#!/usr/bin/env python3
"""
Trajectory evaluation script for the SocialMediaManager-A2A project.

This script evaluates 6 trajectory metrics:
1. Exact match
2. In-order match
3. Any-order match
4. Precision
5. Recall
6. Single-tool use

Features:
- Dynamic reference trajectories: tool calls within each AGENT can be permuted.
- Auto-select the best reference trajectory based on a combined score from
  exact_match, in_order_match, and any_order_match.
"""

import os
import re
import yaml
from pathlib import Path
from typing import List, Dict, Tuple, Set
from collections import defaultdict
import pandas as pd
import math
from itertools import permutations, product


class TrajectoryParser:
    """Parse `execution_path.md` and extract the execution trajectory."""

    def __init__(self, md_file_path: str, extract_types: List[str] = None):
        """
        Args:
            md_file_path: Path to `execution_path.md`
            extract_types: Node types to extract. Default: ['Tool'].
                          Options: 'SPAN', 'Chain', 'Tool', 'AGENT', 'LLM',
                          'Task Created', 'Crew Created'
        """
        self.md_file_path = md_file_path
        self.extract_types = extract_types or ["Tool"]
        self.trajectory = []

    def parse(self) -> List[str]:
        """Parse the file and return the trajectory sequence."""
        if not os.path.exists(self.md_file_path):
            return []

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

        # Extract the "Execution Path Tree" section (inside a code block)
        tree_match = re.search(
            r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL
        )
        if not tree_match:
            return []

        tree_content = tree_match.group(1)
        trajectory = []

        for line in tree_content.split("\n"):
            # Remove tree-drawing characters but keep the node text
            clean_line = re.sub(r"^[│├└─\s]+", "", line).strip()
            if not clean_line:
                continue

            # Remove the error marker ❌
            clean_line = re.sub(r"^❌\s+", "", clean_line)

            # Remove retry markers: (retry N) and [RETRYN]
            clean_line = re.sub(r"\s*\(retry\s+\d+\)", "", clean_line)
            clean_line = re.sub(r"\s*\[RETRY\d+\]", "", clean_line)

            # Remove ERROR info: keep node type/name, drop the [ERROR: ...] part
            clean_line = re.sub(r"\s*\[ERROR:[^\]]*\]", "", clean_line)

            # Extract node info
            node_info = self._extract_node_info(clean_line)

            if node_info and node_info["type"] in self.extract_types:
                trajectory.append(node_info["action"])

        self.trajectory = trajectory
        return trajectory

    def _extract_node_info(self, line: str) -> dict:
        """Extract node info from a single line.

        Note: `line` should already have error markers (❌), retry markers, and
        ERROR info removed.

        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [stats]
        # Use a looser match to tolerate any residual special characters.
        span_match = re.match(r"\[SPAN\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if span_match:
            span_name = span_match.group(1).strip()
            return {"type": "SPAN", "action": f"SPAN: {span_name}"}

        # Chain node: [Chain] chain_name [stats]
        # For Crew_xxx.kickoff, normalize to the wildcard Crew***.kickoff
        chain_match = re.match(r"\[Chain\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if chain_match:
            chain_name = chain_match.group(1).strip()
            # Wildcard normalization: Crew_UUID.kickoff -> Crew***.kickoff
            chain_name = re.sub(
                r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", chain_name
            )
            return {"type": "Chain", "action": f"Chain: {chain_name}"}

        # AGENT node: [AGENT] agent_name._execute_core [stats]
        agent_match = re.match(r"\[AGENT\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if agent_match:
            agent_name = agent_match.group(1).strip()
            # Remove the ._execute_core suffix
            agent_name = re.sub(r"\._execute_core$", "", agent_name)
            return {"type": "Agent", "action": f"Agent: {agent_name}"}

        # Tool node: [Tool] tool_name._use [time]
        tool_match = re.match(
            r"\[Tool\]\s+([^\[\]]+?)(?:\s+\[[\d.]+(?:ms|s)\])?(?:\s*@@@)?\s*$", line
        )
        if tool_match:
            tool_name = tool_match.group(1).strip()
            # Remove the ._use suffix
            tool_name = re.sub(r"\._use$", "", tool_name)
            return {"type": "Tool", "action": f"Tool: {tool_name}"}

        # LLM node: [LLM] model_name (tokens) [time]
        llm_match = re.match(r"\[LLM\]\s+([^\(\[]+)", line)
        if llm_match:
            model_name = llm_match.group(1).strip()
            return {"type": "LLM", "action": f"LLM: {model_name}"}

        # Task Created node: [Task Created] [time]
        task_match = re.match(r"\[Task Created\]", line)
        if task_match:
            return {"type": "Task Created", "action": "Task Created"}

        # Crew Created node: [Crew Created] [time]
        crew_match = re.match(r"\[Crew Created\]", line)
        if crew_match:
            return {"type": "Crew Created", "action": "Crew Created"}

        return None


class TrajectoryEvaluator:
    """Trajectory evaluator implementing 6 metrics."""

    def __init__(self, reference_trajectory: List[str]):
        """
        Args:
            reference_trajectory: Reference trajectory (ground truth)
        """
        self.reference = reference_trajectory

    def _match_action(self, predicted_action: str, reference_action: str) -> bool:
        """
        Match two actions (supports wildcards).

        Args:
            predicted_action: Action from the predicted trajectory
            reference_action: Action from the reference trajectory (may contain wildcards)

        Returns:
            True if match, False otherwise
        """
        # Exact match
        if predicted_action == reference_action:
            return True

        # Wildcard match: "LLM: *" matches any "LLM: <model_name>"
        if reference_action == "LLM: *" and predicted_action.startswith("LLM: "):
            return True

        return False

    def exact_match(self, predicted: List[str]) -> int:
        """
        Exact match: the predicted trajectory must equal the reference (supports wildcards).

        Returns:
            1 if exact match, 0 otherwise
        """
        if len(predicted) != len(self.reference):
            return 0

        for i in range(len(predicted)):
            if not self._match_action(predicted[i], self.reference[i]):
                return 0

        return 1

    def in_order_match(self, predicted: List[str]) -> int:
        """
        In-order match: the reference trajectory must be a subsequence of the predicted
        trajectory (supports wildcards). Extra actions are allowed, but core steps must
        appear in order.

        Returns:
            1 if in-order match, 0 otherwise
        """
        if not self.reference:
            return 1  # An empty reference always matches

        ref_idx = 0
        for pred_action in predicted:
            if ref_idx < len(self.reference) and self._match_action(
                pred_action, self.reference[ref_idx]
            ):
                ref_idx += 1

        # Check whether all reference steps were found in order
        return 1 if ref_idx == len(self.reference) else 0

    def any_order_match(self, predicted: List[str]) -> int:
        """
        Any-order match: the predicted trajectory only needs to include all required
        actions (supports wildcards). Order does not matter; extra actions are allowed.

        Returns:
            1 if any-order match, 0 otherwise
        """
        if not self.reference:
            return 1

        # Copy predicted actions for matching
        pred_remaining = predicted.copy()

        # For each reference action, try to find a match in the prediction
        for ref_action in self.reference:
            matched = False
            for i, pred_action in enumerate(pred_remaining):
                if self._match_action(pred_action, ref_action):
                    pred_remaining.pop(i)  # Remove the matched item
                    matched = True
                    break

            if not matched:
                return 0  # A reference action was not matched

        return 1

    def precision(self, predicted: List[str]) -> float:
        """
        Precision: fraction of predicted actions that are considered correct by the
        reference (supports wildcards).

        Precision = TP / (TP + FP)
        TP: number of correctly predicted actions
        FP: number of incorrect / extra predicted actions

        Returns:
            precision value (0.0 - 1.0)
        """
        if not predicted:
            return 1.0  # No predictions, no false positives

        if not self.reference:
            return 0.0  # Empty reference but non-empty prediction => all incorrect

        # Copy reference actions for matching
        ref_remaining = self.reference.copy()

        tp = 0  # True Positives
        for pred_action in predicted:
            # Try to find a match in the reference
            for i, ref_action in enumerate(ref_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    ref_remaining.pop(i)  # Remove the matched item
                    break

        fp = len(predicted) - tp  # False Positives

        return tp / (tp + fp) if (tp + fp) > 0 else 0.0

    def recall(self, predicted: List[str]) -> float:
        """
        Recall: fraction of reference actions that are covered by the prediction
        (supports wildcards).

        Recall = TP / (TP + FN)
        TP: number of required actions covered by the prediction
        FN: number of required actions missing in the prediction

        Returns:
            recall value (0.0 - 1.0)
        """
        if not self.reference:
            return 1.0  # Empty reference => nothing to recall

        if not predicted:
            return 0.0  # No prediction => recall is 0

        # Copy predicted actions for matching
        pred_remaining = predicted.copy()

        tp = 0  # True Positives
        for ref_action in self.reference:
            # Try to find a match in the prediction
            for i, pred_action in enumerate(pred_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    pred_remaining.pop(i)  # Remove the matched item
                    break

        fn = len(self.reference) - tp  # False Negatives

        return tp / (tp + fn) if (tp + fn) > 0 else 0.0

    def single_tool_use(self, predicted: List[str], tool_name: str) -> int:
        """
        Single-tool use: check whether a specific tool appears in the trajectory
        (supports wildcards).

        Args:
            predicted: Predicted trajectory
            tool_name: Target tool name

        Returns:
            1 if tool is used, 0 otherwise
        """
        # Check whether any predicted action matches the target tool
        for pred_action in predicted:
            if self._match_action(pred_action, tool_name):
                return 1
        return 0

    def evaluate_all(
        self, predicted: List[str], target_tools: List[str] = None
    ) -> Dict[str, float]:
        """
        Evaluate all metrics.

        Args:
            predicted: Predicted trajectory
            target_tools: Tools to check usage for (used by single-tool use)

        Returns:
            A dictionary of all metric values
        """
        results = {
            "exact_match": self.exact_match(predicted),
            "in_order_match": self.in_order_match(predicted),
            "any_order_match": self.any_order_match(predicted),
            "precision": self.precision(predicted),
            "recall": self.recall(predicted),
        }

        # Single-tool use metric: overall usage rate (average across target tools)
        if target_tools:
            tool_usage_count = sum(
                self.single_tool_use(predicted, tool) for tool in target_tools
            )
            results["single_tool_use"] = (
                tool_usage_count / len(target_tools) if target_tools else 0.0
            )

        return results


class DatasetEvaluator:
    """Dataset-level evaluator."""

    def __init__(self, config_file: str):
        """
        Args:
            config_file: YAML config path containing the reference trajectory
        """
        self.config_file = config_file
        self.config = self._load_config()
        self.reference_trajectory = self.config.get("reference_trajectory", [])
        self.target_tools = self.config.get("target_tools", [])
        self.models = self.config.get("models", [])
        self.project_name = self.config.get("project_name", "SocialMediaManager-A2A")
        # Trajectory node types to extract: default is only Tool; can also include
        # ['Tool', 'AGENT', 'Task Created'], etc.
        self.extract_types = self.config.get("extract_types", ["Tool"])
        # Permutable tool group configuration
        self.permutable_tool_groups = self.config.get("permutable_tool_groups", {})

    def _load_config(self) -> Dict:
        """Load the YAML config file."""
        if not os.path.exists(self.config_file):
            print(f"⚠️  Config file not found: {self.config_file}")
            return {}

        with open(self.config_file, "r", encoding="utf-8") as f:
            return yaml.safe_load(f)

    def _generate_permuted_trajectories(
        self, base_trajectory: List[str]
    ) -> List[List[str]]:
        """
        Generate all possible tool-order permutations based on `permutable_tool_groups`.

        Args:
            base_trajectory: Base reference trajectory

        Returns:
            All possible permuted trajectories (including the original)
        """
        if not self.permutable_tool_groups:
            # No permutable tool groups configured; return the original trajectory
            return [base_trajectory]

        # Collect permutable tool groups and their positions in the trajectory
        tool_groups_positions = []

        for group_name, tools in self.permutable_tool_groups.items():
            # Find positions of these tools in the trajectory
            positions = []
            tool_indices = {}

            for i, action in enumerate(base_trajectory):
                for tool in tools:
                    if action == tool:
                        positions.append(i)
                        tool_indices[i] = tool
                        break

            # Only permute when all tools in the group are found
            if len(positions) == len(tools):
                # Record positions and tools for this group
                tools_at_positions = [tool_indices[pos] for pos in positions]
                tool_groups_positions.append((positions, tools_at_positions))

        if not tool_groups_positions:
            # No complete permutable tool groups found
            return [base_trajectory]

        # Generate all permutation combinations
        all_trajectories = []

        # Generate all permutations for each tool group
        group_permutations = []
        for positions, tools in tool_groups_positions:
            # Generate all permutations for this tool group
            perms = list(permutations(tools))
            group_permutations.append([(positions, perm) for perm in perms])

        # Cartesian product: combine permutations across groups
        all_group_combinations = list(product(*group_permutations))

        # Build a new trajectory for each combination
        for combination in all_group_combinations:
            new_trajectory = base_trajectory.copy()

            # Apply all tool permutations in this combination
            for positions, perm in combination:
                for pos, tool in zip(positions, perm):
                    new_trajectory[pos] = tool

            all_trajectories.append(new_trajectory)

        return all_trajectories

    def _find_best_reference_trajectory(
        self, predicted: List[str], candidate_references: List[List[str]]
    ) -> Tuple[List[str], Dict[str, float]]:
        """
        Select the best reference trajectory among multiple candidates.

        Args:
            predicted: Predicted trajectory
            candidate_references: Candidate reference trajectories

        Returns:
            (Best reference trajectory, its matching metric dict)
        """
        best_reference = candidate_references[0]
        best_score = -1
        best_metrics = {}

        for ref_trajectory in candidate_references:
            evaluator = TrajectoryEvaluator(ref_trajectory)

            # Compute the three key matching metrics
            exact = evaluator.exact_match(predicted)
            in_order = evaluator.in_order_match(predicted)
            any_order = evaluator.any_order_match(predicted)

            # Combined score: exact_match is weighted highest, then in_order_match.
            # Weighted sum: exact*3 + in_order*2 + any_order*1
            score = exact * 3 + in_order * 2 + any_order * 1

            if score > best_score:
                best_score = score
                best_reference = ref_trajectory
                best_metrics = {
                    "exact_match": exact,
                    "in_order_match": in_order,
                    "any_order_match": any_order,
                }

        return best_reference, best_metrics

    def collect_execution_paths(
        self, model_name: str, base_dir: str
    ) -> List[Tuple[str, List[str]]]:
        """
        Collect and parse all `execution_path.md` files for a given model.

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            List of (session_id, trajectory)
        """
        model_dir = Path(base_dir) / model_name / self.project_name / "test_results"

        if not model_dir.exists():
            print(f"⚠️  Model directory not found: {model_dir}")
            return []

        results = []

        # Iterate over all session subdirectories
        for session_dir in sorted(model_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            exec_path_file = session_dir / "execution_path.md"
            if not exec_path_file.exists():
                continue

            # Parse trajectory (using configured extract types)
            parser = TrajectoryParser(
                str(exec_path_file), extract_types=self.extract_types
            )
            trajectory = parser.parse()

            results.append((session_dir.name, trajectory))

        return results

    def evaluate_model(self, model_name: str, base_dir: str) -> Dict[str, float]:
        """
        Evaluate a single model over all samples.

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            Dictionary of averaged metrics
        """
        model_dir = Path(base_dir) / model_name / self.project_name / "test_results"

        if not model_dir.exists():
            print(f"⚠️  No trajectory data found for model {model_name}")
            return {}

        # Accumulate per-sample metrics
        all_metrics = defaultdict(list)
        trajectories = []

        # Iterate over all sessions
        for session_dir in sorted(model_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            exec_path_file = session_dir / "execution_path.md"
            if not exec_path_file.exists():
                continue

            # Parse trajectory
            parser = TrajectoryParser(
                str(exec_path_file), extract_types=self.extract_types
            )
            predicted = parser.parse()

            # Use the default reference trajectory
            base_reference = self.reference_trajectory

            # Generate all possible permuted reference trajectories
            candidate_references = self._generate_permuted_trajectories(base_reference)

            # Pick the best reference among candidates
            if len(candidate_references) > 1:
                reference, _ = self._find_best_reference_trajectory(
                    predicted, candidate_references
                )
            else:
                reference = base_reference

            # Evaluate using the selected best reference
            evaluator = TrajectoryEvaluator(reference)
            metrics = evaluator.evaluate_all(predicted, self.target_tools)

            for key, value in metrics.items():
                all_metrics[key].append(value)

            trajectories.append((session_dir.name, predicted))

        # Compute averages
        avg_metrics = {}
        for key, values in all_metrics.items():
            avg_metrics[key] = sum(values) / len(values) if values else 0.0

        num_samples = len(trajectories)

        if num_samples > 0:
            path_counter = defaultdict(int)
            for session_id, predicted in trajectories:
                path_key = tuple(predicted)
                path_counter[path_key] += 1

            unique_paths = len(path_counter)
            unique_path_ratio = unique_paths / num_samples if num_samples > 0 else 0.0

            probs = [count / num_samples for count in path_counter.values()]
            H = -sum(p * math.log(p) for p in probs if p > 0)
            if len(probs) > 1:
                path_entropy = H / math.log(len(probs))
            else:
                path_entropy = 0.0
        else:
            unique_path_ratio = 0.0
            path_entropy = 0.0

        avg_metrics["unique_path_ratio"] = unique_path_ratio
        avg_metrics["path_entropy"] = path_entropy

        # Add sample count
        avg_metrics["num_samples"] = num_samples

        return avg_metrics

    def evaluate_all_models(self, base_dir: str = None) -> pd.DataFrame:
        """
        Evaluate all models and build a summary table.

        Args:
            base_dir: RESULTS directory path. Defaults to two levels above this script.

        Returns:
            A DataFrame containing evaluation results for all models
        """
        if base_dir is None:
            # Default path: two levels above this script
            base_dir = Path(__file__).parent.parent.parent

        results = []

        for model_name in self.models:
            print(f"\n📊 Evaluating model: {model_name}")
            metrics = self.evaluate_model(model_name, str(base_dir))

            if metrics:
                metrics["model"] = model_name
                results.append(metrics)
                print(f"  ✅ Done. Samples: {metrics['num_samples']}")
            else:
                print("  ❌ Skipped (no data)")

        if not results:
            print("\n❌ No model data found")
            return pd.DataFrame()

        # Create DataFrame
        df = pd.DataFrame(results)

        # Reorder columns: model name first
        cols = [
            "model",
            "num_samples",
            "exact_match",
            "in_order_match",
            "any_order_match",
            "precision",
            "recall",
            "single_tool_use",
            "unique_path_ratio",
            "path_entropy",
        ]

        # Keep only columns that exist
        cols = [col for col in cols if col in df.columns]

        df = df[cols]

        return df


def main():
    """Main entry point."""
    import argparse

    parser = argparse.ArgumentParser(
        description="Evaluate trajectory metrics for the SocialMediaManager-A2A project"
    )
    parser.add_argument(
        "--config",
        type=str,
        default="reference_trajectory.yaml",
        help="Reference trajectory config file path (YAML)",
    )
    parser.add_argument(
        "--base-dir",
        type=str,
        default=None,
        help="RESULTS directory path (defaults to two levels above this script)",
    )
    parser.add_argument(
        "--output",
        type=str,
        default="evaluation_results.csv",
        help="Output CSV file path",
    )
    parser.add_argument(
        "--format",
        type=str,
        choices=["csv", "markdown", "both"],
        default="both",
        help="Output format: csv, markdown, or both. (Markdown output is disabled)",
    )

    args = parser.parse_args()

    # If config is a relative path, resolve it under the script directory
    config_path = args.config
    if not os.path.isabs(config_path):
        config_path = os.path.join(os.path.dirname(__file__), config_path)

    print("=" * 80)
    print("Trajectory Evaluation Tool - SocialMediaManager-A2A")
    print("=" * 80)
    print(f"\n📁 Config file: {config_path}")

    # Create evaluator
    evaluator = DatasetEvaluator(config_path)

    print(f"📋 Project: {evaluator.project_name}")
    print(
        f"🎯 Reference trajectory length: {len(evaluator.reference_trajectory)} steps"
    )
    print(f"🔧 Target tools: {evaluator.target_tools}")
    print(f"🤖 Models: {evaluator.models}")

    # Show tool permutation info
    if evaluator.permutable_tool_groups:
        print("\n🔀 Dynamic tool permutation optimization: enabled")
        total_permutations = 1
        for group_name, tools in evaluator.permutable_tool_groups.items():
            num_perms = math.factorial(len(tools))
            total_permutations *= num_perms
            print(f"   - {group_name}: {len(tools)} tools, {num_perms} permutations")
        print(f"   - Total combinations: {total_permutations}")
        print("   - Strategy: select the best-matching permutation per sample")
    else:
        print("\n🔀 Dynamic tool permutation optimization: disabled")

    # Evaluate all models
    df = evaluator.evaluate_all_models(args.base_dir)

    if df.empty:
        print("\n❌ Evaluation failed: no data")
        return

    print("\n" + "=" * 80)
    print("📊 Evaluation Summary")
    print("=" * 80)

    # Configure display formatting
    pd.set_option("display.max_columns", None)
    pd.set_option("display.width", None)
    pd.set_option("display.float_format", lambda x: f"{x:.4f}")

    print("\n" + df.to_string(index=False))

    # Save results
    output_dir = os.path.dirname(args.output) or "."
    os.makedirs(output_dir, exist_ok=True)

    if args.format in ["csv", "both"]:
        csv_file = args.output
        df.to_csv(csv_file, index=False)
        print(f"\n✅ CSV saved: {csv_file}")

    if args.format in ["markdown", "both"]:
        print("\n⚠️  Markdown output is disabled; no .md file will be generated.")

    print("\n" + "=" * 80)
    print("✅ Evaluation completed")
    print("=" * 80)


if __name__ == "__main__":
    main()