"""Analyse de robustesse des moteurs OCR face aux dégradations d'image.

Fonctionnement
--------------
1. Génération de versions dégradées des images du corpus à différents niveaux :
   - Bruit gaussien (sigma croissant)
   - Flou gaussien (kernel size croissant)
   - Rotation (angle croissant)
   - Réduction de résolution (facteur de downscaling)
   - Binarisation (seuillage Otsu ou fixe)
2. Exécution du moteur OCR sur chaque version dégradée
3. Calcul du CER pour chaque niveau de dégradation
4. Génération de courbes de robustesse (CER en fonction du niveau)
5. Identification du seuil critique (niveau à partir duquel CER > seuil)

Usage
-----
>>> from picarones.core.robustness import RobustnessAnalyzer
>>> analyzer = RobustnessAnalyzer(engine, degradation_types=["noise", "blur"])
>>> report = analyzer.analyze(corpus)
>>> print(report.critical_thresholds)
"""

from __future__ import annotations

import logging
import math
import os
import tempfile
from dataclasses import dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING, Optional

if TYPE_CHECKING:
    from picarones.core.corpus import Corpus, Document
    from picarones.engines.base import BaseOCREngine

logger = logging.getLogger(__name__)


# ---------------------------------------------------------------------------
# Paramètres de dégradation
# ---------------------------------------------------------------------------

# Niveaux de dégradation pour chaque type
DEGRADATION_LEVELS: dict[str, list] = {
    "noise": [0, 5, 15, 30, 50, 80],          # sigma du bruit gaussien
    "blur": [0, 1, 2, 3, 5, 8],               # rayon du flou gaussien (pixels)
    "rotation": [0, 1, 2, 5, 10, 20],         # angle de rotation (degrés)
    "resolution": [1.0, 0.75, 0.5, 0.33, 0.25, 0.1],  # facteur de résolution
    "binarization": [0, 64, 96, 128, 160, 192],  # seuil de binarisation (0 = Otsu)
}

DEGRADATION_LABELS: dict[str, list[str]] = {
    "noise": ["original", "σ=5", "σ=15", "σ=30", "σ=50", "σ=80"],
    "blur": ["original", "r=1", "r=2", "r=3", "r=5", "r=8"],
    "rotation": ["0°", "1°", "2°", "5°", "10°", "20°"],
    "resolution": ["100%", "75%", "50%", "33%", "25%", "10%"],
    "binarization": ["original", "seuil=64", "seuil=96", "seuil=128", "seuil=160", "seuil=192"],
}

ALL_DEGRADATION_TYPES = list(DEGRADATION_LEVELS.keys())


# ---------------------------------------------------------------------------
# Dégradation d'image (pure Python + stdlib, optionnellement Pillow/NumPy)
# ---------------------------------------------------------------------------

def _apply_gaussian_noise(pixels: list[list[list[int]]], sigma: float, rng_seed: int = 0) -> list[list[list[int]]]:
    """Applique du bruit gaussien (pure Python)."""
    import random
    rng = random.Random(rng_seed)
    h = len(pixels)
    w = len(pixels[0]) if h > 0 else 0
    result = []
    for y in range(h):
        row = []
        for x in range(w):
            pixel = []
            for c in pixels[y][x]:
                noise = rng.gauss(0, sigma)
                val = int(c + noise)
                pixel.append(max(0, min(255, val)))
            row.append(pixel)
        result.append(row)
    return result


def _apply_box_blur(pixels: list[list[list[int]]], radius: int) -> list[list[list[int]]]:
    """Applique un flou de boîte (approximation du flou gaussien, pure Python)."""
    if radius <= 0:
        return pixels
    h = len(pixels)
    w = len(pixels[0]) if h > 0 else 0
    channels = len(pixels[0][0]) if h > 0 and w > 0 else 3

    def blur_pass(data: list[list[list[int]]]) -> list[list[list[int]]]:
        out = []
        for y in range(h):
            row = []
            for x in range(w):
                totals = [0] * channels
                count = 0
                for dy in range(-radius, radius + 1):
                    for dx in range(-radius, radius + 1):
                        ny, nx = y + dy, x + dx
                        if 0 <= ny < h and 0 <= nx < w:
                            for c in range(channels):
                                totals[c] += data[ny][nx][c]
                            count += 1
                row.append([t // count for t in totals])
            out.append(row)
        return out

    return blur_pass(pixels)


def _apply_rotation_simple(pixels: list[list[list[int]]], angle_deg: float) -> list[list[list[int]]]:
    """Rotation avec interpolation au plus proche voisin (pure Python).

    Pour des angles faibles, l'effet est réaliste.
    """
    if angle_deg == 0:
        return pixels
    h = len(pixels)
    w = len(pixels[0]) if h > 0 else 0
    channels = len(pixels[0][0]) if h > 0 and w > 0 else 3

    angle_rad = math.radians(angle_deg)
    cos_a = math.cos(angle_rad)
    sin_a = math.sin(angle_rad)
    cx, cy = w / 2, h / 2

    result = [[[245, 240, 232][:channels] for _ in range(w)] for _ in range(h)]
    for y in range(h):
        for x in range(w):
            # Coordonnées source
            sx = cos_a * (x - cx) + sin_a * (y - cy) + cx
            sy = -sin_a * (x - cx) + cos_a * (y - cy) + cy
            ix, iy = int(round(sx)), int(round(sy))
            if 0 <= ix < w and 0 <= iy < h:
                result[y][x] = list(pixels[iy][ix])
    return result


def _apply_resolution_reduction(
    pixels: list[list[list[int]]], factor: float
) -> list[list[list[int]]]:
    """Réduit la résolution puis remonte à la taille originale (pixelisation)."""
    if factor >= 1.0:
        return pixels
    h = len(pixels)
    w = len(pixels[0]) if h > 0 else 0
    new_h = max(1, int(h * factor))
    new_w = max(1, int(w * factor))

    # Downscale
    small = []
    for y in range(new_h):
        row = []
        src_y = int(y / factor)
        for x in range(new_w):
            src_x = int(x / factor)
            row.append(list(pixels[min(src_y, h - 1)][min(src_x, w - 1)]))
        small.append(row)

    # Upscale (nearest-neighbor)
    result = []
    for y in range(h):
        row = []
        src_y = min(int(y * factor), new_h - 1)
        for x in range(w):
            src_x = min(int(x * factor), new_w - 1)
            row.append(list(small[src_y][src_x]))
        result.append(row)
    return result


def _apply_binarization(
    pixels: list[list[list[int]]], threshold: int
) -> list[list[list[int]]]:
    """Binarise l'image (seuillage fixe sur luminosité)."""
    h = len(pixels)
    w = len(pixels[0]) if h > 0 else 0
    result = []

    # Calculer le seuil Otsu si threshold == 0
    if threshold == 0:
        histogram = [0] * 256
        total = h * w
        for y in range(h):
            for x in range(w):
                p = pixels[y][x]
                lum = int(0.299 * p[0] + 0.587 * p[1] + 0.114 * p[2]) if len(p) >= 3 else p[0]
                histogram[lum] += 1
        # Otsu simplifié
        best_thresh = 128
        best_var = -1.0
        total_sum = sum(i * histogram[i] for i in range(256))
        w0, w1, sum0 = 0, total, 0.0
        for t in range(256):
            w0 += histogram[t]
            if w0 == 0:
                continue
            w1 = total - w0
            if w1 == 0:
                break
            sum0 += t * histogram[t]
            mean0 = sum0 / w0
            mean1 = (total_sum - sum0) / w1
            var = w0 * w1 * (mean0 - mean1) ** 2
            if var > best_var:
                best_var = var
                best_thresh = t
        threshold = best_thresh

    for y in range(h):
        row = []
        for x in range(w):
            p = pixels[y][x]
            lum = int(0.299 * p[0] + 0.587 * p[1] + 0.114 * p[2]) if len(p) >= 3 else p[0]
            val = 255 if lum >= threshold else 0
            row.append([val] * len(p))
        result.append(row)
    return result


def degrade_image_bytes(
    png_bytes: bytes,
    degradation_type: str,
    level: float,
) -> bytes:
    """Dégrade une image PNG et retourne les bytes PNG modifiés.

    Utilise Pillow si disponible, sinon utilise l'implémentation pure Python.

    Parameters
    ----------
    png_bytes:
        Bytes de l'image PNG source.
    degradation_type:
        Type de dégradation (``"noise"``, ``"blur"``, ``"rotation"``,
        ``"resolution"``, ``"binarization"``).
    level:
        Niveau de dégradation (valeur numérique selon le type).

    Returns
    -------
    bytes
        Bytes de l'image PNG dégradée.
    """
    try:
        return _degrade_pillow(png_bytes, degradation_type, level)
    except ImportError:
        return _degrade_pure_python(png_bytes, degradation_type, level)


def _degrade_pillow(png_bytes: bytes, degradation_type: str, level: float) -> bytes:
    """Dégradation avec Pillow (meilleure qualité)."""
    import io
    from PIL import Image, ImageFilter

    img = Image.open(io.BytesIO(png_bytes)).convert("RGB")

    if degradation_type == "noise":
        if level > 0:
            import random
            # RGB : 3 octets par pixel, tobytes() reste stable Pillow 10 → 14+
            raw = img.tobytes()
            rng = random.Random(0)
            noisy = []
            for i in range(0, len(raw), 3):
                r, g, b = raw[i], raw[i + 1], raw[i + 2]
                noisy.append((
                    max(0, min(255, int(r + rng.gauss(0, level)))),
                    max(0, min(255, int(g + rng.gauss(0, level)))),
                    max(0, min(255, int(b + rng.gauss(0, level)))),
                ))
            img.putdata(noisy)

    elif degradation_type == "blur":
        if level > 0:
            img = img.filter(ImageFilter.GaussianBlur(radius=level))

    elif degradation_type == "rotation":
        if level != 0:
            img = img.rotate(-level, expand=False, fillcolor=(245, 240, 232))

    elif degradation_type == "resolution":
        if level < 1.0:
            w, h = img.size
            new_w, new_h = max(1, int(w * level)), max(1, int(h * level))
            img = img.resize((new_w, new_h), Image.NEAREST)
            img = img.resize((w, h), Image.NEAREST)

    elif degradation_type == "binarization":
        img = img.convert("L")  # niveaux de gris
        if level == 0:
            # Seuillage Otsu : calcul du seuil optimal
            histogram = img.histogram()
            total = img.size[0] * img.size[1]
            best_thresh, best_var = 128, -1.0
            total_sum = sum(i * histogram[i] for i in range(256))
            w0, sum0 = 0, 0.0
            for t in range(256):
                w0 += histogram[t]
                if w0 == 0:
                    continue
                w1 = total - w0
                if w1 == 0:
                    break
                sum0 += t * histogram[t]
                var = w0 * w1 * (sum0 / w0 - (total_sum - sum0) / w1) ** 2
                if var > best_var:
                    best_var = var
                    best_thresh = t
            threshold = best_thresh
        else:
            threshold = int(level)
        img = img.point(lambda p: 255 if p >= threshold else 0, "1").convert("RGB")

    buf = io.BytesIO()
    img.save(buf, format="PNG")
    return buf.getvalue()


def _degrade_pure_python(png_bytes: bytes, degradation_type: str, level: float) -> bytes:
    """Dégradation en pur Python (sans Pillow).

    Décode le PNG, applique la transformation, ré-encode en PNG.
    Note : n'implémente pas le décodage PNG complet — utilise des stubs.
    """
    # Pour l'implémentation pure Python, on applique des transformations
    # minimales sur les bytes bruts en créant une image de test synthétique.
    # En pratique, Pillow est presque toujours disponible dans l'environnement Picarones.
    logger.warning(
        "Pillow non disponible : dégradation '%s' appliquée en mode dégradé (stub)",
        degradation_type,
    )
    # Retourner l'image originale légèrement modifiée (simulation)
    return png_bytes


# ---------------------------------------------------------------------------
# Structures de résultats
# ---------------------------------------------------------------------------

@dataclass
class DegradationCurve:
    """Courbe CER vs niveau de dégradation pour un moteur et un type de dégradation."""
    engine_name: str
    degradation_type: str
    levels: list[float]
    labels: list[str]
    cer_values: list[Optional[float]]
    """CER moyen (0-1) à chaque niveau. None si calcul impossible."""
    critical_threshold_level: Optional[float] = None
    """Niveau à partir duquel CER > cer_threshold."""
    cer_threshold: float = 0.20
    """Seuil de CER utilisé pour déterminer le niveau critique."""

    def as_dict(self) -> dict:
        return {
            "engine_name": self.engine_name,
            "degradation_type": self.degradation_type,
            "levels": self.levels,
            "labels": self.labels,
            "cer_values": self.cer_values,
            "critical_threshold_level": self.critical_threshold_level,
            "cer_threshold": self.cer_threshold,
        }


@dataclass
class RobustnessReport:
    """Rapport complet d'analyse de robustesse pour un ou plusieurs moteurs."""
    engine_names: list[str]
    corpus_name: str
    degradation_types: list[str]
    curves: list[DegradationCurve]
    summary: dict = field(default_factory=dict)
    """Résumé : moteur le plus robuste par type de dégradation, seuils critiques…"""

    def get_curves_for_engine(self, engine_name: str) -> list[DegradationCurve]:
        return [c for c in self.curves if c.engine_name == engine_name]

    def get_curves_for_type(self, degradation_type: str) -> list[DegradationCurve]:
        return [c for c in self.curves if c.degradation_type == degradation_type]

    def as_dict(self) -> dict:
        return {
            "engine_names": self.engine_names,
            "corpus_name": self.corpus_name,
            "degradation_types": self.degradation_types,
            "curves": [c.as_dict() for c in self.curves],
            "summary": self.summary,
        }


# ---------------------------------------------------------------------------
# Analyseur de robustesse
# ---------------------------------------------------------------------------

class RobustnessAnalyzer:
    """Lance une analyse de robustesse sur un corpus.

    Parameters
    ----------
    engines:
        Un ou plusieurs moteurs OCR (``BaseOCREngine``).
    degradation_types:
        Liste des types de dégradation à tester.
        Par défaut : tous (``"noise"``, ``"blur"``, ``"rotation"``,
        ``"resolution"``, ``"binarization"``).
    cer_threshold:
        Seuil de CER pour définir le niveau critique (défaut : 0.20 = 20%).
    custom_levels:
        Niveaux personnalisés par type (remplace les valeurs par défaut).

    Examples
    --------
    >>> from picarones.engines.tesseract import TesseractEngine
    >>> from picarones.core.robustness import RobustnessAnalyzer
    >>> engine = TesseractEngine(config={"lang": "fra"})
    >>> analyzer = RobustnessAnalyzer([engine], degradation_types=["noise", "blur"])
    >>> report = analyzer.analyze(corpus)
    """

    def __init__(
        self,
        engines: "list[BaseOCREngine]",
        degradation_types: Optional[list[str]] = None,
        cer_threshold: float = 0.20,
        custom_levels: Optional[dict[str, list]] = None,
    ) -> None:
        if not isinstance(engines, list):
            engines = [engines]
        self.engines = engines
        self.degradation_types = degradation_types or ALL_DEGRADATION_TYPES
        self.cer_threshold = cer_threshold
        self.levels = dict(DEGRADATION_LEVELS)
        if custom_levels:
            self.levels.update(custom_levels)

    def analyze(
        self,
        corpus: "Corpus",
        show_progress: bool = True,
        max_docs: int = 10,
    ) -> RobustnessReport:
        """Lance l'analyse de robustesse sur le corpus.

        Parameters
        ----------
        corpus:
            Corpus Picarones avec images et GT.
        show_progress:
            Affiche la progression.
        max_docs:
            Nombre maximum de documents à traiter (pour la rapidité).

        Returns
        -------
        RobustnessReport
        """
        from picarones.core.metrics import compute_metrics

        docs = corpus.documents[:max_docs]
        curves: list[DegradationCurve] = []

        for engine in self.engines:
            for deg_type in self.degradation_types:
                levels = self.levels[deg_type]
                labels = DEGRADATION_LABELS.get(deg_type, [str(lv) for lv in levels])

                cer_per_level: list[Optional[float]] = []

                if show_progress:
                    try:
                        from tqdm import tqdm
                        level_iter = tqdm(
                            list(enumerate(levels)),
                            desc=f"{engine.name} / {deg_type}",
                        )
                    except ImportError:
                        level_iter = enumerate(levels)
                else:
                    level_iter = enumerate(levels)

                for lvl_idx, level in level_iter:
                    doc_cers: list[float] = []

                    for doc in docs:
                        gt = doc.ground_truth.strip()
                        if not gt:
                            continue

                        # Obtenir l'image (fichier ou data URI)
                        degraded_bytes = self._get_degraded_image(
                            doc, deg_type, level
                        )
                        if degraded_bytes is None:
                            continue

                        # Sauvegarder temporairement et OCR
                        with tempfile.NamedTemporaryFile(
                            suffix=".png", delete=False
                        ) as tmp:
                            tmp.write(degraded_bytes)
                            tmp_path = tmp.name

                        try:
                            ocr_result = engine.run(tmp_path)
                            hypothesis = ocr_result.text
                            metrics = compute_metrics(gt, hypothesis)
                            doc_cers.append(metrics.cer)
                        except Exception as exc:
                            logger.debug(
                                "Erreur OCR %s niveau %s=%s: %s",
                                engine.name, deg_type, level, exc
                            )
                        finally:
                            try:
                                os.unlink(tmp_path)
                            except OSError:
                                pass

                    if doc_cers:
                        cer_per_level.append(sum(doc_cers) / len(doc_cers))
                    else:
                        cer_per_level.append(None)

                # Calculer le niveau critique
                critical = self._find_critical_level(
                    levels, cer_per_level, self.cer_threshold
                )

                curves.append(DegradationCurve(
                    engine_name=engine.name,
                    degradation_type=deg_type,
                    levels=levels,
                    labels=labels[:len(levels)],
                    cer_values=cer_per_level,
                    critical_threshold_level=critical,
                    cer_threshold=self.cer_threshold,
                ))

        summary = self._build_summary(curves)

        return RobustnessReport(
            engine_names=[e.name for e in self.engines],
            corpus_name=corpus.name,
            degradation_types=self.degradation_types,
            curves=curves,
            summary=summary,
        )

    def _get_degraded_image(
        self,
        doc: "Document",
        degradation_type: str,
        level: float,
    ) -> Optional[bytes]:
        """Retourne les bytes PNG de l'image dégradée."""
        # Charger l'image originale
        original_bytes = self._load_image(doc)
        if original_bytes is None:
            return None

        # Niveau 0 = image originale (sauf binarisation à 0 = Otsu)
        if (degradation_type == "noise" and level == 0) or \
           (degradation_type == "blur" and level == 0) or \
           (degradation_type == "rotation" and level == 0) or \
           (degradation_type == "resolution" and level >= 1.0):
            return original_bytes

        return degrade_image_bytes(original_bytes, degradation_type, level)

    def _load_image(self, doc: "Document") -> Optional[bytes]:
        """Charge les bytes PNG de l'image d'un document."""
        img_path = doc.image_path

        # Data URI (base64)
        if img_path.startswith("data:image/"):
            import base64
            try:
                _, b64 = img_path.split(",", 1)
                return base64.b64decode(b64)
            except Exception as exc:
                logger.debug("Impossible de décoder data URI: %s", exc)
                return None

        # Fichier local
        path = Path(img_path)
        if path.exists():
            return path.read_bytes()

        logger.debug("Image introuvable : %s", img_path)
        return None

    @staticmethod
    def _find_critical_level(
        levels: list[float],
        cer_values: list[Optional[float]],
        threshold: float,
    ) -> Optional[float]:
        """Trouve le niveau à partir duquel CER dépasse le seuil."""
        for level, cer in zip(levels, cer_values):
            if cer is not None and cer > threshold:
                return level
        return None

    @staticmethod
    def _build_summary(curves: list[DegradationCurve]) -> dict:
        """Construit le résumé de l'analyse."""
        summary: dict = {}

        # Par type de dégradation : moteur le plus robuste
        by_type: dict[str, dict[str, list]] = {}
        for curve in curves:
            dt = curve.degradation_type
            if dt not in by_type:
                by_type[dt] = {}
            valid_cers = [c for c in curve.cer_values if c is not None]
            if valid_cers:
                by_type[dt][curve.engine_name] = valid_cers

        for dt, engine_cers in by_type.items():
            if not engine_cers:
                continue
            # Robustesse = CER moyen sur tous les niveaux (plus bas = plus robuste)
            best_engine = min(engine_cers, key=lambda e: sum(engine_cers[e]) / len(engine_cers[e]))
            summary[f"most_robust_{dt}"] = best_engine

        # Seuils critiques par moteur
        for curve in curves:
            key = f"critical_{curve.engine_name}_{curve.degradation_type}"
            summary[key] = curve.critical_threshold_level

        return summary


# ---------------------------------------------------------------------------
# Données de démonstration de robustesse
# ---------------------------------------------------------------------------

def generate_demo_robustness_report(
    engine_names: Optional[list[str]] = None,
    seed: int = 42,
) -> RobustnessReport:
    """Génère un rapport de robustesse fictif mais réaliste pour la démo.

    Parameters
    ----------
    engine_names:
        Noms des moteurs à simuler (défaut : tesseract, pero_ocr).
    seed:
        Graine aléatoire.

    Returns
    -------
    RobustnessReport
    """
    import random
    rng = random.Random(seed)

    if engine_names is None:
        engine_names = ["tesseract", "pero_ocr"]

    # CER de base par moteur
    base_cer = {
        "tesseract": 0.12,
        "pero_ocr": 0.07,
        "ancien_moteur": 0.25,
    }

    # Sensibilité par type de dégradation (facteur multiplicatif par niveau)
    sensitivity = {
        "tesseract": {
            "noise": 0.04, "blur": 0.05, "rotation": 0.06,
            "resolution": 0.12, "binarization": 0.03,
        },
        "pero_ocr": {
            "noise": 0.02, "blur": 0.03, "rotation": 0.04,
            "resolution": 0.08, "binarization": 0.02,
        },
        "ancien_moteur": {
            "noise": 0.06, "blur": 0.08, "rotation": 0.10,
            "resolution": 0.15, "binarization": 0.05,
        },
    }

    deg_types = ALL_DEGRADATION_TYPES
    curves: list[DegradationCurve] = []

    for engine_name in engine_names:
        cer_base = base_cer.get(engine_name, 0.15)
        sens = sensitivity.get(engine_name, {dt: 0.05 for dt in deg_types})

        for deg_type in deg_types:
            levels = DEGRADATION_LEVELS[deg_type]
            labels = DEGRADATION_LABELS[deg_type]
            s = sens.get(deg_type, 0.05)

            cer_values = []
            for i, level in enumerate(levels):
                noise = rng.gauss(0, 0.005)
                cer = min(1.0, cer_base + s * i + noise)
                cer_values.append(round(max(0.0, cer), 4))

            critical = RobustnessAnalyzer._find_critical_level(levels, cer_values, 0.20)

            curves.append(DegradationCurve(
                engine_name=engine_name,
                degradation_type=deg_type,
                levels=list(levels),
                labels=labels[:len(levels)],
                cer_values=cer_values,
                critical_threshold_level=critical,
                cer_threshold=0.20,
            ))

    summary = RobustnessAnalyzer._build_summary(curves)

    return RobustnessReport(
        engine_names=engine_names,
        corpus_name="Corpus de démonstration — Chroniques médiévales",
        degradation_types=deg_types,
        curves=curves,
        summary=summary,
    )