import logging
from typing import Optional

from celery.signals import worker_process_init
from celery import shared_task
from epdb.models import Pathway, Node, Edge, EPModel, Setting
from epdb.logic import SPathway

from utilities.chem import FormatConverter

logger = logging.getLogger(__name__)


@shared_task(queue='background')
def mul(a, b):
    return a * b


@shared_task(queue='predict')
def predict_simple(model_pk: int, smiles: str):
    mod = EPModel.objects.get(id=model_pk)
    res = mod.predict(smiles)
    return res


@shared_task(queue='background')
def send_registration_mail(user_pk: int):
    pass


@shared_task(queue='model')
def build_model(model_pk: int):
    mod = EPModel.objects.get(id=model_pk)
    mod.build_dataset()
    mod.build_model()


@shared_task(queue='model')
def evaluate_model(model_pk: int):
    mod = EPModel.objects.get(id=model_pk)
    mod.evaluate_model()


@shared_task(queue='predict')
def predict(pw_pk: int, pred_setting_pk: int, limit: Optional[int] = None, node_pk: Optional[int] = None) -> Pathway:
    pw = Pathway.objects.get(id=pw_pk)
    setting = Setting.objects.get(id=pred_setting_pk)

    pw.kv.update(**{'status': 'running'})
    pw.save()

    try:
        # regular prediction
        if limit is not None:
            spw = SPathway(prediction_setting=setting, persist=pw)
            level = 0
            while not spw.done:
                spw.predict_step(from_depth=level)
                level += 1

                # break in case we are in incremental mode
                if limit != -1:
                    if level >= limit:
                        break

        elif node_pk is not None:
            n = Node.objects.get(id=node_pk, pathway=pw)
            spw = SPathway.from_pathway(pw)
            spw.predict_step(from_node=n)
        else:
            raise ValueError("Neither limit nor node_pk given!")



    except Exception as e:
        pw.kv.update({'status': 'failed'})
        pw.save()
        raise e

    pw.kv.update(**{'status': 'completed'})
    pw.save()