#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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 os
import platform
import shutil
import warnings
import gc
import itertools
import operator
import random
import sys

import heapq
from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \
    CompressedSerializer, AutoBatchedSerializer
from pyspark.util import fail_on_stopiteration  # type: ignore


try:
    import psutil

    process = None

    def get_used_memory():
        """ Return the used memory in MiB """
        global process
        if process is None or process._pid != os.getpid():
            process = psutil.Process(os.getpid())
        if hasattr(process, "memory_info"):
            info = process.memory_info()
        else:
            info = process.get_memory_info()
        return info.rss >> 20

except ImportError:

    def get_used_memory():
        """ Return the used memory in MiB """
        if platform.system() == 'Linux':
            for line in open('/proc/self/status'):
                if line.startswith('VmRSS:'):
                    return int(line.split()[1]) >> 10

        else:
            warnings.warn("Please install psutil to have better "
                          "support with spilling")
            if platform.system() == "Darwin":
                import resource
                rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
                return rss >> 20
            # TODO: support windows

        return 0


def _get_local_dirs(sub):
    """ Get all the directories """
    path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
    dirs = path.split(",")
    if len(dirs) > 1:
        # different order in different processes and instances
        rnd = random.Random(os.getpid() + id(dirs))
        random.shuffle(dirs, rnd.random)
    return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs]


# global stats
MemoryBytesSpilled = 0
DiskBytesSpilled = 0


class Aggregator(object):

    """
    Aggregator has tree functions to merge values into combiner.

    createCombiner:  (value) -> combiner
    mergeValue:      (combine, value) -> combiner
    mergeCombiners:  (combiner, combiner) -> combiner
    """

    def __init__(self, createCombiner, mergeValue, mergeCombiners):
        self.createCombiner = fail_on_stopiteration(createCombiner)
        self.mergeValue = fail_on_stopiteration(mergeValue)
        self.mergeCombiners = fail_on_stopiteration(mergeCombiners)


class SimpleAggregator(Aggregator):

    """
    SimpleAggregator is useful for the cases that combiners have
    same type with values
    """

    def __init__(self, combiner):
        Aggregator.__init__(self, lambda x: x, combiner, combiner)


class Merger(object):

    """
    Merge shuffled data together by aggregator
    """

    def __init__(self, aggregator):
        self.agg = aggregator

    def mergeValues(self, iterator):
        """ Combine the items by creator and combiner """
        raise NotImplementedError

    def mergeCombiners(self, iterator):
        """ Merge the combined items by mergeCombiner """
        raise NotImplementedError

    def items(self):
        """ Return the merged items ad iterator """
        raise NotImplementedError


def _compressed_serializer(self, serializer=None):
    # always use PickleSerializer to simplify implementation
    ser = PickleSerializer()
    return AutoBatchedSerializer(CompressedSerializer(ser))


class ExternalMerger(Merger):

    """
    External merger will dump the aggregated data into disks when
    memory usage goes above the limit, then merge them together.

    This class works as follows:

    - It repeatedly combine the items and save them in one dict in
      memory.

    - When the used memory goes above memory limit, it will split
      the combined data into partitions by hash code, dump them
      into disk, one file per partition.

    - Then it goes through the rest of the iterator, combine items
      into different dict by hash. Until the used memory goes over
      memory limit, it dump all the dicts into disks, one file per
      dict. Repeat this again until combine all the items.

    - Before return any items, it will load each partition and
      combine them separately. Yield them before loading next
      partition.

    - During loading a partition, if the memory goes over limit,
      it will partition the loaded data and dump them into disks
      and load them partition by partition again.

    `data` and `pdata` are used to hold the merged items in memory.
    At first, all the data are merged into `data`. Once the used
    memory goes over limit, the items in `data` are dumped into
    disks, `data` will be cleared, all rest of items will be merged
    into `pdata` and then dumped into disks. Before returning, all
    the items in `pdata` will be dumped into disks.

    Finally, if any items were spilled into disks, each partition
    will be merged into `data` and be yielded, then cleared.

    Examples
    --------
    >>> agg = SimpleAggregator(lambda x, y: x + y)
    >>> merger = ExternalMerger(agg, 10)
    >>> N = 10000
    >>> merger.mergeValues(zip(range(N), range(N)))
    >>> assert merger.spills > 0
    >>> sum(v for k,v in merger.items())
    49995000

    >>> merger = ExternalMerger(agg, 10)
    >>> merger.mergeCombiners(zip(range(N), range(N)))
    >>> assert merger.spills > 0
    >>> sum(v for k,v in merger.items())
    49995000
    """

    # the max total partitions created recursively
    MAX_TOTAL_PARTITIONS = 4096

    def __init__(self, aggregator, memory_limit=512, serializer=None,
                 localdirs=None, scale=1, partitions=59, batch=1000):
        Merger.__init__(self, aggregator)
        self.memory_limit = memory_limit
        self.serializer = _compressed_serializer(serializer)
        self.localdirs = localdirs or _get_local_dirs(str(id(self)))
        # number of partitions when spill data into disks
        self.partitions = partitions
        # check the memory after # of items merged
        self.batch = batch
        # scale is used to scale down the hash of key for recursive hash map
        self.scale = scale
        # un-partitioned merged data
        self.data = {}
        # partitioned merged data, list of dicts
        self.pdata = []
        # number of chunks dumped into disks
        self.spills = 0
        # randomize the hash of key, id(o) is the address of o (aligned by 8)
        self._seed = id(self) + 7

    def _get_spill_dir(self, n):
        """ Choose one directory for spill by number n """
        return os.path.join(self.localdirs[n % len(self.localdirs)], str(n))

    def _next_limit(self):
        """
        Return the next memory limit. If the memory is not released
        after spilling, it will dump the data only when the used memory
        starts to increase.
        """
        return max(self.memory_limit, get_used_memory() * 1.05)

    def mergeValues(self, iterator):
        """ Combine the items by creator and combiner """
        # speedup attribute lookup
        creator, comb = self.agg.createCombiner, self.agg.mergeValue
        c, data, pdata, hfun, batch = 0, self.data, self.pdata, self._partition, self.batch
        limit = self.memory_limit

        for k, v in iterator:
            d = pdata[hfun(k)] if pdata else data
            d[k] = comb(d[k], v) if k in d else creator(v)

            c += 1
            if c >= batch:
                if get_used_memory() >= limit:
                    self._spill()
                    limit = self._next_limit()
                    batch /= 2
                    c = 0
                else:
                    batch *= 1.5

        if get_used_memory() >= limit:
            self._spill()

    def _partition(self, key):
        """ Return the partition for key """
        return hash((key, self._seed)) % self.partitions

    def _object_size(self, obj):
        """ How much of memory for this obj, assume that all the objects
        consume similar bytes of memory
        """
        return 1

    def mergeCombiners(self, iterator, limit=None):
        """ Merge (K,V) pair by mergeCombiner """
        if limit is None: