import os
# The repo is the working directory at run time, and there is no network.
os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["TRANSFORMERS_OFFLINE"] = "1"
MODEL_ID = "."

import re
import json
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, torch_dtype=torch.float16, device_map="auto"
).eval()

MAX_NEW_TOKENS = 1536

SYSTEM = (
    "You solve International Linguistics Olympiad problems by reasoning from the "
    "data you are given. You may meet a task type you have never seen: read the "
    "instruction and the examples, and answer in the same form they use. "
    "Common task types and what to give -- "
    "translation: the translated form only, in the language the task asks for. "
    "Apply every suffix, prefix, or ending shown in the examples (plurals, cases, "
    "tense, etc.) -- do not give the bare stem if the pattern requires an ending; "
    "fill_blanks: only the missing form for each blank; "
    "match_letters: the option letter ALONE -- for example 'C', never 'word: C' or "
    "any other text around it; "
    "text_to_num: the number in digits; "
    "num_to_text: the number written out in words, in the language asked; "
    "any other type: give exactly what the instruction asks, nothing else. "
    "Reason step by step first. Before you finalize, re-check each answer is in the "
    "minimal bare form the task requires, with no echoed input, no labels, no extra "
    "words attached. Then write a line that says exactly FINAL ANSWERS: "
    "and, below it, one answer per line in the order the items are asked -- the "
    "bare answer only, no numbering, no quotes, no extra text."
)


def parse_answers(text, task_type=None):
    """Keep only the lines after the last 'FINAL ANSWERS:' marker, one answer per line."""
    marker = list(re.finditer(r"(?im)^\s*final answers?\s*:?\s*$", text))
    if marker:
        text = text[marker[-1].end():]
    answers = []
    for line in text.splitlines():
        line = re.sub(r"^\s*\d+[.)]\s*", "", line).strip()
        if line:
            if task_type == "match_letters":
                m = re.search(r"([A-Za-z])\s*$", line)
                if m and (":" in line or len(line) > 3):
                    line = m.group(1)
            answers.append(line)
    return answers


df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")

rows = []
for _, r in df.iterrows():
    messages = [
        {"role": "system", "content": SYSTEM},
        {"role": "user", "content": f"{r['context'].strip()}\n\n{r['query'].strip()}"},
    ]
    enc = tok.apply_chat_template(
        messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,
    ).to(model.device)
    with torch.no_grad():
        out = model.generate(**enc, max_new_tokens=MAX_NEW_TOKENS, do_sample=False)
    text = tok.decode(out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
    answers = parse_answers(text, task_type=r["task_type"])
    rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
    print(f"{len(rows)}/{len(df)} done", flush=True)

os.makedirs("/tmp/model", exist_ok=True)
pd.DataFrame(rows).to_csv("/tmp/model/submission.csv", index=False)
print("wrote submission.csv", flush=True)