import neo4j, json

# dbprep
fsroot = '/autograder/source/'
datasets = ['Neo4J_dataset']
db = neo4j.GraphDatabase.driver('bolt://localhost:7687', auth = ('neo4j', '4Sfz541Lm')).session()

def postproc_str(data : str): # relaxed str matching
    import re
    return re.sub(r'[\s|_]', '', data.lower())

def comparator(a, b):
    cmp = lambda x, y: 1 if x < y else -1 if x > y else 0
    try: 
        return cmp(a, b)
    except Exception:
        from collections.abc import Iterable
        def itcmp(a: Iterable, b: Iterable):
            if len(a) < len(b):
                    return -1
            elif len(a) == len(b):
                for aa, bb in zip(a, b):
                    c = comparator(aa, bb)
                    if  c != 0: 
                        return c
            else: return 1
            return 0
        
        match (a, b):
            case (dict(), dict()):
                return itcmp([*a.keys(), *a.values()], [*b.keys(), *b.values()])
            case (Iterable(), Iterable()):
                return itcmp(a, b)
            case _ if type(a) == type(b):
                return cmp(f'{a}', f'{b}')
            case _:
                return cmp(hash(type(a)), hash(type(b)))
            
def postproc_iter(data): 
    from collections.abc import Iterable
    from functools import cmp_to_key
    try:
        match data:
            case str():
                return postproc_str(data)
            case dict():
                return { postproc_iter(k):postproc_iter(v) for k, v in data.items() }
            case Iterable(): # flatten, remove order and empty iterables
                res = type(data)(
                    sorted(
                        [postproc_iter(d) for d in data 
                            if not isinstance(d, Iterable) or d]
                    , key = cmp_to_key(comparator))
                )
                return res[0] if len(res) == 1 else res
            case _: # primitives
                return data 
    except Exception as e: # fail proof
        print(e)
        return data
     
def evaluate(query : str):
    query = query.strip()
    return postproc_iter(db.run(query).data()) if query else None

while True:
    try:
        db.run('RETURN 0')
        break
    except:
        continue

for d in datasets:    
    with open(fsroot + d + '.txt', encoding = 'utf-8') as f:
        db.run(f.read())

from solution import sols
answers = [evaluate(s) if type(s) is str else tuple(evaluate(k) for k in s) for s in sols ]

# grading
from os import listdir
from importlib.util import module_from_spec, spec_from_file_location
subroot = '/autograder/submission/'
feedback = ''
submissions = [subroot + f for f in listdir(subroot) if f.strip().lower().endswith('.py')]

grade = 0
n_queries = len(sols)

if submissions: 
    submission = submissions[0]
    
    for i in range(n_queries):
        feedback += f'Query {i + 1}: '
        try:
            spec = spec_from_file_location('curr', submission)
            module = module_from_spec(spec)
            spec.loader.exec_module(module)
            q = getattr(module, f'query{i + 1}')()
            def eq(a: list, b):
                if type(b) is tuple:
                    return any(eq(a, bb) for bb in b)
                else: return a == b                
            ans = evaluate(q)
            if eq(ans, answers[i]):
                grade += 1
                feedback += 'Correct.\n'
            else:
                feedback += 'Wrong Answer.\n'
                print('ans: ', ans, '\nsol: ', answers[i])
        except Exception as e:
            feedback += 'Runtime Error.\n'
            print(e)
else:
    feedback += 'No python file in submission.\n'    

# output
results = {
    'output': feedback,
    'score': round(grade * 100 / n_queries, 1), 
    'max_score': 100,
}

with open('/autograder/results/results.json', 'w') as res:
    json.dump(results, res)