"""Value comparison and refusal detection for gold vs. prediction cells.

Used by scoring (`evaluation.scoring`), the refusal cleaning workflow (`evaluation.cleaning`) and
the analysis package. Refusal detection is heuristic and English-only.
"""

import re
from datetime import date, datetime
from enum import Enum
from typing import get_args

from legex.models.classification import Classification

# Columns that carry no label to score against.
NON_LABEL_COLUMNS = {
    "case_id", "link", "full_text", "translated_full_text", "comment", "original_input",
    "model", "error", "inference_date",
}

# Sentinels that mean "no value".
EMPTY_LITERALS = frozenset({"", "none", "null", "nan"})

# Real controlled answers that must never be read as empty or as a refusal.
VALID_TOKENS = frozenset({"nonpecuniary", "no_allocation_possible"})

# Columns whose Classification field is numeric / a date. Loose parsing applies only to these.
NUMERIC_COLUMNS = frozenset(
    (info.alias or name)
    for name, info in Classification.model_fields.items()
    if any(a is int or a is float for a in (get_args(info.annotation) or (info.annotation,)))
)
DATE_COLUMNS = frozenset(
    (info.alias or name)
    for name, info in Classification.model_fields.items()
    if any(a is date for a in (get_args(info.annotation) or (info.annotation,)))
)

BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn")

_NUM_TOKEN_RE = re.compile(r"-?\d[\d',. ]*")
_DATE_SEP_RE = re.compile(r"[_/.\s]+")
_DATE_FALLBACK_FORMATS = ("%d-%m-%Y", "%m-%d-%Y", "%Y%m%d")
_ISO_DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$")


def is_label_column(name: str) -> bool:
    if not name or name.lower() in NON_LABEL_COLUMNS:
        return False
    return not name.startswith("Currency_")


def normalise(value: object) -> str:
    """Make gold and prediction cells comparable as strings."""
    if value is None:
        return ""
    if isinstance(value, datetime):  # Excel stores dates as datetimes
        return value.date().isoformat()
    if isinstance(value, date):
        return value.isoformat()
    if isinstance(value, float):
        if value != value:  # NaN
            return ""
        if value.is_integer():
            return str(int(value))
    s = str(value).strip()
    if s.lower() in EMPTY_LITERALS:
        return ""
    return s


class RefusalReason(str, Enum):
    BARE_TOKEN = "bare non-value token"
    REFUSAL_PHRASE = "refusal phrase"
    LEADING_NONE = "leading None/N-A + explanation"
    TRAILING_NA = "prose ending in N/A"


_REFUSAL_BARE = frozenset({
    "n/a", "na", "n.a.", "unknown", "undetermined", "tbd", "-", "—",
    "not available", "not specified", "not stated", "not applicable",
    "not provided", "not mentioned", "not determinable", "not disclosed",
})
_REFUSAL_PHRASE = re.compile(
    r"not\s+(?:stated|mentioned|specified|provided|indicated|available|determinable|"
    r"applicable|found|listed|given|disclosed|reported|clearly\s+\w+)"
    r"|(?:cannot|could\s+not|can'?t|couldn'?t|unable\s+to|not\s+able\s+to)\s+(?:be\s+)?"
    r"(?:determine|determined|calculate|calculated|establish|established|ascertain|found|identif)"
    r"|does\s+not\s+(?:state|contain|specify|mention|provide|indicate|list|disclose|appear)"
    r"|no\s+(?:specific|explicit|clear)\s+\w+"
    r"|insufficient\s+(?:information|data|detail)",
    re.I,
)
_REFUSAL_LEADING = re.compile(
    r"^\s*(?:none|n/?a|null|unknown|not\s+available|not\s+specified|not\s+stated)\b", re.I
)
_REFUSAL_TRAILING_NA = re.compile(r"(?:^|[\s\n>)\].])n/?a\.?\s*$", re.I)
_PROSE = re.compile(r"[A-Za-z]{3,}\s+[A-Za-z]{3,}")  # two consecutive words → a sentence


def refusal_reason(value: object, column: str | None = None) -> RefusalReason | None:
    """Classify `value` as a long-form refusal, or None if it is a usable answer.

    A refusal must (a) match a refusal marker and (b) carry no extractable value of
    the expected type — if the loose parser can still pull a number/date out of the
    prose it is an answer-with-explanation, which the scorer handles, not a refusal.
    """
    if value is None:
        return None
    s = str(value).strip()
    if not s or s.lower() in VALID_TOKENS:
        return None

    bare = s.lower().strip(" .") in _REFUSAL_BARE
    if bare:
        reason = RefusalReason.BARE_TOKEN
    elif _REFUSAL_LEADING.match(s) and _PROSE.search(s):
        reason = RefusalReason.LEADING_NONE
    elif _REFUSAL_TRAILING_NA.search(s) and _PROSE.search(s):
        reason = RefusalReason.TRAILING_NA
    elif _REFUSAL_PHRASE.search(s):
        reason = RefusalReason.REFUSAL_PHRASE
    else:
        return None

    if column in NUMERIC_COLUMNS and _numeric_value(s, column) is not None:
        return None
    if column in DATE_COLUMNS and _parse_loose_date(s) is not None:
        return None
    return reason


def is_refusal(value: object, column: str | None = None) -> bool:
    return refusal_reason(value, column) is not None


_ISIC_COLUMNS = frozenset({
    "plaintiff_no1_ISIC1_industry_category",
    "defendant_no1_ISIC1_industry_category",
})
_ISIC_TOKEN = re.compile(r"^[a-v]_[a-z_]+$")


def _canonical_number(n: float) -> str:
    return str(int(n)) if float(n).is_integer() else repr(float(n))


_DATE_CLEAN_RE = re.compile(r"[\d/._\-\s]+")  # a date with only separator noise (no citations/prose)
_CITATION_RE = re.compile(r"\[\d+\]")  # Harvey citation markers, anywhere

# Match prose with dates
_MONTHS = {m: i + 1 for i, m in enumerate((
    "january", "february", "march", "april", "may", "june",
    "july", "august", "september", "october", "november", "december",
))}
_PROSE_DATE_RE = re.compile(
    r"^([A-Za-z]+)\s+(\d{1,2}),?\s+(\d{4})$|^(\d{1,2})\.?\s+([A-Za-z]+)\s+(\d{4})$"
)

# A cell that is exactly one number decorated with a currency token and/or a
# Swiss ".--" suffix ("CHF 20'000.--", "5.000 €").
_CUR = r"(?:[€$£¥₹₩₪₾]|[A-Z]{2,3}\.?|(?i:fr|frs|euros?|francs?|dollars?|pounds?)\.?)"
_DECORATED_AMOUNT_RE = re.compile(
    rf"^\s*{_CUR}?\s*(?P<num>-?[\d'\u2019,.\u00a0\u202f ]*\d)\s*{_CUR}?"
    rf"\s*(?:[.,]?\s*(?:-{{1,2}}|\u2013|\u2014))?\s*$"
)
# '5.000' — 5000 (EU) or 5.0 (US)? '538,183' — 538183 (US) or 538.183 (EU)?
# Either way ambiguous without a decimal part → a human decides.
_AMBIGUOUS_GROUPED_RE = re.compile(r"^-?\d{1,3}([.,]\d{3})+$")


def _strict_number(s: str) -> float | None:
    """A single clean number with formatting noise only (apostrophe/space/EU-US separators).

    Returns None for anything else — prose, citations (`[1]`), newlines, multiple numbers —
    so recovery never scrapes a stray digit out of junk."""
    t = s.strip().replace("'", "").replace("’", "").replace(" ", "").replace(" ", "")
    if not t or re.search(r"[^\d.,+\-]", t):
        return None
    if re.fullmatch(r"-?\d{1,3}(\.\d{3})+,\d+", t):        # 1.000.000,50 (EU)
        t = t.replace(".", "").replace(",", ".")
    elif re.fullmatch(r"-?\d+,\d+", t):                    # 1234,56
        t = t.replace(",", ".")
    elif re.fullmatch(r"-?\d{1,3}(,\d{3})+(\.\d+)?", t):   # 1,000,000.50 (US)
        t = t.replace(",", "")
    try:
        f = float(t)
    except ValueError:
        return None
    return f if f == f and f not in (float("inf"), float("-inf")) else None


def _strip_noise(s: str) -> str:
    """Remove citation markers (`[1]`, anywhere) and markdown backticks/fences, then trim."""
    return _CITATION_RE.sub(" ", s).replace("`", " ").strip()


def _parse_prose_date(s: str) -> date | None:
    """A cell that is exactly one prose date with an English month name, else None."""
    m = _PROSE_DATE_RE.match(s.strip())
    if not m:
        return None
    month_name, day, year = (m.group(1), m.group(2), m.group(3)) if m.group(1) else (
        m.group(5), m.group(4), m.group(6))
    month = _MONTHS.get(month_name.lower())
    if month is None:
        return None
    try:
        return date(int(year), month, int(day))
    except ValueError:
        return None


def _decorated_amount(s: str) -> float | None:
    """A cell that is exactly one currency-decorated clean number, else None.

    Grouped integers without a decimal part (`'5.000 €'`, `'538,183 euro'`) are
    left to a human — thousands- vs decimal-separator is ambiguous there."""
    m = _DECORATED_AMOUNT_RE.match(s)
    if not m:
        return None
    num = m.group("num")
    if _AMBIGUOUS_GROUPED_RE.match(num.strip()):
        return None
    return _strict_number(num)


def _review_reason(s: str, column: str | None) -> str:
    r = refusal_reason(s, column)
    return r.value if r else "no recoverable value"


def resolve(value: object, column: str | None) -> tuple[str, str, str | None]:
    """Resolve a cell to ``(status, canonical_value, reason)``.

    status is one of:
      * ``valid``     — a good value already (kept as-is; also the case for empty),
      * ``recovered`` — a value parseable from prose, canonicalised (score-neutral),
      * ``review``    — non-empty but neither valid nor recoverable → human review.

    Recovery reuses the scorer's own loose parsers so the canonical value scores
    identically to the raw one (`values_agree` stays a tolerant safety net).
    """
    s = normalise(value)
    if not s:
        return "valid", "", None
    if column in NUMERIC_COLUMNS:
        if column == "dispute_value_nominal" and s.lower() == "nonpecuniary":
            return "valid", "nonpecuniary", None
        if _try_float(s) is not None:                     # already a clean number
            return "valid", s, None
        # Recover a single number once citations/backticks/whitespace are stripped — never a
        # digit scraped from surviving prose, which would write garbage.
        cand = _strip_noise(s)
        n = _strict_number(cand)
        if n is not None:
            return "recovered", _canonical_number(n), None
        n = _decorated_amount(cand)
        if n is not None:
            return "recovered", _canonical_number(n), None
        return "review", s, _review_reason(s, column)
    if column in DATE_COLUMNS:
        cand = _strip_noise(s)
        if _ISO_DATE_RE.match(cand):
            return ("valid" if cand == s else "recovered"), cand, None
        if _DATE_CLEAN_RE.fullmatch(cand) and (d := _parse_loose_date(cand)) is not None:
            return "recovered", d.isoformat(), None
        if (d := _parse_prose_date(cand)) is not None:
            return "recovered", d.isoformat(), None
        return "review", s, _review_reason(s, column)
    if column in _ISIC_COLUMNS:
        low = s.lower()
        if low == "no_allocation_possible" or _ISIC_TOKEN.match(low):
            # Canonicalise case: gold and the comparator are exact-match.
            return ("valid", s, None) if s == low else ("recovered", low, None)
        return "review", s, "unrecognized ISIC code"
    r = refusal_reason(s, column)
    if r is not None:
        return "review", s, r.value
    return "valid", s, None


def _try_float(s: str) -> float | None:
    if not s:
        return None
    try:
        return float(s)
    except ValueError:
        return None


def _parse_loose_number(s: str) -> float | None:
    """First numeric token from `s`, tolerating apostrophe/space thousand separators
    (`20'000`), EU decimal commas (`1.000,50`), and trailing prose. None if no digit."""
    if not s:
        return None
    m = _NUM_TOKEN_RE.search(s)
    if not m:
        return None
    tok = m.group(0).strip().rstrip(",.' ")
    if not tok:
        return None
    cleaned = tok.replace("'", "").replace(" ", "")
    if "," in cleaned and "." in cleaned:
        if cleaned.rfind(",") > cleaned.rfind("."):
            cleaned = cleaned.replace(".", "").replace(",", ".")
        else:
            cleaned = cleaned.replace(",", "")
    elif "," in cleaned:
        parts = cleaned.split(",")
        if len(parts) == 2 and 1 <= len(parts[1]) <= 2:
            cleaned = parts[0] + "." + parts[1]
        else:
            cleaned = cleaned.replace(",", "")
    try:
        return float(cleaned)
    except ValueError:
        return None


def _numeric_value(s: str, column: str | None) -> float | None:
    n = _try_float(s)
    if n is not None:
        return n
    if column in NUMERIC_COLUMNS:
        return _parse_loose_number(s)
    return None


def _parse_loose_date(s: str) -> date | None:
    """Parse a date, treating `_`, `/`, `.`, whitespace as `-`; ISO plus a small fallback."""
    if not s:
        return None
    t = _DATE_SEP_RE.sub("-", s.strip()).strip("-")
    if not t:
        return None
    try:
        return date.fromisoformat(t)
    except ValueError:
        pass
    for fmt in _DATE_FALLBACK_FORMATS:
        try:
            return datetime.strptime(t, fmt).date()
        except ValueError:
            continue
    return None


def values_agree(gv: str, pv: str, column: str | None = None) -> bool:
    """Compare normalised gold vs prediction cell values."""
    if gv == pv:
        return True
    # Controlled tokens carry no case information: annotators occasionally capitalise
    # them (gold "Nonpecuniary" vs the schema literal "nonpecuniary").
    if gv.lower() == pv.lower() and pv.lower() in VALID_TOKENS:
        return True
    if column in DATE_COLUMNS:
        gd, pd_ = _parse_loose_date(gv), _parse_loose_date(pv)
        if gd is not None and pd_ is not None and gd == pd_:
            return True
    gn = _numeric_value(gv, column)
    if gn is not None and gn == 0:
        if not pv:
            return True
        pn = _numeric_value(pv, column)
        return pn is not None and pn == 0
    if gn is not None:
        pn = _numeric_value(pv, column)
        if pn is not None:
            return gn == pn
    return False


def classify_cell(gv: str, pv: str, column: str | None) -> str:
    """Bucket a (gold, pred) cell. `gv`/`pv` must already be `normalise()`-d.

    tp - gold filled, values agree; mismatch - both filled, differ; missed - gold
    filled, pred empty; hallucinated - gold empty, pred filled; tn - both empty.
    """
    gold_filled = bool(gv)
    if values_agree(gv, pv, column):
        return "tp" if gold_filled else "tn"
    if gold_filled and bool(pv):
        return "mismatch"
    if gold_filled:
        return "missed"
    return "hallucinated"


def derived(c: dict[str, int]) -> tuple[float, float, float]:
    """Per-column precision, recall, F1 from a bucket counter."""
    tp, mism, miss, hallu = c["tp"], c["mismatch"], c["missed"], c["hallucinated"]
    p_denom = tp + mism + hallu
    r_denom = tp + mism + miss
    p = tp / p_denom if p_denom else 0.0
    r = tp / r_denom if r_denom else 0.0
    f1 = 2 * p * r / (p + r) if (p + r) else 0.0
    return p, r, f1