"""Cross-encoder reranker over ONNX Runtime (local, key-free)."""
import json
import os

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer

RERANK_REPO = "Xenova/bge-reranker-base"
RERANK_ONNX = "onnx/model_quantized.onnx"  # int8: ~3x faster on CPU, negligible quality loss
MAX_TOKENS = 512
_MAX_DOC_CHARS = int(os.environ.get("CANLEX_RERANK_DOC_CHARS", "2000"))
                    # 1000 -> 2000 adopted 2026-07-23: the doubled reading
                    # window held every 203-Q eval metric and lifted Hit@5
                    # +0.01; the cross-encoder now judges a full-size piece
                    # rather than 60% of one.
                       # cap doc text before tokenizing. Cross-encoder cost is
                       # ~linear in tokens per doc, so halving this ~halves the
                       # dominant query cost; env-sweepable, eval-gated (a
                       # tighter cap scores long sections on less of their text
                       # -- fine in practice: the median section is ~520 chars,
                       # and sub-chunked pieces cap at 1800). Swept 2026-06:
                       # 3000 -> 1000 (with pool 50 -> 16) held the eval exactly.


def make_ort_session(model_path):
    """A CPU InferenceSession tuned for shared, small-vCPU hosts.

    Two deviations from onnxruntime defaults: (1) spin-waiting between requests
    is disabled -- by default ORT worker threads busy-spin after an inference
    for a microsecond-scale latency win, which on the Space's shared 2 vCPUs
    burns quota between queries; (2) the intra-op thread count is
    env-configurable (CANLEX_ORT_THREADS) so concurrent sessions can be capped
    below core count if contention is ever observed. Unset, ORT's default
    (all cores) is kept, so single-query numerics and speed are unchanged."""
    opts = ort.SessionOptions()
    threads = int(os.environ.get("CANLEX_ORT_THREADS", "0"))
    if threads:
        opts.intra_op_num_threads = threads
    opts.add_session_config_entry("session.intra_op.allow_spinning", "0")
    opts.add_session_config_entry("session.inter_op.allow_spinning", "0")
    return ort.InferenceSession(model_path, sess_options=opts,
                                providers=["CPUExecutionProvider"])


class Reranker:
    """Cross-encoder that scores (query, section) pairs for relevance.

    Loads a BGE reranker as ONNX and runs it on CPU -- no API key; the model is
    downloaded once and cached. A cross-encoder reads the query and section
    jointly, so it judges true relevance far better than the BM25/embedding
    similarities used to build the candidate pool.
    """

    def __init__(self):
        model_path = hf_hub_download(RERANK_REPO, RERANK_ONNX)
        tok_path = hf_hub_download(RERANK_REPO, "tokenizer.json")
        cfg_path = hf_hub_download(RERANK_REPO, "config.json")
        with open(cfg_path, encoding="utf-8") as fh:
            self.pad_id = json.load(fh).get("pad_token_id", 0)

        self.session = make_ort_session(model_path)
        self.input_names = {i.name for i in self.session.get_inputs()}

        self.tokenizer = Tokenizer.from_file(tok_path)
        # only_second keeps the query intact and truncates the (longer) section.
        self.tokenizer.enable_truncation(max_length=MAX_TOKENS, strategy="only_second")

    def score(self, query, documents):
        """Return a relevance logit for each document paired with the query.

        Higher means more relevant. The returned list is aligned with `documents`.
        """
        if not documents:
            return []
        encs = self.tokenizer.encode_batch(
            [(query, doc[:_MAX_DOC_CHARS]) for doc in documents])
        width = max(len(e.ids) for e in encs)
        input_ids = np.full((len(encs), width), self.pad_id, dtype=np.int64)
        attention = np.zeros((len(encs), width), dtype=np.int64)
        type_ids = np.zeros((len(encs), width), dtype=np.int64)
        for row, enc in enumerate(encs):
            n = len(enc.ids)
            input_ids[row, :n] = enc.ids
            attention[row, :n] = enc.attention_mask
            type_ids[row, :n] = enc.type_ids

        feed = {"input_ids": input_ids, "attention_mask": attention}
        if "token_type_ids" in self.input_names:
            feed["token_type_ids"] = type_ids
        logits = self.session.run(None, feed)[0]
        return np.asarray(logits, dtype=np.float32).reshape(-1).tolist()


def main():
    import time
    print(f"Loading {RERANK_REPO} ({RERANK_ONNX}) ...")
    reranker = Reranker()
    query = "powers of arrest without warrant"
    docs = [
        "Arrest without warrant. A peace officer may arrest without warrant a "
        "person who has committed a criminal offence.",
        "Definitions. In this Act, fish means any fish and includes shellfish, "
        "crustaceans and marine animals.",
        "Importation. It is prohibited to import cannabis except as authorized "
        "under this Act.",
    ]
    start = time.perf_counter()
    scores = reranker.score(query, docs)
    elapsed = (time.perf_counter() - start) * 1000
    print(f"\nQuery: {query!r}")
    for doc, score in sorted(zip(docs, scores), key=lambda x: x[1], reverse=True):
        print(f"  {score:8.3f}  {doc[:62]}")
    print(f"\n{elapsed:.0f} ms for {len(docs)} documents")


if __name__ == "__main__":
    main()