"""Variation fonts interpolation models."""

__all__ = [
    "normalizeValue",
    "normalizeLocation",
    "supportScalar",
    "VariationModel",
]

from fontTools.misc.roundTools import noRound
from .errors import VariationModelError


def nonNone(lst):
    return [l for l in lst if l is not None]


def allNone(lst):
    return all(l is None for l in lst)


def allEqualTo(ref, lst, mapper=None):
    if mapper is None:
        return all(ref == item for item in lst)

    mapped = mapper(ref)
    return all(mapped == mapper(item) for item in lst)


def allEqual(lst, mapper=None):
    if not lst:
        return True
    it = iter(lst)
    try:
        first = next(it)
    except StopIteration:
        return True
    return allEqualTo(first, it, mapper=mapper)


def subList(truth, lst):
    assert len(truth) == len(lst)
    return [l for l, t in zip(lst, truth) if t]


def normalizeValue(v, triple):
    """Normalizes value based on a min/default/max triple.

      >>> normalizeValue(400, (100, 400, 900))
      0.0
      >>> normalizeValue(100, (100, 400, 900))
      -1.0
      >>> normalizeValue(650, (100, 400, 900))
      0.5
    """
    lower, default, upper = triple
    if not (lower <= default <= upper):
        raise ValueError(
            f"Invalid axis values, must be minimum, default, maximum: "
            f"{lower:3.3f}, {default:3.3f}, {upper:3.3f}"
        )
    v = max(min(v, upper), lower)
    if v == default:
        v = 0.0
    elif v < default:
        v = (v - default) / (default - lower)
    else:
        v = (v - default) / (upper - default)
    return v


def normalizeLocation(location, axes):
    """Normalizes location based on axis min/default/max values from axes.

      >>> axes = {"wght": (100, 400, 900)}
      >>> normalizeLocation({"wght": 400}, axes)
      {'wght': 0.0}
      >>> normalizeLocation({"wght": 100}, axes)
      {'wght': -1.0}
      >>> normalizeLocation({"wght": 900}, axes)
      {'wght': 1.0}
      >>> normalizeLocation({"wght": 650}, axes)
      {'wght': 0.5}
      >>> normalizeLocation({"wght": 1000}, axes)
      {'wght': 1.0}
      >>> normalizeLocation({"wght": 0}, axes)
      {'wght': -1.0}
      >>> axes = {"wght": (0, 0, 1000)}
      >>> normalizeLocation({"wght": 0}, axes)
      {'wght': 0.0}
      >>> normalizeLocation({"wght": -1}, axes)
      {'wght': 0.0}
      >>> normalizeLocation({"wght": 1000}, axes)
      {'wght': 1.0}
      >>> normalizeLocation({"wght": 500}, axes)
      {'wght': 0.5}
      >>> normalizeLocation({"wght": 1001}, axes)
      {'wght': 1.0}
      >>> axes = {"wght": (0, 1000, 1000)}
      >>> normalizeLocation({"wght": 0}, axes)
      {'wght': -1.0}
      >>> normalizeLocation({"wght": -1}, axes)
      {'wght': -1.0}
      >>> normalizeLocation({"wght": 500}, axes)
      {'wght': -0.5}
      >>> normalizeLocation({"wght": 1000}, axes)
      {'wght': 0.0}
      >>> normalizeLocation({"wght": 1001}, axes)
      {'wght': 0.0}
    """
    out = {}
    for tag, triple in axes.items():
        v = location.get(tag, triple[1])
        out[tag] = normalizeValue(v, triple)
    return out


def supportScalar(location, support, ot=True, extrapolate=False, axisRanges=None):
    """Returns the scalar multiplier at location, for a master
    with support.  If ot is True, then a peak value of zero
    for support of an axis means "axis does not participate".  That
    is how OpenType Variation Font technology works.

      >>> supportScalar({}, {})
      1.0
      >>> supportScalar({'wght':.2}, {})
      1.0
      >>> supportScalar({'wght':.2}, {'wght':(0,2,3)})
      0.1
      >>> supportScalar({'wght':2.5}, {'wght':(0,2,4)})
      0.75
      >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
      0.75
      >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}, ot=False)
      0.375
      >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
      0.75
      >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
      0.75
      >>> supportScalar({'wght':4}, {'wght':(0,2,3)}, extrapolate=True)
      2.0
      >>> supportScalar({'wght':4}, {'wght':(0,2,2)}, extrapolate=True)
      2.0
    """
    if extrapolate and axisRanges is None:
        axisRanges = {}
    scalar = 1.0
    for axis, (lower, peak, upper) in support.items():
        if ot:
            # OpenType-specific case handling
            if peak == 0.0:
                continue
            if lower > peak or peak > upper:
                continue
            if lower < 0.0 and upper > 0.0:
                continue
            v = location.get(axis, 0.0)
        else:
            assert axis in location
            v = location[axis]
        if v == peak:
            continue

        if extrapolate:
            axisMin, axisMax = axisRanges.get(axis, (-1, +1))
            if v < axisMin and lower <= axisMin:
                if peak <= axisMin and peak < upper:
                    scalar *= (v - upper) / (peak - upper)
                    continue
                elif axisMin < peak:
                    scalar *= (v - lower) / (peak - lower)
                    continue
            elif axisMax < v and axisMax <= upper:
                if axisMax <= peak and lower < peak:
                    scalar *= (v - lower) / (peak - lower)
                    continue
                elif peak < axisMax:
                    scalar *= (v - upper) / (peak - upper)
                    continue

        if v <= lower or upper <= v:
            scalar = 0.0
            break

        if v < peak:
            scalar *= (v - lower) / (peak - lower)
        else:  # v > peak
            scalar *= (v - upper) / (peak - upper)
    return scalar


class VariationModel(object):
    """Locations must have the base master at the origin (ie. 0).

    If the extrapolate argument is set to True, then location values are
    interpretted in the normalized space, ie. in the [-1,+1] range, and
    values are extrapolated outside this range.

      >>> from pprint import pprint
      >>> locations = [ \
      {'wght':100}, \
      {'wght':-100}, \
      {'wght':-180}, \
      {'wdth':+.3}, \
      {'wght':+120,'wdth':.3}, \
      {'wght':+120,'wdth':.2}, \
      {}, \
      {'wght':+180,'wdth':.3}, \
      {'wght':+180}, \
      ]
      >>> model = VariationModel(locations, axisOrder=['wght'])
      >>> pprint(model.locations)
      [{},
       {'wght': -100},
       {'wght': -180},
       {'wght': 100},
       {'wght': 180},
       {'wdth': 0.3},
       {'wdth': 0.3, 'wght': 180},
       {'wdth': 0.3, 'wght': 120},
       {'wdth': 0.2, 'wght': 120}]
      >>> pprint(model.deltaWeights)
      [{},
       {0: 1.0},
       {0: 1.0},
       {0: 1.0},
       {0: 1.0},
       {0: 1.0},
       {0: 1.0, 4: 1.0, 5: 1.0},
       {0: 1.0, 3: 0.75, 4: 0.25, 5: 1.0, 6: 0.6666666666666666},
       {0: 1.0,
        3: 0.75,
        4: 0.25,
        5: 0.6666666666666667,
        6: 0.4444444444444445,
        7: 0.6666666666666667}]
    """

    def __init__(self, locations, axisOrder=None, extrapolate=False):

        if len(set(tuple(sorted(l.items())) for l in locations)) != len(locations):
            raise VariationModelError("Locations must be unique.")

        self.origLocations = locations
        self.axisOrder = axisOrder if axisOrder is not None else []
        self.extrapolate = extrapolate
        self.axisRanges = self.computeAxisRanges(locations) if extrapolate else None

        locations = [{k: v for k, v in loc.items() if v != 0.0} for loc in locations]
        keyFunc = self.getMasterLocationsSortKeyFunc(
            locations, axisOrder=self.axisOrder
        )
        self.locations = sorted(locations, key=keyFunc)

        # Mapping from user's master order to our master order
        self.mapping = [self.locations.index(l) for l in locations]
        self.reverseMapping = [locations.index(l) for l in self.locations]

        self._computeMasterSupports()
        self._subModels = {}

    def getSubModel(self, items):
        if None not in items:
            return self, items
        key = tuple(v is not None for v in items)
        subModel = self._subModels.get(key)
        if subModel is None:
            subModel = VariationModel(subList(key, self.origLocations), self.axisOrder)
            self._subModels[key] = subModel
        return subModel, subList(key, items)

    @staticmethod
    def computeAxisRanges(locations):
        axisRanges = {}
        for loc in locations:
            for axis, value in loc.items():
                axisMin, axisMax = axisRanges.get(axis, (value, value))
                axisRanges[axis] = min(value, axisMin), max(value, axisMax)
        return axisRanges

    @staticmethod
    def getMasterLocationsSortKeyFunc(locations, axisOrder=[]):
        if {} not in locations:
            raise VariationModelError("Base master not found.")
        axisPoints = {}
        for loc in locations:
            if len(loc) != 1:
                continue
            axis = next(iter(loc))
            value = loc[axis]
            if axis not in axisPoints:
                axisPoints[axis] = {0.0}
            assert (
                value not in axisPoints[axis]
            ), 'Value "%s" in axisPoints["%s"] -->  %s' % (value, axis, axisPoints)
            axisPoints[axis].add(value)

        def getKey(axisPoints, axisOrder):
            def sign(v):
                return -1 if v < 0 else +1 if v > 0 else 0

            def key(loc):
                rank = len(loc)
                onPointAxes = [
                    axis
                    for axis, value in loc.items()
                    if axis in axisPoints and value in axisPoints[axis]
                ]
                orderedAxes = [axis for axis in axisOrder if axis in loc]
                orderedAxes.extend(
                    [axis for axis in sorted(loc.keys()) if axis not in axisOrder]
                )
                return (
                    rank,  # First, order by increasing rank
                    -len(onPointAxes),  # Next, by decreasing number of onPoint axes
                    tuple(
                        axisOrder.index(axis) if axis in axisOrder else 0x10000
                        for axis in orderedAxes
                    ),  # Next, by known axes
                    tuple(orderedAxes),  # Next, by all axes
                    tuple(
                        sign(loc[axis]) for axis in orderedAxes
                    ),  # Next, by signs of axis values
                    tuple(
                        abs(loc[axis]) for axis in orderedAxes
                    ),  # Next, by absolute value of axis values
                )

            return key

        ret = getKey(axisPoints, axisOrder)
        return ret

    def reorderMasters(self, master_list, mapping):
        # For changing the master data order without
        # recomputing supports and deltaWeights.
        new_list = [master_list[idx] for idx in mapping]
        self.origLocations = [self.origLocations[idx] for idx in mapping]
        locations = [
            {k: v for k, v in loc.items() if v != 0.0} for loc in self.origLocations
        ]
        self.mapping = [self.locations.index(l) for l in locations]
        self.reverseMapping = [locations.index(l) for l in self.locations]
        self._subModels = {}
        return new_list

    def _computeMasterSupports(self):
        self.supports = []
        regions = self._locationsToRegions()
        for i, region in enumerate(regions):
            locAxes = set(region.keys())
            # Walk over previous masters now
            for prev_region in regions[:i]:
                # Master with extra axes do not participte
                if not set(prev_region.keys()).issubset(locAxes):
                    continue
                # If it's NOT in the current box, it does not participate
                relevant = True
                for axis, (lower, peak, upper) in region.items():
                    if axis not in prev_region or not (
                        prev_region[axis][1] == peak
                        or lower < prev_region[axis][1] < upper
                    ):
                        relevant = False
                        break
                if not relevant:
                    continue

                # Split the box for new master; split in whatever direction
                # that has largest range ratio.
                #
                # For symmetry, we actually cut across multiple axes
                # if they have the largest, equal, ratio.
                # https://github.com/fonttools/fonttools/commit/7ee81c8821671157968b097f3e55309a1faa511e#commitcomment-31054804

                bestAxes = {}
                bestRatio = -1
                for axis in prev_region.keys():
                    val = prev_region[axis][1]
                    assert axis in region
                    lower, locV, upper = region[axis]
                    newLower, newUpper = lower, upper
                    if val < locV:
                        newLower = val
                        ratio = (val - locV) / (lower - locV)
                    elif locV < val:
                        newUpper = val
                        ratio = (val - locV) / (upper - locV)
                    else:  # val == locV
                        # Can't split box in this direction.
                        continue
                    if ratio > bestRatio:
                        bestAxes = {}
                        bestRatio = ratio
                    if ratio == bestRatio:
                        bestAxes[axis] = (newLower, locV, newUpper)

                for axis, triple in bestAxes.items():
                    region[axis] = triple
            self.supports.append(region)
        self._computeDeltaWeights()

    def _locationsToRegions(self):
        locations = self.locations
        # Compute min/max across each axis, use it as total range.
        # TODO Take this as input from outside?
        minV = {}
        maxV = {}
        for l in locations:
            for k, v in l.items():
                minV[k] = min(v, minV.get(k, v))
                maxV[k] = max(v, maxV.get(k, v))

        regions = []
        for loc in locations:
            region = {}
            for axis, locV in loc.items():
                if locV > 0:
                    region[axis] = (0, locV, maxV[axis])
                else:
                    region[axis] = (minV[axis], locV, 0)
            regions.append(region)
        return regions

    def _computeDeltaWeights(self):
        self.deltaWeights = []
        for i, loc in enumerate(self.locations):
            deltaWeight = {}
            # Walk over previous masters now, populate deltaWeight
            for j, support in enumerate(self.supports[:i]):
                scalar = supportScalar(loc, support)
                if scalar:
                    deltaWeight[j] = scalar
            self.deltaWeights.append(deltaWeight)

    def getDeltas(self, masterValues, *, round=noRound):
        assert len(masterValues) == len(self.deltaWeights)
        mapping = self.reverseMapping
        out = []
        for i, weights in enumerate(self.deltaWeights):
            delta = masterValues[mapping[i]]
            for j, weight in weights.items():
                if weight == 1:
                    delta -= out[j]
                else:
                    delta -= out[j] * weight
            out.append(round(delta))
        return out

    def getDeltasAndSupports(self, items, *, round=noRound):
        model, items = self.getSubModel(items)
        return model.getDeltas(items, round=round), model.supports

    def getScalars(self, loc):
        return [supportScalar(loc, support, extrapolate=self.extrapolate, axisRanges=self.axisRanges)
                for support in self.supports]

    @staticmethod
    def interpolateFromDeltasAndScalars(deltas, scalars):
        v = None
        assert len(deltas) == len(scalars)
        for delta, scalar in zip(deltas, scalars):
            if not scalar:
                continue
            contribution = delta * scalar
            if v is None:
                v = contribution
            else:
                v += contribution
        return v

    def interpolateFromDeltas(self, loc, deltas):
        scalars = self.getScalars(loc)
        return self.interpolateFromDeltasAndScalars(deltas, scalars)

    def interpolateFromMasters(self, loc, masterValues, *, round=noRound):
        deltas = self.getDeltas(masterValues, round=round)
        return self.interpolateFromDeltas(loc, deltas)

    def interpolateFromMastersAndScalars(self, masterValues, scalars, *, round=noRound):
        deltas = self.getDeltas(masterValues, round=round)
        return self.interpolateFromDeltasAndScalars(deltas, scalars)


def piecewiseLinearMap(v, mapping):
    keys = mapping.keys()
    if not keys:
        return v
    if v in keys:
        return mapping[v]
    k = min(keys)
    if v < k:
        return v + mapping[k] - k
    k = max(keys)
    if v > k:
        return v + mapping[k] - k
    # Interpolate
    a = max(k for k in keys if k < v)
    b = min(k for k in keys if k > v)
    va = mapping[a]
    vb = mapping[b]
    return va + (vb - va) * (v - a) / (b - a)


def main(args=None):
    """Normalize locations on a given designspace"""
    from fontTools import configLogger
    import argparse

    parser = argparse.ArgumentParser(
        "fonttools varLib.models",
        description=main.__doc__,
    )
    parser.add_argument(
        "--loglevel",
        metavar="LEVEL",
        default="INFO",
        help="Logging level (defaults to INFO)",
    )

    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument("-d", "--designspace", metavar="DESIGNSPACE", type=str)
    group.add_argument(
        "-l",
        "--locations",
        metavar="LOCATION",
        nargs="+",
        help="Master locations as comma-separate coordinates. One must be all zeros.",
    )

    args = parser.parse_args(args)

    configLogger(level=args.loglevel)
    from pprint import pprint

    if args.designspace:
        from fontTools.designspaceLib import DesignSpaceDocument

        doc = DesignSpaceDocument()
        doc.read(args.designspace)
        locs = [s.location for s in doc.sources]
        print("Original locations:")
        pprint(locs)
        doc.normalize()
        print("Normalized locations:")
        locs = [s.location for s in doc.sources]
        pprint(locs)
    else:
        axes = [chr(c) for c in range(ord("A"), ord("Z") + 1)]
        locs = [
            dict(zip(axes, (float(v) for v in s.split(",")))) for s in args.locations
        ]

    model = VariationModel(locs)
    print("Sorted locations:")
    pprint(model.locations)
    print("Supports:")
    pprint(model.supports)


if __name__ == "__main__":
    import doctest, sys

    if len(sys.argv) > 1:
        sys.exit(main())

    sys.exit(doctest.testmod().failed)