"""Event-stream loader for GEMEO-CWM.

Reads the same DATASUS SIH/APAC/SIM JSONs that DT-FM-Joint trains on, but
emits cohort sequences with explicit (cohort_key, condition_id, time_zero)
metadata so the diffusion model can be CFG-conditioned on treatment status.

The condition_id is the treatment-assignment code used for CFG:
  0 = null (CFG dropout / unconditional)
  1 = no orphan-drug observed in trajectory
  2-N = specific drug procedure code (one id per APAC drug subgroup)

A trajectory's condition_id is set to the FIRST orphan drug observed,
mimicking real-world treatment assignment at time of first dispensation.
This is the Target-Trial-Emulation "treatment intention" arm.
"""
from __future__ import annotations
import json
import logging
import os
from collections import Counter, defaultdict
from dataclasses import dataclass

import torch

log = logging.getLogger("gemeo.cwm.data")

SPECIAL = ["<PAD>", "<BOS>", "<EOS>", "<SEP>", "<UNK>", "<YEAR_BREAK>"]


def age_bucket(a):
    if a is None: return "age_unk"
    if a < 1: return "age_0_1"
    if a < 2: return "age_1_2"
    if a < 5: return "age_2_5"
    if a < 12: return "age_5_12"
    if a < 18: return "age_12_18"
    if a < 30: return "age_18_30"
    if a < 50: return "age_30_50"
    if a < 70: return "age_50_70"
    return "age_70plus"


def los_bucket(l):
    if l is None: return None
    if l <= 1: return "los_short"
    if l <= 7: return "los_week"
    if l <= 30: return "los_month"
    return "los_long"


def event_ym(r):
    if r.get("type") == "death":
        d = r.get("date_of_death")
        if d and "-" in str(d):
            p = str(d).split("-")
            return (int(p[0]), int(p[1]) if len(p) > 1 else 0)
    return (r.get("year", 0), r.get("month", 0))


def cohort_key(r):
    age = (r.get("age_at_admission_years")
           or r.get("age_at_authorization_years")
           or r.get("age_at_death_years"))
    if age is None or r.get("orpha") is None:
        return None
    yr = r.get("year") or 2020
    birth = ((yr - int(age)) // 5) * 5
    return (r["orpha"], r.get("uf_code", "??"), birth, r.get("sex", "?"))


def event_to_tokens(r):
    out = []
    if r["type"] == "admission":
        out.append(age_bucket(r.get("age_at_admission_years")))
        out.append("EV_ADM")
        cid = r.get("cid_princ", "")
        if cid: out.append(f"cid_{cid}")
        lb = los_bucket(r.get("los_days"))
        if lb: out.append(lb)
        proc = r.get("primary_procedure")
        if proc: out.append(f"proc_{proc[:7]}")
        out.append("outcome_death" if r.get("death_during_stay") else "outcome_discharge")
    elif r["type"] == "treatment":
        out.append(age_bucket(r.get("age_at_authorization_years")))
        out.append("EV_TX")
        cid = r.get("cid", "")
        if cid: out.append(f"cid_{cid}")
        proc = r.get("procedure_code")
        if proc: out.append(f"drug_{proc[:7]}")
        if r.get("is_orphan_drug"): out.append("ORPHAN_DRUG")
    elif r["type"] == "death":
        out.append(age_bucket(r.get("age_at_death_years")))
        out.append("EV_DEATH")
        cid = (r.get("cause_cid") or r.get("cid_princ") or r.get("cid", ""))
        if cid: out.append(f"cid_{cid}")
    out.append("<SEP>")
    return out


def first_drug_token(events_for_cohort):
    """Find the FIRST orphan-drug token in the cohort's event stream."""
    for r in events_for_cohort:
        if r.get("type") == "treatment" and r.get("procedure_code"):
            return f"drug_{r['procedure_code'][:7]}"
    return None


@dataclass
class CWMDataset:
    sequences: list           # list of token-id sequences (each len <= max_seq_len)
    conditions: torch.Tensor  # (N,) condition id per sequence
    cohort_keys: list         # list of (orpha, uf, birth, sex)
    tok2id: dict
    vocab: list
    cond2id: dict
    cond_vocab: list
    max_seq_len: int

    def __len__(self):
        return len(self.sequences)

    def to(self, device):
        seqs = torch.tensor(
            [s + [self.tok2id["<PAD>"]] * (self.max_seq_len - len(s)) for s in self.sequences],
            dtype=torch.long, device=device,
        )
        return seqs, self.conditions.to(device)


def load_events(sih_path=None, apac_path=None, sim_path=None):
    events = []
    for path, t in [(sih_path, "admission"), (apac_path, "treatment"), (sim_path, "death")]:
        if path and os.path.exists(path):
            recs = json.load(open(path))
            for r in recs:
                r["type"] = t
                if t == "death" and r.get("age_at_death_years") is None:
                    r["age_at_death_years"] = r.get("age")
            events.extend(recs)
            log.info(f"loaded {len(recs)} {t} events from {path}")
    return events


def build_cwm_dataset(events, max_seq_len=384, min_events=3,
                      year_filter=None) -> CWMDataset:
    """Build cohort-sequence dataset with treatment-condition labels for CFG."""
    if year_filter is not None:
        events = [e for e in events if event_ym(e)[0] in year_filter]

    by_cohort = defaultdict(list)
    for r in events:
        ck = cohort_key(r)
        if ck is not None:
            by_cohort[ck].append(r)

    # Build sequences
    seqs, conds, keys = [], [], []
    drug_counter = Counter()
    for ck, recs in by_cohort.items():
        if len(recs) < min_events:
            continue
        recs.sort(key=event_ym)
        orpha, uf, birth, sex = ck
        seq = ["<BOS>", f"orpha_{orpha}", f"uf_{uf}", f"sex_{sex}", f"birth_{birth}"]
        last_y = None
        for r in recs:
            y = event_ym(r)[0]
            if last_y is not None and y != last_y:
                seq.append("<YEAR_BREAK>")
            last_y = y
            seq.extend(event_to_tokens(r))
        seq.append("<EOS>")
        seq = seq[:max_seq_len]
        first_drug = first_drug_token(recs)
        seqs.append(seq)
        conds.append(first_drug)
        keys.append(ck)
        if first_drug:
            drug_counter[first_drug] += 1

    # Build token vocab
    vocab_set = set(SPECIAL)
    for s in seqs:
        vocab_set.update(s)
    vocab = sorted(vocab_set)
    tok2id = {t: i for i, t in enumerate(vocab)}

    # Build condition vocab: "<NULL>" + "<NO_TX>" + top-30 drugs (others -> <NO_TX>)
    cond_vocab = ["<NULL>", "<NO_TX>"] + [d for d, _ in drug_counter.most_common(30)]
    cond2id = {c: i for i, c in enumerate(cond_vocab)}
    cond_ids = torch.tensor(
        [cond2id.get(c, cond2id["<NO_TX>"]) if c else cond2id["<NO_TX>"]
         for c in conds],
        dtype=torch.long,
    )

    # Encode sequences
    encoded = []
    for s in seqs:
        ids = [tok2id.get(t, tok2id["<UNK>"]) for t in s]
        encoded.append(ids)

    log.info(f"built {len(encoded)} cohort sequences over {len(vocab)} tokens, "
             f"{len(cond_vocab)} conditions ({drug_counter.most_common(5)})")
    return CWMDataset(
        sequences=encoded, conditions=cond_ids, cohort_keys=keys,
        tok2id=tok2id, vocab=vocab, cond2id=cond2id, cond_vocab=cond_vocab,
        max_seq_len=max_seq_len,
    )


def temporal_split(events, train_years, test_years):
    train = [e for e in events if event_ym(e)[0] in set(train_years)]
    test = [e for e in events if event_ym(e)[0] in set(test_years)]
    return train, test