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# -*- coding: utf-8 -*-
# /// script
# dependencies = ["datasets","huggingface_hub","hf_xet","tqdm"]
# ///
import argparse, json, os, random, hashlib
from pathlib import Path
from datasets import load_dataset
from huggingface_hub import HfApi, upload_folder, hf_hub_download
from tqdm import tqdm
def jd(obj):
return json.dumps(obj, ensure_ascii=False, sort_keys=False, default=str)
def clean(s, max_chars=12000):
if s is None:
return ""
if not isinstance(s, str):
s = jd(s)
s = s.replace("\r\n", "\n").replace("\r", "\n").strip()
if len(s) > max_chars:
s = s[:max_chars].rstrip() + "\n...[TRUNCATED]"
return s
def compact(obj, max_chars=12000):
s = jd(obj)
if len(s) > max_chars:
s = s[:max_chars].rstrip() + "...[TRUNCATED]"
return s
def role_of(m):
r = str(m.get("role", "")).lower().strip()
if r in ("human", "user"):
return "user"
if r in ("assistant", "gpt", "model"):
return "assistant"
if r in ("tool", "function", "observation"):
return "tool"
if r in ("system", "developer"):
return "system"
return r or "user"
def content_of(m):
for k in ("content", "text", "value", "message"):
if k in m and m[k] is not None:
return clean(m[k])
return ""
def normalize_tool_call(c):
if isinstance(c, str):
try:
c = json.loads(c)
except Exception:
return {"raw": c}
if not isinstance(c, dict):
return {"raw": c}
fn = c.get("function") if isinstance(c.get("function"), dict) else c
name = fn.get("name") or c.get("name") or c.get("tool_name") or c.get("tool")
args = fn.get("arguments", c.get("arguments", c.get("args", c.get("parameters", {}))))
if isinstance(args, str):
try:
args = json.loads(args)
except Exception:
pass
out = {}
if c.get("id"):
out["id"] = c.get("id")
if name:
out["name"] = str(name)
out["arguments"] = args if args is not None else {}
return out
def extract_tool_calls(m):
calls = []
if isinstance(m.get("tool_calls"), list):
calls.extend([normalize_tool_call(c) for c in m["tool_calls"]])
if m.get("function_call"):
calls.append(normalize_tool_call(m["function_call"]))
if m.get("tool_call"):
calls.append(normalize_tool_call(m["tool_call"]))
return [c for c in calls if c]
def used_tool_names(conversations):
names = set()
for m in conversations if isinstance(conversations, list) else []:
if not isinstance(m, dict):
continue
for c in extract_tool_calls(m):
if c.get("name"):
names.add(str(c["name"]))
for k in ("name", "tool_name"):
if m.get(k):
names.add(str(m[k]))
return names
def summarize_tool(t, max_desc=220):
if not isinstance(t, dict):
return None
fn = t.get("function") if isinstance(t.get("function"), dict) else t
name = fn.get("name") or t.get("name")
if not name:
return None
desc = clean(fn.get("description", ""), max_desc)
params = fn.get("parameters", {}) or fn.get("arguments", {})
required, props = [], []
if isinstance(params, dict):
required = params.get("required") or []
properties = params.get("properties") or params.get("arguments") or {}
if isinstance(properties, dict):
props = list(properties.keys())[:20]
line = "- " + str(name)
if desc:
line += ": " + desc
if required:
line += " | required: " + ", ".join(map(str, required[:10]))
elif props:
line += " | fields: " + ", ".join(map(str, props[:16]))
return line
def build_system(tools, conversations, max_tools=20, max_chars=5000):
used = used_tool_names(conversations)
lines = ["You can call tools when needed.", "Use only the available tool names and copy arguments exactly.", "", "Available tools:"]
selected = []
if isinstance(tools, list):
for t in tools:
line = summarize_tool(t)
if not line:
continue
name = line[2:].split(":", 1)[0].split(" | ", 1)[0].strip()
if used and name not in used:
continue
selected.append(line)
if not selected:
for t in tools[:max_tools]:
line = summarize_tool(t)
if line:
selected.append(line)
lines.extend(selected[:max_tools] if selected else ["- no_tool: No tool available"])
return clean("\n".join(lines), max_chars)
def tool_call_block(calls):
if len(calls) == 1:
return "TOOL_CALL:\n" + compact(calls[0])
return "TOOL_CALLS:\n" + compact(calls)
def tool_result_block(m):
payload = {}
for k in ("name", "tool_name", "tool_call_id", "id"):
if m.get(k):
payload[k] = m[k]
c = content_of(m)
if c:
payload["content"] = c
else:
for k in ("result", "observation", "output", "data"):
if m.get(k) is not None:
payload[k] = m[k]
break
return "TOOL_RESULT:\n" + compact(payload or m)
def normalize_row(row, source):
conversations = row.get("conversations") or row.get("messages")
tools = row.get("tools") or []
if not isinstance(conversations, list) or not conversations:
return None
out = [{"role": "system", "content": build_system(tools, conversations)}]
saw_user = False
saw_assistant = False
saw_tool_call = False
for m in conversations:
if not isinstance(m, dict):
continue
r = role_of(m)
if r == "system":
c = content_of(m)
if c:
out.append({"role": "system", "content": c})
continue
if r == "tool":
out.append({"role": "user", "content": tool_result_block(m)})
saw_user = True
continue
if r == "assistant":
content = content_of(m)
calls = extract_tool_calls(m)
if calls:
saw_tool_call = True
block = tool_call_block(calls)
content = (content + "\n\n" + block).strip() if content else block
if content:
out.append({"role": "assistant", "content": content})
saw_assistant = True
continue
c = content_of(m)
if c:
out.append({"role": "user", "content": c})
saw_user = True
merged = []
for m in out:
if merged and merged[-1]["role"] == m["role"]:
merged[-1]["content"] = clean(merged[-1]["content"] + "\n\n" + m["content"])
else:
merged.append(m)
if not saw_user or not saw_assistant:
return None
return {"messages": merged, "source": source, "category": "toolmind_tool_call" if saw_tool_call else "toolmind_no_tool_call"}
def stable_key(obj):
return hashlib.sha256(jd(obj.get("messages", [])).encode("utf-8")).hexdigest()
def try_reuse(out_repo_id, out_dir):
out = Path(out_dir)
out.mkdir(parents=True, exist_ok=True)
try:
train = hf_hub_download(repo_id=out_repo_id, filename="train.jsonl", repo_type="dataset")
val = hf_hub_download(repo_id=out_repo_id, filename="validation.jsonl", repo_type="dataset")
(out / "train.jsonl").write_bytes(Path(train).read_bytes())
(out / "validation.jsonl").write_bytes(Path(val).read_bytes())
print("REUSED", out_repo_id, flush=True)
return True
except Exception as e:
print("NO REUSE:", repr(e)[:250], flush=True)
return False
def convert(a):
out = Path(a.out_dir)
out.mkdir(parents=True, exist_ok=True)
if a.reuse and try_reuse(a.out_repo_id, a.out_dir):
return out
print("Loading", a.dataset, a.split, flush=True)
ds = load_dataset(a.dataset, split=a.split)
total = len(ds)
limit = total if a.max_rows <= 0 else min(a.max_rows, total)
print("Rows", total, "limit", limit, flush=True)
rows, seen, counts = [], set(), {}
for i in tqdm(range(limit), desc="convert"):
obj = normalize_row(dict(ds[i]), f"{a.dataset}:{a.split}:{i}")
if not obj:
continue
k = stable_key(obj)
if k in seen:
continue
seen.add(k)
rows.append(obj)
counts[obj["category"]] = counts.get(obj["category"], 0) + 1
random.Random(a.seed).shuffle(rows)
val_n = min(a.val_size, max(1, len(rows)//100))
val = rows[:val_n]
train = rows[val_n:]
with (out/"train.jsonl").open("w", encoding="utf-8") as f:
for r in train:
f.write(jd(r)+"\n")
with (out/"validation.jsonl").open("w", encoding="utf-8") as f:
for r in val:
f.write(jd(r)+"\n")
(out/"README.md").write_text("---\nlicense: apache-2.0\nlanguage:\n- en\ntask_categories:\n- text-generation\n---\n\n# ToolMind converted to Scugnizz format\n\n" + json.dumps(counts, ensure_ascii=False, indent=2), encoding="utf-8")
print("DONE", out, "TRAIN", len(train), "VAL", len(val), flush=True)
print(json.dumps(counts, ensure_ascii=False, indent=2), flush=True)
if rows[:1]:
print("SAMPLE", json.dumps(rows[0], ensure_ascii=False)[:2000], flush=True)
return out
def upload_dataset(folder, repo_id, private=False):
token = os.environ.get("HF_TOKEN") or os.environ.get("UV_SCRIPT_HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN")
api = HfApi(token=token)
api.create_repo(repo_id, repo_type="dataset", private=private, exist_ok=True)
upload_folder(repo_id=repo_id, repo_type="dataset", folder_path=str(folder), commit_message="Convert ToolMind to Scugnizz format", token=token)
print("UPLOADED", repo_id, flush=True)
def main():
p = argparse.ArgumentParser()
p.add_argument("--dataset", default="mlx-community/ToolMind")
p.add_argument("--split", default="graph_syn_datasets")
p.add_argument("--max-rows", type=int, default=50000)
p.add_argument("--val-size", type=int, default=1000)
p.add_argument("--seed", type=int, default=20260709)
p.add_argument("--out-dir", default="data/toolmind-scugnizz-converted")
p.add_argument("--upload", action="store_true")
p.add_argument("--out-repo-id", default="ProjectScugnizz/toolmind-scugnizz-converted")
p.add_argument("--private", action="store_true")
p.add_argument("--reuse", action="store_true")
a = p.parse_args()
folder = convert(a)
if a.upload:
upload_dataset(folder, a.out_repo_id, a.private)
if __name__ == "__main__":
main()
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