"""
src/nlp/icd_mapper.py
────────────────────────────────────────────────────────────────
ICD-10 code mapping module.

Strategy — lookup-first, embedding fallback
────────────────────────────────────────────
This is the industry-standard approach for clinical coding:

  Step 1 — Exact match
    Direct dictionary lookup on the normalised entity text.
    Fast and unambiguous.  Most common clinical terms hit here.

  Step 2 — Fuzzy match (rapidfuzz)
    Levenshtein-based string matching against ICD-10 descriptions.
    Catches spelling variants and minor OCR errors.
    Returns a match when score >= ICD10Config.fuzzy_threshold (80).

  Step 3 — Embedding match (sentence-transformers)
    Encode both the entity and all ICD-10 descriptions into dense
    vectors, then find the closest by cosine similarity.
    Handles genuinely novel phrasing that fuzzy matching misses.
    Returns a match when similarity >= ICD10Config.embedding_threshold (0.75).
    This step is expensive (~100ms per entity) so it only runs
    when steps 1 and 2 produce no confident result.

  Step 4 — No match
    Returns an empty list with a LOW_CONFIDENCE flag so the
    caller can decide how to handle unknown entities.

Each step returns the top-k candidates, not just the best match.
This lets the dashboard show alternative codes and lets a human
reviewer choose the most appropriate one.

Confidence scores
─────────────────
  Exact match   → 1.0
  Fuzzy match   → fuzzy_score / 100.0
  Embedding     → cosine similarity score (0.0–1.0)
────────────────────────────────────────────────────────────────
"""

from __future__ import annotations

import re
from dataclasses import dataclass
from pathlib import Path

import pandas as pd

from src.utils.config import ICD10Config, Paths
from src.utils.logger import get_logger

logger = get_logger(__name__)


def _strip_parens(text: str) -> str:
    """Remove parenthetical qualifiers and collapse whitespace.

    ICD-10 descriptions often carry a parenthetical qualifier, e.g.
    "Essential (primary) hypertension" — about 8% of the ~74k codes.
    Without stripping these, common, simply-phrased clinical terms
    ("essential hypertension") miss the exact match entirely and fall
    through to fuzzy matching, which can rank an unrelated same-length
    term (e.g. "Neonatal hypertension") above the correct one purely
    because it scores closer on string length.
    """
    stripped = re.sub(r"\s*\([^)]*\)", "", text)
    stripped = re.sub(r"\s{2,}", " ", stripped)
    return stripped.strip()


# Words that qualify/modify a clinical term without naming a distinct
# condition. Stripped out before computing "extra word" counts so that
# e.g. "Type 2 diabetes mellitus without complications" and "...with
# other specified complication" are correctly recognised as equally
# generic relative to the query "type 2 diabetes mellitus".
_GENERIC_QUALIFIER_WORDS: frozenset[str] = frozenset({
    "unspecified", "specified", "other", "type", "without", "with",
    "complication", "complications", "due", "to", "of", "in", "and",
    "or", "the", "a", "an", "not", "elsewhere", "classified",
    "organism", "agent", "cause", "origin", "site", "side",
})


def _content_words(text: str) -> set[str]:
    """Extract the clinically meaningful words from a description.

    Strips generic qualifier words (see :data:`_GENERIC_QUALIFIER_WORDS`)
    so that two phrasings of the same underlying condition compare equal
    regardless of which specific qualifier each one uses.
    """
    words = re.findall(r"[a-z0-9]+", text.lower())
    return {w for w in words if w not in _GENERIC_QUALIFIER_WORDS and len(w) > 1}


def _is_unsafe_type_assertion(query: str, description: str) -> bool:
    """True if `description` asserts "type 1"/"type 2" but `query` doesn't.

    `_content_words()` deliberately strips single-character tokens, so
    "1" and "2" are invisible to the generic-vs-specific preference
    logic above — "diabetes mellitus", "type 1 diabetes mellitus", and
    "type 2 diabetes mellitus" all reduce to the same content-word set.
    That means a bare, type-less query is equally "close" to both Type 1
    and Type 2 ICD-10 entries, and whichever one a fuzzy/embedding
    matcher ranks first is essentially arbitrary — asserting the wrong
    type is a different diagnosis, not just a less precise one, so this
    blocks that specific failure mode rather than relying on string
    closeness to pick correctly.
    """
    desc_lower = description.lower()
    has_type1 = "type 1" in desc_lower or "type i " in desc_lower
    has_type2 = "type 2" in desc_lower or "type ii" in desc_lower
    if not (has_type1 or has_type2):
        return False

    query_lower = query.lower()
    query_has_type1 = "type 1" in query_lower or "type i " in f"{query_lower} "
    query_has_type2 = "type 2" in query_lower or "type ii" in query_lower

    if has_type1 and query_has_type1:
        return False
    return not (has_type2 and query_has_type2)


def _is_external_cause_code(icd10_code: str) -> bool:
    """True if `icd10_code` is an ICD-10 "external causes" code (V00-Y99).

    Chapter 20 codes describe HOW an injury happened (a fall, a
    collision, an accident) and are only ever valid as a supplementary
    code paired with an actual injury diagnosis — never a standalone
    match for a SYMPTOM or DISEASE entity, which is all this mapper is
    ever asked to map (see ``map_entities``). Fuzzy/embedding matching
    can still rank one highly off an unrelated word that happens to
    appear in its description (e.g. "stairs", from "worse with
    stairs", matching "Fall on stairs" — fabricating a fall that was
    never mentioned).
    """
    if not icd10_code:
        return False
    return icd10_code[0].upper() in "VWXY"


# ── Output dataclass ──────────────────────────────────────────────

@dataclass
class ICD10Match:
    """A single ICD-10 code candidate for an extracted entity.

    Attributes:
        icd10_code   : ICD-10-CM code (e.g. "I10").
        description  : Human-readable description.
        confidence   : Match confidence (0.0–1.0).
        match_method : How the match was found:
                       "exact", "fuzzy", "embedding", or "none".
        rank         : Position in the top-k list (1 = best).
    """

    icd10_code:   str
    description:  str
    confidence:   float
    match_method: str
    rank:         int = 1

    def to_dict(self) -> dict:
        """Serialise to a plain dictionary for JSON responses."""
        return {
            "icd10_code":   self.icd10_code,
            "description":  self.description,
            "confidence":   round(self.confidence, 3),
            "match_method": self.match_method,
            "rank":         self.rank,
        }


class ICD10Mapper:
    """Maps clinical entity text to ICD-10-CM codes.

    Loads the ICD-10 reference table once at construction time.
    The embedding index is built lazily on first use — it takes
    a few seconds and is cached in memory for subsequent calls.

    Args:
        icd10_path : Path to the ICD-10 CSV file.
                     Defaults to the processed parquet if available,
                     falls back to the raw CSV.
        top_k      : Maximum number of candidate codes to return
                     per entity.

    Example::

        mapper   = ICD10Mapper()
        matches  = mapper.map("hypertension")
        for m in matches:
            print(m.icd10_code, m.description, m.confidence)
        # → I10  Essential (primary) hypertension  1.0
    """

    def __init__(
        self,
        icd10_path: Path | None = None,
        top_k: int = ICD10Config.top_k,
    ) -> None:
        self._top_k       = top_k
        self._df          = self._load_reference_table(icd10_path)
        self._lookup, self._stripped_lookup = self._build_lookup(self._df)
        # Lowercased once up front — fuzz.token_set_ratio is case-sensitive,
        # and the query is always lowercased before matching, so comparing
        # against original-case descriptions silently degraded every score.
        self._descriptions_lower = self._df["description"].str.lower().tolist()
        self._embeddings  = None   # built lazily
        self._embed_model = None   # loaded lazily
        # Set True only after a real load attempt fails (import error,
        # or no local cache + no network). False until then — covers
        # both "never attempted yet" and "loaded successfully".
        self._embedding_unavailable = False
        logger.info(
            "ICD10Mapper ready: %d codes loaded", len(self._df)
        )

    @property
    def embedding_available(self) -> bool:
        """Whether semantic (embedding-based) ICD-10 matching is usable.

        ``True`` until a real load attempt fails. Exact and fuzzy
        matching are unaffected either way — this only governs the
        fallback used for entity text that doesn't match any ICD-10
        description lexically.
        """
        return not self._embedding_unavailable

    # ── Data loading ──────────────────────────────────────────────

    def _load_reference_table(self, path: Path | None) -> pd.DataFrame:
        """Load the ICD-10 reference table from parquet or CSV.

        The ETL pipeline saves a parquet copy to data/processed/
        which is faster to load.  Falls back to the raw CSV if
        the processed version is not yet available.

        Args:
            path: Explicit path override, or None to auto-detect.

        Returns:
            DataFrame with columns ``icd10_code`` and ``description``.

        Raises:
            FileNotFoundError: If no ICD-10 file can be found.
        """
        # Priority: explicit path > processed parquet > raw CSV
        candidates = [
            p for p in [
                path,
                Paths.processed / "icd10_codes.parquet",
                Paths.icd10_csv,
            ]
            if p is not None and p.exists()
        ]

        if not candidates:
            raise FileNotFoundError(
                "ICD-10 reference file not found.\n"
                "Run the ETL pipeline first:\n"
                "  python -m src.etl.pipeline"
            )

        chosen = candidates[0]
        logger.debug("Loading ICD-10 from %s", chosen)

        df = (
            pd.read_parquet(chosen)
            if chosen.suffix == ".parquet"
            else pd.read_csv(chosen, dtype=str)
        )

        # Accept 'code' (from fetch_icd10.py) or 'icd10_code' (processed parquet)
        if "code" in df.columns and "icd10_code" not in df.columns:
            df = df.rename(columns={"code": "icd10_code"})

        # Ensure we have the columns we need
        required = {"icd10_code", "description"}
        missing  = required - set(df.columns)
        if missing:
            raise ValueError(
                f"ICD-10 file is missing columns: {missing}. "
                f"Got: {list(df.columns)}"
            )

        # Normalise: strip whitespace, uppercase codes
        df["icd10_code"]   = df["icd10_code"].str.strip().str.upper()
        df["description"]  = df["description"].str.strip()
        df = df.dropna(subset=["icd10_code", "description"])

        return df.reset_index(drop=True)

    def _build_lookup(
        self, df: pd.DataFrame
    ) -> tuple[dict[str, tuple[str, str]], dict[str, tuple[str, str]]]:
        """Build O(1) exact-match dictionaries from the ICD-10 table.

        Builds two lookups:
          - Primary: lowercase description → (code, original_description).
            Also indexes the code itself so "I10" → hypertension works.
          - Stripped: same, but with parenthetical qualifiers removed
            (see :func:`_strip_parens`).  Only populated when stripping
            actually changes the key, and on collision keeps the
            shortest original description (usually the more general /
            primary code).

        Args:
            df: ICD-10 reference DataFrame.

        Returns:
            Tuple of ``(primary_lookup, stripped_lookup)``.
        """
        lookup = {}
        stripped_lookup: dict[str, tuple[str, str]] = {}

        for _, row in df.iterrows():
            code = row["icd10_code"]
            desc = row["description"]
            key  = desc.lower().strip()

            lookup[key] = (code, desc)
            # Also index by code for reverse lookups
            lookup[code.lower()] = (code, desc)

            stripped_key = _strip_parens(key)
            if stripped_key and stripped_key != key:
                existing = stripped_lookup.get(stripped_key)
                if existing is None or len(desc) < len(existing[1]):
                    stripped_lookup[stripped_key] = (code, desc)

        return lookup, stripped_lookup

    # ── Mapping methods ───────────────────────────────────────────

    def map(self, entity_text: str) -> list[ICD10Match]:
        """Map an entity text string to ICD-10 candidates.

        Tries exact → fuzzy → embedding in order, stopping at the
        first method that returns a result above its threshold.

        Args:
            entity_text: Extracted entity text (e.g. "hypertension").
                         Cleaned text produces better results.

        Returns:
            List of :class:`ICD10Match` objects ordered by confidence.
            Empty list if no match is found above any threshold.
        """
        if not entity_text or not entity_text.strip():
            return []

        normalised = entity_text.lower().strip()

        # Step 1: exact match — O(1), always try first
        exact = self._exact_match(normalised)
        exact = [m for m in exact if not _is_external_cause_code(m.icd10_code)]
        if exact:
            return exact

        # Step 2: fuzzy match — ~10ms, good for spelling variants
        fuzzy = self._fuzzy_match(normalised)
        fuzzy = [
            m for m in fuzzy
            if not _is_unsafe_type_assertion(normalised, m.description)
            and not _is_external_cause_code(m.icd10_code)
        ]
        if fuzzy:
            return fuzzy

        # Step 3: embedding match — ~100ms, for novel phrasing
        embedding = self._embedding_match(normalised)
        embedding = [
            m for m in embedding
            if not _is_unsafe_type_assertion(normalised, m.description)
            and not _is_external_cause_code(m.icd10_code)
        ]
        return embedding

    def map_entities(
        self, entities: list
    ) -> dict[str, list[ICD10Match]]:
        """Map a list of Entity objects to ICD-10 candidates.

        Convenience wrapper around map() that processes a list
        of Entity objects from the NER pipeline.

        Args:
            entities: List of :class:`src.nlp.ner.Entity` objects.

        Returns:
            Dict mapping entity text → list of ICD10Match objects.
        """
        results = {}
        for entity in entities:
            # Only map disease and symptom entities — medications,
            # procedures, and anatomy terms have their own code systems
            if entity.label in ("DISEASE", "SYMPTOM"):
                matches = self.map(entity.text)
                if matches:
                    results[entity.text] = matches
        return results

    # ── Match implementations ─────────────────────────────────────

    def _exact_match(self, text: str) -> list[ICD10Match]:
        """Direct dictionary lookup for exact description matches.

        Tries the primary lookup first, then falls back to matching
        against descriptions with parenthetical qualifiers stripped
        (see :func:`_strip_parens`) — this lets common, simply-phrased
        terms like "essential hypertension" match ``I10`` directly
        instead of falling through to noisy fuzzy matching.

        Args:
            text: Lowercase, stripped entity text.

        Returns:
            List with one match at confidence=1.0, or empty list.
        """
        result = self._lookup.get(text) or self._stripped_lookup.get(_strip_parens(text))
        if result:
            code, description = result
            return [ICD10Match(
                icd10_code   = code,
                description  = description,
                confidence   = 1.0,
                match_method = "exact",
                rank         = 1,
            )]
        return []

    def _fuzzy_match(self, text: str) -> list[ICD10Match]:
        """Fuzzy string matching against ICD-10 descriptions.

        Uses rapidfuzz's token_set_ratio, which scores a query as a
        perfect match against any description that contains all of
        the query's words plus extras — exactly the "base condition
        plus complication/qualifier" pattern ICD-10 uses heavily
        (e.g. "Type 2 diabetes mellitus {without complications,
        with hyperglycemia, with foot ulcer, ...}"). That produces
        many tied top-scoring candidates, so the actual selection
        happens via two tiebreakers, applied in order:

          1. Fewest "extra" clinically-meaningful words beyond the
             query (see :func:`_content_words`) — prefers the
             general/default code over a specific complication when
             the query didn't mention one.
          2. Shortest description — a final tiebreak between
             phrasings that reduce to the same extra-word count
             (e.g. "without complications" vs "with other specified
             complication" both strip to zero extra words).

        Without tiebreaker 1, "essential hypertension" or "unspecified
        pneumonia" rank an unrelated same-length term above the
        correct code purely because it scores closer on raw string
        similarity. Without tiebreaker 2, "type 2 diabetes mellitus"
        picks an arbitrary complication code instead of E11.9.

        A full scan over all descriptions is used rather than a small
        top-N candidate pool — ties at the top score can number in
        the dozens (86 for the diabetes example above), and a small
        pool risks truncating before reaching the correct candidate.
        rapidfuzz is fast enough that this costs well under a second
        even across 74k descriptions.

        Args:
            text: Lowercase, stripped entity text.

        Returns:
            Top-k matches above the fuzzy threshold, or empty list
            (callers should fall through to the embedding match).
        """
        try:
            from rapidfuzz import fuzz, process
        except ImportError:
            logger.warning(
                "rapidfuzz not installed — fuzzy matching disabled. "
                "Run: pip install rapidfuzz"
            )
            return []

        query_words = _content_words(text)

        candidates = process.extract(
            text,
            self._descriptions_lower,
            scorer = fuzz.token_set_ratio,
            limit  = len(self._descriptions_lower),
        )

        qualifying = []
        for desc, score, idx in candidates:
            if score < ICD10Config.fuzzy_threshold:
                break  # sorted descending — nothing further clears the bar
            desc_words = _content_words(desc)
            if query_words and not query_words.issubset(desc_words):
                continue  # candidate doesn't actually contain the query's terms
            extra = len(desc_words - query_words)
            qualifying.append((extra, len(desc), -score, idx, score))

        if not qualifying:
            return []

        qualifying.sort()

        # Only auto-resolve when the best candidate is a true generic/
        # default equivalent (zero extra clinically-meaningful words).
        # If even the closest candidate still requires adding a genuinely
        # distinguishing word (e.g. bare "hypertension" only ever appears
        # alongside "portal", "renal", "essential" etc. — ICD-10 has no
        # plain "Hypertension, unspecified" code), guessing one of those
        # specific variants would silently produce a wrong diagnosis code.
        # Defer to the embedding step instead, same as if fuzzy found
        # nothing at all.
        if qualifying[0][0] != 0:
            return []

        matches = []
        for _extra, _len, _neg_score, idx, score in qualifying[: self._top_k]:
            row = self._df.iloc[idx]
            matches.append(ICD10Match(
                icd10_code   = row["icd10_code"],
                description  = row["description"],
                confidence   = score / 100.0,
                match_method = "fuzzy",
                rank         = len(matches) + 1,
            ))

        return matches

    def _embedding_cache_path(self) -> Path:
        """Return the on-disk cache path for the ICD-10 embedding index."""
        return Paths.processed / "icd10_embeddings.pt"

    def _load_or_build_embedding_index(self):
        """Load the cached ICD-10 embedding index from disk, or build it.

        Encoding 74k descriptions through BERT on CPU takes 15-40+
        minutes, so the result is cached to disk and only rebuilt if
        the ICD-10 table size changes (e.g. a new CMS release).

        Returns:
            A tensor of shape (n_descriptions, embedding_dim).
        """
        import torch

        cache_path = self._embedding_cache_path()
        if cache_path.exists():
            cached = torch.load(cache_path)
            if cached.shape[0] == len(self._df):
                logger.info(
                    "Loaded cached ICD-10 embedding index from %s (%d vectors)",
                    cache_path, cached.shape[0],
                )
                return cached
            logger.info(
                "Cached embedding index size (%d) does not match current "
                "ICD-10 table (%d) — rebuilding.",
                cached.shape[0], len(self._df),
            )

        # Try downloading a precomputed copy from HF Hub before resorting
        # to a 15-40+ minute CPU rebuild — e.g. a fresh deployment
        # container that doesn't bundle data/processed/ locally (the
        # file is too large to commit to git).
        from src.utils.config import ModelConfig
        hub_repo = ModelConfig.icd10_embeddings_hf_hub_fallback
        if hub_repo:
            try:
                from huggingface_hub import hf_hub_download
                logger.info(
                    "No local embedding cache — downloading precomputed "
                    "index from HF Hub (%s) instead of rebuilding from "
                    "scratch.", hub_repo,
                )
                downloaded_path = hf_hub_download(
                    repo_id  = hub_repo,
                    repo_type = "dataset",
                    filename = "icd10_embeddings.pt",
                )
                cached = torch.load(downloaded_path)
                if cached.shape[0] == len(self._df):
                    cache_path.parent.mkdir(parents=True, exist_ok=True)
                    torch.save(cached, cache_path)
                    logger.info(
                        "Loaded embedding index from HF Hub (%d vectors), "
                        "cached locally to %s", cached.shape[0], cache_path,
                    )
                    return cached
                logger.warning(
                    "HF Hub embedding index size (%d) does not match "
                    "current ICD-10 table (%d) — rebuilding instead.",
                    cached.shape[0], len(self._df),
                )
            except Exception as exc:
                logger.warning(
                    "Could not download embedding index from HF Hub "
                    "(%s) — rebuilding from scratch instead.", exc,
                )

        logger.info(
            "Building ICD-10 embedding index (%d descriptions) — "
            "one-time cost on CPU, can take 15-40+ minutes. "
            "Progress bar below; result will be cached to disk.",
            len(self._df),
        )
        embeddings = self._embed_model.encode(
            self._df["description"].tolist(),
            batch_size        = 128,
            show_progress_bar = True,
            convert_to_tensor = True,
        )
        cache_path.parent.mkdir(parents=True, exist_ok=True)
        torch.save(embeddings, cache_path)
        logger.info("Embedding index ready and cached to %s ✓", cache_path)
        return embeddings

    def _embedding_match(self, text: str) -> list[ICD10Match]:
        """Semantic similarity matching using sentence embeddings.

        Encodes the entity text and all ICD-10 descriptions into
        dense vectors, then finds the closest descriptions by
        cosine similarity.

        The embedding index is built once and cached both in memory
        and on disk (see :meth:`_load_or_build_embedding_index`).
        First call ever takes 15-40+ minutes; every call after that
        (including in future notebook sessions) loads from disk in
        under a second.

        Args:
            text: Lowercase, stripped entity text.

        Returns:
            Top-k matches above the embedding threshold, or empty list.
        """
        # Once a real attempt has failed, don't keep retrying a known-
        # broken import/load on every single call — fail fast instead.
        if self._embedding_unavailable:
            return []

        try:
            from sentence_transformers import SentenceTransformer, util

            # Load the embedding model once and cache it.
            # Try the local cache first — sentence-transformers otherwise
            # always does a handful of network round-trips (HEAD requests
            # for adapter_config.json etc.) even when the model is fully
            # cached, which crashes the whole pipeline on any transient
            # network blip. Only fall back to a live download if nothing
            # is cached yet.
            if self._embed_model is None:
                from src.utils.config import ModelConfig, force_offline_hf_env
                model_name = ModelConfig.embedding_model
                try:
                    with force_offline_hf_env():
                        self._embed_model = SentenceTransformer(
                            model_name, local_files_only=True
                        )
                    logger.info(
                        "Loaded embedding model from local cache (offline): %s",
                        model_name,
                    )
                except Exception:
                    logger.info(
                        "Embedding model not found in local cache — "
                        "downloading from Hugging Face Hub: %s (first call only)",
                        model_name,
                    )
                    self._embed_model = SentenceTransformer(model_name)

            # Build (or load from disk cache) the ICD-10 embedding index.
            # The cache is saved from whatever device originally built it
            # (often CPU) -- move it onto the embedding model's device so
            # cos_sim() below never sees a CPU/CUDA tensor mismatch.
            if self._embeddings is None:
                self._embeddings = self._load_or_build_embedding_index()
                self._embeddings = self._embeddings.to(self._embed_model.device)

            query_embedding = self._embed_model.encode(
                text, convert_to_tensor=True
            )
            scores = util.cos_sim(query_embedding, self._embeddings)[0]
            top_indices = scores.argsort(descending=True)[: self._top_k * 2]

            matches = []
            for idx in top_indices:
                score = float(scores[idx])
                if score < ICD10Config.embedding_threshold:
                    break
                row = self._df.iloc[int(idx)]
                matches.append(ICD10Match(
                    icd10_code   = row["icd10_code"],
                    description  = row["description"],
                    confidence   = score,
                    match_method = "embedding",
                    rank         = len(matches) + 1,
                ))
                if len(matches) >= self._top_k:
                    break

            return matches

        except ImportError:
            self._embedding_unavailable = True
            logger.warning(
                "sentence-transformers not installed — "
                "embedding fallback disabled. "
                "Run: pip install sentence-transformers"
            )
            return []
        except Exception as exc:
            # Never let an embedding-fallback failure crash the whole
            # /notes/analyse request — exact/fuzzy matching still works.
            # Mark unavailable so we don't keep retrying a known-broken
            # load (e.g. resource contention during import) on every call.
            self._embedding_unavailable = True
            logger.warning(
                "Embedding fallback failed and is now disabled for this "
                "process (%s: %s). Exact/fuzzy ICD-10 matching is "
                "unaffected; restart the process to retry.",
                type(exc).__name__, exc,
            )
            return []

    # ── Utility ───────────────────────────────────────────────────

    def describe(self, icd10_code: str) -> str | None:
        """Return the description for an ICD-10 code.

        Args:
            icd10_code: ICD-10-CM code (case-insensitive).

        Returns:
            Description string, or None if the code is not found.
        """
        result = self._lookup.get(icd10_code.upper().strip())
        if result:
            return result[1]
        # Try the lower-case key
        result = self._lookup.get(icd10_code.lower().strip())
        return result[1] if result else None