"""
Asynchronous Shared-Memory Scheduler for Dask Graphs.

This scheduler coordinates several workers to execute tasks in a dask graph in
parallel.  It depends on a ``concurrent.futures.Executor``
and a corresponding Queue for worker-to-scheduler communication.

It tries to execute tasks in an order which maintains a small memory footprint
throughout execution.  It does this by running tasks that allow us to release
data resources.


Task Selection Policy
=====================

When we complete a task we add more data in to our set of available data; this
new data makes new tasks available.  We preferentially choose tasks that were
just made available in a last-in-first-out fashion.  We implement this as a
simple stack.  This results in more depth-first rather than breadth first
behavior which encourages us to process batches of data to completion before
starting in on new data when possible.

When the addition of new data readies multiple tasks simultaneously we add
tasks to the stack in sorted order so that tasks with greater keynames are run
first.  This can be handy to break ties in a predictable fashion.


State
=====

Many functions pass around a ``state`` variable that holds the current state of
the computation.  This variable consists of several other dictionaries and
sets, explained below.

Constant state
--------------

1.  dependencies: {x: [a, b ,c]} a,b,c, must be run before x
2.  dependents: {a: [x, y]} a must run before x or y

Changing state
--------------

### Data

1.  cache: available concrete data.  {key: actual-data}
2.  released: data that we've seen, used, and released because it is no longer
    needed

### Jobs

1.  ready: A fifo stack of ready-to-run tasks
2.  running: A set of tasks currently in execution
3.  finished: A set of finished tasks
4.  waiting: which tasks are still waiting on others :: {key: {keys}}
    Real-time equivalent of dependencies
5.  waiting_data: available data to yet-to-be-run-tasks :: {key: {keys}}
    Real-time equivalent of dependents


Examples
--------

>>> import pprint  # doctest: +SKIP
>>> inc = lambda x: x + 1
>>> add = lambda x, y: x + y
>>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')}  # doctest: +SKIP
>>> pprint.pprint(start_state_from_dask(dsk))  # doctest: +SKIP
{'cache': {'x': 1, 'y': 2},
 'dependencies': {'w': {'z', 'y'}, 'x': set(), 'y': set(), 'z': {'x'}},
 'dependents': defaultdict(None, {'w': set(), 'x': {'z'}, 'y': {'w'}, 'z': {'w'}}),
 'finished': set(),
 'ready': ['z'],
 'released': set(),
 'running': set(),
 'waiting': {'w': {'z'}},
 'waiting_data': {'x': {'z'}, 'y': {'w'}, 'z': {'w'}}}

Optimizations
=============

We build this scheduler with out-of-core array operations in mind.  To this end
we have encoded some particular optimizations.

Compute to release data
-----------------------

When we choose a new task to execute we often have many options.  Policies at
this stage are cheap and can significantly impact performance.  One could
imagine policies that expose parallelism, drive towards a particular output,
etc..

Our current policy is to run tasks that were most recently made available.


Inlining computations
---------------------

We hold on to intermediate computations either in memory or on disk.

For very cheap computations that may emit new copies of the data, like
``np.transpose`` or possibly even ``x + 1`` we choose not to store these as
separate pieces of data / tasks.  Instead we combine them with the computations
that require them.  This may result in repeated computation but saves
significantly on space and computation complexity.

See the function ``inline_functions`` for more information.
"""
from __future__ import annotations

import os
from collections.abc import Hashable, Mapping, Sequence
from concurrent.futures import Executor, Future
from functools import partial
from queue import Empty, Queue

from dask import config
from dask.callbacks import local_callbacks, unpack_callbacks
from dask.core import _execute_task, flatten, get_dependencies, has_tasks, reverse_dict
from dask.order import order

if os.name == "nt":
    # Python 3 windows Queue.get doesn't handle interrupts properly. To
    # workaround this we poll at a sufficiently large interval that it
    # shouldn't affect performance, but small enough that users trying to kill
    # an application shouldn't care.
    def queue_get(q):
        while True:
            try:
                return q.get(block=True, timeout=0.1)
            except Empty:
                pass

else:

    def queue_get(q):
        return q.get()


def start_state_from_dask(dsk, cache=None, sortkey=None):
    """Start state from a dask

    Examples
    --------
    >>> inc = lambda x: x + 1
    >>> add = lambda x, y: x + y
    >>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')}  # doctest: +SKIP
    >>> from pprint import pprint  # doctest: +SKIP
    >>> pprint(start_state_from_dask(dsk))  # doctest: +SKIP
    {'cache': {'x': 1, 'y': 2},
     'dependencies': {'w': {'z', 'y'}, 'x': set(), 'y': set(), 'z': {'x'}},
     'dependents': defaultdict(None, {'w': set(), 'x': {'z'}, 'y': {'w'}, 'z': {'w'}}),
     'finished': set(),
     'ready': ['z'],
     'released': set(),
     'running': set(),
     'waiting': {'w': {'z'}},
     'waiting_data': {'x': {'z'}, 'y': {'w'}, 'z': {'w'}}}
    """
    if sortkey is None:
        sortkey = order(dsk).get
    if cache is None:
        cache = config.get("cache", None)
    if cache is None:
        cache = dict()
    data_keys = set()
    for k, v in dsk.items():
        if not has_tasks(dsk, v):
            cache[k] = v
            data_keys.add(k)

    dsk2 = dsk.copy()
    dsk2.update(cache)

    dependencies = {k: get_dependencies(dsk2, k) for k in dsk}
    waiting = {k: v.copy() for k, v in dependencies.items() if k not in data_keys}

    dependents = reverse_dict(dependencies)
    for a in cache:
        for b in dependents.get(a, ()):
            waiting[b].remove(a)
    waiting_data = {k: v.copy() for k, v in dependents.items() if v}

    ready_set = {k for k, v in waiting.items() if not v}
    ready = sorted(ready_set, key=sortkey, reverse=True)
    waiting = {k: v for k, v in waiting.items() if v}

    state = {
        "dependencies": dependencies,
        "dependents": dependents,
        "waiting": waiting,
        "waiting_data": waiting_data,
        "cache": cache,
        "ready": ready,
        "running": set(),
        "finished": set(),
        "released": set(),
    }

    return state


"""
Running tasks
-------------

When we execute tasks we both

1.  Perform the actual work of collecting the appropriate data and calling the function
2.  Manage administrative state to coordinate with the scheduler
"""


def execute_task(key, task_info, dumps, loads, get_id, pack_exception):
    """
    Compute task and handle all administration

    See Also
    --------
    _execute_task : actually execute task
    """
    try:
        task, data = loads(task_info)
        result = _execute_task(task, data)
        id = get_id()
        result = dumps((result, id))
        failed = False
    except BaseException as e:
        result = pack_exception(e, dumps)
        failed = True
    return key, result, failed


def batch_execute_tasks(it):
    """
    Batch computing of multiple tasks with `execute_task`
    """
    return [execute_task(*a) for a in it]


def release_data(key, state, delete=True):
    """Remove data from temporary storage

    See Also
    --------
    finish_task
    """
    if key in state["waiting_data"]:
        assert not state["waiting_data"][key]
        del state["waiting_data"][key]

    state["released"].add(key)

    if delete:
        del state["cache"][key]


def finish_task(
    dsk, key, state, results, sortkey, delete=True, release_data=release_data
):
    """
    Update execution state after a task finishes

    Mutates.  This should run atomically (with a lock).
    """
    for dep in sorted(state["dependents"][key], key=sortkey, reverse=True):
        s = state["waiting"][dep]
        s.remove(key)
        if not s:
            del state["waiting"][dep]
            state["ready"].append(dep)

    for dep in state["dependencies"][key]:
        if dep in state["waiting_data"]:
            s = state["waiting_data"][dep]
            s.remove(key)
            if not s and dep not in results:
                release_data(dep, state, delete=delete)
        elif delete and dep not in results:
            release_data(dep, state, delete=delete)

    state["finished"].add(key)
    state["running"].remove(key)

    return state


def nested_get(ind, coll):
    """Get nested index from collection

    Examples
    --------

    >>> nested_get(1, 'abc')
    'b'
    >>> nested_get([1, 0], 'abc')
    ('b', 'a')
    >>> nested_get([[1, 0], [0, 1]], 'abc')
    (('b', 'a'), ('a', 'b'))
    """
    if isinstance(ind, list):
        return tuple(nested_get(i, coll) for i in ind)
    else:
        return coll[ind]


def default_get_id():
    """Default get_id"""
    return None


def default_pack_exception(e, dumps):
    raise


def reraise(exc, tb=None):
    if exc.__traceback__ is not tb:
        raise exc.with_traceback(tb)
    raise exc


def identity(x):
    """Identity function. Returns x.

    >>> identity(3)
    3
    """
    return x


"""
Task Selection
--------------

We often have a choice among many tasks to run next.  This choice is both
cheap and can significantly impact performance.

We currently select tasks that have recently been made ready.  We hope that
this first-in-first-out policy reduces memory footprint
"""

"""
`get`
-----

The main function of the scheduler.  Get is the main entry point.
"""


def get_async(
    submit,
    num_workers,
    dsk,
    result,
    cache=None,
    get_id=default_get_id,
    rerun_exceptions_locally=None,
    pack_exception=default_pack_exception,
    raise_exception=reraise,
    callbacks=None,
    dumps=identity,
    loads=identity,
    chunksize=None,
    **kwargs,
):
    """Asynchronous get function

    This is a general version of various asynchronous schedulers for dask.  It
    takes a ``concurrent.futures.Executor.submit`` function to form a more
    specific ``get`` method that walks through the dask array with parallel
    workers, avoiding repeat computation and minimizing memory use.

    Parameters
    ----------
    submit : function
        A ``concurrent.futures.Executor.submit`` function
    num_workers : int
        The number of workers that task submissions can be spread over
    dsk : dict
        A dask dictionary specifying a workflow
    result : key or list of keys
        Keys corresponding to desired data
    cache : dict-like, optional
        Temporary storage of results
    get_id : callable, optional
        Function to return the worker id, takes no arguments. Examples are
        `threading.current_thread` and `multiprocessing.current_process`.
    rerun_exceptions_locally : bool, optional
        Whether to rerun failing tasks in local process to enable debugging
        (False by default)
    pack_exception : callable, optional
        Function to take an exception and ``dumps`` method, and return a
        serialized tuple of ``(exception, traceback)`` to send back to the
        scheduler. Default is to just raise the exception.
    raise_exception : callable, optional
        Function that takes an exception and a traceback, and raises an error.
    callbacks : tuple or list of tuples, optional
        Callbacks are passed in as tuples of length 5. Multiple sets of
        callbacks may be passed in as a list of tuples. For more information,
        see the dask.diagnostics documentation.
    dumps: callable, optional
        Function to serialize task data and results to communicate between
        worker and parent.  Defaults to identity.
    loads: callable, optional
        Inverse function of `dumps`.  Defaults to identity.
    chunksize: int, optional
        Size of chunks to use when dispatching work. Defaults to 1.
        If -1, will be computed to evenly divide ready work across workers.

    See Also
    --------
    threaded.get
    """
    chunksize = chunksize or config.get("chunksize", 1)

    queue = Queue()

    if isinstance(result, list):
        result_flat = set(flatten(result))
    else:
        result_flat = {result}
    results = set(result_flat)

    dsk = dict(dsk)
    with local_callbacks(callbacks) as callbacks:
        _, _, pretask_cbs, posttask_cbs, _ = unpack_callbacks(callbacks)
        started_cbs = []
        succeeded = False
        # if start_state_from_dask fails, we will have something
        # to pass to the final block.
        state = {}
        try:
            for cb in callbacks:
                if cb[0]:
                    cb[0](dsk)
                started_cbs.append(cb)

            keyorder = order(dsk)

            state = start_state_from_dask(dsk, cache=cache, sortkey=keyorder.get)

            for _, start_state, _, _, _ in callbacks:
                if start_state:
                    start_state(dsk, state)

            if rerun_exceptions_locally is None:
                rerun_exceptions_locally = config.get("rerun_exceptions_locally", False)

            if state["waiting"] and not state["ready"]:
                raise ValueError("Found no accessible jobs in dask")

            def fire_tasks(chunksize):
                """Fire off a task to the thread pool"""
                # Determine chunksize and/or number of tasks to submit
                nready = len(state["ready"])
                if chunksize == -1:
                    ntasks = nready
                    chunksize = -(ntasks // -num_workers)
                else:
                    used_workers = -(len(state["running"]) // -chunksize)
                    avail_workers = max(num_workers - used_workers, 0)
                    ntasks = min(nready, chunksize * avail_workers)

                # Prep all ready tasks for submission
                args = []
                for _ in range(ntasks):
                    # Get the next task to compute (most recently added)
                    key = state["ready"].pop()
                    # Notify task is running
                    state["running"].add(key)
                    for f in pretask_cbs:
                        f(key, dsk, state)

                    # Prep args to send
                    data = {
                        dep: state["cache"][dep] for dep in get_dependencies(dsk, key)
                    }
                    args.append(
                        (
                            key,
                            dumps((dsk[key], data)),
                            dumps,
                            loads,
                            get_id,
                            pack_exception,
                        )
                    )

                # Batch submit
                for i in range(-(len(args) // -chunksize)):
                    each_args = args[i * chunksize : (i + 1) * chunksize]
                    if not each_args:
                        break
                    fut = submit(batch_execute_tasks, each_args)
                    fut.add_done_callback(queue.put)

            # Main loop, wait on tasks to finish, insert new ones
            while state["waiting"] or state["ready"] or state["running"]:
                fire_tasks(chunksize)
                for key, res_info, failed in queue_get(queue).result():
                    if failed:
                        exc, tb = loads(res_info)
                        if rerun_exceptions_locally:
                            data = {
                                dep: state["cache"][dep]
                                for dep in get_dependencies(dsk, key)
                            }
                            task = dsk[key]
                            _execute_task(task, data)  # Re-execute locally
                        else:
                            raise_exception(exc, tb)
                    res, worker_id = loads(res_info)
                    state["cache"][key] = res
                    finish_task(dsk, key, state, results, keyorder.get)
                    for f in posttask_cbs:
                        f(key, res, dsk, state, worker_id)

            succeeded = True

        finally:
            for _, _, _, _, finish in started_cbs:
                if finish:
                    finish(dsk, state, not succeeded)

    return nested_get(result, state["cache"])


""" Synchronous concrete version of get_async

Usually we supply a ``concurrent.futures.Executor``.  Here we provide a
sequential one.  This is useful for debugging and for code dominated by the
GIL
"""


class SynchronousExecutor(Executor):
    _max_workers = 1

    def submit(self, fn, *args, **kwargs):
        fut = Future()
        try:
            fut.set_result(fn(*args, **kwargs))
        except BaseException as e:
            fut.set_exception(e)
        return fut


synchronous_executor = SynchronousExecutor()


def get_sync(dsk: Mapping, keys: Sequence[Hashable] | Hashable, **kwargs):
    """A naive synchronous version of get_async

    Can be useful for debugging.
    """
    kwargs.pop("num_workers", None)  # if num_workers present, remove it
    return get_async(
        synchronous_executor.submit,
        synchronous_executor._max_workers,
        dsk,
        keys,
        **kwargs,
    )


""" Adaptor for ``multiprocessing.Pool`` instances

Usually we supply a ``concurrent.futures.Executor``.  Here we provide a wrapper
class for ``multiprocessing.Pool`` instances so we can treat them like
``concurrent.futures.Executor`` instances instead.

This is mainly useful for legacy use cases or users that prefer
``multiprocessing.Pool``.
"""


class MultiprocessingPoolExecutor(Executor):
    def __init__(self, pool):
        self.pool = pool
        self._max_workers = len(pool._pool)

    def submit(self, fn, *args, **kwargs):
        return submit_apply_async(self.pool.apply_async, fn, *args, **kwargs)


def submit_apply_async(apply_async, fn, *args, **kwargs):
    fut = Future()
    apply_async(fn, args, kwargs, fut.set_result, fut.set_exception)
    return fut


def get_apply_async(apply_async, num_workers, *args, **kwargs):
    return get_async(
        partial(submit_apply_async, apply_async), num_workers, *args, **kwargs
    )


def sortkey(item):
    """Sorting key function that is robust to different types

    Both strings and tuples are common key types in dask graphs.
    However In Python 3 one can not compare strings with tuples directly.
    This function maps many types to a form where they can be compared

    Examples
    --------
    >>> sortkey('Hello')
    ('str', 'Hello')

    >>> sortkey(('x', 1))
    ('tuple', ('x', 1))
    """
    return (type(item).__name__, item)
