"""LLM Benchmarking Script

Measures token usage, costs, and timing for cancer risk assessments
across different LLM backends.
"""

import argparse
import csv
import functools
import os
import time
from collections import defaultdict
from collections.abc import Callable
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Any

import requests
import yaml
from dotenv import load_dotenv
from langchain_community.callbacks.manager import get_openai_callback
from loguru import logger
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib.units import inch
from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer, Table, TableStyle

from sentinel.config import AppConfig, ModelConfig, ResourcePaths
from sentinel.factory import SentinelFactory
from sentinel.utils import load_user_file

load_dotenv()


@dataclass
class ModelPricing:
    """Pricing per 1 million tokens in USD.

    Attributes:
        input_per_million: Cost per 1M input tokens (USD)
        output_per_million: Cost per 1M output tokens (USD)
    """

    input_per_million: float
    output_per_million: float


@dataclass
class BenchmarkModelConfig:
    """Model configuration for benchmarking.

    Attributes:
        provider: Provider key (google, openai, local)
        model_name: Model identifier used by the provider
        pricing: Pricing information per 1M tokens
    """

    provider: str
    model_name: str
    pricing: ModelPricing


# Sources:
# - https://ai.google.dev/pricing
# - https://openai.com/api/pricing/
BENCHMARK_MODELS = [
    BenchmarkModelConfig(
        provider="google",
        model_name="gemini-2.5-pro",
        pricing=ModelPricing(input_per_million=1.25, output_per_million=10.00),
    ),
    BenchmarkModelConfig(
        provider="google",
        model_name="gemini-2.5-flash-lite",
        pricing=ModelPricing(input_per_million=0.1, output_per_million=0.4),
    ),
]


@dataclass
class TokenUsage:
    """Token usage statistics for a single assessment.

    Attributes:
        input_tokens: Tokens in the prompt/input
        output_tokens: Tokens in the model's response
    """

    input_tokens: int
    output_tokens: int

    @property
    def total_tokens(self) -> int:
        """Total tokens used.

        Returns:
            Sum of input and output tokens
        """
        return self.input_tokens + self.output_tokens


@dataclass
class BenchmarkResult:
    """Results from a single model/profile benchmark run.

    Attributes:
        model_name: Name of the model
        provider: Provider key (openai, google, local)
        profile_name: Name of the profile
        token_usage: Token usage statistics
        cost: Cost in USD
        assessment_time_seconds: Time taken for assessment in seconds
    """

    model_name: str
    provider: str
    profile_name: str
    token_usage: TokenUsage
    cost: float
    assessment_time_seconds: float


def calculate_cost(token_usage: TokenUsage, pricing: ModelPricing) -> float:
    """Calculate cost based on token usage and model pricing.

    Args:
        token_usage: Token usage statistics
        pricing: Model pricing per 1M tokens

    Returns:
        Cost in USD
    """
    input_cost = (token_usage.input_tokens / 1_000_000) * pricing.input_per_million
    output_cost = (token_usage.output_tokens / 1_000_000) * pricing.output_per_million
    return input_cost + output_cost


def validate_directory_input(func: Callable[..., Any]) -> Callable[..., Any]:
    """Decorator to validate directory argument.

    Args:
        func: Function to decorate

    Returns:
        Decorated function that validates directory input
    """

    @functools.wraps(func)
    def wrapper(directory: Path, *args: Any, **kwargs: Any) -> Any:
        """Wrapper function to validate directory input.

        Args:
            directory: Path to directory to validate
            *args: Additional positional arguments
            **kwargs: Additional keyword arguments

        Returns:
            Result of the wrapped function

        Raises:
            FileNotFoundError: If the directory does not exist
            NotADirectoryError: If the path is not a directory
            ValueError: If the directory is empty
        """
        if not directory.exists():
            raise FileNotFoundError(f"Directory not found: {directory}")
        if not directory.is_dir():
            raise NotADirectoryError(f"Not a directory: {directory}")
        if not any(directory.iterdir()):
            raise ValueError(f"Directory is empty: {directory}")
        return func(directory, *args, **kwargs)

    return wrapper


def get_available_models() -> list[BenchmarkModelConfig]:
    """Get list of available models for benchmarking.

    Returns:
        List of configured benchmark models
    """
    return BENCHMARK_MODELS


@validate_directory_input
def load_benchmark_profiles(benchmark_dir: Path) -> list[dict[str, Any]]:
    """Load benchmark profiles.

    Args:
        benchmark_dir: Directory containing benchmark YAML files

    Returns:
        List of dicts with 'name' and 'path' keys
    """
    profiles = []
    for yaml_file in sorted(benchmark_dir.glob("*.yaml")):
        profiles.append({"name": yaml_file.stem, "path": yaml_file})
    return profiles


def create_knowledge_base_paths(workspace_root: Path) -> ResourcePaths:
    """Build resource path configuration from workspace root.

    Args:
        workspace_root: Path to workspace root directory

    Returns:
        ResourcePaths configuration object
    """
    return ResourcePaths(
        persona=workspace_root / "prompts/persona/default.md",
        instruction_assessment=workspace_root / "prompts/instruction/assessment.md",
        instruction_conversation=workspace_root / "prompts/instruction/conversation.md",
        output_format_assessment=workspace_root
        / "configs/output_format/assessment.yaml",
        output_format_conversation=workspace_root
        / "configs/output_format/conversation.yaml",
        cancer_modules_dir=workspace_root / "configs/knowledge_base/cancer_modules",
        dx_protocols_dir=workspace_root / "configs/knowledge_base/dx_protocols",
    )


def validate_backend(provider: str, model_name: str) -> None:
    """Validate that backend is accessible.

    Args:
        provider: Provider key (e.g. "openai", "google", "local")
        model_name: Model identifier

    Raises:
        ValueError: If the backend is not accessible
    """
    if provider == "openai":
        if not os.getenv("OPENAI_API_KEY"):
            raise ValueError("OPENAI_API_KEY not set")
    elif provider == "google":
        if not os.getenv("GOOGLE_API_KEY"):
            raise ValueError("GOOGLE_API_KEY not set")
    elif provider == "local":
        ollama_base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
        response = requests.get(f"{ollama_base_url}/api/tags", timeout=2)
        if response.status_code != 200:
            raise ValueError("Ollama server not responding")
        models = response.json().get("models", [])
        model_names = [m.get("name") for m in models]
        if model_name not in model_names:
            raise ValueError(f"Model not found. Run: ollama pull {model_name}")


def run_assessment(
    model_config: BenchmarkModelConfig, profile_path: Path
) -> BenchmarkResult:
    """Run a single assessment and capture token usage.

    Args:
        model_config: Model configuration with pricing
        profile_path: Path to profile YAML file

    Returns:
        BenchmarkResult with cost and token usage
    """
    validate_backend(model_config.provider, model_config.model_name)

    workspace_root = Path(__file__).parent.parent

    with open(workspace_root / "configs/config.yaml") as f:
        default_config = yaml.safe_load(f)

    app_config = AppConfig(
        model=ModelConfig(
            provider=model_config.provider,
            model_name=model_config.model_name,
        ),
        knowledge_base_paths=create_knowledge_base_paths(workspace_root),
        selected_cancer_modules=default_config["knowledge_base"]["cancer_modules"],
        selected_dx_protocols=default_config["knowledge_base"]["dx_protocols"],
    )

    factory = SentinelFactory(app_config)
    conversation = factory.create_conversation_manager()
    user = load_user_file(str(profile_path))

    start_time = time.perf_counter()
    with get_openai_callback() as cb:
        conversation.initial_assessment(user)
        input_tokens = cb.prompt_tokens
        output_tokens = cb.completion_tokens
    end_time = time.perf_counter()

    assessment_time = end_time - start_time
    token_usage = TokenUsage(input_tokens, output_tokens)
    cost = calculate_cost(token_usage, model_config.pricing)

    return BenchmarkResult(
        model_name=model_config.model_name,
        provider=model_config.provider,
        profile_name=profile_path.stem,
        token_usage=token_usage,
        cost=cost,
        assessment_time_seconds=assessment_time,
    )


def print_results(results: list[BenchmarkResult]) -> None:
    """Print formatted results to console.

    Args:
        results: List of benchmark results
    """
    by_model = defaultdict(list)
    for result in results:
        by_model[result.model_name].append(result)

    lines = []
    lines.append("\n╔══════════════════════════════════════════════════════════════╗")
    lines.append("║              LLM Cost Benchmark Results                      ║")
    lines.append("╚══════════════════════════════════════════════════════════════╝\n")

    for model_name, model_results in sorted(by_model.items()):
        provider = model_results[0].provider
        lines.append(f"Model: {model_name} ({provider})")

        num_results = len(model_results)
        avg_cost = sum(result.cost for result in model_results) / num_results
        avg_input = (
            sum(result.token_usage.input_tokens for result in model_results)
            / num_results
        )
        avg_output = (
            sum(result.token_usage.output_tokens for result in model_results)
            / num_results
        )
        avg_time = (
            sum(result.assessment_time_seconds for result in model_results)
            / num_results
        )

        for result_index, result in enumerate(model_results):
            is_last = result_index == num_results - 1
            prefix = "└─" if is_last else "├─"
            indent = "   " if is_last else "│  "
            lines.append(f"{prefix} Profile: {result.profile_name}")
            lines.append(f"{indent}├─ Input:  {result.token_usage.input_tokens:,}")
            lines.append(f"{indent}├─ Output: {result.token_usage.output_tokens:,}")
            lines.append(f"{indent}├─ Cost: ${result.cost:.4f}")
            lines.append(f"{indent}└─ Time: {result.assessment_time_seconds:.2f}s")

        lines.append(f"└─ Average: ${avg_cost:.4f}")
        lines.append(f"   ├─ Tokens: {avg_input:,.0f} input, {avg_output:,.0f} output")
        lines.append(f"   └─ Time: {avg_time:.2f}s\n")

    lines.append("═══════════════════════════════════════════════════════════════")
    lines.append("Summary - Model Ranking (Cheapest to Most Expensive)")
    lines.append("───────────────────────────────────────────────────────────────")

    model_averages = sorted(
        (
            (
                model_name,
                sum(result.cost for result in model_results) / len(model_results),
            )
            for model_name, model_results in by_model.items()
        ),
        key=lambda model_avg_tuple: model_avg_tuple[1],
    )

    for rank, (model_name, avg_cost) in enumerate(model_averages, 1):
        prefix = (
            "🥇"
            if rank == 1
            else "🥈"
            if rank == 2
            else "🥉"
            if rank == 3
            else f"{rank}."
        )
        lines.append(f"{prefix:<4} {model_name:<25} ${avg_cost:.4f}")

    lines.append("\n═══════════════════════════════════════════════════════════════")
    lines.append("Summary - Timing Performance (Fastest to Slowest)")
    lines.append("───────────────────────────────────────────────────────────────")

    model_timing = sorted(
        (
            (
                model_name,
                sum(result.assessment_time_seconds for result in model_results)
                / len(model_results),
            )
            for model_name, model_results in by_model.items()
        ),
        key=lambda model_time_tuple: model_time_tuple[1],
    )

    for rank, (model_name, avg_time) in enumerate(model_timing, 1):
        prefix = (
            "🥇"
            if rank == 1
            else "🥈"
            if rank == 2
            else "🥉"
            if rank == 3
            else f"{rank}."
        )
        lines.append(f"{prefix:<4} {model_name:<25} {avg_time:.2f}s")

    lines.append(f"\nTotal: {len(results)} assessments across {len(by_model)} models")
    lines.append("═══════════════════════════════════════════════════════════════\n")

    logger.info("\n".join(lines))


def export_to_csv(results: list[BenchmarkResult], output_path: Path) -> None:
    """Export results to CSV file.

    Args:
        results: List of benchmark results
        output_path: Path to output CSV file
    """
    with open(output_path, "w", newline="") as f:
        writer = csv.writer(f)
        writer.writerow(
            [
                "model_name",
                "provider",
                "profile_name",
                "input_tokens",
                "output_tokens",
                "total_tokens",
                "cost_usd",
                "assessment_time_seconds",
            ]
        )
        for result in results:
            writer.writerow(
                [
                    result.model_name,
                    result.provider,
                    result.profile_name,
                    result.token_usage.input_tokens,
                    result.token_usage.output_tokens,
                    result.token_usage.total_tokens,
                    f"{result.cost:.6f}",
                    f"{result.assessment_time_seconds:.3f}",
                ]
            )
    logger.success(f"Results exported to: {output_path}")


def export_to_pdf(
    results: list[BenchmarkResult],
    output_path: Path,
) -> None:
    """Export results to PDF file with formatted table.

    Args:
        results: List of benchmark results
        output_path: Path to output PDF file
    """
    doc = SimpleDocTemplate(
        str(output_path),
        pagesize=letter,
        leftMargin=0.75 * inch,
        rightMargin=0.75 * inch,
        topMargin=0.75 * inch,
        bottomMargin=0.75 * inch,
    )

    elements = []
    styles = getSampleStyleSheet()

    title = Paragraph(
        "<b>LLM Benchmark Report</b>",
        styles["Title"],
    )
    elements.append(title)
    elements.append(Spacer(1, 0.2 * inch))

    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    timestamp_text = Paragraph(
        f"Generated: {timestamp}",
        styles["Normal"],
    )
    elements.append(timestamp_text)
    elements.append(Spacer(1, 0.3 * inch))

    by_model = defaultdict(list)
    for result in results:
        by_model[result.model_name].append(result)

    pricing_lookup = {model.model_name: model.pricing for model in BENCHMARK_MODELS}

    results_desc = Paragraph(
        "Average cost and timing for running a single cancer risk assessment given a completed patient questionnaire.",
        styles["Normal"],
    )
    elements.append(results_desc)
    elements.append(Spacer(1, 0.2 * inch))

    table_data = [
        [
            "Model",
            "Provider",
            "Avg Cost\nper Report",
            "Input Price\n(per 1M)",
            "Output Price\n(per 1M)",
            "Avg Input\nTokens",
            "Avg Output\nTokens",
            "Avg Time\n(seconds)",
        ]
    ]

    # Sort by average cost (cheapest first)
    sorted_models = sorted(
        by_model.items(),
        key=lambda model_tuple: sum(result.cost for result in model_tuple[1])
        / len(model_tuple[1]),
    )

    for model_name, model_results in sorted_models:
        provider = model_results[0].provider
        num_results = len(model_results)
        avg_cost = sum(result.cost for result in model_results) / num_results
        avg_input = (
            sum(result.token_usage.input_tokens for result in model_results)
            / num_results
        )
        avg_output = (
            sum(result.token_usage.output_tokens for result in model_results)
            / num_results
        )
        avg_time = (
            sum(result.assessment_time_seconds for result in model_results)
            / num_results
        )

        pricing = pricing_lookup.get(model_name)
        input_price = f"${pricing.input_per_million:.2f}" if pricing else "N/A"
        output_price = f"${pricing.output_per_million:.2f}" if pricing else "N/A"

        table_data.append(
            [
                model_name,
                provider,
                f"${avg_cost:.4f}",
                input_price,
                output_price,
                f"{avg_input:,.0f}",
                f"{avg_output:,.0f}",
                f"{avg_time:.1f}",
            ]
        )

    table = Table(
        table_data,
        colWidths=[
            1.4 * inch,
            0.75 * inch,
            0.8 * inch,
            0.75 * inch,
            0.75 * inch,
            0.7 * inch,
            0.7 * inch,
            0.65 * inch,
        ],
    )

    table_style = TableStyle(
        [
            # Header styling
            ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#4A90E2")),
            ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
            ("ALIGN", (0, 0), (-1, 0), "CENTER"),
            ("VALIGN", (0, 0), (-1, 0), "MIDDLE"),
            ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
            ("FONTSIZE", (0, 0), (-1, 0), 8),
            ("BOTTOMPADDING", (0, 0), (-1, 0), 10),
            ("TOPPADDING", (0, 0), (-1, 0), 10),
            # Data rows styling
            ("BACKGROUND", (0, 1), (-1, -1), colors.beige),
            ("TEXTCOLOR", (0, 1), (-1, -1), colors.black),
            ("ALIGN", (0, 1), (1, -1), "LEFT"),
            ("ALIGN", (2, 1), (-1, -1), "CENTER"),
            ("VALIGN", (0, 1), (-1, -1), "MIDDLE"),
            ("FONTNAME", (0, 1), (-1, -1), "Helvetica"),
            ("FONTSIZE", (0, 1), (-1, -1), 8),
            ("TOPPADDING", (0, 1), (-1, -1), 7),
            ("BOTTOMPADDING", (0, 1), (-1, -1), 7),
            # Alternating row colors
            ("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.beige, colors.lightgrey]),
            # Grid
            ("GRID", (0, 0), (-1, -1), 1, colors.black),
        ]
    )

    table.setStyle(table_style)
    elements.append(table)
    elements.append(Spacer(1, 0.3 * inch))

    doc.build(elements)
    logger.success(f"PDF report generated: {output_path}")


def parse_args() -> argparse.Namespace:
    """Parse command-line arguments.

    Returns:
        Parsed command-line arguments
    """
    workspace_root = Path(__file__).parent.parent

    parser = argparse.ArgumentParser(description="Benchmark LLM costs")
    parser.add_argument(
        "--benchmark-dir",
        type=Path,
        default=workspace_root / "examples/benchmark",
        help="Benchmark profile directory",
    )
    parser.add_argument(
        "--models",
        nargs="+",
        help="Specific models to test (by name)",
    )
    parser.add_argument(
        "--profiles",
        nargs="+",
        help="Specific profiles to test",
    )
    parser.add_argument(
        "--output",
        type=Path,
        help="Export to CSV",
    )
    return parser.parse_args()


def main() -> None:
    """Main entry point.

    Raises:
        ValueError: If no matching models or profiles found
    """
    args = parse_args()

    logger.info("Loading benchmark configuration...")
    all_models = get_available_models()

    logger.info("Loading profiles...")
    all_profiles = load_benchmark_profiles(args.benchmark_dir)

    if args.models:
        all_models = [model for model in all_models if model.model_name in args.models]
        if not all_models:
            raise ValueError(f"No matching models: {args.models}")

    if args.profiles:
        all_profiles = [
            profile for profile in all_profiles if profile["name"] in args.profiles
        ]
        if not all_profiles:
            raise ValueError(f"No matching profiles: {args.profiles}")

    logger.info(
        f"\nRunning {len(all_models)} model(s) x {len(all_profiles)} profile(s)...\n"
    )

    results = []
    for model_index, model in enumerate(all_models, 1):
        for profile in all_profiles:
            logger.info(
                f"[{model_index}/{len(all_models)}] {model.model_name}: {profile['name']}"
            )
            result = run_assessment(model, profile["path"])
            results.append(result)

    print_results(results)

    # Generate PDF report with timestamp
    workspace_root = Path(__file__).parent.parent
    outputs_dir = workspace_root / "outputs"
    outputs_dir.mkdir(exist_ok=True)

    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    pdf_path = outputs_dir / f"llm_benchmark_{timestamp}.pdf"

    export_to_pdf(results, pdf_path)

    if args.output:
        export_to_csv(results, args.output)


if __name__ == "__main__":
    main()