"""
GAIA Test Bench - Local evaluation for your agent

Usage:
    uv run python test_bench.py                    # Run on 5 random questions
    uv run python test_bench.py --n 10             # Run on 10 random questions
    uv run python test_bench.py --level 1          # Run on level 1 only
    uv run python test_bench.py --level 1 --n 3    # Run on 3 level 1 questions
    uv run python test_bench.py --all              # Run on all validation questions
    uv run python test_bench.py --task-id 1234     # Run on specific task ID

    uv run python test_bench.py --type youtube,file,web,excel,pdf,image,audio,text-only
"""

import argparse
import json
import os
import re
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path

from datasets import load_dataset
from dotenv import load_dotenv
from huggingface_hub import snapshot_download
from pydantic import BaseModel, Field

load_dotenv()


class AnnotatorMetadata(BaseModel):
    """Metadata from GAIA annotators about how to solve the task."""

    steps: str = Field(default="", alias="Steps")
    number_of_steps: str = Field(default="", alias="Number of steps")
    tools: str = Field(default="", alias="Tools")
    number_of_tools: str = Field(default="", alias="Number of tools")
    how_long: str = Field(default="", alias="How long did this take?")


class GaiaQuestion(BaseModel):
    """A single question from the GAIA dataset."""

    model_config = {"populate_by_name": True}

    task_id: str
    question: str = Field(alias="Question")
    level: int = Field(alias="Level")
    final_answer: str = Field(default="", alias="Final answer")
    file_name: str = ""
    file_path: str | None = None
    annotator: AnnotatorMetadata = Field(
        default_factory=AnnotatorMetadata, alias="Annotator Metadata"
    )


@dataclass
class TestResult:
    """Result of running an agent on a GAIA question."""

    task_id: str
    question: str
    expected: str
    actual: str
    correct: bool
    level: int
    file_name: str = ""
    file_path: str | None = None
    annotator_steps: str = ""
    annotator_tools: str = ""
    annotator_number_of_steps: str = ""


def normalize_answer(answer: str) -> str:
    """Normalize answer for comparison."""
    if not answer:
        return ""
    ans = answer.lower().strip()
    ans = re.sub(r"[.,;:!?]+$", "", ans)
    return ans


def answers_match(expected: str, actual: str) -> bool:
    """Check if answers match (with some flexibility)."""
    exp = normalize_answer(expected)
    act = normalize_answer(actual)

    if exp == act:
        return True

    if exp in act:
        return True

    try:
        exp_num = float(re.sub(r"[^\d.-]", "", exp))
        act_num = float(re.sub(r"[^\d.-]", "", act))
        if abs(exp_num - act_num) < 0.01:
            return True
    except (ValueError, TypeError):
        pass

    return False


def filter_by_type(
    questions: list[GaiaQuestion], task_type: str | None
) -> list[GaiaQuestion]:
    """Filter questions by task type."""
    if not task_type:
        return questions

    filtered = []
    for q in questions:
        question_text = q.question.lower()
        file_name = q.file_name.lower()

        match task_type:
            case "youtube":
                if "youtube.com" in question_text or "youtu.be" in question_text:
                    filtered.append(q)
            case "file":
                if q.file_name:
                    filtered.append(q)
            case "web":
                if "http://" in question_text or "https://" in question_text:
                    filtered.append(q)
            case "excel":
                if file_name.endswith((".xlsx", ".xls", ".csv")):
                    filtered.append(q)
            case "pdf":
                if file_name.endswith(".pdf"):
                    filtered.append(q)
            case "image":
                if file_name.endswith((".png", ".jpg", ".jpeg", ".gif", ".webp")):
                    filtered.append(q)
            case "audio":
                if file_name.endswith((".mp3", ".wav", ".m4a", ".flac", ".ogg")):
                    filtered.append(q)
            case "text-only":
                has_url = "http://" in question_text or "https://" in question_text
                has_file = bool(q.file_name)
                if not has_url and not has_file:
                    filtered.append(q)
            case _:
                filtered.append(q)

    return filtered


def filter_by_task_id(
    questions: list[GaiaQuestion], task_id: str | None
) -> list[GaiaQuestion]:
    """Filter questions by task id."""
    if not task_id:
        return questions

    filtered = []
    for q in questions:
        if q.task_id == task_id:
            filtered.append(q)

    return filtered


def load_gaia_data(level: int | None = None) -> list[GaiaQuestion]:
    """Load GAIA validation dataset with all metadata."""
    print("Downloading GAIA dataset...")
    data_dir = snapshot_download(repo_id="gaia-benchmark/GAIA", repo_type="dataset")

    questions: list[GaiaQuestion] = []

    levels = [level] if level else [1, 2, 3]
    for lvl in levels:
        try:
            dataset = load_dataset(data_dir, f"2023_level{lvl}", split="validation")

            for example in dataset:
                question = GaiaQuestion.model_validate(example)
                # Fix file_path to be absolute
                if question.file_path:
                    question.file_path = os.path.join(data_dir, question.file_path)
                questions.append(question)

        except Exception as e:
            print(f"Warning: Could not load level {lvl}: {e}")

    print(f"Loaded {len(questions)} questions")
    return questions


def run_test_bench(
    agent,
    n_questions: int | None = 5,
    level: int | None = None,
    task_type: str | None = None,
    run_all: bool = False,
    save_results: bool = True,
    task_id: str | None = None,
) -> list[TestResult]:
    """
    Run the test bench on the agent.

    Args:
        agent: The agent to test (callable that takes question string)
        n_questions: Number of questions to test (None for all)
        level: Filter by difficulty level (1, 2, or 3)
        task_type: Filter by task type (youtube, file, web, excel, pdf, image, audio, text-only)
        run_all: If True, run on all questions
        save_results: Save results to JSON file

    Returns:
        List of TestResult objects
    """
    import random

    questions = load_gaia_data(level=level)
    questions = filter_by_type(questions, task_type)
    questions = filter_by_task_id(questions, task_id)

    if not questions:
        print(f"No questions found for type '{task_type}'")
        return []

    if not run_all and n_questions and n_questions < len(questions):
        questions = random.sample(questions, n_questions)

    results: list[TestResult] = []
    correct_count = 0

    print(f"\n{'='*60}")
    print(f"Running {len(questions)} questions...")
    print("=" * 60)

    for i, q in enumerate(questions, 1):
        print(f"\n[{i}/{len(questions)}] Level {q.level}: {q.question[:80]}...")

        if q.file_name:
            print(f"  File: {q.file_name}")
        if q.annotator.tools:
            print(f"  Tools needed:\n{q.annotator.tools}")

        try:
            question_with_file = q.question
            if q.file_path:
                question_with_file += f"\n\nFile path: {q.file_path}"
            import asyncio

            actual = asyncio.run(agent(question_with_file))
        except Exception as e:
            actual = f"ERROR: {e}"

        is_correct = answers_match(q.final_answer, actual)
        if is_correct:
            correct_count += 1

        result = TestResult(
            task_id=q.task_id,
            question=q.question,
            expected=q.final_answer,
            actual=actual,
            correct=is_correct,
            level=q.level,
            file_name=q.file_name,
            file_path=q.file_path,
            annotator_steps=q.annotator.steps,
            annotator_tools=q.annotator.tools,
            annotator_number_of_steps=q.annotator.number_of_steps,
        )
        results.append(result)

        status = "CORRECT" if is_correct else "WRONG"
        print(f"  Expected: {q.final_answer}")
        print(f"  Got:      {actual[:100]}...")
        print(f"  Status:   {status}")

    # Summary
    print(f"\n{'='*60}")
    print("SUMMARY")
    print("=" * 60)
    print(f"Total:   {len(results)}")
    print(f"Correct: {correct_count}")
    print(f"Accuracy: {correct_count/len(results)*100:.1f}%")

    # Per-level breakdown
    for lvl in [1, 2, 3]:
        lvl_results = [r for r in results if r.level == lvl]
        if lvl_results:
            lvl_correct = sum(1 for r in lvl_results if r.correct)
            print(
                f"  Level {lvl}: {lvl_correct}/{len(lvl_results)} ({lvl_correct/len(lvl_results)*100:.1f}%)"
            )

    # Tool usage analysis
    print(f"\n{'='*60}")
    print("TOOL REQUIREMENTS ANALYSIS")
    print("=" * 60)
    tool_stats: dict[str, dict] = {}
    for r in results:
        tools = (
            r.annotator_tools
            if isinstance(r.annotator_tools, str)
            else ", ".join(r.annotator_tools)
        )
        if tools:
            if tools not in tool_stats:
                tool_stats[tools] = {"total": 0, "correct": 0}
            tool_stats[tools]["total"] += 1
            if r.correct:
                tool_stats[tools]["correct"] += 1

    for tools, stats in sorted(tool_stats.items(), key=lambda x: -x[1]["total"]):
        acc = stats["correct"] / stats["total"] * 100 if stats["total"] > 0 else 0
        print(f"  {tools}: {stats['correct']}/{stats['total']} ({acc:.0f}%)")

    # Save results
    if save_results:
        results_dir = Path("test_results")
        results_dir.mkdir(exist_ok=True)

        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        results_file = results_dir / f"results_{timestamp}.json"

        with open(results_file, "w") as f:
            json.dump(
                {
                    "timestamp": timestamp,
                    "total": len(results),
                    "correct": correct_count,
                    "accuracy": correct_count / len(results),
                    "results": [
                        {
                            "task_id": r.task_id,
                            "level": r.level,
                            "question": r.question,
                            "expected": r.expected,
                            "actual": r.actual,
                            "correct": r.correct,
                            "file_name": r.file_name,
                            "file_path": r.file_path,
                            "annotator_steps": r.annotator_steps,
                            "annotator_tools": r.annotator_tools,
                            "annotator_number_of_steps": r.annotator_number_of_steps,
                        }
                        for r in results
                    ],
                },
                f,
                indent=2,
            )
        print(f"\nResults saved to: {results_file}")

    return results


def main():
    parser = argparse.ArgumentParser(description="GAIA Test Bench")
    parser.add_argument("--n", type=int, default=5, help="Number of questions to test")
    parser.add_argument("--level", type=int, choices=[1, 2, 3], help="Filter by level")
    parser.add_argument(
        "--type",
        choices=[
            "youtube",
            "file",
            "web",
            "excel",
            "pdf",
            "image",
            "audio",
            "text-only",
        ],
        help="Filter by task type",
    )
    parser.add_argument("--all", action="store_true", help="Run all questions")
    parser.add_argument("--no-save", action="store_true", help="Don't save results")
    parser.add_argument("--task-id", type=str, help="Run specific task ID")
    args = parser.parse_args()

    from agent import BasicAgent

    if callable(BasicAgent) and not hasattr(BasicAgent, "__call__"):
        agent = BasicAgent(question="")
    else:
        agent = BasicAgent

    run_test_bench(
        agent=agent,
        n_questions=args.n,
        level=args.level,
        task_type=args.type,
        run_all=args.all,
        save_results=not args.no_save,
        task_id=args.task_id,
    )


if __name__ == "__main__":
    main()