# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.

from __future__ import absolute_import, print_function, unicode_literals

import logging
import os
import requests

from .graph import Graph
from . import files_changed
from .taskgraph import TaskGraph
from .util.seta import is_low_value_task
from .util.taskcluster import find_task_id
from .util.parameterization import resolve_task_references
from slugid import nice as slugid

logger = logging.getLogger(__name__)

_optimizations = {}


def optimize_task_graph(target_task_graph, params, do_not_optimize, existing_tasks=None):
    """
    Perform task optimization, without optimizing tasks named in
    do_not_optimize.
    """
    named_links_dict = target_task_graph.graph.named_links_dict()
    label_to_taskid = {}

    # This proceeds in two phases.  First, mark all optimized tasks (those
    # which will be removed from the graph) as such, including a replacement
    # taskId where applicable.  Second, generate a new task graph containing
    # only the non-optimized tasks, with all task labels resolved to taskIds
    # and with task['dependencies'] populated.
    annotate_task_graph(target_task_graph=target_task_graph,
                        params=params,
                        do_not_optimize=do_not_optimize,
                        named_links_dict=named_links_dict,
                        label_to_taskid=label_to_taskid,
                        existing_tasks=existing_tasks)
    return get_subgraph(target_task_graph, named_links_dict, label_to_taskid), label_to_taskid


def optimize_task(task, params):
    """
    Optimize a single task by running its optimizations in order until one
    succeeds.
    """
    for opt in task.optimizations:
        opt_type, args = opt[0], opt[1:]
        opt_fn = _optimizations[opt_type]
        opt_result = opt_fn(task, params, *args)
        if opt_result:
            return opt_result

    return False


def annotate_task_graph(target_task_graph, params, do_not_optimize,
                        named_links_dict, label_to_taskid, existing_tasks):
    """
    Annotate each task in the graph with .optimized (boolean) and .task_id
    (possibly None), following the rules for optimization and calling the task
    kinds' `optimize_task` method.

    As a side effect, label_to_taskid is updated with labels for all optimized
    tasks that are replaced with existing tasks.
    """

    # set .optimized for all tasks, and .task_id for optimized tasks
    # with replacements
    for label in target_task_graph.graph.visit_postorder():
        task = target_task_graph.tasks[label]
        named_task_dependencies = named_links_dict.get(label, {})

        # check whether any dependencies have been optimized away
        dependencies = [target_task_graph.tasks[l] for l in named_task_dependencies.itervalues()]
        for t in dependencies:
            if t.optimized and not t.task_id:
                raise Exception(
                    "task {} was optimized away, but {} depends on it".format(
                        t.label, label))

        # if this task is blacklisted, don't even consider optimizing
        replacement_task_id = None
        if label in do_not_optimize:
            optimized = False
        # Let's check whether this task has been created before
        elif existing_tasks is not None and label in existing_tasks:
            optimized = True
            replacement_task_id = existing_tasks[label]
        # otherwise, examine the task itself (which may be an expensive operation)
        else:
            opt_result = optimize_task(task, params)

            # use opt_result to determine values for optimized, replacement_task_id
            optimized = bool(opt_result)
            replacement_task_id = opt_result if opt_result and opt_result is not True else None

        task.optimized = optimized
        task.task_id = replacement_task_id
        if replacement_task_id:
            label_to_taskid[label] = replacement_task_id

        if optimized:
            if replacement_task_id:
                logger.debug("optimizing `{}`, replacing with task `{}`"
                             .format(label, replacement_task_id))
            else:
                logger.debug("optimizing `{}` away".format(label))
                # note: any dependent tasks will fail when they see this
        else:
            if replacement_task_id:
                raise Exception("{}: optimize_task returned False with a taskId".format(label))


def get_subgraph(annotated_task_graph, named_links_dict, label_to_taskid):
    """
    Return the subgraph of annotated_task_graph consisting only of
    non-optimized tasks and edges between them.

    To avoid losing track of taskIds for tasks optimized away, this method
    simultaneously substitutes real taskIds for task labels in the graph, and
    populates each task definition's `dependencies` key with the appropriate
    taskIds.  Task references are resolved in the process.
    """

    # resolve labels to taskIds and populate task['dependencies']
    tasks_by_taskid = {}
    for label in annotated_task_graph.graph.visit_postorder():
        task = annotated_task_graph.tasks[label]
        if task.optimized:
            continue
        task.task_id = label_to_taskid[label] = slugid()
        named_task_dependencies = {
                name: label_to_taskid[label]
                for name, label in named_links_dict.get(label, {}).iteritems()}
        task.task = resolve_task_references(task.label, task.task, named_task_dependencies)
        task.task.setdefault('dependencies', []).extend(named_task_dependencies.itervalues())
        tasks_by_taskid[task.task_id] = task

    # resolve edges to taskIds
    edges_by_taskid = (
        (label_to_taskid.get(left), label_to_taskid.get(right), name)
        for (left, right, name) in annotated_task_graph.graph.edges
        )
    # ..and drop edges that are no longer in the task graph
    edges_by_taskid = set(
        (left, right, name)
        for (left, right, name) in edges_by_taskid
        if left in tasks_by_taskid and right in tasks_by_taskid
        )

    return TaskGraph(
        tasks_by_taskid,
        Graph(set(tasks_by_taskid), edges_by_taskid))


def optimization(name):
    def wrap(func):
        if name in _optimizations:
            raise Exception("multiple optimizations with name {}".format(name))
        _optimizations[name] = func
        return func
    return wrap


@optimization('index-search')
def opt_index_search(task, params, index_path):
    try:
        task_id = find_task_id(
            index_path,
            use_proxy=bool(os.environ.get('TASK_ID')))

        return task_id or True
    except requests.exceptions.HTTPError:
        pass

    return False


@optimization('seta')
def opt_seta(task, params):
    bbb_task = False

    # for bbb tasks we need to send in the buildbot buildername
    if task.task.get('provisionerId', '') == 'buildbot-bridge':
        label = task.task.get('payload').get('buildername')
        bbb_task = True
    else:
        label = task.label

    # we would like to return 'False, None' while it's high_value_task
    # and we wouldn't optimize it. Otherwise, it will return 'True, None'
    if is_low_value_task(label,
                         params.get('project'),
                         params.get('pushlog_id'),
                         params.get('pushdate'),
                         bbb_task):
        # Always optimize away low-value tasks
        return True
    else:
        return False


@optimization('skip-unless-changed')
def opt_files_changed(task, params, file_patterns):
    # pushlog_id == -1 - this is the case when run from a cron.yml job
    if params.get('pushlog_id') == -1:
        return True

    changed = files_changed.check(params, file_patterns)
    if not changed:
        logger.debug('no files found matching a pattern in `skip-unless-changed` for ' +
                     task.label)
        return True
    return False