import json
import re

from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline

try:
    from langdetect import DetectorFactory, detect

    DetectorFactory.seed = 0
    HAS_LANGDETECT = True
except Exception:
    HAS_LANGDETECT = False

try:
    import spacy

    HAS_SPACY = True
except Exception:
    HAS_SPACY = False

MODEL_NAME = "pythainlp/thainer-corpus-v2-base-model"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)

hf_ner = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple",
)

spacy_en_nlp = None

CANONICAL_TAGS = {
    "ID_CARD",
    "TIME",
    "EMAIL",
    "LEN",
    "LOCATION",
    "ORGANIZATION",
    "PERSON",
    "PHONE",
    "TEMPERATURE",
    "URL",
    "ZIP",
    "MONEY",
    "LAW",
    "PERCENT",
}

ENTITY_PRIORITY = {
    "ID_CARD": 100,
    "EMAIL": 95,
    "URL": 93,
    "PHONE": 92,
    "PERSON": 90,
    "ORGANIZATION": 80,
    "LOCATION": 78,
    "MONEY": 75,
    "ZIP": 72,
    "PERCENT": 70,
    "TIME": 65,
    "TEMPERATURE": 60,
    "LEN": 55,
    "LAW": 50,
}

SOURCE_PRIORITY = {
    "regex": 4,
    "wangchanberta_hf": 2,
    "spacy_en": 1,
}

THAI_FUNCTION_WORDS = {
    "ที่",
    "จาก",
    "ใน",
    "บน",
    "และ",
    "หรือ",
    "คือ",
    "ได้",
    "กับ",
    "ของ",
}


def _normalize_label(entity: dict) -> str:
    """Return a stable label from model output."""
    return (entity.get("entity_group") or entity.get("entity") or "PII").upper()


def init_models() -> None:
    """Lazy-load optional models so the core pipeline can still run without them."""
    global spacy_en_nlp

    if spacy_en_nlp is None and HAS_SPACY:
        try:
            spacy_en_nlp = spacy.load("en_core_web_sm")
        except Exception:
            spacy_en_nlp = None


def detect_text_profile(text: str) -> dict:
    """Return language profile with mode: th/en/mixed."""
    thai_chars = len(re.findall(r"[ก-๙]", text))
    en_chars = len(re.findall(r"[A-Za-z]", text))
    total_chars = max(1, thai_chars + en_chars)

    th_ratio = thai_chars / total_chars
    en_ratio = en_chars / total_chars
    mode = "mixed"

    if th_ratio >= 0.65:
        mode = "th"
    elif en_ratio >= 0.65:
        mode = "en"

    if HAS_LANGDETECT:
        try:
            detected = detect(text)
            if detected == "th" and th_ratio >= 0.2:
                mode = "th"
            elif detected == "en" and en_ratio >= 0.2:
                mode = "en"
            elif detected not in {"th", "en"} and th_ratio > 0.2 and en_ratio > 0.2:
                mode = "mixed"
        except Exception:
            pass

    return {
        "th_ratio": th_ratio,
        "en_ratio": en_ratio,
        "mode": mode,
    }


def _to_tag(label: str) -> str:
    """Map model labels to requested canonical tags."""
    if "PER" in label or "PERSON" in label or "NAME" in label:
        return "PERSON"
    if "ORG" in label or "COMPANY" in label:
        return "ORGANIZATION"
    if "LOC" in label or "ADDRESS" in label or "GPE" in label:
        return "LOCATION"
    if "EMAIL" in label or "MAIL" in label:
        return "EMAIL"
    if "URL" in label or "WEB" in label:
        return "URL"
    if "ZIP" in label or "POSTAL" in label:
        return "ZIP"
    if "MONEY" in label or "PRICE" in label or "CURRENCY" in label:
        return "MONEY"
    if "LAW" in label or "ACT" in label:
        return "LAW"
    if "PERCENT" in label:
        return "PERCENT"
    if "TEMP" in label:
        return "TEMPERATURE"
    if "LEN" in label or "LENGTH" in label or "AGE" in label:
        return "LEN"
    if "TIME" in label:
        return "TIME"
    if "DATE" in label or "DATA" in label:
        return "DATA"
    if "ID_CARD" in label or ("ID" in label and "CARD" in label):
        return "ID_CARD"
    if "PHONE" in label or "TEL" in label or "MOBILE" in label:
        return "PHONE"
    return "PERSON"


def normalize_entity(raw: dict, source: str, text: str) -> dict | None:
    """Normalize source-specific entity shape into common schema."""
    start, end = _trim_span(text, int(raw["start"]), int(raw["end"]))
    if start >= end:
        return None

    label = _to_tag(raw.get("label", ""))
    if label == "DATA":
        # Business rule: drop DATE/DATA-like entities.
        return None

    value = text[start:end]
    if _is_noise_span(value, label, source):
        return None

    return {
        "start": start,
        "end": end,
        "label": label,
        "source": source,
    }


def _is_phone_number(text: str, label: str) -> bool:
    """Skip masking for phone numbers as requested."""
    digits = re.sub(r"\D", "", text)
    has_phone_like_length = 9 <= len(digits) <= 12
    starts_with_zero_or_plus = text.strip().startswith("0") or text.strip().startswith("+")
    phone_label = any(key in label for key in ("PHONE", "TEL", "MOBILE"))
    return phone_label or (has_phone_like_length and starts_with_zero_or_plus)


def _trim_span(text: str, start: int, end: int) -> tuple:
    """Trim whitespace around entity span to reduce broken tags."""
    while start < end and text[start].isspace():
        start += 1
    while end > start and text[end - 1].isspace():
        end -= 1
    return start, end


def _is_noise_span(value: str, tag: str, source: str) -> bool:
    """Drop low-information spans that often appear from model noise."""
    stripped = value.strip()
    if not stripped:
        return True
    if len(stripped) == 1 and tag not in {"ZIP", "PERCENT", "MONEY", "LEN"}:
        return True
    if source in {"model", "wangchanberta_hf"} and stripped in THAI_FUNCTION_WORDS:
        return True
    if re.fullmatch(r"[\W_]+", stripped):
        return True
    return False


def _collect_regex_entities(text: str) -> list:
    """Force-detect patterns that are often missed by NER."""
    patterns = [
        ("ID_CARD", r"(?<!\d)(?:\d{13}|\d-\d{4}-\d{5}-\d{2}-\d)(?!\d)"),
        ("EMAIL", r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}"),
        ("URL", r"https?://[^\s]+|www\.[^\s]+"),
        ("PERCENT", r"\b\d+(?:\.\d+)?%"),
        ("MONEY", r"(?:฿|THB\s?)\d+(?:,\d{3})*(?:\.\d+)?|\b\d+(?:,\d{3})*(?:\.\d+)?\s?บาท"),
        ("ZIP", r"\b\d{5}\b"),
        ("TIME", r"\b\d{1,2}:\d{2}\b"),
        ("TEMPERATURE", r"\b\d+(?:\.\d+)?\s?(?:°C|°F|องศา)\b"),
        ("PHONE", r"(?:\+66|0)\d(?:[\s-]?\d){7,9}"),
        ("PERSON", r"(?:นาย|นางสาว|นาง|ดร\.?|คุณ)\s?[ก-๙]{2,}(?:\s[ก-๙]{2,}){0,2}"),
        ("PERSON", r"\b(?:Mr|Mrs|Ms|Miss|Dr)\.?\s+[A-Z][a-z]+(?:\s+[A-Z][a-z]+){0,2}\b"),
    ]

    found = []
    for label, pattern in patterns:
        for match in re.finditer(pattern, text, flags=re.IGNORECASE):
            found.append(
                {
                    "start": match.start(),
                    "end": match.end(),
                    "label": label,
                    "source": "regex",
                }
            )
    return found


def collect_model_entities_th(text: str) -> list:
    """Collect Thai entities from HF model only."""
    found = []

    hf_entities = hf_ner(text)
    for entity in hf_entities:
        found.append(
            {
                "start": entity["start"],
                "end": entity["end"],
                "label": _normalize_label(entity),
                "source": "wangchanberta_hf",
            }
        )

    return found


def collect_model_entities_en(text: str) -> list:
    """Collect English entities from spaCy if available."""
    if spacy_en_nlp is None:
        return []

    found = []
    doc = spacy_en_nlp(text)
    for ent in doc.ents:
        found.append(
            {
                "start": ent.start_char,
                "end": ent.end_char,
                "label": ent.label_,
                "source": "spacy_en",
            }
        )
    return found


def _entity_score(entity: dict) -> tuple:
    return (
        ENTITY_PRIORITY.get(entity["label"], 0),
        SOURCE_PRIORITY.get(entity["source"], 0),
        entity["end"] - entity["start"],
    )


def _is_overlap(a: dict, b: dict) -> bool:
    return not (a["end"] <= b["start"] or b["end"] <= a["start"])


def merge_entities_with_priority(entities: list) -> list:
    """Resolve overlap by label priority, source confidence, then span length."""
    if not entities:
        return []

    entities = sorted(
        entities,
        key=lambda x: (
            x["start"],
            -_entity_score(x)[0],
            -_entity_score(x)[1],
            -_entity_score(x)[2],
        ),
    )

    resolved = []
    for item in entities:
        conflict_idx = None
        for idx, kept in enumerate(resolved):
            if _is_overlap(item, kept):
                conflict_idx = idx
                break

        if conflict_idx is None:
            resolved.append(item)
            continue

        kept = resolved[conflict_idx]
        if item["label"] == kept["label"]:
            # Same label: keep longer span.
            if (item["end"] - item["start"]) > (kept["end"] - kept["start"]):
                resolved[conflict_idx] = item
        elif _entity_score(item) > _entity_score(kept):
            # Different labels: use priority score.
            resolved[conflict_idx] = item

    return sorted(resolved, key=lambda x: x["start"])


def _merge_entities(text: str) -> list:
    """Collect from all enabled sources, normalize, and merge by priority."""
    init_models()
    profile = detect_text_profile(text)
    mode = profile["mode"]

    raw_entities = []
    raw_entities.extend(_collect_regex_entities(text))

    if mode in {"th", "mixed"}:
        raw_entities.extend(collect_model_entities_th(text))
    # Run spaCy when text is English/mixed or still contains enough English signal.
    if mode in {"en", "mixed"} or profile["en_ratio"] >= 0.12:
        raw_entities.extend(collect_model_entities_en(text))

    normalized = []
    for entity in raw_entities:
        cleaned = normalize_entity(entity, entity["source"], text)
        if cleaned is not None:
            normalized.append(cleaned)

    return merge_entities_with_priority(normalized)


def mark_pii_context(text: str) -> dict:
    """Return tagged sentence and long context, excluding phone masking."""
    entities = _merge_entities(text)

    parts = []
    cursor = 0
    tagged_entities = []
    last_was_placeholder = False
    last_placeholder_tag = None

    for entity in entities:
        start, end = entity["start"], entity["end"]
        value = text[start:end]
        label = entity["label"]

        if start < cursor:
            continue

        if _is_noise_span(value, label, entity.get("source", "model")):
            continue

        parts.append(text[cursor:start])
        last_was_placeholder = False
        last_placeholder_tag = None

        if _is_phone_number(value, label):
            parts.append(value)
            action = "not_marked_phone"
            tag_name = "PHONE"
        else:
            tag_name = label if label in CANONICAL_TAGS else _to_tag(label)
            if tag_name == "DATA":
                # Defensive guard: date-like entity should not be masked.
                parts.append(value)
                action = "kept_original"
                tagged_entities.append(
                    {
                        "text": value,
                        "label": label,
                        "tag": tag_name,
                        "start": start,
                        "end": end,
                        "action": action,
                        "source": entity.get("source", "model"),
                    }
                )
                cursor = end
                continue
            placeholder = f"<{tag_name}>"
            if not (last_was_placeholder and last_placeholder_tag == tag_name):
                parts.append(placeholder)
            action = "marked"
            last_was_placeholder = True
            last_placeholder_tag = tag_name

        tagged_entities.append(
            {
                "text": value,
                "label": label,
                "tag": tag_name,
                "start": start,
                "end": end,
                "action": action,
                "source": entity.get("source", "model"),
            }
        )
        cursor = end

    parts.append(text[cursor:])
    tagged_text = re.sub(r"\s+", " ", "".join(parts)).strip()

    context = (
        "PII Context Output\n"
        f"Original: {text}\n\n"
        f"Tagged: {tagged_text}\n\n"
        "Entities:\n"
        f"{json.dumps(tagged_entities, ensure_ascii=False, indent=2)}"
    )

    return {
        "original_text": text,
        "sentence": tagged_text,
        "tagged_text": tagged_text,
        "entities": tagged_entities,
        "context": context,
    }


if __name__ == "__main__":
    sample_text = (
        "ฉันชื่อ นางสาวมะลิวา บุญสระดี อาศัยอยู่ที่อำเภอนางรอง จังหวัดบุรีรัมย์ "
        "เรียนจบจากมหาวิทยาลัยขอนแก่น ติดต่อได้ที่ 089-123-4567 และอีเมล abc@example.com"
    )
    output = mark_pii_context(sample_text)
    print(output["sentence"])
    



# ในไฟล์ test_model.py

# ใส่ local_files_only=True เพื่อรองรับคำสั่ง Offline ที่คุณจะใช้
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, local_files_only=True)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME, local_files_only=True)

ner = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple",
)