"""kdc ingest command — full PDF ingestion pipeline."""

from __future__ import annotations

from pathlib import Path

import typer
from rich.console import Console
from rich.panel import Panel
from rich.progress import (
    BarColumn,
    MofNCompleteColumn,
    Progress,
    SpinnerColumn,
    TextColumn,
    TimeElapsedColumn,
)
from rich.table import Table

from app.collectors.local import LocalCollector
from app.collectors.pipeline import IngestionPipeline, IngestionStats
from app.models.document import DocumentMeta
from app.utils.container import Container
from app.utils.file import human_size

app = typer.Typer(help="Ingest PDFs, calculate SHA256, deduplicate, store, and build metadata.")
console = Console()


@app.command()
def run(
    ctx: typer.Context,
    source: Path = typer.Option(
        Path("pdf"),
        "--source",
        "-s",
        "--pdf-dir",
        help="Input directory containing PDFs.",
    ),
    pdf_dir: Path = typer.Option(
        Path("pdf"),
        "--pdf-dir-out",
        help="Output directory to store organized corpus PDFs.",
    ),
    meta_dir: Path = typer.Option(
        Path("metadata"), "--meta-dir", help="Metadata JSON output directory."
    ),
    render_previews: bool = typer.Option(
        True, "--preview/--no-preview", help="Render preview images."
    ),
    export_dataset: bool = typer.Option(
        True, "--export/--no-export", help="Export Parquet/JSONL datasets."
    ),
) -> None:
    """Ingest PDFs from SOURCE folder, calculate SHA256, assign UUIDs, store PDFs, build metadata, skip duplicates."""
    container: Container = ctx.obj
    source_dir = source.resolve()
    pdf_dir_out = pdf_dir.resolve()
    meta_dir_out = meta_dir.resolve()

    meta_dir_out.mkdir(parents=True, exist_ok=True)
    pdf_dir_out.mkdir(parents=True, exist_ok=True)

    collector = LocalCollector(pdf_dir=source_dir)
    collected_files = list(collector.collect())

    if not collected_files:
        console.print(f"[yellow]No PDF files found in {source_dir}[/yellow]")
        raise typer.Exit()

    pipeline = IngestionPipeline(
        extractor=container.extractor,
        schema_validator=container.schema_validator,
        content_validator=container.content_validator,
        dedup_registry=container.dedup_registry,
        preview_renderer=container.renderer,
        parquet_exporter=container.parquet_exporter,
        jsonl_exporter=container.jsonl_exporter,
    )

    stats = IngestionStats(total_found=len(collected_files))
    valid_docs: list[DocumentMeta] = []

    console.print(
        Panel.fit(
            f"[bold cyan]Ingesting {stats.total_found} PDF(s) from [yellow]{source_dir}[/yellow]"
        )
    )

    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        BarColumn(),
        MofNCompleteColumn(),
        TimeElapsedColumn(),
        console=console,
    ) as progress:
        task = progress.add_task("Processing PDFs...", total=stats.total_found)

        for item in collected_files:
            progress.update(task, description=f"[cyan]Ingesting {item.path.name[:30]}[/cyan]")
            doc_meta, status = pipeline.process_file(
                pdf_path=item.path,
                category_hint=item.category_hint,
                target_pdf_dir=pdf_dir_out,
                target_meta_dir=meta_dir_out,
                render_preview=render_previews,
            )

            if status == "success" and doc_meta is not None:
                valid_docs.append(doc_meta)
                stats.ingested += 1
                stats.total_pages += doc_meta.pages
                stats.total_bytes += doc_meta.file_size_bytes
            elif status == "duplicate":
                stats.duplicates_skipped += 1
            elif status == "invalid":
                stats.invalid_skipped += 1
            else:
                stats.errors += 1

            progress.advance(task)

    if container.dedup_registry:
        container.dedup_registry.save()

    # Rich summary table
    table = Table(
        title="Ingestion Pipeline Summary",
        show_header=True,
        header_style="bold green",
    )
    table.add_column("Metric", style="cyan")
    table.add_column("Value", justify="right", style="bold white")

    table.add_row("Total Files Discovered", str(stats.total_found))
    table.add_row("Successfully Ingested & Stored", f"[green]{stats.ingested}[/green]")
    table.add_row("Duplicates Skipped (SHA256)", f"[yellow]{stats.duplicates_skipped}[/yellow]")
    table.add_row("Invalid Files Skipped", f"[red]{stats.invalid_skipped}[/red]")
    table.add_row("Extraction Errors", f"[red]{stats.errors}[/red]")
    table.add_row("Total Pages Extracted", str(stats.total_pages))
    table.add_row("Total Corpus Size", human_size(stats.total_bytes))

    console.print()
    console.print(table)

    # Export datasets
    if export_dataset and valid_docs:
        if container.parquet_exporter:
            p_path = container.parquet_exporter.export(valid_docs)
            console.print(f"[green]✓ Parquet dataset exported to {p_path}[/green]")
        if container.jsonl_exporter:
            j_path = container.jsonl_exporter.export(valid_docs)
            console.print(f"[green]✓ JSONL dataset exported to {j_path}[/green]")