, int32_t, unsigned short>,
+    _CallbackAndBlockSize<false, int32_t, unsigned short>,
+    _CallbackAndBlockSize<true, int64_t, unsigned short>,
+    _CallbackAndBlockSize<false, int64_t, unsigned short>>;
+#else
+struct _EmbeddingBagKernelCache {
+    explicit _EmbeddingBagKernelCache(std::optional<int64_t> /* maybe_block_size */) {}
+};
+#endif
+
+void _embedding_bag_cpu_impl_out(Tensor& output, Tensor& offset2bag,
+    Tensor& bag_size, Tensor* max_indices,
+    const Tensor &weight, const Tensor &indices,
+    const Tensor &offsets, const int64_t mode = 0,
+    const std::optional<Tensor>& per_sample_weights = std::nullopt,
+    bool include_last_offset = false,
+    int64_t padding_idx = -1,
+    _EmbeddingBagKernelCache* fbgemm_kernel_cache = nullptr);
+
+void _embedding_bag_cpu_out(
+    at::Tensor& output,
+    at::Tensor& offset2bag,
+    at::Tensor& bag_size,
+    at::Tensor* p_max_indices,
+    const at::Tensor& weight,
+    const at::Tensor& indices,
+    const at::Tensor& offsets,
+    const bool scale_grad_by_freq,
+    const int64_t mode,
+    const bool sparse,
+    const std::optional<at::Tensor>& per_sample_weights,
+    const bool include_last_offset,
+    const std::optional<int64_t>& padding_idx,
+    _EmbeddingBagKernelCache* fbgemm_kernel_cache = nullptr);
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/FusedSGD.h

@@ -0,0 +1,21 @@
+#include <ATen/core/Tensor.h>
+#include <ATen/native/DispatchStub.h>
+
+namespace at::native {
+
+using fused_sgd_fn = void (*)(
+    const at::Tensor& param,
+    const at::Tensor& grad,
+    const at::Tensor& momentum_buffer,
+    const double weight_decay,
+    const double momentum,
+    const double lr,
+    const double dampening,
+    const bool nesterov,
+    const bool maximize,
+    const bool is_first_step,
+    const float* grad_scale_ptr);
+
+DECLARE_DISPATCH(fused_sgd_fn, fused_sgd_stub);
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/GridSampler.h

@@ -0,0 +1,298 @@
+#pragma once
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <utility>
+
+#include <ATen/native/GridSamplerUtils.h>
+
+namespace at::native {
+
+using detail::GridSamplerInterpolation;
+using detail::GridSamplerPadding;
+
+// Unnormalizes a coordinate from the -1 to +1 scale to its pixel index value,
+// where we view each pixel as an area between (idx - 0.5) and (idx + 0.5).
+// if align_corners: -1 and +1 get sent to the centers of the corner pixels
+//     -1 --> 0
+//     +1 --> (size - 1)
+//     scale_factor = (size - 1) / 2
+// if not align_corners: -1 and +1 get sent to the image edges
+//     -1 --> -0.5
+//     +1 --> (size - 1) + 0.5 == size - 0.5
+//     scale_factor = size / 2
+template <typename scalar_t>
+static inline scalar_t grid_sampler_unnormalize(scalar_t coord, int64_t size,
+                                                bool align_corners) {
+  if (align_corners) {
+    // unnormalize coord from [-1, 1] to [0, size - 1]
+    return ((coord + 1) / 2) * (size - 1);
+  } else {
+    // unnormalize coord from [-1, 1] to [-0.5, size - 0.5]
+    return ((coord + 1) * size - 1) / 2;
+  }
+}
+
+// grid_sampler_unnormalize_set_grad works the same as grid_sampler_unnormalize
+// except that it also returns the `d output / d input` via pointer argument
+// `grad_in`.
+// This is useful in the backward pass of grid_sampler.
+template <typename scalar_t>
+static inline scalar_t grid_sampler_unnormalize_set_grad(scalar_t coord, int64_t size,
+                                                         bool align_corners, scalar_t *grad_in) {
+  if (align_corners) {
+    // unnormalize coord from [-1, 1] to [0, size - 1]
+    *grad_in = static_cast<scalar_t>(size - 1) / 2;
+    return ((coord + 1) / 2) * (size - 1);
+  } else {
+    // unnormalize coord from [-1, 1] to [-0.5, size - 0.5]
+    *grad_in = static_cast<scalar_t>(size) / 2;
+    return ((coord + 1) * size - 1) / 2;
+  }
+}
+
+// Clips coordinates to between 0 and clip_limit - 1
+template<typename scalar_t>
+static inline scalar_t clip_coordinates(scalar_t in, int64_t clip_limit) {
+  return std::min(static_cast<scalar_t>(clip_limit - 1), std::max(in, static_cast<scalar_t>(0)));
+}
+
+// clip_coordinates_set_grad works similarly to clip_coordinates except that
+// it also returns the `d output / d input` via pointer argument `grad_in`.
+// This is useful in the backward pass of grid_sampler.
+template<typename scalar_t>
+static inline scalar_t clip_coordinates_set_grad(scalar_t in, int64_t clip_limit,
+                                                 scalar_t *grad_in) {
+  // Note that it is important for the gradient calculation that borders
+  // are considered out of bounds.
+  if (in <= static_cast<scalar_t>(0)) {
+    *grad_in = static_cast<scalar_t>(0);
+    return static_cast<scalar_t>(0);
+  } else {
+    scalar_t max = static_cast<scalar_t>(clip_limit - 1);
+    if (in >= max) {
+      *grad_in = static_cast<scalar_t>(0);
+      return max;
+    } else {
+      *grad_in = static_cast<scalar_t>(1);
+      return in;
+    }
+  }
+}
+
+// Reflects coordinates until they fall between low and high (inclusive).
+// The bounds are passed as twice their value so that half-integer values
+// can be represented as ints.
+template<typename scalar_t>
+static inline scalar_t reflect_coordinates(scalar_t in, int64_t twice_low,
+                                           int64_t twice_high) {
+  if (twice_low == twice_high) {
+    return static_cast<scalar_t>(0);
+  }
+  scalar_t min = static_cast<scalar_t>(twice_low) / 2;
+  scalar_t span = static_cast<scalar_t>(twice_high - twice_low) / 2;
+  in = std::fabs(in - min);
+  // `fmod` returns same sign as `in`, which is positive after the `fabs` above.
+  scalar_t extra = std::fmod(in, span);
+  int flips = static_cast<int>(std::floor(in / span));
+  if (flips % 2 == 0) {
+    return extra + min;
+  } else {
+    return span - extra + min;
+  }
+}
+
+// reflect_coordinates_set_grad works similarly to reflect_coordinates except
+// that it also returns the `d output / d input` via pointer argument
+// `grad_in`.
+// This is useful in the backward pass of grid_sampler.
+template<typename scalar_t>
+static inline scalar_t reflect_coordinates_set_grad(scalar_t in, int64_t twice_low,
+                                                    int64_t twice_high, scalar_t *grad_in) {
+  if (twice_low == twice_high) {
+    *grad_in = static_cast<scalar_t>(0);
+    return static_cast<scalar_t>(0);
+  }
+  int grad_in_mult_;
+  scalar_t min = static_cast<scalar_t>(twice_low) / 2;
+  scalar_t span = static_cast<scalar_t>(twice_high - twice_low) / 2;
+  in = in - min;
+  if (in < static_cast<scalar_t>(0)) {
+    grad_in_mult_ = -1;
+    in = -in;
+  } else {
+    grad_in_mult_ = 1;
+  }
+  // `fmod` returns same sign as `in`, which is positive after the `if` above.
+  scalar_t extra = std::fmod(in, span);
+  int flips = static_cast<int>(std::floor(in / span));
+  if (flips % 2 == 0) {
+    *grad_in = static_cast<scalar_t>(grad_in_mult_);
+    return extra + min;
+  } else {
+    *grad_in = static_cast<scalar_t>(-grad_in_mult_);
+    return span - extra + min;
+  }
+}
+
+// Mapping the out-of-boundary points back into boundary
+// This would only affect padding_mode=border or reflection
+template<typename scalar_t>
+static inline scalar_t compute_coordinates(scalar_t coord, int64_t size,
+                                           GridSamplerPadding padding_mode,
+                                           bool align_corners) {
+  if (padding_mode == GridSamplerPadding::Border) {
+    // clip coordinates to image borders
+    coord = clip_coordinates(coord, size);
+  } else if (padding_mode == GridSamplerPadding::Reflection) {
+    // reflect coordinates by image borders
+    if (align_corners) {
+      coord = reflect_coordinates(coord, 0, 2*(size - 1));
+    } else {
+      coord = reflect_coordinates(coord, -1, 2*size - 1);
+    }
+    // clip coordinates to image borders
+    coord = clip_coordinates(coord, size);
+  }
+  return coord;
+}
+
+// Computes the pixel source index value for a grid coordinate
+template <typename scalar_t>
+static inline scalar_t grid_sampler_compute_source_index(
+    scalar_t coord,
+    int64_t size,
+    GridSamplerPadding padding_mode,
+    bool align_corners) {
+  coord = grid_sampler_unnormalize(coord, size, align_corners);
+  coord = compute_coordinates(coord, size, padding_mode, align_corners);
+  return coord;
+}
+
+// grid_sampler_compute_source_index_set_grad works similarly to
+// grid_sampler_compute_source_index except that it also returns the
+// `d output / d input` via pointer argument `grad_in`.
+// This is useful in the backward pass of grid_sampler.
+template <typename scalar_t>
+static inline scalar_t grid_sampler_compute_source_index_set_grad(
+    scalar_t coord,
+    int64_t size,
+    GridSamplerPadding padding_mode,
+    bool align_corners,
+    scalar_t *grad_in) {
+  scalar_t grad_clip, grad_refl;
+  coord = grid_sampler_unnormalize_set_grad(coord, size, align_corners, grad_in);
+  if (padding_mode == GridSamplerPadding::Border) {
+    // clip coordinates to image borders
+    coord = clip_coordinates_set_grad(coord, size, &grad_clip);
+    *grad_in = (*grad_in) * grad_clip;
+  } else if (padding_mode == GridSamplerPadding::Reflection) {
+    // reflect coordinates by image borders
+    if (align_corners) {
+      coord = reflect_coordinates_set_grad(coord, 0, 2*(size - 1), &grad_refl);
+    } else {
+      coord = reflect_coordinates_set_grad(coord, -1, 2*size - 1, &grad_refl);
+    }
+    // clip coordinates to image borders
+    coord = clip_coordinates_set_grad(coord, size, &grad_clip);
+    *grad_in = (*grad_in) * grad_refl * grad_clip;
+  }
+  return coord;
+}
+
+static inline bool within_bounds_2d(int64_t h, int64_t w, int64_t H, int64_t W) {
+  return h >= 0 && h < H && w >= 0 && w < W;
+}
+
+static inline bool within_bounds_3d(int64_t d, int64_t h, int64_t w, int64_t D, int64_t H, int64_t W) {
+  return d >= 0 && d < D && h >= 0 && h < H && w >= 0 && w < W;
+}
+
+template<typename scalar_t>
+static inline scalar_t get_value_bounded(
+    const scalar_t* data,
+    scalar_t x,
+    scalar_t y,
+    int64_t W,
+    int64_t H,
+    int64_t sW,
+    int64_t sH,
+    GridSamplerPadding padding_mode,
+    bool align_corners) {
+
+  x = compute_coordinates(x, W, padding_mode, align_corners);
+  y = compute_coordinates(y, H, padding_mode, align_corners);
+
+  int64_t ix = static_cast<int64_t>(x);
+  int64_t iy = static_cast<int64_t>(y);
+
+  if (within_bounds_2d(iy, ix, H, W)) {
+    return data[iy * sH + ix * sW];
+  }
+  return static_cast<scalar_t>(0);
+}
+
+template<typename scalar_t>
+static inline void safe_add_2d(scalar_t *data, int64_t h, int64_t w,
+                               int64_t sH, int64_t sW, int64_t H, int64_t W,
+                               scalar_t delta) {
+  if (within_bounds_2d(h, w, H, W)) {
+    data[h * sH + w * sW] += delta;
+  }
+}
+
+template<typename scalar_t>
+static inline void safe_add_3d(scalar_t *data, int64_t d, int64_t h, int64_t w,
+                               int64_t sD, int64_t sH, int64_t sW,
+                               int64_t D, int64_t H, int64_t W,
+                               scalar_t delta) {
+  if (within_bounds_3d(d, h, w, D, H, W)) {
+    data[d * sD + h * sH + w * sW] += delta;
+  }
+}
+
+template<typename scalar_t>
+static inline void add_value_bounded(
+    scalar_t* data,
+    scalar_t x,
+    scalar_t y,
+    int64_t W,
+    int64_t H,
+    int64_t sW,
+    int64_t sH,
+    scalar_t delta,
+    GridSamplerPadding padding_mode,
+    bool align_corners) {
+
+  x = compute_coordinates(x, W, padding_mode, align_corners);
+  y = compute_coordinates(y, H, padding_mode, align_corners);
+
+  int64_t ix = static_cast<int64_t>(x);
+  int64_t iy = static_cast<int64_t>(y);
+
+  safe_add_2d(data, iy, ix, sH, sW, H, W, delta);
+}
+
+// Calculate the differential of the cubic convolution, i.e. `d coeff / d x`
+template<typename scalar_t>
+static inline void get_cubic_coefficients_grad(
+    scalar_t coeffs[4],
+    scalar_t t) {
+
+  // Must be the same as forward calculation in
+  // aten/src/ATen/native/UpSample.h:get_cubic_upsample_coefficients
+  scalar_t A = -0.75;
+
+  scalar_t x;
+  x = -1 - t; // 1 < x = |-1 - tx| < 2
+  coeffs[0] = (-3 * A * x - 10 * A ) * x - 8 * A;
+  x = -t;     // x = |0 - tx| <= 1
+  coeffs[1] = (-3 * (A + 2) * x - 2 * (A + 3)) * x;
+  x = 1 - t;  // x = |1 - tx| <= 1
+  coeffs[2] = (3 * (A + 2) * x - 2 * (A + 3)) * x;
+  x = 2 - t;  // 1 < x = |2 - tx| < 2
+  coeffs[3] = (3 * A * x - 10 * A) * x + 8 * A;
+}
+
+}  // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/GridSamplerUtils.h

@@ -0,0 +1,105 @@
+#pragma once
+
+// See NOTE: [Tensor vs. TensorBase]
+// https://github.com/pytorch/pytorch/pull/66979
+#include <ATen/core/TensorBase.h>
+#include <ATen/native/TensorProperties.h>
+#include <ATen/native/CanUse32BitIndexMath.h>
+
+namespace at::native {
+
+namespace detail {
+
+enum class GridSamplerInterpolation {Bilinear, Nearest, Bicubic};
+enum class GridSamplerPadding {Zeros, Border, Reflection};
+
+} // namespace detail
+
+using detail::GridSamplerInterpolation;
+using detail::GridSamplerPadding;
+
+// See NOTE [ grid_sampler Native Functions ].
+inline void check_grid_sampler_common(
+  const TensorBase& input,
+  const TensorBase& grid
+) {
+  auto input_opt = input.options();
+  auto grid_opt = grid.options();
+
+  TORCH_CHECK(
+    input.defined(),
+    "grid_sampler(): expected input to not be undefined");
+  TORCH_CHECK(
+    grid.defined(),
+    "grid_sampler(): expected grid to not be undefined");
+  TORCH_CHECK(
+    input_opt.device() == grid_opt.device(),
+    "grid_sampler(): expected input and grid to be on same device, but input "
+    "is on ", input_opt.device(), " and grid is on ", grid_opt.device());
+  TORCH_CHECK(
+    input_opt.layout() == kStrided && grid_opt.layout() == kStrided,
+    "grid_sampler(): expected input and grid to have torch.strided layout, but "
+    "input has ", input_opt.layout(), " and grid has ", grid_opt.layout());
+  TORCH_CHECK(
+    input.size(0) == grid.size(0),
+    "grid_sampler(): expected grid and input to have same batch size, but got "
+    "input with sizes ", input.sizes(), " and grid with sizes ", grid.sizes());
+  TORCH_CHECK(
+    grid.size(-1) == input.dim() - 2,
+    "grid_sampler(): expected grid to have size ", input.dim() - 2, " in last "
+    "dimension, but got grid with sizes ", grid.sizes());
+
+  for (const auto i : c10::irange(2, input.dim())) {
+    TORCH_CHECK(input.size(i) > 0,
+      "grid_sampler(): expected input to have non-empty spatial dimensions, "
+      "but input has sizes ", input.sizes(), " with dimension ", i, " being "
+      "empty");
+  }
+}
+
+// See NOTE [ grid_sampler Native Functions ].
+inline void check_grid_sampler_2d(
+  const TensorBase& input,
+  const TensorBase& grid
+) {
+  TORCH_CHECK(
+    input.dim() == 4 && input.dim() == grid.dim(),
+    "grid_sampler(): expected 4D input and grid with same number of "
+    "dimensions, but got input with sizes ", input.sizes(),
+    " and grid with sizes ", grid.sizes());
+}
+
+// See NOTE [ grid_sampler Native Functions ].
+inline void check_grid_sampler_3d(
+  const TensorBase& input,
+  const TensorBase& grid,
+  int64_t interpolation_mode
+) {
+  TORCH_CHECK(
+    input.dim() == 5 && input.dim() == grid.dim(),
+    "grid_sampler(): expected 5D input and grid with same number of "
+    "dimensions, but got input with sizes ", input.sizes(),
+    " and grid with sizes ", grid.sizes());
+  TORCH_CHECK(
+    !(input.dim() == 5 &&
+      static_cast<GridSamplerInterpolation>(interpolation_mode) ==
+        GridSamplerInterpolation::Bicubic),
+    "grid_sampler(): bicubic interpolation only supports 4D input");
+}
+
+// See NOTE [ grid_sampler Native Functions ].
+// cudnn does not support inputs larger than 1024.
+inline bool cond_cudnn_grid_sampler(
+  const TensorBase& input,
+  const TensorBase& grid
+) {
+  return (
+    at::native::cudnn_is_acceptable(input) &&
+    at::native::cudnn_is_acceptable(grid) &&
+    at::native::canUse32BitIndexMath(input) &&
+    at::native::canUse32BitIndexMath(grid) &&
+    input.dim() == 4 &&
+    input.sym_size(1) <= 1024);
+}
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/IndexKernel.h

@@ -0,0 +1,41 @@
+#pragma once
+#include <ATen/native/DispatchStub.h>
+#include <c10/util/ArrayRef.h>
+
+namespace at {
+class Tensor;
+class TensorBase;
+struct TensorIterator;
+struct TensorIteratorBase;
+}
+
+namespace c10 {
+class Scalar;
+}
+
+namespace at::native {
+
+using index_fn = void(*)(TensorIteratorBase &, IntArrayRef indexed_sizes, IntArrayRef indexed_strides);
+using index_fill_fn = void(*)(TensorIterator & iter, int64_t dim, int64_t self_dim_size, int64_t self_dim_stride, const Scalar& source);
+using index_copy_fn = void(*)(TensorIterator & iter, int64_t dim, int64_t self_dim_size, int64_t self_dim_stride);
+using index_put_fn = void(*)(TensorIterator &, IntArrayRef indexed_sizes, IntArrayRef indexed_strides, bool accumulate);
+using put_fn = void(*)(TensorIterator & iter, const TensorBase& self, const bool accumulate);
+using take_fn = void(*)(TensorIterator & iter, const TensorBase& input);
+using flip_fn = void(*)(TensorIterator &, const bool);
+using masked_fill_fn = void(*)(TensorIterator &, const Scalar& scalar);
+using masked_select_fn = void(*)(TensorIterator &, int64_t orig_stride);
+using masked_scatter_fn = void(*)(TensorIterator &, const TensorBase &);
+
+DECLARE_DISPATCH(index_fn, index_stub);
+DECLARE_DISPATCH(index_fill_fn, index_fill_stub);
+DECLARE_DISPATCH(index_copy_fn, index_copy_stub);
+DECLARE_DISPATCH(index_put_fn, index_put_stub);
+DECLARE_DISPATCH(put_fn, put_stub);
+DECLARE_DISPATCH(take_fn, take_stub);
+DECLARE_DISPATCH(flip_fn, flip_stub);
+DECLARE_DISPATCH(masked_fill_fn, masked_fill_stub);
+DECLARE_DISPATCH(masked_select_fn, masked_select_serial_stub);
+DECLARE_DISPATCH(masked_select_fn, masked_select_stub);
+DECLARE_DISPATCH(masked_scatter_fn, masked_scatter_stub);
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/Lerp.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include <ATen/native/DispatchStub.h>
+#include <ATen/OpMathType.h>
+#include <ATen/TensorIterator.h>
+#include <c10/core/Scalar.h>
+
+namespace at::native {
+
+template <typename scalar_t>
+C10_HOST_DEVICE C10_ALWAYS_INLINE bool is_lerp_weight_small(scalar_t weight) {
+  return std::abs(weight) < scalar_t(0.5);
+}
+template <typename scalar_t>
+C10_HOST_DEVICE C10_ALWAYS_INLINE bool is_lerp_weight_small(c10::complex<scalar_t> weight) {
+  // Avoid the sqrt in abs(weight)
+  return (weight.real() * weight.real() + weight.imag() * weight.imag()) < scalar_t(0.25);
+}
+
+template <typename scalar_t, typename weight_t>
+C10_HOST_DEVICE C10_ALWAYS_INLINE scalar_t lerp(scalar_t self_, scalar_t end_, weight_t weight_) {
+  using opmath_t = at::opmath_type<scalar_t>;
+  using opmath_weight_t = at::opmath_type<weight_t>;
+
+  opmath_t self = self_;
+  opmath_t end = end_;
+  opmath_weight_t weight = weight_;
+
+  // Conditional for better numeric. This has been discussed in
+  // https://github.com/pytorch/pytorch/pull/18871
+  return is_lerp_weight_small(weight)
+      ? self + weight * (end - self)
+      : end - (end - self) * (opmath_t(1) - weight);
+}
+
+using lerp_fn_scalar = void (*)(
+    at::TensorIteratorBase& iter,
+    const Scalar& weight);
+
+using lerp_fn_tensor = void (*)(
+    at::TensorIteratorBase& iter);
+
+DECLARE_DISPATCH(lerp_fn_scalar, lerp_kernel_scalar_weight);
+DECLARE_DISPATCH(lerp_fn_tensor, lerp_kernel_tensor_weight);
+
+} // namespace at::native

vllm/lib/python3.10/site-packages/torch/include/ATen/native/LinearAlgebraUtils.h

@@ -0,0 +1,623 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/util/irange.h>
+#include <c10/util/Exception.h>
+#include <c10/util/strides.h>
+#include <ATen/core/Tensor.h>
+#include <ATen/ExpandUtils.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/native/TensorIterator.h>
+#include <ATen/native/TransposeType.h>
+#include <limits>
+#include <type_traits>
+#include <sstream>
+#include <cstring>
+#include <cctype>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/arange.h>
+#include <ATen/ops/empty.h>
+#include <ATen/ops/empty_like.h>
+#include <ATen/ops/empty_strided.h>
+#include <ATen/ops/zeros.h>
+#endif
+
+namespace at::native {
+
+inline c10::MaybeOwned<Tensor> expect_resolved_conj(const Tensor& tensor) {
+  if (tensor.is_conj()) {
+    return c10::MaybeOwned<Tensor>::owned(tensor.resolve_conj());
+  } else {
+    return c10::MaybeOwned<Tensor>::borrowed(tensor);
+  }
+}
+
+inline DimVector batched_matrix_contiguous_strides(
+    const IntArrayRef sizes,
+    const bool f_contig = false) {
+  // f_contig chooses between the strides of a batch of Fortran (F-contiguous)
+  // and C-contiguous matrices
+  auto strides = c10::contiguous_strides(sizes);
+  auto dim = strides.size();
+
+  if (f_contig && dim >= 2) {
+    // Fix the strides of the last two dimensions, so that we return
+    // C-contiguous batches of F-contiguous matrices.
+    strides[dim - 1] = std::max(sizes[dim - 2], static_cast<int64_t>(1));
+    strides[dim - 2] = 1;
+  }
+  return strides;
+}
+
+/*
+ * Clones a Tensor so that the following conditions hold:
+ * If we think of a Tensor of having size (B, M, N), where B is any number
+ * of batch dimensions, then:
+ * - Each (M, N) matrix is in column major form
+ * - Let Tensor P have size (B, M, N) and Q have size (B, M', N').
+ *   Then when laid out in memory, the M by N matrix starting at
+ *   P.data_ptr()[B * M * N] is of the same corresponding batch as the M' by N'
+ *   matrix starting at Q.data_ptr()[B * M' * N'].
+ */
+inline Tensor cloneBatchedColumnMajor(const Tensor& src) {
+  // If src is already in batched column major format, then
+  // this will be efficient (no reordering of the data will occur)
+  // because the first transpose will make the tensor contiguous,
+  // and cloning a contiguous tensor is fast.
+  auto result = src.mT().clone(at::MemoryFormat::Contiguous);
+  result.transpose_(-2, -1);
+  return result;
+}
+
+/*
+ * contig chooses between C-contig (true) and F-contig (false)
+ */
+inline c10::MaybeOwned<Tensor> borrow_else_clone(const bool cond, const Tensor& borrow, const Tensor& clone, const bool contig) {
+  return cond ? c10::MaybeOwned<Tensor>::borrowed(borrow)
+              : c10::MaybeOwned<Tensor>::owned(contig ? clone.clone(MemoryFormat::Contiguous)
+                                                      : cloneBatchedColumnMajor(clone));
+}
+
+/*
+ * This method is designed to be a faster alternative to
+ * `cloneBatchedColumnMajor` with some additional features,
+ * namely:
+ * 1. It uses `copy` instead of `clone` which could be much faster.
+ * 2. `nrows` parameter used to create inputs with the number of rows larger
+ *  than the original input, which is required for some LAPACK/MAGMA methods.
+ * 3. `desired_batch_size` is used to create copies with the batch size
+ *  which is either the original batch size of the input, or its larger
+ *  broadcasted shape.
+ */
+inline Tensor copyBatchedColumnMajor(const Tensor& src, int64_t nrows = -1,
+    at::OptionalIntArrayRef desired_batch_sizes = std::nullopt) {
+  nrows = (nrows == -1) ? src.size(-2) : nrows;
+  auto copy_sizes = desired_batch_sizes.has_value()
+    ? desired_batch_sizes.value().vec()
+    : IntArrayRef(src.sizes().data(), src.dim() - 2).vec();
+  copy_sizes.insert(copy_sizes.end(), {nrows, src.size(-1)});
+  const auto copy_strides = batched_matrix_contiguous_strides(copy_sizes, /*f-contig*/true);
+  auto copy = at::empty_strided(copy_sizes, copy_strides, src.options());
+  copy.narrow(-2, 0, src.size(-2)).copy_(src);
+  return copy;
+}
+
+/*
+ * Given batches of matrices with arbitrary batch dim,
+ * computes the number of batches.
+ */
+inline int64_t batchCount(const Tensor& batched_matrices) {
+  int64_t result = 1;
+  for (int64_t i = 0; i < batched_matrices.ndimension() - 2; i++) {
+    result *= batched_matrices.size(i);
+  }
+  return result;
+}
+
+// Computes the number of elements of a matrix in a batched matrix tensor
+inline int64_t matrixStride(const Tensor& batched_matrices) {
+  return batched_matrices.size(-1) * batched_matrices.size(-2);
+}
+
+// Validates input shapes for operations on batches of square matrices (inverse, cholesky, symeig, eig)
+inline void checkIsMatrix(const Tensor& A, const char* const f_name, const char* const arg_name = "A") {
+  TORCH_CHECK(A.dim() >= 2, f_name, ": The input tensor ", arg_name, " must have at least 2 dimensions.");
+}
+inline void squareCheckInputs(const Tensor& self, const char* const f_name, const char* const arg_name = "A") {
+  checkIsMatrix(self, f_name, arg_name);
+  TORCH_CHECK(self.sym_size(-1) == self.sym_size(-2),
+              f_name,
+              ": ", arg_name, " must be batches of square matrices, "
+              "but they are ", self.sym_size(-2), " by ", self.sym_size(-1), " matrices");
+}
+
+inline void checkInputsSolver(const Tensor& A,
+                                     const Tensor& B,
+                                     const bool left,
+                                     const char* const f_name) {
+  squareCheckInputs(A, f_name, "A");
+  checkIsMatrix(B, f_name, "B");
+  TORCH_CHECK(left ? A.size(-2) == B.size(-2) : A.size(-1) == B.size(-1),
+              f_name, ": Incompatible shapes of A and B for the equation ",
+              left ? "AX = B" : "XA = B",
+              " (", A.size(-2), "x", A.size(-1), " and ", B.size(-2), "x", B.size(-1), ")");
+}
+
+inline bool is_row_or_column_contiguous(const Tensor& t) {
+  // This could be made more general, similar to how it's checked in matmul, which would allow to
+  // ellide the copy with strides such as (6, 12, 1, 3) or (3, 1, 9), but this is quite tricky.
+  // We choose to be conservative for simplicity
+  return t.is_contiguous() || t.transpose(-2, -1).is_contiguous();
+}
+
+inline TransposeType to_transpose_type(const bool contig, const bool conj) {
+  if (conj) {
+    if (contig) { TORCH_INTERNAL_ASSERT(false, "Invalid transpose type"); }
+    else {        return TransposeType::ConjTranspose; }
+  } else {
+    if (contig) { return TransposeType::NoTranspose; }
+    else {        return TransposeType::Transpose; }
+  }
+}
+
+
+// This function is designed to be used with linear algebra methods that minimize
+// L(ax - b) = 0, where L is generally the identity map (`solve`, for example)
+// or the L2 norm (`lstsq`).
+// It is expected that `a` and `b` are contiguous tensors of column-major matrices
+// (so that a.view({-1, a.size(-2), a.size(-1)}) succeeds, same for `b`),
+// with the following additional properties:
+//
+// 1. a.dim() == b.dim()
+// 2. a.shape[:-2] broadcasts over b.shape[:-2]
+// 3. a.size(i) <= b.size(i) for i=0,..., a.dim() - 3 (only for batch dimensions)
+//
+// MAGMA/LAPACK modify tensor `a` in-place, and the main goal of this method
+// is to be memory efficient, which means that if there exists an index i such that
+// a.shape[i] < b.shape[i], 0 <= i <= a.dim() - 3,
+// then instead of materializing copies of `a` in the broadcasted shape, we keep
+// a buffer copy of `a` along with flags that check whether specific batch dimension
+// indices for `a` were already accessed. If they were, we copy the data from the buffer
+// into `a`. The number of copies does not exceed
+// prod(max(a.shape[:-2], b.shape[:-2]) - a.shape[:-2] + 1)
+// and this value is attained by tensors with non-empty batch dimensions.
+//
+// func_t `f` is a callable that is being supplied with
+// scalar_t* a_working_ptr, scalar_t* b_working_ptr, int64_t a_linear_batch_idx.
+// a_working_ptr and b_working_ptr can directly be passed to LAPACK/MAGMA routines,
+// and a_linear_batch_idx is an index in the 3d representation which corresponds to
+// the memory a_working_ptr points to, in other words:
+// a_working_ptr == a.view({-1, a.size(-2), a.size(-1)}.select(0, a_linear_batch_idx).data_ptr<scalar_t>();
+// a_linear_batch_idx is useful to store metadata related to `a`, such as, for example,
+// its rank or singular values (see linalg_lstsq).
+template<typename scalar_t, typename func_t>
+void batch_iterator_with_broadcasting(const Tensor& a, const Tensor& b, const func_t& f) {
+  IntArrayRef a_batch_sizes(a.sizes().data(), a.dim() - 2);
+  IntArrayRef b_batch_sizes(b.sizes().data(), b.dim() - 2);
+
+  auto a_linear_batch_idx = at::arange(batchCount(a)).view(a_batch_sizes);
+  auto b_linear_batch_idx = at::arange(batchCount(b)).view(b_batch_sizes);
+
+  TensorIterator iter = TensorIteratorConfig()
+    .set_check_mem_overlap(false)
+    .check_all_same_dtype(false)
+    .resize_outputs(false)
+    .add_output(b_linear_batch_idx)
+    .add_input(a_linear_batch_idx)
+    .build();
+
+  auto m = a.size(-2);
+  auto n = a.size(-1);
+  auto a_3d = a.view({batchCount(a), m, n});
+  auto b_3d = b.view({batchCount(b), b.size(-2), b.size(-1)});
+
+  auto a_broadcasts_over_b = (a_batch_sizes != b_batch_sizes);
+  Tensor a_buffer, a_was_accessed, a_buffer_3d;
+  std::function<void(int64_t)> check_if_copy_needed_for_a
+    = [](int64_t /*a_curr_linear_batch_idx*/){};
+  if (a_broadcasts_over_b) {
+    a_buffer = at::empty_strided(a.sizes(), a.strides(), a.options())
+      .copy_(a);
+    a_was_accessed = at::zeros(batchCount(a), at::kBool);
+    a_buffer_3d = a_buffer.view({batchCount(a), m, n});
+    check_if_copy_needed_for_a = [&](int64_t a_curr_linear_batch_idx) {
+      auto* a_was_accessed_flag = a_was_accessed
+        .select(0, a_curr_linear_batch_idx)
+        .data_ptr<bool>();
+      if (!(*a_was_accessed_flag)) {
+        *a_was_accessed_flag = true;
+      }
+      else {
+        a_3d.select(0, a_curr_linear_batch_idx)
+          .copy_(a_buffer_3d.select(0, a_curr_linear_batch_idx));
+      }
+    };
+  }
+
+  auto loop = [&](char** data, const int64_t* strides, int64_t nelems) {
+    auto* b_batch_idx_ptr = data[0];
+    auto* a_batch_idx_ptr = data[1];
+
+    for (const auto elem C10_UNUSED : c10::irange(nelems)) {
+      auto b_curr_linear_batch_idx = *reinterpret_cast<int64_t*>(b_batch_idx_ptr);
+      auto a_curr_linear_batch_idx = *reinterpret_cast<int64_t*>(a_batch_idx_ptr);
+
+      check_if_copy_needed_for_a(a_curr_linear_batch_idx);
+
+      auto* a_working_ptr = a_3d.select(0, a_curr_linear_batch_idx)
+        .data_ptr<scalar_t>();
+      auto* b_working_ptr = b_3d.select(0, b_curr_linear_batch_idx)
+        .data_ptr<scalar_t>();
+      f(a_working_ptr, b_working_ptr, a_curr_linear_batch_idx);
+
+      b_batch_idx_ptr += strides[0];
+      a_batch_idx_ptr += strides[1];
+    }
+  };
+  iter.serial_for_each(loop, {0, batchCount(b)});
+}
+
+// Returns the epsilon value for floating types except half
+inline double _get_epsilon(const ScalarType& sc_type) {
+  switch (sc_type) {
+    case at::ScalarType::Float:
+      return static_cast<double>(std::numeric_limits<float>::epsilon());
+    case at::ScalarType::Double:
+      return std::numeric_limits<double>::epsilon();
+    default:
+      AT_ERROR("This function doesn't handle types other than float and double");
+  }
+}
+
+// Validates input shapes and devices
+// for linear solve methods (solve, cholesky_solve, lu_solve, triangular_solve)
+inline void linearSolveCheckInputs(const Tensor& self, const Tensor& A, const char* name) {
+  TORCH_CHECK(self.device() == A.device(),
+              "Expected b and A to be on the same device, but found b on ",
+              self.device(), " and A on ", A.device(), " instead.");
+
+  TORCH_CHECK(self.scalar_type() == A.scalar_type(),
+              "Expected b and A to have the same dtype, but found b of type ",
+              self.scalar_type(), " and A of type ", A.scalar_type(), " instead.");
+
+  TORCH_CHECK(A.size(-1) == A.size(-2),
+              "A must be batches of square matrices, "
+              "but they are ", A.size(-2), " by ", A.size(-1), " matrices");
+
+  TORCH_CHECK(A.size(-1) == self.size(-2),
+              "Incompatible matrix sizes for ", name, ": each A "
+              "matrix is ", A.size(-1), " by ", A.size(-1),
+              " but each b matrix is ", self.size(-2), " by ", self.size(-1));
+}
+
+inline void checkFloatingOrComplex(const Tensor& t, const char* const f_name, const bool allow_low_precision_dtypes=true) {
+  auto dtype = t.scalar_type();
+  TORCH_CHECK((at::isFloatingType(dtype) || at::isComplexType(dtype)),
+              f_name, ": Expected a floating point or complex tensor as input. Got ", dtype);
+  if (!allow_low_precision_dtypes) {
+    TORCH_CHECK(dtype == kFloat || dtype == kDouble || dtype == kComplexFloat || dtype == kComplexDouble,
+                f_name, ": Low precision dtypes not supported. Got ", dtype);
+  }
+}
+
+
+// Checks if all the Tensors in a TensorList are of the same dimensions
+inline void checkAllSameDim(TensorList tensors, int64_t dim) {
+  for (auto &t : tensors) {
+    TORCH_CHECK(t.dim() == dim, "Tensor dimension is ", t.dim(), ", expected ", dim, " instead.");
+  }
+}
+
+inline std::tuple<std::vector<int64_t>, std::vector<int64_t>> _linalg_broadcast_batch_dims(const Tensor& arg1, const Tensor& arg2) {
+  // broadcast the batch dimensions of arg1 and arg2.
+  IntArrayRef arg1_batch_sizes(arg1.sizes().data(), arg1.ndimension() - 2);
+  IntArrayRef arg2_batch_sizes(arg2.sizes().data(), arg2.ndimension() - 2);
+  std::vector<int64_t> expand_batch_portion = infer_size(arg1_batch_sizes, arg2_batch_sizes);
+
+  std::vector<int64_t> arg1_expand_size({expand_batch_portion});
+  arg1_expand_size.insert(arg1_expand_size.end(), { arg1.size(-2), arg1.size(-1) });
+
+  std::vector<int64_t> arg2_expand_size({expand_batch_portion});
+  arg2_expand_size.insert(arg2_expand_size.end(), { arg2.size(-2), arg2.size(-1) });
+  return std::make_tuple(std::move(arg1_expand_size), std::move(arg2_expand_size));
+}
+
+inline std::tuple<Tensor,Tensor> _linalg_broadcast_batch_dims(const Tensor& arg1, const Tensor& arg2, const char* name) {
+  // If there's no name we assume we don't want to check the errors
+  if (name != nullptr) {
+    linearSolveCheckInputs(arg1, arg2, name);
+  }
+
+  auto [arg1_expand_size, arg2_expand_size] = at::native::_linalg_broadcast_batch_dims(arg1, arg2);
+
+  auto arg1_broadcasted  = arg1_expand_size == arg1.sizes() ? arg1 : arg1.expand(arg1_expand_size);
+  auto arg2_broadcasted  = arg2_expand_size == arg2.sizes() ? arg2 : arg2.expand(arg2_expand_size);
+  return std::make_tuple(arg1_broadcasted, arg2_broadcasted);
+}
+
+inline std::vector<int64_t> broadcast_batch_size(const Tensor& t1, const Tensor& t2, int64_t n_batch_dims) {
+  IntArrayRef t1_batch_sizes(t1.sizes().data(), n_batch_dims);
+  IntArrayRef t2_batch_sizes(t2.sizes().data(), n_batch_dims);
+  auto broadcasted_batch_sizes = infer_size(t1_batch_sizes, t2_batch_sizes);
+  return broadcasted_batch_sizes;
+}
+
+// Return a permutation with the given axes moved to the end.
+inline Tensor _move_to_end(const Tensor& self, IntArrayRef axes) {
+  const std::vector<int64_t> a = axes.vec();
+  const int64_t ndim = self.ndimension();
+  std::vector<int64_t> perm;
+
+  for (const auto i : c10::irange(ndim)) {
+    auto it = std::find(a.begin(), a.end(), i);
+    if (it == a.end()) {
+       perm.push_back(i);
+    }
+  }
+  for (auto i : a) {
+    perm.push_back(i);
+  }
+
+  TORCH_CHECK((int64_t)perm.size() == ndim,
+    "duplicate or invalid axis in 'dim' argument for tensor with ndim==", ndim);
+
+  return self.permute(perm);
+}
+
+// parse the "mode" param in linalg_qr: return a tuple of bools (compute_q, reduced)
+inline std::tuple<bool, bool> _parse_qr_mode(c10::string_view mode) {
+  bool compute_q;
+  bool reduced;
+  if (mode == "reduced") {
+    compute_q = true;
+    reduced = true;
+  } else if (mode == "complete") {
+    compute_q = true;
+    reduced = false;
+  } else if (mode == "r") {
+    compute_q = false;
+    reduced = true; // this is actually irrelevant in this mode
+  } else {
+      TORCH_CHECK(false, "qr received unrecognized mode '", mode,
+                  "' but expected one of 'reduced' (default), 'r', or 'complete'");
+  }
+  return std::make_tuple(compute_q, reduced);
+}
+
+// Function to compute sizes, strides and the extra columns for the Q matrix in the QR Decomposition
+inline std::tuple<DimVector, DimVector, int64_t> _compute_geometry_for_Q(
+    const Tensor& input,
+    bool reduced) {
+  int64_t m = input.size(-2), n = input.size(-1);
+  int64_t n_columns_q;
+
+  // We need to compute the required size of Q based on the `reduced` option
+  DimVector q_sizes(input.sizes());
+  if (!reduced && m > n) {
+    q_sizes[input.dim() - 1] = m;
+    n_columns_q = m;
+  } else {
+    q_sizes[input.dim() - 1] = n;
+    n_columns_q = std::min(m, n);
+  }
+  auto q_strides = batched_matrix_contiguous_strides(q_sizes, /*f-contig*/true);
+  return std::make_tuple(q_sizes, q_strides, n_columns_q);
+}
+
+inline bool svd_uses_cusolver(const Tensor& A) {
+  // if cusolver is available, it is used unconditionally
+  return A.is_cuda()
+         && at::globalContext().hasCuSOLVER()
+         && at::globalContext().linalgPreferredBackend() != at::LinalgBackend::Magma;
+}
+
+
+// Function used instead of .to so that the original strides are retained
+// .to doesn't retain strides and make the output tensor contiguous
+inline Tensor same_stride_to(const Tensor& original_tensor, const at::TensorOptions& options) {
+  auto strided_to = at::empty_strided(original_tensor.sizes(),
+                                      original_tensor.strides(),
+                                      options);
+  strided_to.copy_(original_tensor);
+  return strided_to;
+}
+
+// Creates a dimension permutation array that can be given to `at::permute()`, which will shift
+// the two specified dimensions to the end of a tensor, without changing the order of
+// the other dimensions. `dim1` will be placed at the very end, and `dim0` will be
+// placed just to the left of it.
+//
+// For instance, given a 4-D tensor, dimensions 1 and 3 can be shifted to the end by
+// calling `create_dim_backshift_permutation(1, 3, 4)`. The resulting vector will
+// be `vec(0, 2, 1, 3)`.
+inline std::vector<int64_t> create_dim_backshift_permutation(int64_t dim0, int64_t dim1, int64_t ndim) {
+  TORCH_CHECK(
+    (dim0 != dim1) && (dim0 < ndim) && (dim0 >= 0) && (dim1 < ndim) && (dim1 >= 0),
+    "duplicate or invalid dimensions");
+  std::vector<int64_t> permutation(ndim);
+  int64_t cur_permuted_dim = 0;
+  for (const auto dim_ind : c10::irange(ndim)) {
+    if ((dim_ind != dim0) && (dim_ind != dim1)) {
+      permutation[cur_permuted_dim++] = dim_ind;
+    }
+  }
+  permutation[cur_permuted_dim++] = dim0;
+  permutation[cur_permuted_dim] = dim1;
+  return permutation;
+}
+
+// Creates a dimension permutation array that can be given to `at::permute()`, which
+// will reverse a given permutation.
+// The reverse permutation array is created by swapping the indices and their
+// associated values from the given permutation array.
+inline std::vector<int64_t> create_reverse_permutation(std::vector<int64_t> permutation) {
+  int64_t ndim = permutation.size();
+  std::vector<int64_t> reverse_permutation(ndim);
+  for (const auto dim_ind : c10::irange(ndim)) {
+    reverse_permutation[permutation[dim_ind]] = dim_ind;
+  }
+  return reverse_permutation;
+}
+
+// Compute R-work array size for MAGMA/LAPACK cgesdd/zgesdd
+// See https://github.com/Reference-LAPACK/lapack/blob/122506cd8b6ce050a200920c3d4c0b153b150fd8/SRC/cgesdd.f#L186
+inline int64_t computeLRWorkDim(const char jobz, int64_t m, int64_t n) {
+  auto mn = std::min(m, n);
+  auto mx = std::max(m, n);
+  if (jobz == 'N') {
+#ifdef __APPLE__
+    // According to `vecLib.framework/Headers/clapack.h` Accelerate.framework is based on LAPACK 3.2.1
+    return 7 * mn;
+#else
+    // These setting is valid for on LAPACK 3.6+
+    return 5 * mn;
+#endif
+  }
+  if (mx > 10 * mn) {
+    return 5 * mn * mn + 5 * mn;
+  }
+  return std::max(5 * mn * mn + 5 * mn, 2 * mx * mn + 2 * mn * mn + mn);
+}
+
+// This function checks whether the uplo argument input is valid
+// Allowed strings are "u", "U", "l", "L"
+inline void checkUplo(const c10::string_view uplo) {
+  // To use std::toupper safely with plain chars (or signed chars), the argument should first be converted to unsigned char
+  char uplo_uppercase = static_cast<char>(std::toupper(static_cast<unsigned char>(uplo[0])));
+  TORCH_CHECK(uplo.size() == 1 && (uplo_uppercase == 'U' || uplo_uppercase == 'L'),
+    "Expected UPLO argument to be 'L' or 'U', but got ", uplo);
+}
+
+inline void checkSameDevice(const std::string& fn_name, Tensor result, Tensor input, const std::string& result_name = "result") {
+  TORCH_CHECK(
+      result.device() == input.device(),
+      fn_name,
+      ": Expected ", result_name, " and input tensors to be on the same device, but got ",
+      result_name, " on ", result.device(), " and input on ", input.device());
+}
+
+// Check the dtype of result and input tensors (for _out variants).
+// Most linear algebra functions have the same dtype for input and output
+// (either floating or complex type input), so we can check whether input's dtype can be casted to result's dtype.
+// According to https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
+// c10::canCast is used for checking the "safe copy" dtype requirements.
+inline void checkLinalgCompatibleDtype(const std::string& fn_name, Tensor result, Tensor input, const std::string& result_name = "result") {
+  bool can_cast = c10::canCast(input.scalar_type(), result.scalar_type());
+  TORCH_CHECK(
+      can_cast,
+      fn_name,
+      ": Expected ", result_name, " to be safely castable from ", input.scalar_type(), " dtype, but got ",
+      result_name, " with dtype ", result.scalar_type());
+}
+
+// Alternatively, we can check whether the specific expected output type (result_type) can be safely casted to out tensor dtype (out_type)
+inline void checkLinalgCompatibleDtype(const std::string& fn_name, ScalarType out_type, ScalarType result_type, const std::string& out_name = "result") {
+  bool can_cast = c10::canCast(result_type, out_type);
+  TORCH_CHECK(
+      can_cast,
+      fn_name,
+      ": Expected ", out_name, " to be safely castable from ", result_type, " dtype, but got ",
+      out_name, " with dtype ", out_type);
+}
+
+inline void checkNotComplexTolerance(const Tensor& tol, const c10::string_view f_name, const c10::string_view tol_name) {
+  TORCH_CHECK(!at::isComplexType(tol.scalar_type()),
+              f_name, ": ", tol_name, " tensor of complex type is not supported. Got ", tol.scalar_type());
+}
+
+/*
+  Two types of 'other' tensors are supported when solving
+  a system of linear equations matmul(input, x) = other:
+  * 1-dimensional (1D) tensor or batch of 1D tensors (vector case)
+  * 2-dimensional (2D) tensor or batch of 2D tensors (matrix case).
+  The original torch.solve supported only the matrix case, while NumPy works for both cases.
+  For the batched input we need to be able to distinguish them.
+  Let input.shape = (batch_dimensions, m, n), then 'other' is of vector type if other.shape == (batch_dimensions, m).
+  This rule is compatible with NumPy, see https://github.com/numpy/numpy/blob/v1.20.0/numpy/linalg/linalg.py#L384-L389
+*/
+inline bool linalg_solve_is_vector_rhs(const Tensor& input, const Tensor& other) {
+  auto expected_batched_rhs_shape = SymIntArrayRef(input.sym_sizes().data(), input.dim() - 1); // input.shape[:-1]
+  bool vector_case = other.dim() == 1 || (input.dim() - 1 == other.dim() && other.sym_sizes().equals(expected_batched_rhs_shape));
+  return vector_case;
+}
+
+/*
+  Computes linear indices for a tensor with original_shape to access its elements like it was a materialized broadcast tensor.
+*/
+inline Tensor get_linear_indices(int64_t numel, IntArrayRef original_shape, IntArrayRef broadcast_shape) {
+  TensorOptions options = at::TensorOptions().dtype(at::kLong).device(at::kCPU);
+  return at::arange(numel, options).view(original_shape).broadcast_to(broadcast_shape).contiguous();
+}
+
+class BroadcastLinearIndices {
+ private:
+  Tensor linear_indices_;
+  bool is_broadcasting_;
+
+ public:
+  BroadcastLinearIndices(
+      int64_t numel,
+      IntArrayRef original_shape,
+      IntArrayRef broadcast_shape) : is_broadcasting_(!original_shape.equals(broadcast_shape)) {
+    // The assumption is that the broadcast_shape is a materialized broadcast
+    // shape of the original_shape. We need to compute the linear indices
+    // compatible with the original_shape to access the elements in the original
+    // tensor corresponding to the broadcast tensor.
+    if (is_broadcasting_) {
+      linear_indices_ =
+          get_linear_indices(numel, original_shape, broadcast_shape);
+    }
+  }
+  int64_t operator()(int64_t broadcast_linear_index) {
+    return is_broadcasting_
+        ? linear_indices_.data_ptr<int64_t>()[broadcast_linear_index]
+        : broadcast_linear_index;
+  }
+};
+
+inline bool is_blas_compatible_column_major_order(const Tensor& input) {
+  IntArrayRef input_strides = input.strides();
+  IntArrayRef input_sizes = input.sizes();
+  auto ndim = input.dim();
+  TORCH_INTERNAL_ASSERT_DEBUG_ONLY(ndim >= 2);
+  if (ndim > 3) {
+    return input.transpose(-2, -1).is_contiguous();
+  }
+  auto leading_dimension = input_strides[ndim - 1];
+  auto rows = input_sizes[ndim - 2];
+  bool batch_stride_compatible = true;
+  if (ndim == 3) {
+    auto cols = input_sizes[ndim - 1];
+    batch_stride_compatible =
+        input_strides[ndim - 3] >= leading_dimension * cols;
+  }
+  return (input_strides[ndim - 2] == 1) &&
+      (leading_dimension >= std::max<int64_t>(1, rows)) &&
+      batch_stride_compatible;
+}
+
+inline bool is_blas_compatible_row_major_order(const Tensor& input) {
+  IntArrayRef input_strides = input.strides();
+  IntArrayRef input_sizes = input.sizes();
+  auto ndim = input.dim();
+  TORCH_INTERNAL_ASSERT_DEBUG_ONLY(ndim >= 2);
+  if (ndim > 3) {
+    return input.is_contiguous();
+  }
+  auto leading_dimension = input_strides[ndim - 2];
+  auto cols = input_sizes[ndim - 1];
+  bool batch_stride_compatible = true;
+  if (ndim == 3) {
+    auto rows = input_sizes[ndim - 2];
+    batch_stride_compatible =
+        input_strides[ndim - 3] >= leading_dimension * rows;
+  }
+  return (input_strides[ndim - 1] == 1) &&
+      (leading_dimension >= std::max<int64_t>(1, cols)) &&
+      batch_stride_compatible;
+}
+
+}  // namespace at::native