"""normalize video titles for display; keep the raw one in original_title

Revision ID: 0039_title_normalize
Revises: 0038_asset_gc_notified
Create Date: 2026-07-03

Adds videos.original_title (the raw YouTube title) and rewrites videos.title to a normalized,
display-friendly form (emoji stripped, ALL-CAPS de-shouted, trailing SEO hashtags removed —
see app.titles). Reversible: original_title preserves the source, and title can be re-derived.
The generated search_vector column regenerates automatically as each title is updated.
"""
from typing import Sequence, Union

import sqlalchemy as sa
from alembic import op

from app.titles import normalize_title

revision: str = "0039_title_normalize"
down_revision: Union[str, None] = "0038_asset_gc_notified"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None

_BATCH = 2000


def upgrade() -> None:
    op.add_column("videos", sa.Column("original_title", sa.Text(), nullable=True))
    conn = op.get_bind()
    # Preserve the raw title first (fast, set-based).
    conn.execute(sa.text("UPDATE videos SET original_title = title WHERE title IS NOT NULL"))
    # Then rewrite title to the normalized form, batched (each update regenerates its FTS vector).
    rows = conn.execute(
        sa.text("SELECT id, original_title FROM videos WHERE original_title IS NOT NULL")
    ).fetchall()
    changes = []
    for vid, raw in rows:
        norm = normalize_title(raw)
        if norm != raw:
            changes.append({"i": vid, "t": norm})
    stmt = sa.text("UPDATE videos SET title = :t WHERE id = :i")
    for start in range(0, len(changes), _BATCH):
        conn.execute(stmt, changes[start : start + _BATCH])


def downgrade() -> None:
    conn = op.get_bind()
    conn.execute(sa.text("UPDATE videos SET title = original_title WHERE original_title IS NOT NULL"))
    op.drop_column("videos", "original_title")