"""Validation functions for the Chessbench dataset.

This script validates the integrity of the Chessbench dataset by checking that each record adheres to expected formats and value ranges.
It ensures that FEN strings and move UCI strings are valid, and that win probabilities and mate labels are consistent.
"""

import chess
from tqdm import tqdm

from athena.datasets.chessbenchmate.dataset import ChessbenchDataset


def validate_record(record):
    fen, move, win_prob, mate = record
    assert isinstance(fen, str)
    assert isinstance(move, str)

    # Check for valid fen
    board = chess.Board(fen)
    assert board.is_valid(), f"Invalid FEN: {fen}"

    # Check for valid move
    uci_move = chess.Move.from_uci(move)
    assert uci_move in board.legal_moves, f"Illegal move {move} for board {fen}"

    if mate == "#":
        assert win_prob == 1.0, f"Unexpected win_prob {win_prob} for mate {mate}"
    elif isinstance(mate, int):
        # If mate is "#" or an integer, win_prob should be 1.0 or 0.0
        assert mate != 0, "Mate cannot be zero"
        assert win_prob in (0.0, 1.0), f"Unexpected win_prob {win_prob} for mate {mate}"
        if mate > 0:
            assert win_prob == 1.0, f"Unexpected win_prob {win_prob} for mate {mate}"
        else:
            assert win_prob == 0.0, f"Unexpected win_prob {win_prob} for mate {mate}"
    elif mate == "-":
        assert isinstance(win_prob, float) and 0.0 < win_prob < 1.0


test_dataset = ChessbenchDataset(dir="src/athena/datasets/chessbenchmate/data", mode="test")
train_dataset = ChessbenchDataset(dir="src/athena/datasets/chessbenchmate/data", mode="train")


for i in tqdm(range(len(test_dataset))):
    record = test_dataset[i]
    try:
        validate_record(record)
    except AssertionError as e:
        print(f"Validation error in test dataset at index {i}: {e}")
        raise

for i in tqdm(range(len(train_dataset))):
    record = train_dataset[i]
    try:
        validate_record(record)
    except AssertionError as e:
        print(f"Validation error in train dataset at index {i}: {e}")
        raise