from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path
from typing import Callable, Optional, TextIO

from PIL import Image

from .pipeline import CoRGIPipeline
from .qwen_client import Qwen3VLClient, QwenGenerationConfig
from .types import GroundedEvidence, ReasoningStep

DEFAULT_MODEL_ID = "Qwen/Qwen3-VL-2B-Instruct"


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        prog="corgi-cli",
        description="Run the CoRGI reasoning pipeline over an image/question pair.",
    )
    parser.add_argument("--image", type=Path, required=True, help="Path to the input image (jpg/png/etc.)")
    parser.add_argument("--question", type=str, required=True, help="Visual question for the image")
    parser.add_argument("--max-steps", type=int, default=3, help="Maximum number of reasoning steps to request")
    parser.add_argument(
        "--max-regions",
        type=int,
        default=3,
        help="Maximum number of grounded regions per visual step",
    )
    parser.add_argument(
        "--model-id",
        type=str,
        default=None,
        help="Optional override for the Qwen3-VL model identifier",
    )
    parser.add_argument(
        "--json-out",
        type=Path,
        default=None,
        help="Optional path to write the pipeline result as JSON",
    )
    return parser


def _format_step(step: ReasoningStep) -> str:
    needs = "yes" if step.needs_vision else "no"
    suffix = f"; reason: {step.reason}" if step.reason else ""
    return f"[{step.index}] {step.statement} (needs vision: {needs}{suffix})"


def _format_evidence_item(evidence: GroundedEvidence) -> str:
    bbox = ", ".join(f"{coord:.2f}" for coord in evidence.bbox)
    parts = [f"Step {evidence.step_index} | bbox=({bbox})"]
    if evidence.description:
        parts.append(f"desc: {evidence.description}")
    if evidence.confidence is not None:
        parts.append(f"conf: {evidence.confidence:.2f}")
    return " | ".join(parts)


def _default_pipeline_factory(model_id: Optional[str]) -> CoRGIPipeline:
    config = QwenGenerationConfig(model_id=model_id or DEFAULT_MODEL_ID)
    client = Qwen3VLClient(config=config)
    return CoRGIPipeline(vlm_client=client)


def execute_cli(
    *,
    image_path: Path,
    question: str,
    max_steps: int,
    max_regions: int,
    model_id: Optional[str],
    json_out: Optional[Path],
    pipeline_factory: Callable[[Optional[str]], CoRGIPipeline] | None = None,
    output_stream: TextIO | None = None,
) -> None:
    if output_stream is None:
        output_stream = sys.stdout
    factory = pipeline_factory or _default_pipeline_factory

    with Image.open(image_path) as img:
        image = img.convert("RGB")
        pipeline = factory(model_id)
        result = pipeline.run(
            image=image,
            question=question,
            max_steps=max_steps,
            max_regions=max_regions,
        )

    print(f"Question: {question}", file=output_stream)
    print("-- Steps --", file=output_stream)
    for step in result.steps:
        print(_format_step(step), file=output_stream)
    if not result.steps:
        print("(no reasoning steps returned)", file=output_stream)

    print("-- Evidence --", file=output_stream)
    if result.evidence:
        for evidence in result.evidence:
            print(_format_evidence_item(evidence), file=output_stream)
    else:
        print("(no visual evidence)", file=output_stream)

    print("-- Answer --", file=output_stream)
    print(f"Answer: {result.answer}", file=output_stream)

    if json_out is not None:
        json_out.parent.mkdir(parents=True, exist_ok=True)
        with json_out.open("w", encoding="utf-8") as handle:
            json.dump(result.to_json(), handle, ensure_ascii=False, indent=2)


def main(argv: Optional[list[str]] = None) -> int:
    parser = build_parser()
    args = parser.parse_args(argv)
    execute_cli(
        image_path=args.image,
        question=args.question,
        max_steps=args.max_steps,
        max_regions=args.max_regions,
        model_id=args.model_id,
        json_out=args.json_out,
    )
    return 0


__all__ = ["build_parser", "execute_cli", "main"]