#!/usr/bin/env python3
"""
Agent-Level Time Comparison Analysis Script

This script analyzes agent time data from Part2 directories, comparing:
- MCP vs Hardcoded
- MCP vs A2A
- A2A vs A2A_mix

For each comparison, it shows:
- Agent time proportions (percentage of total time)
- Actual agent times (mean time per occurrence)
- Differences in both absolute and percentage terms
"""

import csv
import re
from pathlib import Path
from collections import defaultdict
from typing import Dict, List, Tuple, Optional
import logging

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)


def parse_agent_map(agent_map_path: Path) -> Dict[str, str]:
    """
    Parse agent_map.md file to extract agent name mappings.

    Format example:
    --agent-map "Expert SQL Query Generator:SQL Query Generator"

    Returns:
        Dict mapping original name -> standardized name
    """
    agent_map = {}

    if not agent_map_path.exists():
        logger.warning(f"Agent map not found: {agent_map_path}")
        return agent_map

    try:
        with open(agent_map_path, "r", encoding="utf-8") as f:
            content = f.read()

        # Pattern to match --agent-map "Original Name:Standardized Name"
        pattern = r'--agent-map\s+"([^:]+):([^"]+)"'
        matches = re.findall(pattern, content)

        for original, standardized in matches:
            agent_map[original.strip()] = standardized.strip()

        logger.info(f"Loaded {len(agent_map)} agent mappings from {agent_map_path}")
    except Exception as e:
        logger.error(f"Failed to parse agent map {agent_map_path}: {e}")

    return agent_map


def load_agent_time_data(
    csv_path: Path, agent_map: Dict[str, str]
) -> Dict[str, Dict[str, float]]:
    """
    Load agent time data from agent_llm_tool_breakdown_by_model.csv.

    Returns:
        Dict[model][agent] = {
            'mean_time': mean time per occurrence in seconds,
            'total_time': total time in seconds,
            'occurrences': number of occurrences
        }
    """
    agent_data = defaultdict(lambda: defaultdict(dict))

    if not csv_path.exists():
        logger.warning(f"CSV file not found: {csv_path}")
        return agent_data

    try:
        with open(csv_path, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for row in reader:
                model = row["model"]
                agent_name = row["agent_name"]

                # Apply agent name mapping if available
                standardized_name = agent_map.get(agent_name, agent_name)

                occurrences = int(row["occurrences"])
                total_time_ms = float(row["total_agent_llm_tool_time_ms"])
                total_time_s = total_time_ms / 1000.0  # Convert to seconds

                mean_time = total_time_s / occurrences if occurrences > 0 else 0

                agent_data[model][standardized_name] = {
                    "mean_time": mean_time,
                    "total_time": total_time_s,
                    "occurrences": occurrences,
                }

        logger.info(f"Loaded agent data from {csv_path}")
    except Exception as e:
        logger.error(f"Failed to load agent data from {csv_path}: {e}")

    return agent_data


def calculate_agent_proportions(
    agent_data: Dict[str, Dict[str, float]],
) -> Dict[str, Dict[str, float]]:
    """
    Calculate what proportion of total time each agent takes per model.

    Returns:
        Dict[model][agent] = proportion (0-1)
    """
    proportions = defaultdict(dict)

    for model, agents in agent_data.items():
        # Calculate total time across all agents for this model
        total_time = sum(data["total_time"] for data in agents.values())

        if total_time > 0:
            for agent, data in agents.items():
                proportions[model][agent] = data["total_time"] / total_time
        else:
            for agent in agents.keys():
                proportions[model][agent] = 0.0

    return proportions


def generate_project_comparison(
    project_name: str,
    version_a_suffix: str,
    version_b_suffix: str,
    part2_dir: Path,
    version_a_name: str,
    version_b_name: str,
) -> str:
    """Generate agent-level comparison for a single project, organized by agent."""
    lines = []
    lines.append(f"# {project_name}: {version_a_name} vs {version_b_name}\n\n")

    # Get paths
    scenario_a = f"{project_name}{version_a_suffix}"
    scenario_b = f"{project_name}{version_b_suffix}"

    dir_a = part2_dir / scenario_a
    dir_b = part2_dir / scenario_b

    if not dir_a.exists() or not dir_b.exists():
        lines.append("_Data not available for comparison_\n\n")
        return "".join(lines)

    # Load agent maps
    map_a = parse_agent_map(dir_a / "agent_map.md")
    map_b = parse_agent_map(dir_b / "agent_map.md")

    # Load agent data
    data_a = load_agent_time_data(
        dir_a / "agent_llm_tool_breakdown_by_model.csv", map_a
    )
    data_b = load_agent_time_data(
        dir_b / "agent_llm_tool_breakdown_by_model.csv", map_b
    )

    if not data_a or not data_b:
        lines.append("_No agent data available_\n\n")
        return "".join(lines)

    # Calculate proportions
    prop_a = calculate_agent_proportions(data_a)
    prop_b = calculate_agent_proportions(data_b)

    # Get all models and agents
    all_models = sorted(set(data_a.keys()) | set(data_b.keys()))

    # Get all unique agents across all models
    all_agents = set()
    for model in all_models:
        all_agents.update(data_a.get(model, {}).keys())
        all_agents.update(data_b.get(model, {}).keys())
    all_agents = sorted(all_agents)

    # Organize by agent
    for agent in all_agents:
        lines.append(f"## Agent: {agent}\n\n")

        # Per-model comparison for this agent
        lines.append(f"### Per-Model Comparison\n\n")
        lines.append(
            f"| Model | {version_a_name} Time (s) | {version_b_name} Time (s) | Time Diff | "
        )
        lines.append(f"{version_a_name} % | {version_b_name} % | Proportion Diff |\n")
        lines.append("| --- | --- | --- | --- | --- | --- | --- |\n")

        # Collect data for overall average
        overall_time_a = []
        overall_time_b = []
        overall_prop_a = []
        overall_prop_b = []

        for model in all_models:
            data_a_agent = data_a.get(model, {}).get(
                agent, {"mean_time": 0, "total_time": 0}
            )
            data_b_agent = data_b.get(model, {}).get(
                agent, {"mean_time": 0, "total_time": 0}
            )

            time_a = data_a_agent["mean_time"]
            time_b = data_b_agent["mean_time"]

            # Skip if both are 0 (agent not present in this model)
            if time_a == 0 and time_b == 0:
                continue

            prop_a_val = prop_a.get(model, {}).get(agent, 0) * 100
            prop_b_val = prop_b.get(model, {}).get(agent, 0) * 100

            time_diff = time_a - time_b
            time_pct = (time_diff / time_b * 100) if time_b > 0 else 0

            prop_diff = prop_a_val - prop_b_val

            lines.append(
                f"| {model} | {time_a:.2f} | {time_b:.2f} | "
                f"{time_diff:+.2f}s ({time_pct:+.1f}%) | "
                f"{prop_a_val:.1f}% | {prop_b_val:.1f}% | "
                f"{prop_diff:+.1f}pp |\n"
            )

            # Collect for average
            if time_a > 0:
                overall_time_a.append(time_a)
                overall_prop_a.append(prop_a_val)
            if time_b > 0:
                overall_time_b.append(time_b)
                overall_prop_b.append(prop_b_val)

        lines.append("\n")

        # Overall average for this agent across all models
        if overall_time_a or overall_time_b:
            lines.append(f"### Overall Average Across All Models\n\n")
            lines.append(
                f"| Metric | {version_a_name} | {version_b_name} | Difference |\n"
            )
            lines.append("| --- | --- | --- | --- |\n")

            avg_time_a = (
                sum(overall_time_a) / len(overall_time_a) if overall_time_a else 0
            )
            avg_time_b = (
                sum(overall_time_b) / len(overall_time_b) if overall_time_b else 0
            )
            avg_prop_a = (
                sum(overall_prop_a) / len(overall_prop_a) if overall_prop_a else 0
            )
            avg_prop_b = (
                sum(overall_prop_b) / len(overall_prop_b) if overall_prop_b else 0
            )

            time_diff_avg = avg_time_a - avg_time_b
            time_pct_avg = (time_diff_avg / avg_time_b * 100) if avg_time_b > 0 else 0
            prop_diff_avg = avg_prop_a - avg_prop_b

            lines.append(
                f"| Mean Time (s) | {avg_time_a:.2f} | {avg_time_b:.2f} | "
                f"{time_diff_avg:+.2f}s ({time_pct_avg:+.1f}%) |\n"
            )
            lines.append(
                f"| Time Proportion (%) | {avg_prop_a:.1f}% | {avg_prop_b:.1f}% | "
                f"{prop_diff_avg:+.1f}pp |\n"
            )
            lines.append("\n")

        lines.append("---\n\n")

    return "".join(lines)


def generate_overall_comparison(
    projects: List[str],
    version_a_suffix: str,
    version_b_suffix: str,
    part2_dir: Path,
    version_a_name: str,
    version_b_name: str,
    comparison_title: str,
) -> str:
    """Generate overall agent-level comparison across multiple projects."""
    lines = []
    lines.append(f"# Overall {comparison_title}\n\n")
    lines.append(f"Averaged across all projects: {', '.join(projects)}\n\n")

    # Collect data from all projects
    overall_data_a = defaultdict(
        lambda: defaultdict(lambda: {"total_time": 0, "count": 0})
    )
    overall_data_b = defaultdict(
        lambda: defaultdict(lambda: {"total_time": 0, "count": 0})
    )

    for project in projects:
        scenario_a = f"{project}{version_a_suffix}"
        scenario_b = f"{project}{version_b_suffix}"

        dir_a = part2_dir / scenario_a
        dir_b = part2_dir / scenario_b

        if not dir_a.exists() or not dir_b.exists():
            continue

        # Load agent maps
        map_a = parse_agent_map(dir_a / "agent_map.md")
        map_b = parse_agent_map(dir_b / "agent_map.md")

        # Load agent data
        data_a = load_agent_time_data(
            dir_a / "agent_llm_tool_breakdown_by_model.csv", map_a
        )
        data_b = load_agent_time_data(
            dir_b / "agent_llm_tool_breakdown_by_model.csv", map_b
        )

        # Calculate proportions
        prop_a = calculate_agent_proportions(data_a)
        prop_b = calculate_agent_proportions(data_b)

        # Aggregate data
        for model, agents in data_a.items():
            for agent, agent_data in agents.items():
                overall_data_a[model][agent]["total_time"] += agent_data["total_time"]
                overall_data_a[model][agent]["count"] += agent_data["occurrences"]

        for model, agents in data_b.items():
            for agent, agent_data in agents.items():
                overall_data_b[model][agent]["total_time"] += agent_data["total_time"]
                overall_data_b[model][agent]["count"] += agent_data["occurrences"]

    # Calculate overall proportions and means
    all_models = sorted(set(overall_data_a.keys()) | set(overall_data_b.keys()))

    for model in all_models:
        lines.append(f"## {model}\n\n")

        agents_a = set(overall_data_a[model].keys())
        agents_b = set(overall_data_b[model].keys())
        all_agents = sorted(agents_a | agents_b)

        if not all_agents:
            lines.append("_No agent data for this model_\n\n")
            continue

        # Calculate total time for proportions
        total_time_a = sum(d["total_time"] for d in overall_data_a[model].values())
        total_time_b = sum(d["total_time"] for d in overall_data_b[model].values())

        # Table header
        lines.append(
            f"| Agent | {version_a_name} Mean (s) | {version_b_name} Mean (s) | Time Diff | "
        )
        lines.append(f"{version_a_name} % | {version_b_name} % | Proportion Diff |\n")
        lines.append("| --- | --- | --- | --- | --- | --- | --- |\n")

        for agent in all_agents:
            data_a = overall_data_a[model][agent]
            data_b = overall_data_b[model][agent]

            mean_a = (
                data_a["total_time"] / data_a["count"] if data_a["count"] > 0 else 0
            )
            mean_b = (
                data_b["total_time"] / data_b["count"] if data_b["count"] > 0 else 0
            )

            prop_a = (
                (data_a["total_time"] / total_time_a * 100) if total_time_a > 0 else 0
            )
            prop_b = (
                (data_b["total_time"] / total_time_b * 100) if total_time_b > 0 else 0
            )

            time_diff = mean_a - mean_b
            time_pct = (time_diff / mean_b * 100) if mean_b > 0 else 0

            prop_diff = prop_a - prop_b

            lines.append(
                f"| {agent} | {mean_a:.2f} | {mean_b:.2f} | "
                f"{time_diff:+.2f}s ({time_pct:+.1f}%) | "
                f"{prop_a:.1f}% | {prop_b:.1f}% | "
                f"{prop_diff:+.1f}pp |\n"
            )
    return "".join(lines)


def generate_overall_summary(
    projects: List[str],
    version_a_suffix: str,
    version_b_suffix: str,
    part2_dir: Path,
    version_a_name: str,
    version_b_name: str,
) -> str:
    """Generate overall summary across all projects and models."""
    lines = []
    lines.append("## Overall Summary (All Projects, All Models)\n\n")

    # Collect data from all projects
    overall_data_a = defaultdict(lambda: {"total_time": 0, "count": 0})
    overall_data_b = defaultdict(lambda: {"total_time": 0, "count": 0})

    for project in projects:
        scenario_a = f"{project}{version_a_suffix}"
        scenario_b = f"{project}{version_b_suffix}"

        dir_a = part2_dir / scenario_a
        dir_b = part2_dir / scenario_b

        if not dir_a.exists() or not dir_b.exists():
            continue

        # Load agent maps
        map_a = parse_agent_map(dir_a / "agent_map.md")
        map_b = parse_agent_map(dir_b / "agent_map.md")

        # Load agent data
        data_a = load_agent_time_data(
            dir_a / "agent_llm_tool_breakdown_by_model.csv", map_a
        )
        data_b = load_agent_time_data(
            dir_b / "agent_llm_tool_breakdown_by_model.csv", map_b
        )

        # Aggregate data across all models
        for model, agents in data_a.items():
            for agent, agent_data in agents.items():
                overall_data_a[agent]["total_time"] += agent_data["total_time"]
                overall_data_a[agent]["count"] += agent_data["occurrences"]

        for model, agents in data_b.items():
            for agent, agent_data in agents.items():
                overall_data_b[agent]["total_time"] += agent_data["total_time"]
                overall_data_b[agent]["count"] += agent_data["occurrences"]

    # Get all agents
    all_agents = sorted(set(overall_data_a.keys()) | set(overall_data_b.keys()))

    if not all_agents:
        lines.append("_No data available_\n\n")
        return "".join(lines)

    # Calculate total time for proportions
    total_time_a = sum(d["total_time"] for d in overall_data_a.values())
    total_time_b = sum(d["total_time"] for d in overall_data_b.values())

    # Table header
    lines.append(
        f"| Agent | {version_a_name} Mean (s) | {version_b_name} Mean (s) | Time Diff | "
    )
    lines.append(f"{version_a_name} % | {version_b_name} % | Proportion Diff |\n")
    lines.append("| --- | --- | --- | --- | --- | --- | --- |\n")

    for agent in all_agents:
        data_a = overall_data_a[agent]
        data_b = overall_data_b[agent]

        mean_a = data_a["total_time"] / data_a["count"] if data_a["count"] > 0 else 0
        mean_b = data_b["total_time"] / data_b["count"] if data_b["count"] > 0 else 0

        prop_a = (data_a["total_time"] / total_time_a * 100) if total_time_a > 0 else 0
        prop_b = (data_b["total_time"] / total_time_b * 100) if total_time_b > 0 else 0

        time_diff = mean_a - mean_b
        time_pct = (time_diff / mean_b * 100) if mean_b > 0 else 0

        prop_diff = prop_a - prop_b

        lines.append(
            f"| {agent} | {mean_a:.2f} | {mean_b:.2f} | "
            f"{time_diff:+.2f}s ({time_pct:+.1f}%) | "
            f"{prop_a:.1f}% | {prop_b:.1f}% | "
            f"{prop_diff:+.1f}pp |\n"
        )

    lines.append("\n---\n\n")
    return "".join(lines)


def generate_comparisons_for_projects(
    projects: List[str],
    version_a_suffix: str,
    version_b_suffix: str,
    part2_dir: Path,
    version_a_name: str,
    version_b_name: str,
    comparison_title: str,
) -> str:
    """Generate project-by-project agent-level comparisons."""
    lines = []
    lines.append(f"# {comparison_title}\n\n")
    lines.append(f"Projects included: {', '.join(projects)}\n\n")
    lines.append("---\n\n")

    # Add overall summary first
    overall_summary = generate_overall_summary(
        projects,
        version_a_suffix,
        version_b_suffix,
        part2_dir,
        version_a_name,
        version_b_name,
    )
    lines.append(overall_summary)

    # Generate comparison for each project
    for project in projects:
        project_comparison = generate_project_comparison(
            project,
            version_a_suffix,
            version_b_suffix,
            part2_dir,
            version_a_name,
            version_b_name,
        )
        lines.append(project_comparison)

    return "".join(lines)


def main():
    """Main execution function"""
    part2_dir = Path("/Users/wzr/TOSEM-2025/RESULTS/RQ2")
    output_dir = Path("/Users/wzr/TOSEM-2025/RESULTS/RQ3/agent_time_reports")

    output_dir.mkdir(parents=True, exist_ok=True)

    logger.info("Starting agent-level time comparison analysis...")

    # 1. MCP vs Hardcoded comparisons
    logger.info("Generating MCP vs Hardcoded comparisons...")
    mcp_hardcoded_projects = [
        "MarkdownValidator",
        "GameBuilder",
        "EmailResponder",
    ]

    comparison_content = generate_comparisons_for_projects(
        mcp_hardcoded_projects,
        "-MCP",
        "",
        part2_dir,
        "MCP",
        "Hardcoded",
        "MCP vs Hardcoded Agent-Level Comparison",
    )

    output_path = output_dir / "Agent_Time_Comparison_MCP_vs_Hardcoded.md"
    output_path.write_text(comparison_content, encoding="utf-8")
    logger.info(f"Created: {output_path}")

    # 2. MCP vs A2A comparisons
    logger.info("Generating MCP vs A2A comparisons...")
    version_projects = [
        "SQL_assistant",
        "intelligent_recruitment_platform",
        "landing_page_generator",
        "self_evaluation_loop_flow",
        "write_a_book_with_flows",
    ]

    comparison_content = generate_comparisons_for_projects(
        version_projects,
        "-MCP",
        "-A2A",
        part2_dir,
        "MCP",
        "A2A",
        "MCP vs A2A Agent-Level Comparison",
    )

    output_path = output_dir / "Agent_Time_Comparison_MCP_vs_A2A.md"
    output_path.write_text(comparison_content, encoding="utf-8")
    logger.info(f"Created: {output_path}")

    # 3. A2A vs A2A_mix comparisons
    logger.info("Generating A2A vs A2A_mix comparisons...")

    comparison_content = generate_comparisons_for_projects(
        version_projects,
        "-A2A",
        "-A2A_mix",
        part2_dir,
        "A2A",
        "A2A_mix",
        "A2A vs A2A_mix Agent-Level Comparison",
    )

    output_path = output_dir / "Agent_Time_Comparison_A2A_vs_A2A_mix.md"
    output_path.write_text(comparison_content, encoding="utf-8")
    logger.info(f"Created: {output_path}")

    logger.info("Agent-level time comparison analysis complete!")


if __name__ == "__main__":
    main()