feat(tools): add 7 wired+bench utility scripts (A+B classification)

- A (wired, imports project modules): e2e_map_roles, anonymize_demo, grounding_e2e_resolve_engine
- B (orphan projection, standalone benches): enrichment_eval_multi, extract_easily_bench_cases, extract_record_bench_cases, grounding_eval_multi

tools/e2e_map_roles.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""E2E — valide le MODULE `core.extraction.role_mapper` en conditions réelles.
+
+Remplace le POC ad hoc (`poc_lecture_ecran.py`) : au lieu de logique inline, on
+appelle la brique TESTÉE `map_roles` avec un vrai client vLLM. Prouve la parité
+module ↔ POC sur un vrai écran DGX.
+
+Pipeline : extract_grid_from_image (OCR) → tokens_from_grid → map_roles(client réel).
+Sortie masquée (PII) ; détail complet dumpé dans /tmp (reste sur le DGX).
+"""
+import argparse
+import base64
+import json
+import re
+import sys
+import time
+from io import BytesIO
+from pathlib import Path
+
+import requests
+from PIL import Image
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from core.llm.ocr_extractor import extract_grid_from_image  # noqa: E402
+from core.extraction.role_mapper import tokens_from_grid, map_roles  # noqa: E402
+
+VLLM_URL = "http://localhost:8001/v1/chat/completions"
+MODEL = "Qwen/Qwen3-VL-4B-Instruct"
+
+
+def _img_data_url(path, max_w=1280):
+    img = Image.open(path).convert("RGB")
+    if img.width > max_w:
+        h = int(img.height * max_w / img.width)
+        img = img.resize((max_w, h), Image.LANCZOS)
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
+
+
+def make_client(max_tokens=1500, max_w=1280):
+    """Construit un client VLM (image_path, prompt) -> texte, branché sur vLLM:8001."""
+    def client(image_path, prompt):
+        body = {
+            "model": MODEL,
+            "messages": [{"role": "user", "content": [
+                {"type": "image_url", "image_url": {"url": _img_data_url(image_path, max_w)}},
+                {"type": "text", "text": prompt},
+            ]}],
+            "temperature": 0.0,
+            "max_tokens": max_tokens,
+            "chat_template_kwargs": {"enable_thinking": False},
+        }
+        r = requests.post(VLLM_URL, json=body, timeout=120)
+        if r.status_code != 200:
+            raise RuntimeError(f"vLLM {r.status_code}: {r.text[:300]}")
+        return r.json()["choices"][0]["message"]["content"]
+    return client
+
+
+def _mask(v):
+    v = str(v)
+    if not v:
+        return "<vide>"
+    if re.fullmatch(r"[\d .,/:%€-]+", v):
+        k = "num/date"
+    elif len(v.split()) >= 4:
+        k = "texte"
+    else:
+        k = "court"
+    return f"<{k}:{len(v)}c>"
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--extract", required=True)
+    ap.add_argument("--roles", default="", help="rôles attendus, séparés par des virgules (mode guidé)")
+    a = ap.parse_args()
+    roles = [r.strip() for r in a.roles.split(",") if r.strip()] or None
+
+    t0 = time.time()
+    grid = extract_grid_from_image(a.extract)
+    t_ocr = time.time() - t0
+    tokens = tokens_from_grid(grid)
+    confs = sorted(t.confidence for t in tokens)
+    med = confs[len(confs) // 2] if confs else 0.0
+
+    client = make_client()
+    t1 = time.time()
+    fields = map_roles(a.extract, tokens, client, roles)
+    t_vlm = time.time() - t1
+
+    out = Path(f"/tmp/e2e_{Path(a.extract).stem}.json")
+    out.write_text(json.dumps(
+        [{"label": f.label, "value": f.value, "confidence": f.confidence,
+          "anchored": f.anchored, "value_ids": f.value_ids} for f in fields],
+        ensure_ascii=False, indent=2))
+
+    anc = sum(1 for f in fields if f.anchored)
+    print(f"# Image    : {Path(a.extract).name}")
+    print(f"# Mode     : {'guidé ' + str(roles) if roles else 'libre'}")
+    print(f"# OCR      : {len(tokens)} tokens, conf médiane {med:.2f}, {t_ocr:.1f}s")
+    print(f"# VLM      : {t_vlm:.1f}s  | via map_roles (module testé)")
+    print(f"# Champs   : {len(fields)} (ancrés OCR: {anc})")
+    for f in fields:
+        flag = "·" if f.anchored else "∅"
+        print(f"  {flag} {str(f.label)[:28]:28s} = {_mask(f.value)}")
+    print(f"# Ancrage strict : {anc}/{len(fields)} | détail PII -> {out} (DGX, NE PAS rapatrier)")
+
+
+if __name__ == "__main__":
+    main()