"""
EvidenceNER model definition.

Architecture: distilbert-base-uncased with token classification head.
Task:         Named Entity Recognition on redacted complaint text.
Entity types: ORG | AMOUNT | DATE | REF_ID | ACCOUNT | PERSON
Input:        Redacted complaint text (str, max 512 tokens after tokenisation).
Output:       List of Entity(text, label, start, end, confidence).

BIO scheme: O + B-/I- prefix for each of the 6 entity types → 13 labels total.

Note: PERSON entities surviving Presidio redaction are role-references
      (e.g. "customer care executive"), not personal names.
"""

from __future__ import annotations

import logging
from dataclasses import dataclass
from typing import Optional

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Label constants — shared by model.py, train.py, predict.py
# ---------------------------------------------------------------------------

NER_LABELS = ["ORG", "AMOUNT", "DATE", "REF_ID", "ACCOUNT", "PERSON"]

# O first, then B-/I- pairs in the same order as NER_LABELS → 13 labels
BIO_LABELS: list[str] = ["O"] + [
    f"{bio}-{label}" for label in NER_LABELS for bio in ("B", "I")
]

LABEL2ID: dict[str, int] = {label: i for i, label in enumerate(BIO_LABELS)}
ID2LABEL: dict[int, str] = {i: label for label, i in LABEL2ID.items()}

NUM_LABELS = len(BIO_LABELS)  # 13


# ---------------------------------------------------------------------------
# Public output type
# ---------------------------------------------------------------------------

@dataclass
class Entity:
    """A single recognised entity span."""
    text: str
    label: str
    start: int
    end: int
    confidence: float


# ---------------------------------------------------------------------------
# EvidenceNER
# ---------------------------------------------------------------------------

class EvidenceNER:
    """
    DistilBERT token classifier for complaint evidence extraction.

    Loads a fine-tuned checkpoint produced by train.py.  Uses the tokenizer's
    offset_mapping to convert subword-level BIO predictions back to
    character-level spans without any secondary tokenisation step.
    """

    BASE_MODEL = "distilbert-base-uncased"

    def __init__(self, model_dir: str) -> None:
        """Load a fine-tuned NER checkpoint from *model_dir*."""
        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
        self.model = AutoModelForTokenClassification.from_pretrained(model_dir)
        self.model.eval()
        self._device = torch.device(
            "cuda" if torch.cuda.is_available()
            else "mps" if torch.backends.mps.is_available()
            else "cpu"
        )
        self.model.to(self._device)
        logger.info("EvidenceNER loaded from %s on %s", model_dir, self._device)

    # ------------------------------------------------------------------
    # Public API
    # ------------------------------------------------------------------

    def extract(self, text: str) -> list[Entity]:
        """
        Extract entity spans from *text* and return a list of Entity objects.

        Spans are character-level (start/end index into the original string).
        Returns [] for empty or whitespace-only input.
        """
        if not text or not text.strip():
            return []

        encoding = self.tokenizer(
            text,
            return_tensors="pt",
            truncation=True,
            max_length=512,
            return_offsets_mapping=True,
        )

        # offset_mapping is not a model input — pop before forward pass
        offset_mapping: list[tuple[int, int]] = (
            encoding.pop("offset_mapping")[0].tolist()
        )

        model_inputs = {k: v.to(self._device) for k, v in encoding.items()}

        with torch.no_grad():
            logits = self.model(**model_inputs).logits[0]  # (seq_len, num_labels)

        probs = torch.softmax(logits, dim=-1).cpu()
        pred_ids: list[int] = probs.argmax(dim=-1).tolist()
        conf_scores: list[float] = probs.max(dim=-1).values.tolist()

        return self._aggregate_spans(text, offset_mapping, pred_ids, conf_scores)

    # ------------------------------------------------------------------
    # Span aggregation
    # ------------------------------------------------------------------

    def _aggregate_spans(
        self,
        text: str,
        offset_mapping: list[tuple[int, int]],
        pred_ids: list[int],
        conf_scores: list[float],
    ) -> list[Entity]:
        """
        Convert per-subtoken BIO predictions into character-level Entity spans.

        Special tokens ([CLS], [SEP]) have offset (0, 0) — i.e. start == end —
        and are skipped.  An I- tag that does not continue the current B- entity
        type is treated as O (broken sequence).
        """
        entities: list[Entity] = []
        current: Optional[dict] = None
        current_confs: list[float] = []

        def _flush() -> None:
            if current is not None:
                entities.append(Entity(
                    text=current["text"],
                    label=current["label"],
                    start=current["start"],
                    end=current["end"],
                    confidence=sum(current_confs) / len(current_confs),
                ))

        for (start, end), label_id, conf in zip(offset_mapping, pred_ids, conf_scores):
            # Special tokens have zero-length offset spans
            if start == end:
                _flush()
                current = None
                current_confs = []
                continue

            label = ID2LABEL[label_id]

            if label.startswith("B-"):
                _flush()
                entity_type = label[2:]
                current = {
                    "text": text[start:end],
                    "label": entity_type,
                    "start": start,
                    "end": end,
                }
                current_confs = [conf]

            elif (
                label.startswith("I-")
                and current is not None
                and label[2:] == current["label"]
            ):
                # Extend the current span (including any whitespace between subwords)
                current["text"] = text[current["start"]: end]
                current["end"] = end
                current_confs.append(conf)

            else:  # O or mismatched I- → close current span
                _flush()
                current = None
                current_confs = []

        # Flush any span still open at end of sequence
        _flush()

        return entities