import hashlib
import math
import json
from dataclasses import asdict
from public_context import feedback_text
def choose(predictions):
    if len(predictions) != 5:
        raise ValueError('Expected four candidates and one current program')
    utility = [row['utility'] for row in predictions]
    if not all((math.isfinite(v) and 0 <= v <= 1.25 for v in utility)):
        raise ValueError('Invalid utility')
    best = max(range(4), key=lambda i: utility[i])
    return best if utility[best] > utility[4] else 4

class Predictor:

    def __init__(self, checkpoint, backbone):
        from semantic_world import SemanticExecutionWorld
        self.model = SemanticExecutionWorld.load(checkpoint, backbone, device='cuda').eval()
        self.identity = self.model.identity

    def predict(self, problem, code, feedback, tests, candidates):
        public = [asdict(test) for test in tests if test.test_id.startswith('public_')]
        observed = asdict(feedback)
        observed['results'] = [row for row in observed['results'] if row['test_id'].startswith('public_')]
        contexts = [f'Whole-training-suite execution forecast. Only public observations are supplied.\nProblem:\n{problem}\nCurrent program:\n{code}\nTrusted previous public execution:\n{feedback_text(observed)}\nCandidate action:\n{candidate.code}\nOfficial public test suite:\n' + json.dumps(public, ensure_ascii=False) for candidate in candidates]
        values = self.model.score_suite(contexts)
        return [dict(candidate_id=c.candidate_id, utility=float(values['utility'][i]), full_suite_pass_probability=float(values['full_suite_pass_probability'][i]), predicted_pass_fraction=float(values['predicted_pass_fraction'][i])) for (i, c) in enumerate(candidates)]

def state_key(code, feedback, step):
    outcomes = [{k: getattr(row, k) for k in ('test_id', 'status', 'actual', 'expected', 'stderr')} for row in feedback.results]
    return hashlib.sha256(json.dumps(dict(code=code, feedback=outcomes, step=step), sort_keys=True).encode()).hexdigest()