from __future__ import annotations

import cv2
from enum import Enum
from functools import lru_cache
from pathlib import Path
from typing import NewType, NamedTuple, Dict, Tuple

import numpy as np
from sklearn.externals import joblib


class COLOR(Enum):
    WHITE = "white"
    BLACK = "black"

    def __str__(self) -> str:
        return self.value


class PIECE(Enum):
    PAWN = "pawn"
    ROOK = "rook"
    KNIGHT = "knight"
    BISHOP = "bishop"
    QUEEN = "queen"
    KING = "king"
    EMPTY = "empty"

    def __str__(self) -> str:
        return self.value


PieceAndColor = Tuple[PIECE, COLOR]

OUR_PIECES = (
    PIECE.ROOK,
    PIECE.KNIGHT,
)


class FILE(int, Enum):
    A = 1
    B = 2
    C = 3
    D = 4
    E = 5
    F = 6
    G = 7
    H = 8


class RANK(int, Enum):
    EIGHT = 8
    SEVEN = 7
    SIX = 6
    FIVE = 5
    FOUR = 4
    THREE = 3
    TWO = 2
    ONE = 1


class _Position(NamedTuple):
    file: FILE
    rank: RANK

    def __str__(self) -> str:
        return f"{self.file.name}{self.rank}"

    @property
    def color(self):
        if (self.file + self.rank) % 2:
            return COLOR.WHITE
        return COLOR.BLACK


# POSITION.{A8, A7, ..., H1}
POSITION = Enum("POSITION", {str(_Position(f, r)): _Position(f, r) for f in FILE for r in RANK}, type=_Position)

# Squares is a dict mapping positions to square images, i.e. a board container during image processing
Squares = NewType("Squares", Dict[POSITION, np.ndarray])


class Board(Dict[POSITION, PIECE]):
    """Board is a dict mapping positions to a piece, i.e. a board configuration after all image processing"""


def imwrite(*args, **kwargs):
    Path(args[0]).parent.mkdir(parents=True, exist_ok=True)
    return cv2.imwrite(*args, **kwargs)


@lru_cache()
def load_classifier(filename):
    # print(f"Loading classifier {filename}")
    return joblib.load(filename)