from collections import OrderedDict

import chess
import torch

from .feature_block import FeatureBlock


NUM_SQ = 64
NUM_PT_REAL = 11
NUM_PT_VIRTUAL = 12
NUM_PLANES_REAL = NUM_SQ * NUM_PT_REAL
NUM_PLANES_VIRTUAL = NUM_SQ * NUM_PT_VIRTUAL
NUM_INPUTS = NUM_PLANES_REAL * NUM_SQ


def orient(is_white_pov: bool, sq: int) -> int:
    return (56 * (not is_white_pov)) ^ sq


def halfka_idx(is_white_pov: bool, king_sq: int, sq: int, p: chess.Piece) -> int:
    p_idx = (p.piece_type - 1) * 2 + (p.color != is_white_pov)
    if p_idx == 11:
        p_idx -= 1
    return orient(is_white_pov, sq) + p_idx * NUM_SQ + king_sq * NUM_PLANES_REAL


def halfka_psqts() -> list[int]:
    # values copied from stockfish, in stockfish internal units
    piece_values = {
        chess.PAWN: 126,
        chess.KNIGHT: 781,
        chess.BISHOP: 825,
        chess.ROOK: 1276,
        chess.QUEEN: 2538,
    }

    values = [0] * (NUM_PLANES_REAL * NUM_SQ)

    for ksq in range(64):
        for s in range(64):
            for pt, val in piece_values.items():
                idxw = halfka_idx(True, ksq, s, chess.Piece(pt, chess.WHITE))
                idxb = halfka_idx(True, ksq, s, chess.Piece(pt, chess.BLACK))
                values[idxw] = val
                values[idxb] = -val

    return values


class Features(FeatureBlock):
    def __init__(self):
        super().__init__(
            "HalfKAv2",
            0x5F234CB8,
            OrderedDict([("HalfKAv2", NUM_PLANES_REAL * NUM_SQ)]),
        )

    def get_active_features(
        self, board: chess.Board
    ) -> tuple[torch.Tensor, torch.Tensor]:
        def piece_features(turn):
            indices = torch.zeros(NUM_PLANES_REAL * NUM_SQ)
            ksq = board.king(turn)
            assert ksq is not None
            for sq, p in board.piece_map().items():
                indices[halfka_idx(turn, orient(turn, ksq), sq, p)] = 1.0
            return indices

        return (piece_features(chess.WHITE), piece_features(chess.BLACK))

    def get_initial_psqt_features(self) -> list[int]:
        return halfka_psqts()


class FactorizedFeatures(FeatureBlock):
    def __init__(self):
        super().__init__(
            "HalfKAv2^",
            0x5F234CB8,
            OrderedDict(
                [("HalfKAv2", NUM_PLANES_REAL * NUM_SQ), ("A", NUM_PLANES_VIRTUAL)]
            ),
        )

    def get_active_features(self, board: chess.Board):
        raise Exception(
            "Not supported yet, you must use the c++ data loader for factorizer support during training"
        )

    def get_feature_factors(self, idx: int) -> list[int]:
        if idx >= self.num_real_features:
            raise Exception("Feature must be real")

        a_idx = idx % NUM_PLANES_REAL
        k_idx = idx // NUM_PLANES_REAL

        if a_idx // NUM_SQ == 10 and k_idx != a_idx % NUM_SQ:
            a_idx += NUM_SQ

        return [idx, self.get_factor_base_feature("A") + a_idx]

    def get_initial_psqt_features(self) -> list[int]:
        return halfka_psqts() + [0] * NUM_PLANES_VIRTUAL


"""
This is used by the features module for discovery of feature blocks.
"""


def get_feature_block_clss() -> list[type[FeatureBlock]]:
    return [Features, FactorizedFeatures]