from __future__ import annotations
from datetime import datetime
from sqlalchemy import DateTime, Float, ForeignKey, Index, Integer, SmallInteger, String, Text
from sqlalchemy.orm import Mapped, mapped_column
from .base import Base
from steward.core.ids import time_ordered_id


class MetricNorm(Base):
    """What one metric usually does in one time slot (#5739), learned from
    its own history by steward.core.norms.rebuild_norms.

    `kind` says what a slot is: "day" (0–23, the UTC hour of day) or "week"
    (0–167, Monday 00:00 UTC first). A key has slots of one kind at a time.
    The whole table is rebuilt each pass, so a row is never edited."""
    __tablename__ = "metric_norms"

    source_module: Mapped[str] = mapped_column(String(64), primary_key=True)
    resource_name: Mapped[str] = mapped_column(String(255), primary_key=True)
    metric_name: Mapped[str] = mapped_column(String(128), primary_key=True)
    slot: Mapped[int] = mapped_column(SmallInteger, primary_key=True)
    kind: Mapped[str] = mapped_column(String(8), nullable=False)
    # The level the slot's hours usually held, and how high they usually
    # peaked: the 90th percentile, across past days, of each hour's p95 and max.
    usual_hold: Mapped[float] = mapped_column(Float, nullable=False)
    usual_peak: Mapped[float] = mapped_column(Float, nullable=False)
    days_seen: Mapped[int] = mapped_column(Integer, nullable=False)
    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)


class MetricAnomaly(Base):
    """A metric that departed from its normal (#5740): one item to review.

    A departure that recurs while the item is still to review joins it as
    another occurrence (a MetricAnomalySpan) rather than adding a row, so the
    list stays one row per resource and metric. `review` is to_review until
    someone marks it normal (its hours are learned) or real (they are not);
    an ended item nobody reviewed within anomalies.review_days becomes lapsed,
    and is not learned either."""
    __tablename__ = "metric_anomalies"

    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
    source_module: Mapped[str] = mapped_column(String(64), nullable=False)
    resource_name: Mapped[str] = mapped_column(String(255), nullable=False)
    metric_name: Mapped[str] = mapped_column(String(128), nullable=False)
    # The first occurrence's start, the latest's start, and the latest's end:
    # NULL while an occurrence is still going.
    started_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
    last_started_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
    ended_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
    occurrences: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
    # The highest 15-minute level any occurrence held, and the normal for the
    # hour it was reached in.
    peak_level: Mapped[float] = mapped_column(Float, nullable=False)
    peak_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
    usual_hold: Mapped[float] = mapped_column(Float, nullable=False)
    usual_peak: Mapped[float] = mapped_column(Float, nullable=False)
    review: Mapped[str] = mapped_column(String(12), nullable=False, default="to_review")
    reviewed_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
    reviewed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
    note: Mapped[str | None] = mapped_column(Text, nullable=True)

    __table_args__ = (
        Index("ix_metric_anomalies_key", "source_module", "resource_name", "metric_name"),
        Index("ix_metric_anomalies_review_last", "review", "last_started_at"),
    )


class MetricAnomalySpan(Base):
    """One occurrence of an anomaly: when the metric was out of its normal.
    Learning skips the hours a span covers unless its anomaly was marked
    normal, and a resource's chart shades them."""
    __tablename__ = "metric_anomaly_spans"

    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=time_ordered_id)
    anomaly_id: Mapped[str] = mapped_column(
        String(36), ForeignKey("metric_anomalies.id", ondelete="CASCADE"), nullable=False, index=True)
    started_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
    ended_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
    peak_level: Mapped[float] = mapped_column(Float, nullable=False)