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"""Persistent JSONL sidecar for the local BetterWright 2.0 alpha."""
from __future__ import annotations
import argparse
import bisect
import json
import sys
import time
from pathlib import Path
import torch
from transformers import AutoModel, AutoTokenizer
from tree_utils import references, structural_windows
MUST_KEEP = ("[active]", "[selected]", "[checked]", "dialog", "alert", "error", "textbox")
def ancestors(lines: list[str], index: int) -> set[int]:
keep = set()
indent = len(lines[index]) - len(lines[index].lstrip())
for i in range(index - 1, -1, -1):
candidate = len(lines[i]) - len(lines[i].lstrip())
if candidate < indent:
keep.add(i)
indent = candidate
if indent == 0:
break
return keep
def render_indices(tree: str, indices: set[int]) -> str:
lines = [line.rstrip() for line in tree.splitlines() if line.strip()]
indices = {index for index in indices if 0 <= index < len(lines)}
for i in list(indices):
indices.update(ancestors(lines, i))
out = []
previous = -1
for i in sorted(indices):
if i > previous + 1:
out.append("- text: … irrelevant subtree pruned …")
out.append(lines[i])
previous = i
return "\n".join(out)
def coarse_ref_indices(
tree: str,
windows,
scores: list[float],
max_ranked_windows: int,
ref_context_lines: int,
) -> set[int]:
"""Mirror the validated coarse-ref-context policy exactly."""
lines = [line.rstrip() for line in tree.splitlines() if line.strip()]
ranked_windows = sorted(
range(len(windows)), key=lambda index: scores[index], reverse=True
)[:max_ranked_windows]
candidate_lines = set()
for window_index in ranked_windows:
window = windows[window_index]
candidate_lines.update(
range(window.start_line, min(window.end_line, len(lines)))
)
chosen = {
index
for index in candidate_lines
if references(lines[index])
or any(term in lines[index].casefold() for term in MUST_KEEP)
}
for index in list(chosen):
base_indent = len(lines[index]) - len(lines[index].lstrip())
kept = 0
for child in range(index + 1, len(lines)):
child_indent = len(lines[child]) - len(lines[child].lstrip())
if child_indent <= base_indent:
break
if child in candidate_lines:
chosen.add(child)
kept += 1
if kept >= ref_context_lines:
break
return chosen
class Server:
def __init__(self, model_id: str, config_path: Path):
self.config = json.loads(config_path.read_text())
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
self.model = AutoModel.from_pretrained(
model_id, trust_remote_code=True, torch_dtype=torch.bfloat16
).to(device).eval()
@torch.inference_mode()
def prune(self, query: str, tree: str, max_chars: int | None = None) -> dict:
started = time.perf_counter()
if not query.strip() or len(tree) < 1800:
return {"tree": tree, "fallback": True, "reason": "missing-query-or-small-tree", "savings": 0}
validated_max_chars = int(self.config.get("validated_max_chars", 10_000))
if max_chars is not None and max_chars < validated_max_chars:
return {
"tree": tree,
"fallback": True,
"reason": "requested-budget-below-validated-budget",
"savings": 0,
}
window_chars = int(self.config.get("window_chars", 3600))
strategy = self.config.get("strategy", "token-lines")
windows = structural_windows(tree, max_chars=window_chars, overlap_lines=4)
scores = []
confidences = []
total_lines = max((window.end_line for window in windows), default=0)
line_scores = [float("-inf")] * total_lines
for start in range(0, len(windows), 16):
part = windows[start : start + 16]
prefixes = [
f"[BETTERWRIGHT_TASK]\n{query}\n[ACCESSIBILITY_SUBTREE]\n"
for _ in part
]
texts = [prefix + window.text for prefix, window in zip(prefixes, part)]
tokenizer_args = {
"padding": True,
"truncation": True,
"max_length": 2048,
"return_tensors": "pt",
}
if strategy != "coarse-ref-context":
tokenizer_args["return_offsets_mapping"] = True
batch = self.tokenizer(texts, **tokenizer_args)
offsets = batch.pop("offset_mapping", None)
output = self.model(**batch.to(self.device))
scores.extend(torch.sigmoid(output.logits).float().cpu().tolist())
confidences.extend(torch.sigmoid(output.uncertainty_logits).float().cpu().tolist())
if strategy == "coarse-ref-context":
continue
token_scores = torch.sigmoid(output.token_logits).float().cpu()
for batch_index, window in enumerate(part):
prefix_chars = len(prefixes[batch_index])
line_starts = []
offset = 0
for line in window.text.splitlines():
line_starts.append(offset)
offset += len(line) + 1
for token_index, (token_start, token_end) in enumerate(
offsets[batch_index].tolist()
):
if token_end <= token_start or token_end <= prefix_chars:
continue
relative = max(token_start, prefix_chars) - prefix_chars
if relative >= len(window.text):
continue
local_line = bisect.bisect_right(line_starts, relative) - 1
if local_line < 0:
continue
global_line = window.start_line + local_line
if global_line < len(line_scores):
line_scores[global_line] = max(
line_scores[global_line],
float(token_scores[batch_index, token_index]),
)
if not scores:
return {"tree": tree, "fallback": True, "reason": "no-windows", "savings": 0}
threshold = self.config["relevance_threshold"]
if strategy == "coarse-ref-context":
chosen = coarse_ref_indices(
tree,
windows,
scores,
int(self.config["max_ranked_windows"]),
int(self.config["ref_context_lines"]),
)
else:
lines = [line.rstrip() for line in tree.splitlines() if line.strip()]
chosen = {
index
for index, line in enumerate(lines)
if any(term in line.casefold() for term in MUST_KEEP)
}
ranked = sorted(
(
index
for index, score in enumerate(line_scores)
if score != float("-inf")
),
key=lambda index: line_scores[index],
reverse=True,
)
chosen.update(ranked[: int(self.config["max_ranked_lines"])])
peak_i = max(range(len(scores)), key=lambda i: scores[i])
if (
not chosen
or scores[peak_i] < threshold
or confidences[peak_i] < self.config["confidence_threshold"]
):
return {"tree": tree, "fallback": True, "reason": "low-confidence", "savings": 0}
pruned = render_indices(tree, chosen)
savings = 1 - len(pruned) / max(1, len(tree))
if len(pruned) > validated_max_chars:
return {
"tree": tree,
"fallback": True,
"reason": "candidate-over-limit",
"savings": 0,
"candidate_chars": len(pruned),
"candidate_savings": savings,
}
if savings < 0.08:
return {
"tree": tree,
"fallback": True,
"reason": "insufficient-benefit",
"savings": 0,
"candidate_chars": len(pruned),
"candidate_savings": savings,
}
return {
"tree": pruned, "fallback": False, "reason": "model", "savings": savings,
"latency_ms": round((time.perf_counter() - started) * 1000, 2),
"peak_score": scores[peak_i], "peak_confidence": confidences[peak_i],
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="ProCreations/betterwright-encoder-350m")
parser.add_argument("--config", type=Path, required=True)
args = parser.parse_args()
server = Server(args.model, args.config)
print(json.dumps({"ready": True, "model": args.model}), flush=True)
for line in sys.stdin:
request = {}
try:
request = json.loads(line)
raw_max_chars = request.get("max_chars")
max_chars = int(raw_max_chars) if raw_max_chars is not None else None
response = {
"id": request.get("id"),
**server.prune(
str(request.get("query", "")),
str(request.get("tree", "")),
max_chars=max_chars,
),
}
except Exception as error:
response = {"id": request.get("id"), "fallback": True, "error": str(error)}
print(json.dumps(response, ensure_ascii=False), flush=True)
if __name__ == "__main__":
main()
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