"""Concept token sets C_t for TOFU forget10 authors (proposal §1.2).

For each author: tokenization variants of name parts (leading-space /
no-space, first subword token of each). Prompt partition: name_absent
questions (author name not in question text) are the H1 primary signal;
name_present questions are reported separately (copy confound).
"""

import json
import re

FORGET10_AUTHORS = [
    "Hsiao Yun-Hwa", "Carmen Montenegro", "Elvin Mammadov", "Rajeev Majumdar",
    "Jad Ambrose Al-Shamary", "Adib Jarrah", "Ji-Yeon Park", "Behrouz Rohani",
    "Wei-Jun Chen", "Tae-ho Park", "Hina Ameen", "Xin Lee Williams",
    "Moshe Ben-David", "Kalkidan Abera", "Takashi Nakamura", "Raven Marais",
    "Aysha Al-Hashim", "Edward Patrick Sullivan", "Basil Mahfouz Al-Kuwaiti",
    "Nikolai Abilov",
]


def name_parts(full_name: str) -> list[str]:
    """'Hsiao Yun-Hwa' -> ['Hsiao', 'Yun-Hwa', 'Yun', 'Hwa'] (space and hyphen splits)."""
    parts = full_name.split()
    for p in list(parts):
        if "-" in p:
            parts.extend(seg for seg in p.split("-") if len(seg) > 2)
    return parts


def concept_token_ids(tokenizer, full_name: str) -> list[int]:
    """First subword token of each name variant, leading-space and bare."""
    ids = []
    variants = [full_name] + name_parts(full_name)
    for v in variants:
        for s in (f" {v}", v):
            t = tokenizer(s, add_special_tokens=False).input_ids
            if t:
                ids.append(t[0])
    return sorted(set(ids))


def author_in_text(full_name: str, text: str) -> bool:
    """Any name part (len>2) appears in text (case-insensitive, word-ish match)."""
    low = text.lower()
    for p in name_parts(full_name):
        if len(p) > 2 and re.search(re.escape(p.lower()), low):
            return True
    return False


def load_forget10_rows():
    """Rows of forget10 with author index/name and name_present flag."""
    from datasets import load_dataset

    ds = load_dataset("locuslab/TOFU", "forget10")["train"]
    rows = []
    for i in range(len(ds)):
        author = FORGET10_AUTHORS[i // 20]
        rows.append(
            {
                "idx": i,
                "author_idx": i // 20,
                "author": author,
                "question": ds[i]["question"],
                "answer": ds[i]["answer"],
                "name_present": author_in_text(author, ds[i]["question"]),
            }
        )
    return rows


if __name__ == "__main__":
    import transformers

    tok = transformers.AutoTokenizer.from_pretrained("/workspace/models/tofu_1b_full")
    rows = load_forget10_rows()
    n_absent = sum(not r["name_present"] for r in rows)
    print(f"forget10: {len(rows)} rows, {n_absent} name-absent")
    from collections import Counter

    per_author = Counter(r["author"] for r in rows if not r["name_present"])
    for a in FORGET10_AUTHORS:
        ids = concept_token_ids(tok, a)
        toks = [tok.decode([t]) for t in ids]
        print(f"{a:26s} absent={per_author.get(a, 0):2d}  C_t={toks}")
    json.dump(rows, open("/workspace/jspace-unlearning/results/forget10_rows.json", "w"), indent=1)