from functools import partial

from utils import Configer
from algorithms.neat import Pipeline
from time_utils import using_cprofile
from problems import Sin, Xor, DIY


# xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
# xor_outputs = np.array([[0], [1], [1], [0]])
#
#
# def evaluate(forward_func: Callable) -> List[float]:
#     """
#     :param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
#     :return:
#     """
#     outs = forward_func(xor_inputs)
#     outs = jax.device_get(outs)
#     fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
#     return fitnesses.tolist()  # returns a list


# @using_cprofile
@partial(using_cprofile, root_abs_path='/mnt/e/neat-jax/', replace_pattern="/mnt/e/neat-jax/")
def main():
    config = Configer.load_config()
    config.neat.population.pop_size = 50
    problem = Xor()
    # problem = Sin()
    # problem = DIY(func=lambda x: (np.sin(x) + np.exp(x) - x ** 2) / (np.cos(x) + np.sqrt(x)) - np.log(x + 1))
    problem.refactor_config(config)
    pipeline = Pipeline(config, seed=0)
    best_nodes, best_connections = pipeline.auto_run(problem.evaluate)
    # print(best_nodes, best_connections)
    # func = pipeline.function_factory.ask_batch_forward(best_nodes, best_connections)
    # problem.print(func)


if __name__ == '__main__':
    main()