# -*- coding: utf-8 -*-

#    Copyright (C) 2013 Rackspace Hosting Inc. All Rights Reserved.
#    Copyright (C) 2013 Yahoo! 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.

import abc

import enum
import six

from taskflow import atom
from taskflow import exceptions as exc
from taskflow.utils import misc


@enum.unique
class Decision(misc.StrEnum):
    """Decision results/strategy enumeration."""

    REVERT = "REVERT"
    """Reverts only the surrounding/associated subflow.

    This strategy first consults the parent atom before reverting the
    associated subflow to determine if the parent retry object provides a
    different reconciliation strategy.  This allows for safe nesting of
    flows with different retry strategies.

    If the parent flow has no retry strategy, the default behavior is
    to just revert the atoms in the associated subflow.  This is
    generally not the desired behavior, but is left as the default in
    order to keep backwards-compatibility.  The ``defer_reverts``
    engine option will let you change this behavior.  If that is set
    to True, a REVERT will always defer to the parent, meaning that
    if the parent has no retry strategy, it will be reverted as well.
    """

    REVERT_ALL = "REVERT_ALL"
    """Reverts the entire flow, regardless of parent strategy.

    This strategy will revert every atom that has executed thus
    far, regardless of whether the parent flow has a separate
    retry strategy associated with it.
    """

    #: Retries the surrounding/associated subflow again.
    RETRY = "RETRY"

# Retain these aliases for a number of releases...
REVERT = Decision.REVERT
REVERT_ALL = Decision.REVERT_ALL
RETRY = Decision.RETRY

# Constants passed into revert/execute kwargs.
#
# Contains information about the past decisions and outcomes that have
# occurred (if available).
EXECUTE_REVERT_HISTORY = 'history'
#
# The cause of the flow failure/s
REVERT_FLOW_FAILURES = 'flow_failures'


class History(object):
    """Helper that simplifies interactions with retry historical contents."""

    def __init__(self, contents, failure=None):
        self._contents = contents
        self._failure = failure

    @property
    def failure(self):
        """Returns the retries own failure or none if not existent."""
        return self._failure

    def outcomes_iter(self, index=None):
        """Iterates over the contained failure outcomes.

        If the index is not provided, then all outcomes are iterated over.

        NOTE(harlowja): if the retry itself failed, this will **not** include
        those types of failures. Use the :py:attr:`.failure` attribute to
        access that instead (if it exists, aka, non-none).
        """
        if index is None:
            contents = self._contents
        else:
            contents = [
                self._contents[index],
            ]
        for (provided, outcomes) in contents:
            for (owner, outcome) in six.iteritems(outcomes):
                yield (owner, outcome)

    def __len__(self):
        return len(self._contents)

    def provided_iter(self):
        """Iterates over all the values the retry has attempted (in order)."""
        for (provided, outcomes) in self._contents:
            yield provided

    def __getitem__(self, index):
        return self._contents[index]

    def caused_by(self, exception_cls, index=None, include_retry=False):
        """Checks if the exception class provided caused the failures.

        If the index is not provided, then all outcomes are iterated over.

        NOTE(harlowja): only if ``include_retry`` is provided as true (defaults
                        to false) will the potential retries own failure be
                        checked against as well.
        """
        for (name, failure) in self.outcomes_iter(index=index):
            if failure.check(exception_cls):
                return True
        if include_retry and self._failure is not None:
            if self._failure.check(exception_cls):
                return True
        return False

    def __iter__(self):
        """Iterates over the raw contents of this history object."""
        return iter(self._contents)


@six.add_metaclass(abc.ABCMeta)
class Retry(atom.Atom):
    """A class that can decide how to resolve execution failures.

    This abstract base class is used to inherit from and provide different
    strategies that will be activated upon execution failures. Since a retry
    object is an atom it may also provide :meth:`~taskflow.retry.Retry.execute`
    and :meth:`~taskflow.retry.Retry.revert` methods to alter the inputs of
    connected atoms (depending on the desired strategy to be used this can be
    quite useful).

    NOTE(harlowja): the :meth:`~taskflow.retry.Retry.execute` and
    :meth:`~taskflow.retry.Retry.revert` and
    :meth:`~taskflow.retry.Retry.on_failure` will automatically be given
    a ``history`` parameter, which contains information about the past
    decisions and outcomes that have occurred (if available).
    """

    def __init__(self, name=None, provides=None, requires=None,
                 auto_extract=True, rebind=None):
        super(Retry, self).__init__(name=name, provides=provides,
                                    requires=requires, rebind=rebind,
                                    auto_extract=auto_extract,
                                    ignore_list=[EXECUTE_REVERT_HISTORY])

    @property
    def name(self):
        return self._name

    @name.setter
    def name(self, name):
        self._name = name

    @abc.abstractmethod
    def execute(self, history, *args, **kwargs):
        """Executes the given retry.

        This execution activates a given retry which will typically produce
        data required to start or restart a connected component using
        previously provided values and a ``history`` of prior failures from
        previous runs. The historical data can be analyzed to alter the
        resolution strategy that this retry controller will use.

        For example, a retry can provide the same values multiple times (after
        each run), the latest value or some other variation. Old values will be
        saved to the history of the retry atom automatically, that is a list of
        tuples (result, failures) are persisted where failures is a dictionary
        of failures indexed by task names and the result is the execution
        result returned by this retry during that failure resolution
        attempt.

        :param args: positional arguments that retry requires to execute.
        :param kwargs: any keyword arguments that retry requires to execute.
        """

    def revert(self, history, *args, **kwargs):
        """Reverts this retry.

        On revert call all results that had been provided by previous tries
        and all errors caused during reversion are provided. This method
        will be called *only* if a subflow must be reverted without the
        retry (that is to say that the controller has ran out of resolution
        options and has either given up resolution or has failed to handle
        a execution failure).

        :param args: positional arguments that the retry required to execute.
        :param kwargs: any keyword arguments that the retry required to
                       execute.
        """

    @abc.abstractmethod
    def on_failure(self, history, *args, **kwargs):
        """Makes a decision about the future.

        This method will typically use information about prior failures (if
        this historical failure information is not available or was not
        persisted the provided history will be empty).

        Returns a retry constant (one of):

        * ``RETRY``: when the controlling flow must be reverted and restarted
          again (for example with new parameters).
        * ``REVERT``: when this controlling flow must be completely reverted
          and the parent flow (if any) should make a decision about further
          flow execution.
        * ``REVERT_ALL``: when this controlling flow and the parent
          flow (if any) must be reverted and marked as a ``FAILURE``.
        """


class AlwaysRevert(Retry):
    """Retry that always reverts subflow."""

    def on_failure(self, *args, **kwargs):
        return REVERT

    def execute(self, *args, **kwargs):
        pass


class AlwaysRevertAll(Retry):
    """Retry that always reverts a whole flow."""

    def on_failure(self, **kwargs):
        return REVERT_ALL

    def execute(self, **kwargs):
        pass


class Times(Retry):
    """Retries subflow given number of times. Returns attempt number.

    :param attempts: number of attempts to retry the associated subflow
                     before giving up
    :type attempts: int
    :param revert_all: when provided this will cause the full flow to revert
                       when the number of attempts that have been tried
                       has been reached (when false, it will only locally
                       revert the associated subflow)
    :type revert_all: bool

    Further arguments are interpreted as defined in the
    :py:class:`~taskflow.atom.Atom` constructor.
    """

    def __init__(self, attempts=1, name=None, provides=None, requires=None,
                 auto_extract=True, rebind=None, revert_all=False):
        super(Times, self).__init__(name, provides, requires,
                                    auto_extract, rebind)
        self._attempts = attempts

        if revert_all:
            self._revert_action = REVERT_ALL
        else:
            self._revert_action = REVERT

    def on_failure(self, history, *args, **kwargs):
        if len(history) < self._attempts:
            return RETRY
        return self._revert_action

    def execute(self, history, *args, **kwargs):
        return len(history) + 1


class ForEachBase(Retry):
    """Base class for retries that iterate over a given collection."""

    def __init__(self, name=None, provides=None, requires=None,
                 auto_extract=True, rebind=None, revert_all=False):
        super(ForEachBase, self).__init__(name, provides, requires,
                                          auto_extract, rebind)

        if revert_all:
            self._revert_action = REVERT_ALL
        else:
            self._revert_action = REVERT

    def _get_next_value(self, values, history):
        # Fetches the next resolution result to try, removes overlapping
        # entries with what has already been tried and then returns the first
        # resolution strategy remaining.
        remaining = misc.sequence_minus(values, history.provided_iter())
        if not remaining:
            raise exc.NotFound("No elements left in collection of iterable "
                               "retry controller %s" % self.name)
        return remaining[0]

    def _on_failure(self, values, history):
        try:
            self._get_next_value(values, history)
        except exc.NotFound:
            return self._revert_action
        else:
            return RETRY


class ForEach(ForEachBase):
    """Applies a statically provided collection of strategies.

    Accepts a collection of decision strategies on construction and returns the
    next element of the collection on each try.

    :param values: values collection to iterate over and provide to
                   atoms other in the flow as a result of this functions
                   :py:meth:`~taskflow.atom.Atom.execute` method, which
                   other dependent atoms can consume (for example, to alter
                   their own behavior)
    :type values: list
    :param revert_all: when provided this will cause the full flow to revert
                       when the number of attempts that have been tried
                       has been reached (when false, it will only locally
                       revert the associated subflow)
    :type revert_all: bool

    Further arguments are interpreted as defined in the
    :py:class:`~taskflow.atom.Atom` constructor.
    """

    def __init__(self, values, name=None, provides=None, requires=None,
                 auto_extract=True, rebind=None, revert_all=False):
        super(ForEach, self).__init__(name, provides, requires,
                                      auto_extract, rebind, revert_all)
        self._values = values

    def on_failure(self, history, *args, **kwargs):
        return self._on_failure(self._values, history)

    def execute(self, history, *args, **kwargs):
        # NOTE(harlowja): This allows any connected components to know the
        # current resolution strategy being attempted.
        return self._get_next_value(self._values, history)


class ParameterizedForEach(ForEachBase):
    """Applies a dynamically provided collection of strategies.

    Accepts a collection of decision strategies from a predecessor (or from
    storage) as a parameter and returns the next element of that collection on
    each try.

    :param revert_all: when provided this will cause the full flow to revert
                       when the number of attempts that have been tried
                       has been reached (when false, it will only locally
                       revert the associated subflow)
    :type revert_all: bool

    Further arguments are interpreted as defined in the
    :py:class:`~taskflow.atom.Atom` constructor.
    """

    def __init__(self, name=None, provides=None, requires=None,
                 auto_extract=True, rebind=None, revert_all=False):
        super(ParameterizedForEach, self).__init__(name, provides, requires,
                                                   auto_extract, rebind,
                                                   revert_all)

    def on_failure(self, values, history, *args, **kwargs):
        return self._on_failure(values, history)

    def execute(self, values, history, *args, **kwargs):
        return self._get_next_value(values, history)
