"""
Modèles SQLAlchemy pour PriceWatch Phase 2.

Schéma normalisé pour persistence PostgreSQL:
- products: Catalogue produits (déduplication sur source + reference)
- price_history: Historique prix time-series
- product_images: Images produit (N par produit)
- product_specs: Caractéristiques produit (key-value)
- scraping_logs: Logs observabilité pour debugging

Justification technique:
- Normalisation: products séparée de price_history (catalogue vs time-series)
- Clé naturelle: (source, reference) comme unique constraint (ASIN Amazon, etc.)
- Pas de JSONB pour données structurées: tables séparées pour images/specs
- JSONB uniquement pour données variables: errors, notes dans logs
"""

from datetime import datetime, timezone
from decimal import Decimal
from typing import List, Optional

from sqlalchemy import (
    TIMESTAMP,
    CheckConstraint,
    Column,
    ForeignKey,
    Index,
    Integer,
    JSON,
    Numeric,
    Boolean,
    String,
    Text,
    UniqueConstraint,
)
from sqlalchemy.dialects.postgresql import JSONB
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship


class Base(DeclarativeBase):
    """Base class pour tous les modèles SQLAlchemy."""

    pass


def utcnow() -> datetime:
    return datetime.now(timezone.utc)


class Product(Base):
    """
    Catalogue produits (1 ligne par produit unique).

    Clé naturelle: (source, reference) - Ex: (amazon, B08N5WRWNW)
    Mise à jour: title, category, url à chaque scraping
    Historique prix: relation 1-N vers PriceHistory
    """

    __tablename__ = "products"

    # Primary key
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)

    # Natural key (unique)
    source: Mapped[str] = mapped_column(
        String(50), nullable=False, comment="Store ID (amazon, cdiscount, etc.)"
    )
    reference: Mapped[str] = mapped_column(
        String(100), nullable=False, comment="Product reference (ASIN, SKU, etc.)"
    )

    # Product metadata
    url: Mapped[str] = mapped_column(Text, nullable=False, comment="Canonical product URL")
    title: Mapped[Optional[str]] = mapped_column(Text, nullable=True, comment="Product title")
    category: Mapped[Optional[str]] = mapped_column(
        Text, nullable=True, comment="Product category (breadcrumb)"
    )
    description: Mapped[Optional[str]] = mapped_column(
        Text, nullable=True, comment="Product description"
    )
    currency: Mapped[Optional[str]] = mapped_column(
        String(3), nullable=True, comment="Currency code (EUR, USD, GBP)"
    )
    msrp: Mapped[Optional[Decimal]] = mapped_column(
        Numeric(10, 2), nullable=True, comment="Recommended price"
    )

    # Timestamps
    first_seen_at: Mapped[datetime] = mapped_column(
        TIMESTAMP, nullable=False, default=utcnow, comment="First scraping timestamp"
    )
    last_updated_at: Mapped[datetime] = mapped_column(
        TIMESTAMP,
        nullable=False,
        default=utcnow,
        onupdate=utcnow,
        comment="Last metadata update",
    )

    # Relationships
    price_history: Mapped[List["PriceHistory"]] = relationship(
        "PriceHistory", back_populates="product", cascade="all, delete-orphan"
    )
    images: Mapped[List["ProductImage"]] = relationship(
        "ProductImage", back_populates="product", cascade="all, delete-orphan"
    )
    specs: Mapped[List["ProductSpec"]] = relationship(
        "ProductSpec", back_populates="product", cascade="all, delete-orphan"
    )
    logs: Mapped[List["ScrapingLog"]] = relationship(
        "ScrapingLog", back_populates="product", cascade="all, delete-orphan"
    )

    # Constraints
    __table_args__ = (
        UniqueConstraint("source", "reference", name="uq_product_source_reference"),
        Index("ix_product_source", "source"),
        Index("ix_product_reference", "reference"),
        Index("ix_product_last_updated", "last_updated_at"),
    )

    def __repr__(self) -> str:
        return f"<Product(id={self.id}, source={self.source}, reference={self.reference})>"


class PriceHistory(Base):
    """
    Historique prix (time-series).

    Une ligne par scraping réussi avec extraction prix.
    Unique constraint sur (product_id, fetched_at) évite doublons.
    """

    __tablename__ = "price_history"

    # Primary key
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)

    # Foreign key
    product_id: Mapped[int] = mapped_column(
        Integer, ForeignKey("products.id", ondelete="CASCADE"), nullable=False
    )

    # Price data
    price: Mapped[Optional[Decimal]] = mapped_column(
        Numeric(10, 2), nullable=True, comment="Product price"
    )
    shipping_cost: Mapped[Optional[Decimal]] = mapped_column(
        Numeric(10, 2), nullable=True, comment="Shipping cost"
    )
    stock_status: Mapped[Optional[str]] = mapped_column(
        String(20), nullable=True, comment="Stock status (in_stock, out_of_stock, unknown)"
    )

    # Fetch metadata
    fetch_method: Mapped[str] = mapped_column(
        String(20), nullable=False, comment="Fetch method (http, playwright)"
    )
    fetch_status: Mapped[str] = mapped_column(
        String(20), nullable=False, comment="Fetch status (success, partial, failed)"
    )
    fetched_at: Mapped[datetime] = mapped_column(
        TIMESTAMP, nullable=False, comment="Scraping timestamp"
    )

    # Relationship
    product: Mapped["Product"] = relationship("Product", back_populates="price_history")

    # Constraints
    __table_args__ = (
        UniqueConstraint("product_id", "fetched_at", name="uq_price_history_product_time"),
        Index("ix_price_history_product_id", "product_id"),
        Index("ix_price_history_fetched_at", "fetched_at"),
        CheckConstraint("stock_status IN ('in_stock', 'out_of_stock', 'unknown')"),
        CheckConstraint("fetch_method IN ('http', 'playwright')"),
        CheckConstraint("fetch_status IN ('success', 'partial', 'failed')"),
    )

    def __repr__(self) -> str:
        return f"<PriceHistory(id={self.id}, product_id={self.product_id}, price={self.price}, fetched_at={self.fetched_at})>"


class ProductImage(Base):
    """
    Images produit (N images par produit).

    Unique constraint sur (product_id, image_url) évite doublons.
    Position permet de garder l'ordre des images.
    """

    __tablename__ = "product_images"

    # Primary key
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)

    # Foreign key
    product_id: Mapped[int] = mapped_column(
        Integer, ForeignKey("products.id", ondelete="CASCADE"), nullable=False
    )

    # Image data
    image_url: Mapped[str] = mapped_column(Text, nullable=False, comment="Image URL")
    position: Mapped[int] = mapped_column(
        Integer, nullable=False, default=0, comment="Image position (0=main)"
    )

    # Relationship
    product: Mapped["Product"] = relationship("Product", back_populates="images")

    # Constraints
    __table_args__ = (
        UniqueConstraint("product_id", "image_url", name="uq_product_image_url"),
        Index("ix_product_image_product_id", "product_id"),
    )

    def __repr__(self) -> str:
        return f"<ProductImage(id={self.id}, product_id={self.product_id}, position={self.position})>"


class ProductSpec(Base):
    """
    Caractéristiques produit (key-value).

    Unique constraint sur (product_id, spec_key) évite doublons.
    Permet queries efficaces par clé.
    """

    __tablename__ = "product_specs"

    # Primary key
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)

    # Foreign key
    product_id: Mapped[int] = mapped_column(
        Integer, ForeignKey("products.id", ondelete="CASCADE"), nullable=False
    )

    # Spec data
    spec_key: Mapped[str] = mapped_column(
        String(200), nullable=False, comment="Specification key (e.g., 'Brand', 'Color')"
    )
    spec_value: Mapped[str] = mapped_column(Text, nullable=False, comment="Specification value")

    # Relationship
    product: Mapped["Product"] = relationship("Product", back_populates="specs")

    # Constraints
    __table_args__ = (
        UniqueConstraint("product_id", "spec_key", name="uq_product_spec_key"),
        Index("ix_product_spec_product_id", "product_id"),
        Index("ix_product_spec_key", "spec_key"),
    )

    def __repr__(self) -> str:
        return f"<ProductSpec(id={self.id}, product_id={self.product_id}, key={self.spec_key})>"


class ScrapingLog(Base):
    """
    Logs observabilité pour debugging.

    FK optionnelle vers products (permet logs même si produit non créé).
    JSONB pour errors/notes car structure variable.
    Permet analytics: taux succès, durée moyenne, etc.
    """

    __tablename__ = "scraping_logs"

    # Primary key
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)

    # Foreign key (optional)
    product_id: Mapped[Optional[int]] = mapped_column(
        Integer, ForeignKey("products.id", ondelete="SET NULL"), nullable=True
    )

    # Scraping metadata
    url: Mapped[str] = mapped_column(Text, nullable=False, comment="Scraped URL")
    source: Mapped[str] = mapped_column(
        String(50), nullable=False, comment="Store ID (amazon, cdiscount, etc.)"
    )
    reference: Mapped[Optional[str]] = mapped_column(
        String(100), nullable=True, comment="Product reference (if extracted)"
    )

    # Fetch metadata
    fetch_method: Mapped[str] = mapped_column(
        String(20), nullable=False, comment="Fetch method (http, playwright)"
    )
    fetch_status: Mapped[str] = mapped_column(
        String(20), nullable=False, comment="Fetch status (success, partial, failed)"
    )
    fetched_at: Mapped[datetime] = mapped_column(
        TIMESTAMP, nullable=False, default=utcnow, comment="Scraping timestamp"
    )

    # Performance metrics
    duration_ms: Mapped[Optional[int]] = mapped_column(
        Integer, nullable=True, comment="Fetch duration in milliseconds"
    )
    html_size_bytes: Mapped[Optional[int]] = mapped_column(
        Integer, nullable=True, comment="HTML response size in bytes"
    )

    # Debug data (JSONB)
    errors: Mapped[Optional[list[str]]] = mapped_column(
        JSON().with_variant(JSONB, "postgresql"),
        nullable=True,
        comment="Error messages (list of strings)",
    )
    notes: Mapped[Optional[list[str]]] = mapped_column(
        JSON().with_variant(JSONB, "postgresql"),
        nullable=True,
        comment="Debug notes (list of strings)",
    )

    # Relationship
    product: Mapped[Optional["Product"]] = relationship("Product", back_populates="logs")

    # Constraints
    __table_args__ = (
        Index("ix_scraping_log_product_id", "product_id"),
        Index("ix_scraping_log_source", "source"),
        Index("ix_scraping_log_fetched_at", "fetched_at"),
        Index("ix_scraping_log_fetch_status", "fetch_status"),
        CheckConstraint("fetch_method IN ('http', 'playwright')"),
        CheckConstraint("fetch_status IN ('success', 'partial', 'failed')"),
    )

    def __repr__(self) -> str:
        return f"<ScrapingLog(id={self.id}, url={self.url}, status={self.fetch_status}, fetched_at={self.fetched_at})>"


class Webhook(Base):
    """
    Webhooks pour notifications externes.
    """

    __tablename__ = "webhooks"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    event: Mapped[str] = mapped_column(String(50), nullable=False, comment="Event name")
    url: Mapped[str] = mapped_column(Text, nullable=False, comment="Webhook URL")
    enabled: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
    secret: Mapped[Optional[str]] = mapped_column(
        String(200), nullable=True, comment="Secret optionnel"
    )
    created_at: Mapped[datetime] = mapped_column(
        TIMESTAMP, nullable=False, default=utcnow, comment="Creation timestamp"
    )

    __table_args__ = (
        Index("ix_webhook_event", "event"),
        Index("ix_webhook_enabled", "enabled"),
    )

    def __repr__(self) -> str:
        return f"<Webhook(id={self.id}, event={self.event}, url={self.url})>"