from asyncio import (
    gather,
    ensure_future,
    get_event_loop,
    iscoroutine,
    iscoroutinefunction,
)
from collections import namedtuple
from collections.abc import Iterable
from functools import partial

from typing import List

Loader = namedtuple("Loader", "key,future")


def iscoroutinefunctionorpartial(fn):
    return iscoroutinefunction(fn.func if isinstance(fn, partial) else fn)


class DataLoader(object):
    batch = True
    max_batch_size = None  # type: int
    cache = True

    def __init__(
        self,
        batch_load_fn=None,
        batch=None,
        max_batch_size=None,
        cache=None,
        get_cache_key=None,
        cache_map=None,
        loop=None,
    ):
        self._loop = loop

        if batch_load_fn is not None:
            self.batch_load_fn = batch_load_fn

        assert iscoroutinefunctionorpartial(
            self.batch_load_fn
        ), "batch_load_fn must be coroutine. Received: {}".format(self.batch_load_fn)

        if not callable(self.batch_load_fn):
            raise TypeError(  # pragma: no cover
                (
                    "DataLoader must be have a batch_load_fn which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but got: {}."
                ).format(batch_load_fn)
            )

        if batch is not None:
            self.batch = batch  # pragma: no cover

        if max_batch_size is not None:
            self.max_batch_size = max_batch_size

        if cache is not None:
            self.cache = cache  # pragma: no cover

        self.get_cache_key = get_cache_key or (lambda x: x)

        self._cache = cache_map if cache_map is not None else {}
        self._queue: List[Loader] = []

    @property
    def loop(self):
        if not self._loop:
            self._loop = get_event_loop()

        return self._loop

    def load(self, key=None):
        """
        Loads a key, returning a `Future` for the value represented by that key.
        """
        if key is None:
            raise TypeError(  # pragma: no cover
                (
                    "The loader.load() function must be called with a value, "
                    "but got: {}."
                ).format(key)
            )

        cache_key = self.get_cache_key(key)

        # If caching and there is a cache-hit, return cached Future.
        if self.cache:
            cached_result = self._cache.get(cache_key)
            if cached_result:
                return cached_result

        # Otherwise, produce a new Future for this value.
        future = self.loop.create_future()
        # If caching, cache this Future.
        if self.cache:
            self._cache[cache_key] = future

        self.do_resolve_reject(key, future)
        return future

    def do_resolve_reject(self, key, future):
        # Enqueue this Future to be dispatched.
        self._queue.append(Loader(key=key, future=future))
        # Determine if a dispatch of this queue should be scheduled.
        # A single dispatch should be scheduled per queue at the time when the
        # queue changes from "empty" to "full".
        if len(self._queue) == 1:
            if self.batch:
                # If batching, schedule a task to dispatch the queue.
                enqueue_post_future_job(self.loop, self)
            else:
                # Otherwise dispatch the (queue of one) immediately.
                dispatch_queue(self)  # pragma: no cover

    def load_many(self, keys):
        """
        Loads multiple keys, returning a list of values

        >>> a, b = await my_loader.load_many([ 'a', 'b' ])

        This is equivalent to the more verbose:

        >>> a, b = await gather(
        >>>    my_loader.load('a'),
        >>>    my_loader.load('b')
        >>> )
        """
        if not isinstance(keys, Iterable):
            raise TypeError(  # pragma: no cover
                (
                    "The loader.load_many() function must be called with Iterable<key> "
                    "but got: {}."
                ).format(keys)
            )

        return gather(*[self.load(key) for key in keys])

    def clear(self, key):
        """
        Clears the value at `key` from the cache, if it exists. Returns itself for
        method chaining.
        """
        cache_key = self.get_cache_key(key)
        self._cache.pop(cache_key, None)
        return self

    def clear_all(self):
        """
        Clears the entire cache. To be used when some event results in unknown
        invalidations across this particular `DataLoader`. Returns itself for
        method chaining.
        """
        self._cache.clear()
        return self

    def prime(self, key, value):
        """
        Adds the provied key and value to the cache. If the key already exists, no
        change is made. Returns itself for method chaining.
        """
        cache_key = self.get_cache_key(key)

        # Only add the key if it does not already exist.
        if cache_key not in self._cache:
            # Cache a rejected future if the value is an Error, in order to match
            # the behavior of load(key).
            future = self.loop.create_future()
            if isinstance(value, Exception):
                future.set_exception(value)
            else:
                future.set_result(value)

            self._cache[cache_key] = future

        return self


def enqueue_post_future_job(loop, loader):
    async def dispatch():
        dispatch_queue(loader)

    loop.call_soon(ensure_future, dispatch())


def get_chunks(iterable_obj, chunk_size=1):
    chunk_size = max(1, chunk_size)
    return (
        iterable_obj[i : i + chunk_size]
        for i in range(0, len(iterable_obj), chunk_size)
    )


def dispatch_queue(loader):
    """
    Given the current state of a Loader instance, perform a batch load
    from its current queue.
    """
    # Take the current loader queue, replacing it with an empty queue.
    queue = loader._queue
    loader._queue = []

    # If a max_batch_size was provided and the queue is longer, then segment the
    # queue into multiple batches, otherwise treat the queue as a single batch.
    max_batch_size = loader.max_batch_size

    if max_batch_size and max_batch_size < len(queue):
        chunks = get_chunks(queue, max_batch_size)
        for chunk in chunks:
            ensure_future(dispatch_queue_batch(loader, chunk))
    else:
        ensure_future(dispatch_queue_batch(loader, queue))


async def dispatch_queue_batch(loader, queue):
    # Collect all keys to be loaded in this dispatch
    keys = [loaded.key for loaded in queue]

    # Call the provided batch_load_fn for this loader with the loader queue's keys.
    batch_future = loader.batch_load_fn(keys)

    # Assert the expected response from batch_load_fn
    if not batch_future or not iscoroutine(batch_future):
        return failed_dispatch(  # pragma: no cover
            loader,
            queue,
            TypeError(
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Coroutine: {}."
                ).format(batch_future)
            ),
        )

    try:
        values = await batch_future
        if not isinstance(values, Iterable):
            raise TypeError(  # pragma: no cover
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Future of a Iterable: {}."
                ).format(values)
            )

        values = list(values)
        if len(values) != len(keys):
            raise TypeError(  # pragma: no cover
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Future of a Iterable with the same length as the Iterable "
                    "of keys."
                    "\n\nKeys:\n{}"
                    "\n\nValues:\n{}"
                ).format(keys, values)
            )

        # Step through the values, resolving or rejecting each Future in the
        # loaded queue.
        for loaded, value in zip(queue, values):
            if isinstance(value, Exception):
                loaded.future.set_exception(value)
            else:
                loaded.future.set_result(value)

    except Exception as e:
        return failed_dispatch(loader, queue, e)


def failed_dispatch(loader, queue, error):
    """
    Do not cache individual loads if the entire batch dispatch fails,
    but still reject each request so they do not hang.
    """
    for loaded in queue:
        loader.clear(loaded.key)
        loaded.future.set_exception(error)