"""FINSENSE stage-1 trainer — deberta-v3-base on Financial PhraseBank 50agree.

Research-verified recipe (FINSENSE.md): generic encoder, no DAPT, our own
published stratified split (FPB has no official one), macro-F1 primary,
confusion matrix logged (pos/neutral confusion = the known ceiling).

Usage: python3 finsense_train.py [--seed 42] [--smoke] [--base microsoft/deberta-v3-base]
Runs on MPS (Mac) or CUDA. Smoke = 200 steps sanity.
"""
import argparse
import json
import os

import io
import zipfile

import numpy as np
import torch
from huggingface_hub import hf_hub_download
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
                          Trainer, TrainingArguments)

p = argparse.ArgumentParser()
p.add_argument("--seed", type=int, default=42)
p.add_argument("--smoke", action="store_true")
p.add_argument("--base", default="microsoft/deberta-v3-base")
p.add_argument("--corpus", default=None,
               help="optional finsense_arm_*.jsonl — replaces the FPB train split "
                    "as training data (val/test splits stay FPB, untouched)")
args = p.parse_args()

_arm = ("-" + os.path.basename(args.corpus).replace("finsense_arm_", "arm").replace(".jsonl", "")) if args.corpus else ""
OUT = os.path.expanduser(f"~/finsense_runs/{args.base.split('/')[-1]}-s{args.seed}{_arm}")
os.makedirs(OUT, exist_ok=True)
LABELS = ["negative", "neutral", "positive"]  # FPB label order 0/1/2

# parse the raw FPB zip directly (script-datasets unsupported, no parquet branch)
zp = hf_hub_download("takala/financial_phrasebank", "data/FinancialPhraseBank-v1.0.zip",
                     repo_type="dataset")
LAB2ID = {"negative": 0, "neutral": 1, "positive": 2}
rows = []
with zipfile.ZipFile(zp) as z:
    with z.open("FinancialPhraseBank-v1.0/Sentences_50Agree.txt") as fh:
        for line in io.TextIOWrapper(fh, encoding="latin-1"):
            line = line.strip()
            if not line or "@" not in line:
                continue
            sent, lab = line.rsplit("@", 1)
            rows.append({"sentence": sent.strip(), "label": LAB2ID[lab.strip()]})
print(f"FPB 50agree rows: {len(rows)}", flush=True)

# published split: stratified 80/10/10, split-seed FIXED at 42 regardless of
# training seed (same rule as Parable corpus splits)
import random as _rnd
_rnd.Random(42).shuffle(rows)
by_label = {i: [] for i in range(3)}
for r in rows:
    by_label[r["label"]].append(r)
train, val, test = [], [], []
for lab, rows in by_label.items():
    n = len(rows)
    test += rows[: int(n * 0.10)]
    val += rows[int(n * 0.10): int(n * 0.20)]
    train += rows[int(n * 0.20):]
print(f"split: train={len(train)} val={len(val)} test={len(test)}", flush=True)

if args.corpus:
    train = [json.loads(l) for l in open(args.corpus)]
    import random as _r2
    _r2.Random(args.seed).shuffle(train)
    print(f"corpus override: {args.corpus} -> train={len(train)}", flush=True)

_trc = {"trust_remote_code": True} if "NeoBERT" in args.base else {}
tok = AutoTokenizer.from_pretrained(args.base, **_trc)

def enc(rows):
    from datasets import Dataset
    d = Dataset.from_list(rows)
    return d.map(lambda b: tok(b["sentence"], truncation=True, max_length=128),
                 batched=True, remove_columns=["sentence"])

torch.manual_seed(args.seed)
model = AutoModelForSequenceClassification.from_pretrained(
    args.base, num_labels=3, **_trc,
    id2label=dict(enumerate(LABELS)), label2id={l: i for i, l in enumerate(LABELS)})

def metrics(pred):
    y, yhat = pred.label_ids, pred.predictions.argmax(-1)
    return {"accuracy": accuracy_score(y, yhat),
            "macro_f1": f1_score(y, yhat, average="macro")}

from transformers import TrainerCallback

class MPSCacheFlush(TrainerCallback):
    """torch-MPS leaks on deberta attention kernels; flush or the kernel
    OOM-kills the process silently around step ~100 on a 16GB machine."""
    def on_step_end(self, args, state, control, **kw):
        if state.global_step % 25 == 0 and torch.backends.mps.is_available():
            torch.mps.empty_cache()

import transformers as _tf
_tok_kw = {"processing_class": tok} if int(_tf.__version__.split(".")[0]) >= 5 else {"tokenizer": tok}
trainer = Trainer(
    model=model,
    **_tok_kw,
    callbacks=[MPSCacheFlush()],
    train_dataset=enc(train),
    eval_dataset=enc(val),
    compute_metrics=metrics,
    args=TrainingArguments(
        output_dir=f"{OUT}/ckpt",
        num_train_epochs=1 if args.smoke else 5,
        max_steps=50 if args.smoke else -1,
        per_device_train_batch_size=8, gradient_accumulation_steps=2, per_device_eval_batch_size=16,
        learning_rate=(1e-5 if "deberta" in args.base else 2e-5), warmup_ratio=0.1, weight_decay=0.01,
        eval_strategy="epoch" if not args.smoke else "no",
        save_strategy="epoch" if not args.smoke else "no",
        load_best_model_at_end=not args.smoke,
        metric_for_best_model="macro_f1",
        logging_steps=25, seed=args.seed, report_to=[],
        use_cpu=False,
        # deberta-v3's disentangled attention overflows under fp16 -> class
        # collapse; transformers v5 auto-enables mixed precision on CUDA
        fp16=False, bf16=False,
    ),
)
trainer.train()

# final: held-out test (never used for selection)
pred = trainer.predict(enc(test))
y, yhat = pred.label_ids, pred.predictions.argmax(-1)
res = {"base": args.base, "seed": args.seed, "smoke": args.smoke,
       "test_accuracy": float(accuracy_score(y, yhat)),
       "test_macro_f1": float(f1_score(y, yhat, average="macro")),
       "confusion_matrix": confusion_matrix(y, yhat).tolist(),
       "labels": LABELS, "split": "stratified 80/10/10, split-seed 42",
       "corpus": args.corpus or "fpb-train",
       "dataset": "financial_phrasebank sentences_50agree"}
json.dump(res, open(f"{OUT}/result.json", "w"), indent=1)
print("FINSENSE_RESULT", json.dumps(res), flush=True)
if not args.smoke:
    model.save_pretrained(f"{OUT}/model")
    tok.save_pretrained(f"{OUT}/model")