#!/usr/bin/env python3
"""
Script Fidelity Rate (SFR) metric for multilingual ASR evaluation.

SFR = fraction of non-whitespace, non-punctuation characters in the ASR hypothesis
that belong to the expected target script block(s).

A value of 1.0 means every output character is in the correct script.
A value near 0 means the model produced output entirely in the wrong script —
a condition termed *script collapse* (e.g. Latin transliteration or Arabic when
Devanagari was expected).

SFR is reference-free: it requires only the hypothesis string and a target
language identifier, not a ground-truth transcription.  This makes it usable
as a deployment-time audit metric even when no labelled data is available.

This generalises a Pashto-specific script-check heuristic to ten target
languages and scripts.

Usage
-----
>>> from script_fidelity import compute_sfr, SCRIPT_CONFIGS
>>> sfr = compute_sfr("کابل کې ښه هوا ده", "pashto")
>>> assert sfr == 1.0

>>> sfr = compute_sfr("this is entirely Latin", "pashto")
>>> assert sfr == 0.0
"""

import re
import unicodedata
from dataclasses import dataclass, field
from typing import Optional


# ── UNICODE BLOCK RANGES ──────────────────────────────────────────────────────
# Each language config lists one or more (lo, hi) inclusive code-point ranges
# that define the "target script" for that language.  An output character is
# in-script if it falls in ANY of the target ranges OR is in the optional
# unique_codepoints set.

@dataclass
class ScriptConfig:
    """Configuration for a single target script."""
    name: str                          # human-readable label
    ranges: list[tuple[int, int]]      # [(lo, hi), ...] inclusive
    unique_codepoints: set[str] = field(default_factory=set)
    # Optional: codepoints that CONFIRM the script even if below 70% threshold.
    # Useful for Pashto which shares Arabic script with Urdu/Dari but has
    # language-unique glyphs.
    fleurs_code: str = ''              # google/fleurs dataset code
    mms_lang: list[str] = field(default_factory=list)   # MMS language adapter IDs


# Pashto-unique codepoints (not found in standard Arabic or Urdu):
#   ټ U+0679  ډ U+0688  ڼ U+06BC  ړ U+0693  ښ U+069A  ږ U+0696  ځ U+0681
#   ۍ U+06CD  ګ U+06AB  ې U+06D0  ۀ U+06C0  څ U+0685
_PASHTO_UNIQUE = set('ټډڼړښږځۍګېۀڅ')

SCRIPT_CONFIGS: dict[str, ScriptConfig] = {

    # Pashto — Perso-Arabic + Pashto-unique glyphs
    'pashto': ScriptConfig(
        name='Pashto (Perso-Arabic)',
        ranges=[
            (0x0600, 0x06FF),   # Arabic block (covers all Perso-Arabic letters)
            (0x0750, 0x077F),   # Arabic Supplement
            (0xFB50, 0xFDFF),   # Arabic Presentation Forms-A
            (0xFE70, 0xFEFF),   # Arabic Presentation Forms-B
        ],
        unique_codepoints=_PASHTO_UNIQUE,
        fleurs_code='ps_af',
        mms_lang=['pbu', 'pbt', 'pus'],
    ),

    # Urdu — Perso-Arabic (shares blocks with Pashto; no unique codepoints beyond Urdu-specific)
    # Urdu-unique: ں U+06BA  ٹ U+0679  ڈ U+0688  ڑ U+0691  ے U+06D2
    'urdu': ScriptConfig(
        name='Urdu (Perso-Arabic)',
        ranges=[
            (0x0600, 0x06FF),
            (0x0750, 0x077F),
            (0xFB50, 0xFDFF),
            (0xFE70, 0xFEFF),
        ],
        unique_codepoints=set('ںٹڈڑے'),
        fleurs_code='ur_pk',
        # MMS 1B: no bare 'urd'; Urdu adapters are urd-script_* (not 'udu', a different lang).
        mms_lang=['urd-script_arabic', 'urd-script_devanagari', 'urd-script_latin'],
    ),

    # Hindi — Devanagari
    'hindi': ScriptConfig(
        name='Hindi (Devanagari)',
        ranges=[
            (0x0900, 0x097F),   # Devanagari
            (0xA8E0, 0xA8FF),   # Devanagari Extended
        ],
        fleurs_code='hi_in',
        mms_lang=['hin'],
    ),

    # Bengali — Bengali script
    'bengali': ScriptConfig(
        name='Bengali (Bengali script)',
        ranges=[
            (0x0980, 0x09FF),   # Bengali
        ],
        fleurs_code='bn_in',
        mms_lang=['ben'],
    ),

    # Malayalam — Malayalam script
    'malayalam': ScriptConfig(
        name='Malayalam (Malayalam script)',
        ranges=[
            (0x0D00, 0x0D7F),   # Malayalam
        ],
        fleurs_code='ml_in',
        mms_lang=['mal'],
    ),

    # Somali — Latin script (historically used Arabic; modern standard = Latin)
    # Somali uses basic Latin + no diacritics in standard orthography; the
    # main failure mode for Whisper is outputting Arabic on Somali audio.
    # Two separate ranges for A-Z and a-z: a single 0x41-0x7A range would
    # include ^ (U+005E) and ` (U+0060), both category Sk, which pass
    # _is_countable and would be falsely counted as in-script.
    'somali': ScriptConfig(
        name='Somali (Latin)',
        ranges=[
            (0x0041, 0x005A),   # Basic Latin A–Z
            (0x0061, 0x007A),   # Basic Latin a–z
            (0x00C0, 0x024F),   # Latin Extended-A and Extended-B
        ],
        fleurs_code='so_so',
        mms_lang=['som'],
    ),

    # Arabic (Modern Standard Arabic) — same script family as Pashto/Urdu
    # Added as a control: MSA models should produce correct Arabic script.
    # Interesting case: does Whisper confuse Arabic audio with Urdu/Pashto?
    'arabic': ScriptConfig(
        name='Arabic (Modern Standard Arabic)',
        ranges=[
            (0x0600, 0x06FF),
            (0x0750, 0x077F),
            (0xFB50, 0xFDFF),
            (0xFE70, 0xFEFF),
        ],
        fleurs_code='ar_eg',
        mms_lang=['ara'],   # MMS uses ISO 639-3; SeamlessM4T uses 'arb' (FLORES-200)
    ),

    # Persian/Farsi — Perso-Arabic script with Persian-specific letters
    # پ U+067E  چ U+0686  ژ U+0698  گ U+06AF are unique to Persian vs Arabic
    'persian': ScriptConfig(
        name='Persian/Farsi (Perso-Arabic)',
        ranges=[
            (0x0600, 0x06FF),
            (0x0750, 0x077F),
            (0xFB50, 0xFDFF),
            (0xFE70, 0xFEFF),
        ],
        unique_codepoints=set('پچژگ'),
        fleurs_code='fa_ir',
        mms_lang=['fas'],   # MMS uses ISO 639-3; SeamlessM4T uses 'pes' (FLORES-200)
    ),

    # Tamil — Tamil script
    'tamil': ScriptConfig(
        name='Tamil',
        ranges=[
            (0x0B80, 0x0BFF),   # Tamil block
        ],
        fleurs_code='ta_in',
        mms_lang=['tam'],
    ),

    # Georgian — Mkhedruli script (unique, no Latin/Arabic overlap)
    # Georgian is a strong positive control: script collapse would be easy to detect.
    'georgian': ScriptConfig(
        name='Georgian (Mkhedruli)',
        ranges=[
            (0x10A0, 0x10FF),   # Georgian (Asomtavruli + Mkhedruli)
            (0x2D00, 0x2D2F),   # Georgian Supplement (Nuskhuri)
            (0x1C90, 0x1CBF),   # Georgian Extended (Mtavruli capitals)
        ],
        fleurs_code='ka_ge',
        mms_lang=['kat'],
    ),
}


# ── HELPER FUNCTIONS ──────────────────────────────────────────────────────────

def _is_in_range(cp: int, ranges: list[tuple[int, int]]) -> bool:
    return any(lo <= cp <= hi for lo, hi in ranges)


def _is_countable(ch: str) -> bool:
    """Return True for characters that should count toward the SFR denominator.

    Whitespace, punctuation (Unicode category P*), and combining marks are
    excluded so that diacritics and punctuation do not artificially inflate or
    deflate the metric.
    """
    cat = unicodedata.category(ch)
    return (
        not ch.isspace()
        and not cat.startswith('P')   # punctuation
        and not cat.startswith('Z')   # separators
        and not cat.startswith('C')   # control / format chars
    )


def compute_sfr(
    text: str,
    language: str,
    config: Optional[ScriptConfig] = None,
) -> float:
    """Compute Script Fidelity Rate for a single ASR hypothesis string.

    SFR is reference-free: no ground-truth transcription is needed.
    It can be computed in production deployments to detect script collapse
    without any labelled data.

    Parameters
    ----------
    text : str
        Raw ASR hypothesis (unnormalized is fine; NFC is applied internally).
    language : str
        Key into SCRIPT_CONFIGS, e.g. 'pashto', 'hindi', 'somali'.
        Ignored if `config` is supplied directly.
    config : ScriptConfig, optional
        Use a custom config instead of looking up `language`.

    Returns
    -------
    float
        Fraction in [0.0, 1.0].  Returns ``None`` if the text has no countable
        characters (empty / whitespace-only / punctuation-only hypothesis).
        A value near 0 indicates script collapse.
    """
    if config is None:
        if language not in SCRIPT_CONFIGS:
            raise ValueError(
                f"Unknown language '{language}'. "
                f"Available: {sorted(SCRIPT_CONFIGS)}"
            )
        config = SCRIPT_CONFIGS[language]

    text = unicodedata.normalize('NFC', text) if text else ''
    chars = [ch for ch in text if _is_countable(ch)]

    if not chars:
        return None  # hypothesis is empty / only punctuation

    in_script = sum(
        1 for ch in chars
        if (ch in config.unique_codepoints)
        or _is_in_range(ord(ch), config.ranges)
    )
    return in_script / len(chars)


def dominant_script(text: str) -> str:
    """Classify the dominant script of a text string.

    Returns one of: 'pashto', 'arabic_dari_urdu', 'devanagari', 'bengali',
    'malayalam', 'latin', 'empty', or 'other'.

    This is a fast heuristic for tallying script distributions across a corpus.
    For the SF metric, use compute_sf() with a specific language config.
    """
    if not text or not text.strip():
        return 'empty'

    # Pashto-unique glyphs confirm Pashto unambiguously
    if any(ch in _PASHTO_UNIQUE for ch in text):
        return 'pashto'

    chars = [ch for ch in text if _is_countable(ch)]
    if not chars:
        return 'empty'

    counts: dict[str, int] = {
        'arabic_dari_urdu': 0,
        'devanagari': 0,
        'bengali': 0,
        'malayalam': 0,
        'tamil': 0,
        'georgian': 0,
        'latin': 0,
        'other': 0,
    }
    for ch in chars:
        cp = ord(ch)
        if 0x0600 <= cp <= 0x06FF or 0xFB50 <= cp <= 0xFDFF or 0xFE70 <= cp <= 0xFEFF:
            counts['arabic_dari_urdu'] += 1
        elif 0x0900 <= cp <= 0x097F or 0xA8E0 <= cp <= 0xA8FF:
            counts['devanagari'] += 1
        elif 0x0980 <= cp <= 0x09FF:
            counts['bengali'] += 1
        elif 0x0D00 <= cp <= 0x0D7F:
            counts['malayalam'] += 1
        elif 0x0B80 <= cp <= 0x0BFF:
            counts['tamil'] += 1
        elif (0x10A0 <= cp <= 0x10FF) or (0x2D00 <= cp <= 0x2D2F) or (0x1C90 <= cp <= 0x1CBF):
            counts['georgian'] += 1
        elif (0x0041 <= cp <= 0x007A) or (0x00C0 <= cp <= 0x024F):
            counts['latin'] += 1
        else:
            counts['other'] += 1

    total = len(chars)
    best  = max(counts, key=counts.__getitem__)
    if counts[best] / total >= 0.5:
        return best
    return 'other'


def compute_sfr_batch(
    texts: list[str],
    language: str,
) -> tuple[list[Optional[float]], list[str]]:
    """Vectorised version of compute_sfr + dominant_script.

    Returns
    -------
    sfr_scores : list of float | None
    dom_scripts : list of str
    """
    config     = SCRIPT_CONFIGS[language]
    sfr_scores = [compute_sfr(t, language, config) for t in texts]
    dom        = [dominant_script(t) for t in texts]
    return sfr_scores, dom


# Backward-compatibility aliases
compute_sf = compute_sfr
compute_sf_batch = compute_sfr_batch


# ── VALIDATION ────────────────────────────────────────────────────────────────

def _validate_pashto_calibration() -> None:
    """Smoke-test the Pashto SFR implementation.

    Checks that Pashto-unique detection works on a known positive and a known
    negative.
    """
    ps_text  = 'کابل کې ښه هوا ده'   # contains ښ (U+069A), Pashto-unique
    lat_text = 'this is entirely latin output'

    sfr_ps  = compute_sfr(ps_text,  'pashto')
    sfr_lat = compute_sfr(lat_text, 'pashto')

    assert sfr_ps  == 1.0,  f'Expected SFR=1.0 for Pashto text, got {sfr_ps}'
    assert sfr_lat == 0.0,  f'Expected SFR=0.0 for Latin text against Pashto config, got {sfr_lat}'

    dom_ps  = dominant_script(ps_text)
    dom_lat = dominant_script(lat_text)
    assert dom_ps  == 'pashto', f'dominant_script failed for Pashto: {dom_ps}'
    assert dom_lat == 'latin',  f'dominant_script failed for Latin: {dom_lat}'

    print('Pashto calibration: PASS')


def _validate_devanagari() -> None:
    hi_text  = 'नमस्ते'   # Hindi Devanagari
    lat_text = 'namaste'

    sfr_hi  = compute_sfr(hi_text,  'hindi')
    sfr_lat = compute_sfr(lat_text, 'hindi')

    assert sfr_hi  == 1.0, f'Expected SFR=1.0 for Hindi, got {sfr_hi}'
    assert sfr_lat == 0.0, f'Expected SFR=0.0 for Latin vs Hindi config, got {sfr_lat}'
    print('Hindi (Devanagari) calibration: PASS')


def _validate_somali() -> None:
    so_text = 'Somali waa luuqad'   # basic Latin
    ar_text = 'كابل في هواء جيد'   # Arabic

    sfr_so = compute_sfr(so_text, 'somali')
    sfr_ar = compute_sfr(ar_text, 'somali')

    assert sfr_so == 1.0, f'Expected SFR=1.0 for Somali Latin, got {sfr_so}'
    assert sfr_ar == 0.0, f'Expected SFR=0.0 for Arabic vs Somali config, got {sfr_ar}'
    print('Somali (Latin) calibration: PASS')


if __name__ == '__main__':
    _validate_pashto_calibration()
    _validate_devanagari()
    _validate_somali()

    # Print config summary
    print('\nScript configurations:')
    for lang, cfg in SCRIPT_CONFIGS.items():
        n_ranges  = len(cfg.ranges)
        n_unique  = len(cfg.unique_codepoints)
        print(f'  {lang:12s}  {cfg.name:35s}  ranges={n_ranges}  unique_codepoints={n_unique}')