<commit_before>from math import sqrt, pi, sin, cos, atan2
import random
import itertools
import math
from typing import List, Dict, Set, Tuple, Any, Optional, Union # for mypy type checking

FLOAT_DIGITS = 8
EPSILON = 10**(-FLOAT_DIGITS)

def _sign(num):
    if num == 0:
        return 0
    return 1 if num > 0 else -1

class Pointlike(tuple):
    @property
    def rounded(self) -> "Pointlike":
        return self.__class__(round(q) for q in self)

    @property
    def position(self) -> "Pointlike":
        return self

    def distance_to(self, p: Union["Unit", "Point2", "Point3"]) -> Union[int, float]:
        p = p.position
        assert isinstance(p, Pointlike)
        if self == p:
            return 0
        return sqrt(sum(self.__class__((b-a)**2 for a, b in itertools.zip_longest(self, p, fillvalue=0))))

    def distance_to_point2(self, p2: "Point2") -> Union[int, float]:
        """ Same as the function above, but should be 3-4 times faster because of the dropped asserts and conversions and because it doesnt use a loop (itertools or zip). """
        return ((self[0]-p2[0])**2 + (self[1]-p2[1])**2)**0.5

    def _distance_squared(self, p2: "Point2") -> Union[int, float]:
        """ Function used to not take the square root as the distances will stay proportionally the same. This is to speed up the sorting process. """
        return ((self[0]-p2[0])**2 + (self[1]-p2[1])**2)

    def sort_by_distance(self, ps: Union["Units", List["Point2"]]):
        """ This returns the target points sorted. You should not pass a set or dict since those are not sortable. """
        if ps and all(isinstance(p, Point2) for p in ps):
            return sorted(ps, key=lambda p: self._distance_squared(p))
        return sorted(ps, key=lambda p: self.distance_to(p))

    def closest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union["Unit", "Point2"]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        closest_distance_squared = math.inf
        for p2 in ps:
            p2pos = p2
            if not isinstance(p2pos, Point2):
                p2pos = p2.position
            distance = (self[0] - p2pos[0])**2 + (self[1] - p2pos[1])**2
            if distance < closest_distance_squared:
                closest_distance_squared = distance
                closest_element = p2
        return closest_element

    def distance_to_closest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union[int, float]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        closest_distance_squared = math.inf
        for p2 in ps:
            if not isinstance(p2, Point2):
                p2 = p2.position
            distance = (self[0] - p2[0])**2 + (self[1] - p2[1])**2
            if distance < closest_distance_squared:
                closest_distance_squared = distance
        return closest_distance_squared**0.5

    def furthest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union["Unit", "Pointlike"]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        furthest_distance_squared = 0
        for p2 in ps:
            p2pos = p2
            if not isinstance(p2pos, Point2):
                p2pos = p2.position
            distance = (self[0] - p2pos[0])**2 + (self[1] - p2pos[1])**2
            if furthest_distance_squared < distance:
                furthest_distance_squared = distance
                furthest_element = p2
        return furthest_element

    def distance_to_furthest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union[int, float]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        furthest_distance_squared = 0
        for p2 in ps:
            if not isinstance(p2, Point2):
                p2 = p2.position
            distance = (self[0] - p2[0])**2 + (self[1] - p2[1])**2
            if furthest_distance_squared < distance:
                furthest_distance_squared = distance
        return furthest_distance_squared**0.5

    def offset(self, p) -> "Pointlike":
        return self.__class__(a+b for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def unit_axes_towards(self, p):
        return self.__class__(_sign(b - a) for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def towards(self, p: Union["Unit", "Pointlike"], distance: Union[int, float]=1, limit: bool=False) -> "Pointlike":
        assert self != p
        d = self.distance_to(p)
        if limit:
            distance = min(d, distance)
        return self.__class__(a + (b - a) / d * distance for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def __eq__(self, other):
        if not isinstance(other, tuple):
            return False
        return all(abs(a - b) < EPSILON for a, b in itertools.zip_longest(self, other, fillvalue=0))

    def __hash__(self):
        return hash(tuple(int(c * FLOAT_DIGITS)  for c in self))


class Point2(Pointlike):
    @classmethod
    def from_proto(cls, data):
        return cls((data.x, data.y))

    @property
    def x(self) -> Union[int, float]:
        return self[0]

    @property
    def y(self) -> Union[int, float]:
        return self[1]

    @property
    def to2(self) -> "Point2":
        return Point2(self[:2])

    @property
    def to3(self) -> "Point3":
        return Point3((*self, 0))

    def random_on_distance(self, distance):
        if isinstance(distance, (tuple, list)): # interval
            distance = distance[0] + random.random() * (distance[1] - distance[0])

        assert distance > 0
        angle = random.random() * 2 * pi

        dx, dy = cos(angle), sin(angle)
        return Point2((self.x + dx * distance, self.y + dy * distance))

    def towards_with_random_angle(self, p: Union["Point2", "Point3"], distance: Union[int, float]=1, max_difference: Union[int, float]=(pi/4)) -> "Point2":
        tx, ty = self.to2.towards(p.to2, 1)
        angle = atan2(ty - self.y, tx - self.x)
        angle = (angle - max_difference) + max_difference * 2 * random.random()
        return Point2((self.x + cos(angle) * distance, self.y + sin(angle) * distance))

    def circle_intersection(self, p: "Point2", r: Union[int, float]) -> Set["Point2"]:
        """ self is point1, p is point2, r is the radius for circles originating in both points
        Used in ramp finding """
        assert self != p
        distanceBetweenPoints = self.distance_to(p)
        assert r > distanceBetweenPoints / 2
        # remaining distance from center towards the intersection, using pythagoras
        remainingDistanceFromCenter = (r**2 - (distanceBetweenPoints/2)**2)**0.5
        # center of both points
        offsetToCenter = Point2(((p.x - self.x) / 2, (p.y - self.y) / 2))
        center = self.offset(offsetToCenter)

        # stretch offset vector in the ratio of remaining distance from center to intersection
        vectorStretchFactor = remainingDistanceFromCenter / (distanceBetweenPoints / 2)
        v = offsetToCenter
        offsetToCenterStretched = Point2((v.x * vectorStretchFactor, v.y * vectorStretchFactor))

        # rotate vector by 90° and -90°
        vectorRotated1 = Point2((offsetToCenterStretched.y, -offsetToCenterStretched.x))
        vectorRotated2 = Point2((-offsetToCenterStretched.y, offsetToCenterStretched.x))
        intersect1 = center.offset(vectorRotated1)
        intersect2 = center.offset(vectorRotated2)
        return {intersect1, intersect2}

    @property
    def neighbors4(self) -> set:
        return {
            Point2((self.x - 1, self.y)),
            Point2((self.x + 1, self.y)),
            Point2((self.x, self.y - 1)),
            Point2((self.x, self.y + 1)),
        }

    @property
    def neighbors8(self) -> set:
        return self.neighbors4 | {
            Point2((self.x - 1, self.y - 1)),
            Point2((self.x - 1, self.y + 1)),
            Point2((self.x + 1, self.y - 1)),
            Point2((self.x + 1, self.y + 1)),
        }


class Point3(Point2):
    @classmethod
    def from_proto(cls, data):
        return cls((data.x, data.y, data.z))

    @property
    def z(self) -> Union[int, float]:
        return self[2]

    @property
    def to3(self) -> "Point3":
        return Point3(self)

class Size(Point2):<commit_msg>Add Point2 vector functions and operators<commit_after>from math import sqrt, pi, sin, cos, atan2
import random
import itertools
import math
from typing import List, Dict, Set, Tuple, Any, Optional, Union # for mypy type checking

FLOAT_DIGITS = 8
EPSILON = 10**(-FLOAT_DIGITS)

def _sign(num):
    if num == 0:
        return 0
    return 1 if num > 0 else -1

class Pointlike(tuple):
    @property
    def rounded(self) -> "Pointlike":
        return self.__class__(round(q) for q in self)

    @property
    def position(self) -> "Pointlike":
        return self

    def distance_to(self, p: Union["Unit", "Point2", "Point3"]) -> Union[int, float]:
        p = p.position
        assert isinstance(p, Pointlike)
        if self == p:
            return 0
        return sqrt(sum(self.__class__((b-a)**2 for a, b in itertools.zip_longest(self, p, fillvalue=0))))

    def distance_to_point2(self, p2: "Point2") -> Union[int, float]:
        """ Same as the function above, but should be 3-4 times faster because of the dropped asserts and conversions and because it doesnt use a loop (itertools or zip). """
        return ((self[0]-p2[0])**2 + (self[1]-p2[1])**2)**0.5

    def _distance_squared(self, p2: "Point2") -> Union[int, float]:
        """ Function used to not take the square root as the distances will stay proportionally the same. This is to speed up the sorting process. """
        return ((self[0]-p2[0])**2 + (self[1]-p2[1])**2)

    def sort_by_distance(self, ps: Union["Units", List["Point2"]]):
        """ This returns the target points sorted. You should not pass a set or dict since those are not sortable. """
        if ps and all(isinstance(p, Point2) for p in ps):
            return sorted(ps, key=lambda p: self._distance_squared(p))
        return sorted(ps, key=lambda p: self.distance_to(p))

    def closest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union["Unit", "Point2"]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        closest_distance_squared = math.inf
        for p2 in ps:
            p2pos = p2
            if not isinstance(p2pos, Point2):
                p2pos = p2.position
            distance = (self[0] - p2pos[0])**2 + (self[1] - p2pos[1])**2
            if distance < closest_distance_squared:
                closest_distance_squared = distance
                closest_element = p2
        return closest_element

    def distance_to_closest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union[int, float]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        closest_distance_squared = math.inf
        for p2 in ps:
            if not isinstance(p2, Point2):
                p2 = p2.position
            distance = (self[0] - p2[0])**2 + (self[1] - p2[1])**2
            if distance < closest_distance_squared:
                closest_distance_squared = distance
        return closest_distance_squared**0.5

    def furthest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union["Unit", "Pointlike"]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        furthest_distance_squared = 0
        for p2 in ps:
            p2pos = p2
            if not isinstance(p2pos, Point2):
                p2pos = p2.position
            distance = (self[0] - p2pos[0])**2 + (self[1] - p2pos[1])**2
            if furthest_distance_squared < distance:
                furthest_distance_squared = distance
                furthest_element = p2
        return furthest_element

    def distance_to_furthest(self, ps: Union["Units", List["Point2"], Set["Point2"]]) -> Union[int, float]:
        """ This function assumes the 2d distance is meant """
        assert len(ps) > 0
        furthest_distance_squared = 0
        for p2 in ps:
            if not isinstance(p2, Point2):
                p2 = p2.position
            distance = (self[0] - p2[0])**2 + (self[1] - p2[1])**2
            if furthest_distance_squared < distance:
                furthest_distance_squared = distance
        return furthest_distance_squared**0.5

    def offset(self, p) -> "Pointlike":
        return self.__class__(a+b for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def unit_axes_towards(self, p):
        return self.__class__(_sign(b - a) for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def towards(self, p: Union["Unit", "Pointlike"], distance: Union[int, float]=1, limit: bool=False) -> "Pointlike":
        assert self != p
        d = self.distance_to(p)
        if limit:
            distance = min(d, distance)
        return self.__class__(a + (b - a) / d * distance for a, b in itertools.zip_longest(self, p[:len(self)], fillvalue=0))

    def __eq__(self, other):
        if not isinstance(other, tuple):
            return False
        return all(abs(a - b) < EPSILON for a, b in itertools.zip_longest(self, other, fillvalue=0))

    def __hash__(self):
        return hash(tuple(int(c * FLOAT_DIGITS)  for c in self))


class Point2(Pointlike):
    @classmethod
    def from_proto(cls, data):
        return cls((data.x, data.y))

    @property
    def x(self) -> Union[int, float]:
        return self[0]

    @property
    def y(self) -> Union[int, float]:
        return self[1]

    @property
    def to2(self) -> "Point2":
        return Point2(self[:2])

    @property
    def to3(self) -> "Point3":
        return Point3((*self, 0))

    def random_on_distance(self, distance):
        if isinstance(distance, (tuple, list)): # interval
            distance = distance[0] + random.random() * (distance[1] - distance[0])

        assert distance > 0
        angle = random.random() * 2 * pi

        dx, dy = cos(angle), sin(angle)
        return Point2((self.x + dx * distance, self.y + dy * distance))

    def towards_with_random_angle(self, p: Union["Point2", "Point3"], distance: Union[int, float]=1, max_difference: Union[int, float]=(pi/4)) -> "Point2":
        tx, ty = self.to2.towards(p.to2, 1)
        angle = atan2(ty - self.y, tx - self.x)
        angle = (angle - max_difference) + max_difference * 2 * random.random()
        return Point2((self.x + cos(angle) * distance, self.y + sin(angle) * distance))

    def circle_intersection(self, p: "Point2", r: Union[int, float]) -> Set["Point2"]:
        """ self is point1, p is point2, r is the radius for circles originating in both points
        Used in ramp finding """
        assert self != p
        distanceBetweenPoints = self.distance_to(p)
        assert r > distanceBetweenPoints / 2
        # remaining distance from center towards the intersection, using pythagoras
        remainingDistanceFromCenter = (r**2 - (distanceBetweenPoints/2)**2)**0.5
        # center of both points
        offsetToCenter = Point2(((p.x - self.x) / 2, (p.y - self.y) / 2))
        center = self.offset(offsetToCenter)

        # stretch offset vector in the ratio of remaining distance from center to intersection
        vectorStretchFactor = remainingDistanceFromCenter / (distanceBetweenPoints / 2)
        v = offsetToCenter
        offsetToCenterStretched = Point2((v.x * vectorStretchFactor, v.y * vectorStretchFactor))

        # rotate vector by 90° and -90°
        vectorRotated1 = Point2((offsetToCenterStretched.y, -offsetToCenterStretched.x))
        vectorRotated2 = Point2((-offsetToCenterStretched.y, offsetToCenterStretched.x))
        intersect1 = center.offset(vectorRotated1)
        intersect2 = center.offset(vectorRotated2)
        return {intersect1, intersect2}

    @property
    def neighbors4(self) -> set:
        return {
            Point2((self.x - 1, self.y)),
            Point2((self.x + 1, self.y)),
            Point2((self.x, self.y - 1)),
            Point2((self.x, self.y + 1)),
        }

    @property
    def neighbors8(self) -> set:
        return self.neighbors4 | {
            Point2((self.x - 1, self.y - 1)),
            Point2((self.x - 1, self.y + 1)),
            Point2((self.x + 1, self.y - 1)),
            Point2((self.x + 1, self.y + 1)),
        }

    def negative_offset(self, other: "Point2") -> "Point2":
        return self.__class__((self.x - other.x, self.y - other.y))

    def __add__(self, other: "Point2") -> "Point2":
        return self.offset(other)

    def __sub__(self, other: "Point2") -> "Point2":
        return self.negative_offset(other)

    def __neg__(self) -> "Point2":
        return self.__class__(-a for a in self)

    def __abs__(self) -> Union[int, float]:
        return math.hypot(self.x, self.y)

    def __bool__(self) -> "Float":
        return self.x != 0 or self.y != 0

    def __mul__(self, other: Union[int, float, "Point2"]) -> "Point2":
        if isinstance(other, self.__class__):
            return self.__class__((self.x * other.x, self.y * other.y))
        return self.__class__((self.x * other, self.y * other))

    def __rmul__(self, other: Union[int, float, "Point2"]) -> "Point2":
        return self.__mul__(other)

    def __truediv__(self, other: Union[int, float, "Point2"]) -> "Point2":
        if isinstance(other, self.__class__):
            return self.__class__((self.x / other.x, self.y / other.y))
        return self.__class__((self.x / other, self.y / other))

    def is_same_as(self, other: "Point2", dist=0.1) -> "Bool":
        return self._distance_squared(other) <= dist ** 2

    def square_vector(self, other: "Point2") -> "Point2":
        return self.__class__((_sign(other.x - self.x), _sign(other.y - self.y)))

    def manhattan_distance(self, other: "Point2") -> Union[int, float]:
        return abs(other.x - self.x) + abs(other.y - self.y)


def center_point2(a: [Point2]) -> "Point2":
    s = Point2((0, 0))
    for p in a:
        s += p
    return s / len(a)


class Point3(Point2):
    @classmethod
    def from_proto(cls, data):
        return cls((data.x, data.y, data.z))

    @property
    def z(self) -> Union[int, float]:
        return self[2]

    @property
    def to3(self) -> "Point3":
        return Point3(self)

class Size(Point2):
{'fraction_non_alphanumeric': 0.1326431575541896, 'fraction_numerical': 0.024641432114741724, 'mean_word_length': 2.5007550018875047, 'pattern_counts': {'":': 27, '<': 14, '<?xml version=': 0, '>': 72, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2433606', 'n_tokens_mistral': 6084, 'n_tokens_neox': 5792, 'n_words': 1635}