"""Intégration eScriptorium — import et export via API REST.

Fonctionnement
--------------
1. Authentification par token (settings → API key dans eScriptorium)
2. Listing et import de projets, documents et transcriptions
3. Export des résultats de benchmark Picarones comme couche OCR dans eScriptorium

API eScriptorium
----------------
eScriptorium expose une API REST documentée à /api/.
Les endpoints principaux utilisés ici :
- GET  /api/projects/                → liste des projets
- GET  /api/documents/               → liste des documents (filtrables par projet)
- GET  /api/documents/{pk}/parts/    → liste des pages d'un document
- GET  /api/documents/{pk}/parts/{pk}/transcriptions/  → transcriptions d'une page
- POST /api/documents/{pk}/parts/{pk}/transcriptions/  → créer une couche OCR

Usage
-----
>>> from picarones.importers.escriptorium import EScriptoriumClient
>>> client = EScriptoriumClient("https://escriptorium.example.org", token="abc123")
>>> projects = client.list_projects()
>>> corpus = client.import_document(doc_id=42, transcription_layer="manual")
"""

from __future__ import annotations

import json
import logging
import urllib.error
import urllib.parse
import urllib.request
from dataclasses import dataclass, field
from pathlib import Path
from typing import TYPE_CHECKING, Optional

from picarones.core.corpus import Corpus, Document

if TYPE_CHECKING:
    from picarones.core.results import BenchmarkResult

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Structures de données eScriptorium
# ---------------------------------------------------------------------------

@dataclass
class EScriptoriumProject:
    """Représentation d'un projet eScriptorium."""
    pk: int
    name: str
    slug: str
    owner: str = ""
    document_count: int = 0

    def as_dict(self) -> dict:
        return {
            "pk": self.pk,
            "name": self.name,
            "slug": self.slug,
            "owner": self.owner,
            "document_count": self.document_count,
        }


@dataclass
class EScriptoriumDocument:
    """Représentation d'un document eScriptorium."""
    pk: int
    name: str
    project: str = ""
    part_count: int = 0
    transcription_layers: list[str] = field(default_factory=list)

    def as_dict(self) -> dict:
        return {
            "pk": self.pk,
            "name": self.name,
            "project": self.project,
            "part_count": self.part_count,
            "transcription_layers": self.transcription_layers,
        }


@dataclass
class EScriptoriumPart:
    """Une page (part) d'un document eScriptorium."""
    pk: int
    title: str
    image_url: str
    order: int = 0
    transcriptions: list[dict] = field(default_factory=list)


# ---------------------------------------------------------------------------
# Client API eScriptorium
# ---------------------------------------------------------------------------

class EScriptoriumClient:
    """Client pour l'API REST d'eScriptorium.

    Parameters
    ----------
    base_url:
        URL racine de l'instance (ex : ``"https://escriptorium.example.org"``).
    token:
        Token d'authentification API (depuis Settings > API dans eScriptorium).
    timeout:
        Timeout HTTP en secondes.

    Examples
    --------
    >>> client = EScriptoriumClient("https://escriptorium.example.org", token="abc123")
    >>> projects = client.list_projects()
    >>> corpus = client.import_document(42, transcription_layer="manual")
    """

    def __init__(
        self,
        base_url: str,
        token: str,
        timeout: int = 30,
    ) -> None:
        self.base_url = base_url.rstrip("/")
        self.token = token
        self.timeout = timeout

    # ------------------------------------------------------------------
    # HTTP helpers
    # ------------------------------------------------------------------

    def _headers(self) -> dict[str, str]:
        return {
            "Authorization": f"Token {self.token}",
            "Accept": "application/json",
            "Content-Type": "application/json",
        }

    def _get(self, path: str, params: Optional[dict] = None) -> dict:
        """Effectue une requête GET et retourne le JSON."""
        url = f"{self.base_url}/api/{path.lstrip('/')}"
        if params:
            url += "?" + urllib.parse.urlencode(params)
        req = urllib.request.Request(url, headers=self._headers())
        try:
            with urllib.request.urlopen(req, timeout=self.timeout) as resp:
                return json.loads(resp.read().decode("utf-8"))
        except urllib.error.HTTPError as exc:
            raise RuntimeError(
                f"eScriptorium API erreur {exc.code} sur {url}: {exc.reason}"
            ) from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(
                f"Impossible de joindre {self.base_url}: {exc.reason}"
            ) from exc

    def _post(self, path: str, payload: dict) -> dict:
        """Effectue une requête POST avec payload JSON."""
        url = f"{self.base_url}/api/{path.lstrip('/')}"
        data = json.dumps(payload).encode("utf-8")
        req = urllib.request.Request(
            url, data=data, headers=self._headers(), method="POST"
        )
        try:
            with urllib.request.urlopen(req, timeout=self.timeout) as resp:
                body = resp.read().decode("utf-8")
                return json.loads(body) if body else {}
        except urllib.error.HTTPError as exc:
            raise RuntimeError(
                f"eScriptorium API erreur {exc.code} sur {url}: {exc.reason}"
            ) from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(
                f"Impossible de joindre {self.base_url}: {exc.reason}"
            ) from exc

    def _paginate(self, path: str, params: Optional[dict] = None) -> list[dict]:
        """Parcourt toutes les pages de résultats paginés."""
        results: list[dict] = []
        current_params = dict(params or {})
        current_params.setdefault("page_size", 100)
        page_num = 1
        while True:
            current_params["page"] = page_num
            data = self._get(path, current_params)
            if isinstance(data, list):
                results.extend(data)
                break
            results.extend(data.get("results", []))
            if not data.get("next"):
                break
            page_num += 1
        return results

    # ------------------------------------------------------------------
    # API publique
    # ------------------------------------------------------------------

    def test_connection(self) -> bool:
        """Vérifie que l'URL et le token sont valides.

        Returns
        -------
        bool
            True si l'authentification réussit.
        """
        try:
            self._get("projects/", {"page_size": 1})
            return True
        except RuntimeError:
            return False

    def list_projects(self) -> list[EScriptoriumProject]:
        """Retourne la liste des projets accessibles.

        Returns
        -------
        list[EScriptoriumProject]
        """
        raw = self._paginate("projects/")
        projects = []
        for item in raw:
            projects.append(EScriptoriumProject(
                pk=item["pk"],
                name=item.get("name", ""),
                slug=item.get("slug", ""),
                owner=item.get("owner", {}).get("username", "") if isinstance(item.get("owner"), dict) else str(item.get("owner", "")),
                document_count=item.get("documents_count", 0),
            ))
        return projects

    def list_documents(
        self,
        project_pk: Optional[int] = None,
    ) -> list[EScriptoriumDocument]:
        """Retourne la liste des documents, filtrés par projet si fourni.

        Parameters
        ----------
        project_pk:
            PK du projet eScriptorium (optionnel).

        Returns
        -------
        list[EScriptoriumDocument]
        """
        params: dict = {}
        if project_pk is not None:
            params["project"] = project_pk
        raw = self._paginate("documents/", params)
        docs = []
        for item in raw:
            layers = [
                t.get("name", "") if isinstance(t, dict) else str(t)
                for t in item.get("transcriptions", [])
            ]
            docs.append(EScriptoriumDocument(
                pk=item["pk"],
                name=item.get("name", ""),
                project=str(item.get("project", "")),
                part_count=item.get("parts_count", 0),
                transcription_layers=layers,
            ))
        return docs

    def list_parts(self, doc_pk: int) -> list[EScriptoriumPart]:
        """Retourne les pages (parts) d'un document.

        Parameters
        ----------
        doc_pk:
            PK du document eScriptorium.

        Returns
        -------
        list[EScriptoriumPart]
        """
        raw = self._paginate(f"documents/{doc_pk}/parts/")
        parts = []
        for item in raw:
            parts.append(EScriptoriumPart(
                pk=item["pk"],
                title=item.get("title", "") or f"Part {item.get('order', 0) + 1}",
                image_url=item.get("image", "") or "",
                order=item.get("order", 0),
            ))
        return parts

    def get_transcriptions(self, doc_pk: int, part_pk: int) -> list[dict]:
        """Retourne les transcriptions disponibles pour une page.

        Parameters
        ----------
        doc_pk:
            PK du document.
        part_pk:
            PK de la page.

        Returns
        -------
        list[dict]
            Chaque dict contient ``{"name": str, "content": str}``.
        """
        raw = self._get(f"documents/{doc_pk}/parts/{part_pk}/transcriptions/")
        if isinstance(raw, list):
            return raw
        return raw.get("results", [])

    def import_document(
        self,
        doc_pk: int,
        transcription_layer: str = "manual",
        output_dir: Optional[str] = None,
        download_images: bool = True,
        show_progress: bool = True,
    ) -> Corpus:
        """Importe un document eScriptorium comme corpus Picarones.

        Télécharge les images et récupère les transcriptions de la couche
        spécifiée comme vérité terrain.

        Parameters
        ----------
        doc_pk:
            PK du document dans eScriptorium.
        transcription_layer:
            Nom de la couche de transcription à utiliser comme GT.
        output_dir:
            Dossier local pour les images téléchargées. Si None, les images
            sont stockées en mémoire (pas de sauvegarde sur disque).
        download_images:
            Si True, télécharge les images dans output_dir.
        show_progress:
            Affiche une barre de progression tqdm.

        Returns
        -------
        Corpus
            Corpus Picarones avec documents et GT.
        """
        # Récupérer les métadonnées du document
        doc_info = self._get(f"documents/{doc_pk}/")
        doc_name = doc_info.get("name", f"document_{doc_pk}")

        parts = self.list_parts(doc_pk)
        if not parts:
            raise ValueError(f"Aucune page trouvée dans le document {doc_pk}")

        if show_progress:
            try:
                from tqdm import tqdm
                iterator = tqdm(parts, desc=f"Import {doc_name}")
            except ImportError:
                iterator = iter(parts)
        else:
            iterator = iter(parts)

        out_path: Optional[Path] = None
        if output_dir and download_images:
            out_path = Path(output_dir)
            out_path.mkdir(parents=True, exist_ok=True)

        documents: list[Document] = []
        for part in iterator:
            # Récupérer les transcriptions
            transcriptions = self.get_transcriptions(doc_pk, part.pk)
            gt_text = ""
            for t in transcriptions:
                layer_name = t.get("transcription", {}).get("name", "") if isinstance(t.get("transcription"), dict) else t.get("name", "")
                if layer_name == transcription_layer or not transcription_layer:
                    # Le contenu est dans "content" ou dans les lignes
                    lines = t.get("lines", []) or []
                    if lines:
                        gt_text = "\n".join(
                            line.get("content", "") or ""
                            for line in lines
                            if line.get("content")
                        )
                    else:
                        gt_text = t.get("content", "") or ""
                    break

            # Image
            image_path = part.image_url or f"escriptorium://doc{doc_pk}/part{part.pk}"
            if out_path and part.image_url and download_images:
                ext = Path(urllib.parse.urlparse(part.image_url).path).suffix or ".jpg"
                local_img = out_path / f"part_{part.pk:05d}{ext}"
                try:
                    urllib.request.urlretrieve(part.image_url, local_img)
                    image_path = str(local_img)
                except Exception as exc:
                    logger.warning("Impossible de télécharger l'image %s: %s", part.image_url, exc)

                # Sauvegarder la GT
                gt_path = out_path / f"part_{part.pk:05d}.gt.txt"
                gt_path.write_text(gt_text, encoding="utf-8")

            documents.append(Document(
                doc_id=f"part_{part.pk:05d}",
                image_path=image_path,
                ground_truth=gt_text,
                metadata={
                    "source": "escriptorium",
                    "doc_pk": doc_pk,
                    "part_pk": part.pk,
                    "part_title": part.title,
                    "transcription_layer": transcription_layer,
                },
            ))

        return Corpus(
            name=doc_name,
            source=f"{self.base_url}/document/{doc_pk}/",
            documents=documents,
            metadata={
                "escriptorium_url": self.base_url,
                "doc_pk": doc_pk,
                "transcription_layer": transcription_layer,
            },
        )

    def export_benchmark_as_layer(
        self,
        benchmark_result: "BenchmarkResult",
        doc_pk: int,
        engine_name: str,
        layer_name: Optional[str] = None,
        part_mapping: Optional[dict[str, int]] = None,
    ) -> int:
        """Exporte les résultats Picarones comme couche OCR dans eScriptorium.

        Parameters
        ----------
        benchmark_result:
            Résultats du benchmark Picarones.
        doc_pk:
            PK du document cible dans eScriptorium.
        engine_name:
            Nom du moteur dont on exporte les transcriptions.
        layer_name:
            Nom de la couche à créer (défaut : ``"picarones_{engine_name}"``).
        part_mapping:
            Correspondance ``doc_id → part_pk`` eScriptorium. Si None,
            la correspondance est inférée depuis les métadonnées des documents.

        Returns
        -------
        int
            Nombre de pages exportées avec succès.
        """
        if layer_name is None:
            layer_name = f"picarones_{engine_name}"

        # Trouver le rapport du moteur
        engine_report = None
        for report in benchmark_result.engine_reports:
            if report.engine_name == engine_name:
                engine_report = report
                break
        if engine_report is None:
            raise ValueError(f"Moteur '{engine_name}' introuvable dans les résultats.")

        exported = 0
        for doc_result in engine_report.document_results:
            if doc_result.engine_error:
                continue

            # Déterminer le part_pk
            part_pk: Optional[int] = None
            if part_mapping and doc_result.doc_id in part_mapping:
                part_pk = part_mapping[doc_result.doc_id]
            else:
                # Essayer d'extraire depuis doc_id (ex: "part_00042")
                try:
                    part_pk = int(doc_result.doc_id.replace("part_", "").lstrip("0") or "0")
                except ValueError:
                    logger.warning("Impossible de déterminer part_pk pour %s", doc_result.doc_id)
                    continue

            try:
                self._post(
                    f"documents/{doc_pk}/parts/{part_pk}/transcriptions/",
                    {
                        "name": layer_name,
                        "content": doc_result.hypothesis,
                        "source": "picarones",
                    },
                )
                exported += 1
                logger.debug("Exporté part %d → couche '%s'", part_pk, layer_name)
            except RuntimeError as exc:
                logger.warning("Erreur export part %d: %s", part_pk, exc)

        return exported


# ---------------------------------------------------------------------------
# Interface de niveau module
# ---------------------------------------------------------------------------

def connect_escriptorium(
    base_url: str,
    token: str,
    timeout: int = 30,
) -> EScriptoriumClient:
    """Crée et retourne un client eScriptorium authentifié.

    Parameters
    ----------
    base_url:
        URL de l'instance eScriptorium.
    token:
        Token API.
    timeout:
        Timeout HTTP.

    Returns
    -------
    EScriptoriumClient

    Raises
    ------
    RuntimeError
        Si la connexion échoue (URL invalide, token incorrect, serveur inaccessible).
    """
    client = EScriptoriumClient(base_url, token, timeout)
    if not client.test_connection():
        raise RuntimeError(
            f"Impossible de se connecter à {base_url}. "
            "Vérifiez l'URL et le token API."
        )
    return client