#!/usr/bin/env python
"""MTEB(por, v2) random baseline encoder — the FLOOR reference.

Design: each text -> a deterministic L2-normalized random vector, seeded by
sha256(SEED|text). Deterministic-per-text => reproducible + zero semantic signal
(textually-different but semantically-similar sentences get unrelated vectors).
Gives chance-level performance across STS / retrieval / classification / clustering.
No GPU. Runs locally with the same pinned-SHA mteb_pt tasks as the real models.
"""
import os
import hashlib
import numpy as np
import mteb
import mteb_pt  # noqa: F401 (registers task modules)
import mteb_pt.register as register
from mteb.models.abs_encoder import AbsEncoder
from mteb.models.model_meta import ModelMeta

DIM = 768
SEED = 42
_EXCLUDED = {"OffComBR", "CSTNewsClustering", "BBCNewsPTClustering", "TweetSentBR"}


class RandomEncoder(AbsEncoder):
    def __init__(self):
        self.mteb_model_meta = ModelMeta(
            loader=None,
            name="mteb-pt/baseline-random-encoder",
            revision="1.0",
            release_date="2026-06-28",
            languages=["por-Latn"],
            n_parameters=0,
            memory_usage_mb=None,
            max_tokens=None,
            embed_dim=DIM,
            license=None,
            open_weights=True,
            public_training_code=None,
            public_training_data=None,
            framework=[],
            similarity_fn_name="cosine",
            use_instructions=False,
            training_datasets=None,
        )

    def encode(self, inputs, *, task_metadata, hf_split, hf_subset, prompt_type=None, **kwargs):
        texts = [t for batch in inputs for t in batch["text"]]
        out = np.empty((len(texts), DIM), dtype=np.float32)
        for i, t in enumerate(texts):
            h = int(hashlib.sha256((str(SEED) + "|" + (t or "")).encode("utf-8")).hexdigest(), 16) % (2**32)
            v = np.random.default_rng(h).standard_normal(DIM).astype(np.float32)
            out[i] = v / (np.linalg.norm(v) + 1e-9)
        return out


def load_tasks(only=None):
    tasks = [cls() for cls in register._TASKS_TO_REGISTER if cls.metadata.name not in _EXCLUDED]
    tasks.append(mteb.get_task("Assin2STS"))
    if only:
        keep = {x.strip() for x in only.split(",")}
        tasks = [t for t in tasks if t.metadata.name in keep]
    return tasks


if __name__ == "__main__":
    tasks = load_tasks(os.environ.get("RB_TASKS"))
    print(f"[random-baseline] {len(tasks)} tasks: {sorted(t.metadata.name for t in tasks)}", flush=True)
    mteb.evaluate(RandomEncoder(), tasks=tasks, overwrite_strategy="always", raise_error=False, show_progress_bar=False)
    print("[random-baseline] DONE", flush=True)