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"""``ParallelRunner`` is an ``AbstractRunner`` implementation. It can
be used to run the ``Pipeline`` in parallel groups formed by toposort.
"""

from collections import Counter
from concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait
from itertools import chain
from multiprocessing.managers import BaseProxy, SyncManager  # type: ignore
from multiprocessing.reduction import ForkingPickler
from pickle import PicklingError
from typing import Iterable, Set

from kedro.io import AbstractDataSet, DataCatalog, MemoryDataSet
from kedro.pipeline import Pipeline
from kedro.pipeline.node import Node
from kedro.runner.runner import AbstractRunner, run_node


class ParallelRunnerManager(SyncManager):
    """``ParallelRunnerManager`` is used to create shared ``MemoryDataSet``
    objects as default data sets in a pipeline.
    """

    pass


ParallelRunnerManager.register(  # pylint: disable=no-member
    "MemoryDataSet", MemoryDataSet
)


class ParallelRunner(AbstractRunner):
    """``ParallelRunner`` is an ``AbstractRunner`` implementation. It can
    be used to run the ``Pipeline`` in parallel groups formed by toposort.
    """

    def __init__(self):
        """Instantiates the runner by creating a Manager.
        """
        self._manager = ParallelRunnerManager()
        self._manager.start()

    def create_default_data_set(self, ds_name: str) -> AbstractDataSet:
        """Factory method for creating the default data set for the runner.

        Args:
            ds_name: Name of the missing data set

        Returns:
            An instance of an implementation of AbstractDataSet to be used
            for all unregistered data sets.

        """
        # pylint: disable=no-member
        return self._manager.MemoryDataSet()

    @classmethod
    def _validate_nodes(cls, nodes: Iterable[Node]):
        """Ensure all tasks are serializable."""
        unserializable = []
        for node in nodes:
            try:
                ForkingPickler.dumps(node)
            except (AttributeError, PicklingError):
                unserializable.append(node)

        if unserializable:
            raise AttributeError(
                "The following nodes cannot be serialized: {}\nIn order to "
                "utilize multiprocessing you need to make sure all nodes are "
                "serializable, i.e. nodes should not include lambda "
                "functions, nested functions, closures, etc.\nIf you "
                "are using custom decorators ensure they are correctly using "
                "functools.wraps().".format(unserializable)
            )

    @classmethod
    def _validate_catalog(cls, catalog: DataCatalog, pipeline: Pipeline):
        """Ensure that all data sets are serializable and that we do not have
        any non proxied memory data sets being used as outputs as their content
        will not be synchronized across threads.
        """

        data_sets = catalog._data_sets  # pylint: disable=protected-access

        unserializable = []
        for name, data_set in data_sets.items():
            try:
                ForkingPickler.dumps(data_set)
            except (AttributeError, PicklingError):
                unserializable.append(name)

        if unserializable:
            raise AttributeError(
                "The following data_sets cannot be serialized: {}\nIn order "
                "to utilize multiprocessing you need to make sure all data "
                "sets are serializable, i.e. data sets should not make use of "
                "lambda functions, nested functions, closures etc.\nIf you "
                "are using custom decorators ensure they are correctly using "
                "functools.wraps().".format(unserializable)
            )

        memory_data_sets = []
        for name, data_set in data_sets.items():
            if (
                name in pipeline.all_outputs()
                and isinstance(data_set, MemoryDataSet)
                and not isinstance(data_set, BaseProxy)
            ):
                memory_data_sets.append(name)

        if memory_data_sets:
            raise AttributeError(
                "The following data sets are memory data sets: {}\n"
                "ParallelRunner does not support output to externally created "
                "MemoryDataSets".format(memory_data_sets)
            )

    def _run(  # pylint: disable=too-many-locals
        self, pipeline: Pipeline, catalog: DataCatalog
    ) -> None:
        """The abstract interface for running pipelines.

        Args:
            pipeline: The ``Pipeline`` to run.
            catalog: The ``DataCatalog`` from which to fetch data.

        Raises:
            AttributeError: when the provided pipeline is not suitable for
                parallel execution.

        """
        nodes = pipeline.nodes
        self._validate_catalog(catalog, pipeline)
        self._validate_nodes(nodes)

        load_counts = Counter(chain.from_iterable(n.inputs for n in nodes))
        node_dependencies = pipeline.node_dependencies
        todo_nodes = set(node_dependencies.keys())
        done_nodes = set()  # type: Set[Node]
        futures = set()
        done = None
        with ProcessPoolExecutor() as pool:
            while True:
                ready = {n for n in todo_nodes if node_dependencies[n] <= done_nodes}
                todo_nodes -= ready
                for node in ready:
                    futures.add(pool.submit(run_node, node, catalog))
                if not futures:
                    assert not todo_nodes, (todo_nodes, done_nodes, ready, done)
                    break
                done, futures = wait(futures, return_when=FIRST_COMPLETED)
                for future in done:
                    node = future.result()
                    done_nodes.add(node)

                    # decrement load counts and release any data sets we've finished with
                    # this is particularly important for the shared datasets we create above
                    for data_set in node.inputs:
                        load_counts[data_set] -= 1
                        if (
                            load_counts[data_set] < 1
                            and data_set not in pipeline.inputs()
                        ):
                            catalog.release(data_set)
                    for data_set in node.outputs:
                        if (
                            load_counts[data_set] < 1
                            and data_set not in pipeline.outputs()
                        ):
                            catalog.release(data_set)
