from __future__ import absolute_import
import os
import cloudpickle
import pysnptools.util as pstutil
from itertools import *
from unittest.mock import patch


def work_sequence_to_result_sequence(work_sequence):
    """
    Does all the work in a sequence of work items and returns a sequence of results.
    """
    for work in work_sequence:
        if callable(work):
            result = work()
        else:
            result = _run_all_in_memory(work)
        yield result


def _run_all_in_memory(work):
    """
    If callable, just calls
    If distributable, does all the work and then calls reduce on the results.
    """
    if callable(work):
        return work()
    else:
        assert hasattr(
            work, "work_sequence"
        ), "Your work item doesn't have a work_sequence. This can be caused by having 'map_reduce(nested=something,...)' where you should have 'map_reduce(mapper=something,...)' because the 'something' is not a nested 'map_reduce'. The exception can also be caused by giving a 'runner' on an inner, nested map_reduce."
        work_sequence = work.work_sequence()
        result_sequence = work_sequence_to_result_sequence(work_sequence)
        return work.reduce(result_sequence)


def create_task_file_name(workdirectory, taskindex, taskcount):
    """
    Creates a file name to use for saving a set of results.
    """
    task_file_name = os.path.join(
        workdirectory, "{0}.{1}.p".format(taskindex, taskcount)
    )
    return task_file_name


def _shape_to_desired_workcount(distributable, taskcount, weights=None):
    workcount = distributable.work_count
    if workcount > taskcount:
        distributable = BatchUpWork(distributable, workcount, taskcount, weights)
    else:
        if workcount < taskcount:
            if workcount == 0:
                distributable = MakeWork(distributable, taskcount)
            else:
                distributable = ExpandWork(distributable, workcount, taskcount)
    return distributable


def _run_one_task(
    original_distributable,
    taskindex,
    taskcount,
    workdirectory,
    weights=None,
    environ=None,
):
    """
    Does a fraction of the work (e.g. 1 of every 1000 work items) and then saves the results to single file.
    if taskindex == taskcount, does the reduce step
    """

    if not 0 < taskcount:
        raise Exception("Expect taskcount to be positive")
    if not (0 <= taskindex < taskcount + 1):
        raise Exception(
            "Expect taskindex to be between 0 (inclusive) and taskcount (exclusive)"
        )

    shaped_distributable = _shape_to_desired_workcount(
        original_distributable, taskcount, weights=weights
    )

    if shaped_distributable.work_count != taskcount:
        raise Exception("Assert: expect workcount == taskcount")

    pstutil.create_directory_if_necessary(workdirectory, isfile=False, robust=True)

    with patch.dict("os.environ", environ if environ is not None else {}) as _:
        if taskindex < taskcount:
            doMainWorkForOneIndex(
                shaped_distributable, taskcount, taskindex, workdirectory
            )
            return None
        else:
            result_sequence = work_sequence_from_disk(workdirectory, taskcount)
            return shaped_distributable.reduce(result_sequence)


def _work_sequence_for_one_index(distributable, taskAndWorkcount, taskindex):
    if hasattr(distributable, "work_sequence_range"):
        is_first_and_only = True
        for work in distributable.work_sequence_range(taskindex, taskindex + 1):
            assert is_first_and_only, "real assert"
            is_first_and_only = False
            yield work
    else:
        for workIndex, work in enumerate(distributable.work_sequence()):
            if workIndex == taskAndWorkcount:
                raise Exception(
                    "Expect len(work_sequence) to match work_count, but work_sequence was too long"
                )
            if workIndex == taskindex:
                yield work
                workDone = True
                if (
                    workIndex != taskAndWorkcount - 1
                ):  # the work is done, so quit enumerating work (but don't quit early if you're the last workIndex because we want to double check that the work_sequence and work_count match up)
                    break
        if not workDone:
            raise Exception(
                "Expect len(work_sequence) to match work_count, but work_sequence was too short"
            )


def doMainWorkForOneIndex(distributable, taskAndWorkcount, taskindex, workdirectory):
    task_file_name = create_task_file_name(workdirectory, taskindex, taskAndWorkcount)
    workDone = False
    with open(task_file_name, mode="wb") as f:
        for work in _work_sequence_for_one_index(
            distributable, taskAndWorkcount, taskindex
        ):
            result = _run_all_in_memory(work)
            cloudpickle.dump(result, f)  # save a result to the temp results file


def work_sequence_from_disk(workdirectory, taskAndWorkcount):
    """
    Reads all the result sets from the temporary files and returns a sequence results
    """
    for taskindex in range(taskAndWorkcount):
        task_file_name = create_task_file_name(
            workdirectory, taskindex, taskAndWorkcount
        )
        with open(task_file_name, mode="rb") as f:
            try:
                result = cloudpickle.load(f)
            except Exception as detail:
                raise Exception(
                    "Error trying to unpickle '{0}'. {1}".format(task_file_name, detail)
                )
        # if True:
        #    logging.debug("\tAbout to yield result {0} of {1}".format(taskindex,taskAndWorkcount))
        yield result


class BatchUpWork(object):  # implements IDistributable
    """
    A wrapper.
    Takes a distributable that has more work items that wanted and turns it into one with exactly the right number of work items.
    It does this by batching up work items, block style.

    If weights is given, it must be an array of integers of length "taskcount". Each value gives the relative weight
    of that task compared to the others. For example, if all values are 1, then all task will be assigned
    the same amount of work. If the first value is 2 and the others are 1, then the first task (index 0)
    will be assigned twice as much work as the other tasks.
    """

    def __init__(self, distributable, workcount, taskcount, weights=None):
        self.sub_distributable = distributable
        self.sub_workcount = workcount
        self._workcount = taskcount

        if taskcount <= 0:
            raise Exception("Expect taskcount to be at least 1.")

        if weights is None:
            self._partial_sum_weight = list(range(0, taskcount + 1))
        else:
            if len(weights) != taskcount:
                raise Exception("Expect weights to have length of taskcount.")
            self._partial_sum_weight = [0]
            for weight in weights:
                if weight != int(weight) or weight < 0:
                    raise Exception("Expect weights be non-negative integers.")
                self._partial_sum_weight.append(weight + self._partial_sum_weight[-1])
        if self._partial_sum_weight[-1] == 0:
            raise Exception("Weights cannot be all zero.")

    @property
    def work_count(self):
        return self._workcount

    def work_sequence(self):
        return work_sequence_range(0, self._workcount)

    def work_sequence_range(self, start, stop):
        assert 0 <= start <= stop <= self._workcount, "real assert"
        for workIndex in range(start, stop):
            yield lambda workIndex=workIndex: self.work(workIndex)

    def work(self, workIndex):
        result = [workIndex]
        start, stop = self.createSubWorkIndexList(workIndex)
        for sub_workIndex, sub_work in self.pull_sub_work(start, stop):
            sub_result = _run_all_in_memory(sub_work)
            result.append((sub_workIndex, sub_result))
        return result

    def pull_sub_work(self, start, stop):
        if hasattr(self.sub_distributable, "work_sequence_range"):
            index = start
            for sub_work in self.sub_distributable.work_sequence_range(start, stop):
                yield index, sub_work
                index += 1
            assert index == stop, "real assert"
        else:
            sub_workIndexList = list(range(stop - 1, start - 1, -1))
            for sub_workIndex, sub_work in enumerate(
                self.sub_distributable.work_sequence()
            ):
                if sub_workIndex == self.sub_workcount:
                    raise Exception("Expect len(work_sequence) to match work_count")
                if sub_workIndex == sub_workIndexList[-1]:
                    yield sub_workIndex, sub_work
                    sub_workIndexList.pop()
                    if (
                        len(sub_workIndexList) == 0
                        and sub_workIndex != self.sub_workcount - 1
                    ):  # there are no more work items for this task, so quit (but don't quit early if you're the last workIndex because we want to double check that the work_sequence and work_count match up)
                        break
            if len(sub_workIndexList) != 0:
                raise Exception(
                    "no work was enumerated for workIndex={0}".format(
                        sub_workIndexList[-1]
                    )
                )

    def reduce(self, result_sequence):
        sub_result_sequence = self.create_sub_result_sequence(result_sequence)
        result = self.sub_distributable.reduce(sub_result_sequence)
        return result

    def create_sub_result_sequence(self, result_sequence):
        for result in result_sequence:
            workIndex = result.pop(0)
            start, stop = self.createSubWorkIndexList(workIndex)
            if (stop - start) != len(result):
                raise Exception("Assert: batched results not expected size")
            for sub_workIndex, pair in zip(range(start, stop), result):
                sub_workIndex_check, sub_result = pair
                if sub_workIndex != sub_workIndex_check:
                    raise Exception("Assert: Unexpected workindex in batched result")
                yield sub_result

    @property
    def tempdirectory(self):
        return self.sub_distributable.tempdirectory

    # optional
    def __str__(self):
        return "{0}({1},{2},{3})".format(
            self.__class__.__name__,
            self.sub_distributable,
            self.sub_workcount,
            self._workcount,
        )

    def createSubWorkIndexList(self, workindex):
        assert 0 <= workindex < self._workcount, "real assert"
        start = (
            self._partial_sum_weight[workindex]
            * self.sub_workcount
            // self._partial_sum_weight[-1]
        )  # assuming high precision integer math
        stop = (
            self._partial_sum_weight[workindex + 1]
            * self.sub_workcount
            // self._partial_sum_weight[-1]
        )  # assuming high precision integer math
        return start, stop


class ExpandWork(object):  # implements IDistributable
    def __init__(self, distributable, workcount, taskcount):
        assert 1 <= workcount, "Expect workcount to be at least 1"
        assert (
            workcount < taskcount
        ), "ExpandWork expects workcount to be less than taskcount"
        self.sub_distributable = distributable
        self.sub_workcount = workcount
        self._workcount = taskcount
        self.floor = taskcount // workcount  # all work gets this many tasks
        self.excess = (
            taskcount - self.floor * workcount
        )  # but there are this many extra tasks
        self.extra_index = (
            self.floor + 1
        ) * self.excess  # tasks before here, get one extra task

        if hasattr(distributable, "work_sequence_range"):
            self.work_sequence_range = self._work_sequence_range

    @property
    def work_count(self):
        return self._workcount

    def expand_to(self, sub_workIndex):
        if sub_workIndex < self.excess:
            return self.floor + 1
        else:
            return self.floor

    def index_to_sub_index(self, index):
        if index > self.extra_index:
            sub_index = self.excess + (index - self.extra_index) // self.floor
        else:
            sub_index = index // (self.floor + 1)
        return sub_index

    def sub_index_to_start(self, sub_index):
        if sub_index < self.excess:
            start = sub_index * (self.floor + 1)
        else:
            start = self.extra_index + (sub_index - self.excess) * self.floor
        return start

    def sub_sub_start_stop(self, sub_start, start, stop):
        full_start = self.sub_index_to_start(sub_start)
        full_end = self.sub_index_to_start(sub_start + 1)
        if full_start < start:
            sub_sub_start = start - full_start
        else:
            sub_sub_start = 0

        if stop < full_end:
            sub_sub_stop = stop - full_start
        else:
            sub_sub_stop = full_end - full_start

        return sub_sub_start, sub_sub_stop

    def sub_distributable_work_sequence_range(self, start, stop):
        if hasattr(self.sub_distributable, "work_sequence_range"):
            return self.sub_distributable.work_sequence_range(start, stop)
        else:
            return islice(self.sub_distributable.work_sequence(), start, stop)

    def _work_sequence_range(self, start, stop):

        sub_start = self.index_to_sub_index(start)
        sub_stop = self.index_to_sub_index(stop - 1) + 1
        sub_workIndex = sub_start
        workIndex = start
        for sub_work in self.sub_distributable_work_sequence_range(sub_start, sub_stop):
            expand_to = self.expand_to(sub_workIndex)
            sub_sub_start, sub_sub_stop = self.sub_sub_start_stop(
                sub_workIndex, start, stop
            )
            if not callable(
                sub_work
            ):  # i.e. a distributable job. If our expand_to is 3 and i's work_count is 9, then it is batched up 3x3. On the other hand, if its work_count is 2, then we ExpandWork from 2 to 3.
                shaped_distributable = _shape_to_desired_workcount(sub_work, expand_to)
                for sub_sub_work in shaped_distributable.work_sequence_range(
                    sub_sub_start, sub_sub_stop
                ):
                    yield sub_sub_work
                    workIndex += 1
                #    expand_to -= 1
                # if expand_to != 0 :
                #    raise Exception("Assert: shaped_distributable doesn't have right number of work items")
                # assert sub_sub_index == sub_sub_end, "real assert"
            else:
                sub_sub_index = sub_sub_start
                if sub_sub_index == 0:
                    yield sub_work
                    workIndex += 1
                    sub_sub_index += 1
                for sub_sub_index2 in range(sub_sub_index, sub_sub_stop):
                    assert sub_sub_index2 == sub_sub_index, "real assert"
                    yield lambda: None
                    workIndex += 1
                    sub_sub_index += 1
                assert sub_sub_index == sub_sub_stop, "real assert"
            sub_workIndex += 1
        assert sub_workIndex == sub_stop, "real assert"
        assert workIndex == stop, "real assert"

    def work_sequence(self):
        return self._work_sequence_range(0, self.work_count)

    def reduce(self, result_sequence):
        sub_result_sequence = self.create_sub_result_sequence(result_sequence)
        result = self.sub_distributable.reduce(sub_result_sequence)
        return result

    def create_sub_result_sequence(self, result_sequence):
        # e.g. we had 11 work items, but we wanted 300 so each work item was expanded to 28 or 27 work items.
        # now we have 300 results that we must turn to 11 results.
        # If we had just padded with empty work we could just filter those out and we'd have 11. But we might
        # have gone down another level of map reduce.
        # We assume that the 300 items must arrive in order.
        sub_workIndex = 0
        workIndex = 0
        for sub_work in self.sub_distributable.work_sequence():
            expand_to = self.expand_to(sub_workIndex)
            if not callable(sub_work):
                shaped_distributable = _shape_to_desired_workcount(sub_work, expand_to)
                sub_result = SubGen(result_sequence, expand_to)
                result = shaped_distributable.reduce(sub_result)
                if not hasattr(shaped_distributable, "suppress_yield"):
                    yield result
                workIndex += expand_to
            else:
                yield next(result_sequence)
                workIndex += 1
                for workindex in range(1, expand_to):
                    paddedResultToIgnore = next(result_sequence)
                    if paddedResultToIgnore != None:
                        raise Exception("Assert: Expected 'None' result")
                    workIndex += 1
            sub_workIndex += 1
        if sub_workIndex != self.sub_workcount:
            raise Excpetion("Assert:  expect len(result_sequence) to match workcount")
        if workIndex != self._workcount:
            raise Exception("Assert: expect len(result_sequence) to match workcount")
        try:
            next(
                result_sequence
            )  # should get an StopIternation here, which will be ignored.
            raise Exception(
                "Assert: expect len(result_sequence) to match workcount. This can be caused by a 'reducer' that doesn't pull every input from its input sequence."
            )
        except StopIteration:
            pass  # do nothing

    @property
    def tempdirectory(self):
        return self.sub_distributable.tempdirectory

    # optional
    def __str__(self):
        return "{0}({1},{2},{3})".format(
            self.__class__.__name__,
            self.sub_distributable,
            self.sub_workcount,
            self._workcount,
        )


class MakeWork(object):  # implements IDistributable
    def __init__(self, distributable, taskcount):
        self._workcount = taskcount
        self.sub_distributable = distributable

    suppress_yield = None  # Just defining it is enough. The value doesn't matter.

    @property
    def work_count(self):
        return self._workcount

    def work_sequence_range(self, start, stop):
        for i in range(start, stop):
            yield lambda: None

    def work_sequence(self):
        return self._work_sequence_range(0, self.work_count)

    def reduce(self, result_sequence):
        for (
            result
        ) in result_sequence:  # We iterate through, just so everything is accounted for
            pass
        return "This return value should always be ignored"

    def __str__(self):
        return "{0}({1})".format(self.__class__.__name__, self._workcount)


class SubGen:
    def __init__(self, gen, count):
        self.gen = gen
        self.count = count

    def __iter__(self):
        return self

    def __next__(self):
        if self.count > 0:
            self.count -= 1
            return next(self.gen)
        else:
            raise StopIteration()


from pysnptools.util.mapreduce1.runner.runner import Runner
from pysnptools.util.mapreduce1.runner.local import Local, _JustCheckExists
from pysnptools.util.mapreduce1.runner.localmultiproc import LocalMultiProc
from pysnptools.util.mapreduce1.runner.localmultithread import LocalMultiThread
from pysnptools.util.mapreduce1.runner.localinparts import LocalInParts
