"""DATA FRAUD detection — statistical fingerprints of
fabricated numbers. Text plagiarism tools ignore this entirely.

  BENFORD'S LAW : in genuine natural datasets the leading digit follows
                  P(d) = log10(1 + 1/d) (1 appears ~30%, 9 ~4.6%).
                  Fabricated/manually-invented numbers are too uniform ->
                  chi-square + MAD divergence from Benford flags them.
  GRIM TEST     : a reported mean of integer-valued items over N items must
                  be one of only N possible fractions. A mean that is
                  arithmetically impossible for its sample size is a red flag
                  for fabricated or mistyped summary statistics.

Pure numpy, no dependencies. Returns evidence, never a hard verdict — these
are leads for a human reviewer (consistent with the recall-first philosophy).
"""
import re

import numpy as np

BENFORD = np.array([np.log10(1 + 1 / d) for d in range(1, 10)])

# numbers like 12, 3.4, 1,234.5, 67% — capture the numeric token
_NUM = re.compile(r"(?<![\w.])(\d{1,3}(?:,\d{3})+|\d+)(?:\.(\d+))?")
# "mean/average ... of N" patterns for GRIM
_MEAN_CTX = re.compile(
    r"(?:mean|average|M)\s*(?:=|of|was|is|:)?\s*(\d+\.\d+)", re.I)
_N_CTX = re.compile(r"\b[nN]\s*=\s*(\d{1,5})\b")


def _leading_digit(num_str):
    for ch in num_str.replace(",", "").lstrip("0."):
        if ch.isdigit() and ch != "0":
            return int(ch)
    return None


def benford_analysis(text):
    digits = []
    for m in _NUM.finditer(text):
        whole = m.group(1)
        # skip years and tiny integers that aren't "data" (1900-2099, 0-9)
        clean = whole.replace(",", "")
        if len(clean) == 1:
            continue
        if re.fullmatch(r"(19|20)\d{2}", clean):
            continue
        d = _leading_digit(whole)
        if d:
            digits.append(d)
    if len(digits) < 30:                 # too few numbers to judge
        return None
    digits = np.array(digits)
    counts = np.array([(digits == d).sum() for d in range(1, 10)], dtype=float)
    observed = counts / counts.sum()
    expected = BENFORD
    exp_counts = expected * counts.sum()
    chi2 = float(((counts - exp_counts) ** 2 / exp_counts).sum())
    mad = float(np.mean(np.abs(observed - expected)))   # mean abs deviation
    # Nigrini's MAD conformity thresholds for first-digit Benford
    conformity = ("close" if mad < 0.006 else
                  "acceptable" if mad < 0.012 else
                  "marginal" if mad < 0.015 else "nonconformant")
    return {
        "n_numbers": int(len(digits)),
        "observed": [round(float(x), 4) for x in observed],
        "expected": [round(float(x), 4) for x in expected],
        "chi_square": round(chi2, 2),
        "chi2_critical_p05": 15.51,        # df=8
        "mad": round(mad, 5),
        "conformity": conformity,
        "suspicious": bool(chi2 > 15.51 and mad >= 0.012),
    }


def grim_test(text):
    """Check reported means against GRIM-possible values for their N."""
    means = [(m.group(1), m.start()) for m in _MEAN_CTX.finditer(text)]
    ns = [(int(m.group(1)), m.start()) for m in _N_CTX.finditer(text)]
    if not means or not ns:
        return None
    flagged = []
    for mean_str, mpos in means:
        decimals = len(mean_str.split(".")[1])
        if decimals == 0 or decimals > 3:
            continue
        mean_val = float(mean_str)
        # nearest reported N (same sentence-ish window)
        n = min(ns, key=lambda x: abs(x[1] - mpos))[0]
        if not 1 <= n <= 10000:
            continue
        # GRIM: mean*N should be (close to) an integer
        prod = mean_val * n
        frac = abs(prod - round(prod))
        tol = 0.5 / n + 1e-9              # rounding tolerance for given N
        if frac > tol and min(frac, 1 - frac) > tol:
            flagged.append({"reported_mean": mean_val, "n": n,
                            "implied_total": round(prod, 3),
                            "off_by": round(min(frac, 1 - frac), 4)})
    if not flagged:
        return None
    return {"inconsistent_means": flagged[:10], "count": len(flagged)}


def detect_fraud(text):
    """Returns {benford, grim, flags} or None when there's too little data."""
    benford = benford_analysis(text)
    grim = grim_test(text)
    if benford is None and grim is None:
        return None
    flags = []
    if benford and benford["suspicious"]:
        flags.append(f"leading-digit distribution diverges from Benford's law "
                     f"(MAD {benford['mad']}, chi2 {benford['chi_square']}) — "
                     f"numbers may be fabricated or hand-invented")
    if grim:
        flags.append(f"{grim['count']} reported mean(s) arithmetically "
                     f"impossible for the stated sample size (GRIM test)")
    return {"benford": benford, "grim": grim, "flags": flags,
            "suspicious": bool(flags)}