"""Pretty printing for the Lint pass output.

Called from scripts/10_run_lint.py after a run, and from
scripts/11_show_contradictions.py for ad-hoc inspection of the state file
without re-running classification.
"""

from __future__ import annotations

from rich.console import Console
from rich.panel import Panel
from rich.table import Table

from lint._state import LintState, LintStats, PairVerdict

console = Console()


def render_stats(stats: LintStats) -> None:
    t = Table(title="Lint pass stats", show_lines=False)
    t.add_column("Metric", style="bold")
    t.add_column("Count", justify="right")
    t.add_row("atoms seen", str(stats.atoms_seen))
    t.add_row("candidate pairs", str(stats.candidates_generated))
    t.add_row("[dim]skipped (cached)[/dim]", str(stats.pairs_skipped_cached))
    t.add_row("pairs evaluated", str(stats.pairs_evaluated))
    t.add_row("[red]CONTRADICTORY[/red]", str(stats.contradictory))
    t.add_row("[yellow]EQUIVALENT[/yellow]", str(stats.equivalent))
    t.add_row("[dim]INCOMPARABLE[/dim]", str(stats.incomparable))
    t.add_row("errors", str(stats.errors))
    console.print(t)


def render_contradictions(
    state: LintState,
    *,
    top_n: int = 10,
    min_confidence: float = 0.7,
) -> None:
    contras = [v for v in state.all_contradictions if v.confidence >= min_confidence]
    contras.sort(key=lambda v: (-v.confidence, -v.similarity))

    header = (
        f"[bold red]CONTRADICTIONS[/bold red] "
        f"({len(contras)} with confidence >= {min_confidence:.2f}, showing top {top_n})"
    )
    console.print(Panel.fit(header))

    if not contras:
        console.print(
            "[dim]no contradictions above threshold. "
            "The current Wikipedia-only corpus is self-consistent by design, "
            "which is expected for a single well-curated source. "
            "Add diverse sources to surface real disagreement.[/dim]"
        )
        return

    for i, v in enumerate(contras[:top_n], 1):
        console.print()
        console.print(
            f"[bold]#{i}[/bold]  "
            f"confidence=[red]{v.confidence:.2f}[/red]  "
            f"(embed sim {v.similarity:.2f})"
        )
        console.print(f"  [green]A:[/green] {v.atom_a_claim[:240]}")
        console.print(f"     [dim]→ {v.atom_a_url}[/dim]")
        console.print(f"  [red]B:[/red] {v.atom_b_claim[:240]}")
        console.print(f"     [dim]→ {v.atom_b_url}[/dim]")


def render_equivalents(
    state: LintState, *, top_n: int = 10, min_confidence: float = 0.85
) -> None:
    equivs = [v for v in state.all_equivalents if v.confidence >= min_confidence]
    equivs.sort(key=lambda v: (-v.confidence, -v.similarity))

    header = (
        f"[bold yellow]PARAPHRASE CLUSTERS[/bold yellow] "
        f"({len(equivs)} with confidence >= {min_confidence:.2f}, showing top {top_n})"
    )
    console.print()
    console.print(Panel.fit(header))

    if not equivs:
        console.print("[dim]no paraphrase clusters above threshold[/dim]")
        return

    for i, v in enumerate(equivs[:top_n], 1):
        console.print()
        console.print(f"[bold]#{i}[/bold]  confidence=[yellow]{v.confidence:.2f}[/yellow]")
        console.print(f"  · {v.atom_a_claim[:200]}")
        console.print(f"  · {v.atom_b_claim[:200]}")