#!/usr/bin/env python3
"""Trajectory evaluation script - BookWriter-H_A2A project.

Supports 6 metrics: Exact / In-order / Any-order / Precision / Recall / Single-tool use.

Specialization:
- Chapter count (3-5): dynamically expand the chapter reference trajectory based on
  `repeatable_patterns` and the actual trajectory.
- LangGraph `review_book`: dynamically build the Stage 3 reference trajectory based on
  the per-sample `Chain: tools` parallel grouping pattern
  (1+1+1+1, 2+1+1, 1+2+1, 1+1+2, 3+1, 1+3, 4).
  Under the same `Chain: tools`, multiple tools are treated as one execution group in
  the ideal order:
  count_book_words → analyze_book_quality → extract_book_keywords → validate_book_markdown.
"""

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


# ======================  Trajectory Parsing  ======================


class TrajectoryParser:
    """Parse `execution_path.md` and extract SPAN/Chain/AGENT/LLM/Tool nodes."""

    def __init__(self, md_file_path: str, extract_types: List[str] = None):
        self.md_file_path = md_file_path
        self.extract_types = extract_types or ["Tool"]
        self.trajectory: List[str] = []

    def parse(self) -> List[str]:
        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()

        m = re.search(r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL)
        if not m:
            return []

        tree = m.group(1)
        traj: List[str] = []

        for line in tree.split("\n"):
            clean = re.sub(r"^[│├└─\s]+", "", line).strip()
            if not clean:
                continue
            clean = re.sub(r"^❌\s+", "", clean)
            clean = re.sub(r"\s*\(retry\s+\d+\)", "", clean)
            clean = re.sub(r"\s*\[RETRY\d+\]", "", clean)
            clean = re.sub(r"\s*\[ERROR:[^\]]*\]", "", clean)

            node = self._extract_node_info(clean)
            if node and node["type"] in self.extract_types:
                traj.append(node["action"])

        self.trajectory = traj
        return traj

    def _extract_node_info(self, line: str) -> Optional[Dict[str, str]]:
        # SPAN
        span_match = re.match(r"\[SPAN\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if span_match:
            span_name = span_match.group(1).strip()
            # a2a_call_chapter_writer_(chapter_title) → a2a_call_chapter_writer_*
            span_name = re.sub(
                r"a2a_call_chapter_writer_\([^)]*\)",
                "a2a_call_chapter_writer_*",
                span_name,
            )
            return {"type": "SPAN", "action": f"SPAN: {span_name}"}

        # Chain (Crew_xxx.kickoff → Crew***.kickoff)
        # Only take the part before the first "["; allow trailing stats and 📚BATCH tags
        chain_match = re.match(r"\[Chain\]\s+([^\[]+)", line)
        if chain_match:
            chain_name = chain_match.group(1).strip()
            chain_name = re.sub(
                r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", chain_name
            )
            return {"type": "Chain", "action": f"Chain: {chain_name}"}

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

        # Tool (keep execute_tool prefix / ._use suffix / no prefix or suffix)
        tool_match = re.match(
            r"\[Tool\]\s+([^\[\]]+?)(?:\s+\[[\d.]+(?:ms|s)\])?(?:\s*@@@)?\s*$",
            line,
        )
        if tool_match:
            tool_name = tool_match.group(1).strip()
            return {"type": "Tool", "action": f"Tool: {tool_name}"}

        # LLM
        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 / Crew Created (usually not extracted)
        if re.match(r"\[Task Created\]", line):
            return {"type": "Task Created", "action": "Task Created"}
        if re.match(r"\[Crew Created\]", line):
            return {"type": "Crew Created", "action": "Crew Created"}

        return None


# ======================  Evaluator (with dynamic reference)  ======================


class TrajectoryEvaluator:
    def __init__(
        self,
        reference_trajectory: List[str],
        repeatable_patterns: List[Dict] = None,
        actual_chapter_count: Optional[int] = None,
        review_book_pattern: Optional[List[int]] = None,
        autogen_outline_pattern: Optional[str] = None,
    ) -> None:
        self.base_reference = reference_trajectory[:]
        self.repeatable_patterns = repeatable_patterns or []
        self.review_book_pattern = review_book_pattern
        self.autogen_outline_pattern = autogen_outline_pattern

        # 1) Dynamic expansion for chapters
        if actual_chapter_count is not None and self.repeatable_patterns:
            ref_after_ch = self._build_dynamic_reference_for_chapters(
                actual_chapter_count
            )
        else:
            ref_after_ch = self.base_reference[:]

        # 2) Dynamic expansion for AutoGen outline
        if self.autogen_outline_pattern:
            ref_after_autogen = self._build_dynamic_autogen_outline_reference(
                ref_after_ch, self.autogen_outline_pattern
            )
        else:
            ref_after_autogen = ref_after_ch

        # 3) Dynamic expansion for LangGraph review_book
        if self.review_book_pattern:
            self.reference = self._build_dynamic_review_book_reference(
                ref_after_autogen, self.review_book_pattern
            )
        else:
            self.reference = ref_after_autogen

    # ---------- Chapter count ----------

    @staticmethod
    def detect_chapter_count(predicted: List[str]) -> int:
        write_idx = -1
        review_idx = -1
        for i, s in enumerate(predicted):
            if s == "SPAN: write_chapters":
                write_idx = i
            elif s == "SPAN: review_book":
                review_idx = i
                break
        if write_idx == -1:
            return 4
        end = review_idx if review_idx != -1 else len(predicted)
        cnt = 0
        for i in range(write_idx + 1, end):
            if predicted[i] == "Chain: Crew***.kickoff":
                cnt += 1
        return cnt if cnt > 0 else 4

    def _build_dynamic_reference_for_chapters(self, chapter_count: int) -> List[str]:
        if not self.repeatable_patterns:
            return self.base_reference[:]
        pattern = self.repeatable_patterns[0]
        ps, pe = pattern["start"], pattern["end"]
        before = self.base_reference[:ps]
        pat = self.base_reference[ps : pe + 1]
        after = self.base_reference[pe + 1 :]
        mn, mx = pattern.get("min", 3), pattern.get("max", 5)
        if chapter_count < mn or chapter_count > mx:
            repeat = 4
        else:
            repeat = chapter_count
        out: List[str] = before.copy()
        for _ in range(repeat):
            out.extend(pat)
        out.extend(after)
        return out

    # ---------- LangGraph review_book tools pattern ----------

    @staticmethod
    def detect_review_book_pattern(predicted: List[str]) -> Optional[List[int]]:
        """Infer the `Chain: tools` grouping pattern from actual LangGraph execution.

        Returns a pattern like [1,1,1,1] / [2,1,1] / ... / [4], or None if detection fails.
        """
        canonical = [
            "Tool: count_book_words",
            "Tool: analyze_book_quality",
            "Tool: extract_book_keywords",
            "Tool: validate_book_markdown",
        ]

        # Start from Chain: LangGraph (fallback to SPAN: review_book)
        start = -1
        for i, s in enumerate(predicted):
            if s == "Chain: LangGraph":
                start = i
                break
        if start == -1:
            for i, s in enumerate(predicted):
                if s == "SPAN: review_book":
                    start = i
                    break
        if start == -1:
            return None

        group_counts: List[int] = []
        need = len(canonical)
        idx = 0  # matched canonical index

        i = start
        while i < len(predicted) and idx < need:
            s = predicted[i]
            if s == "Chain: tools":
                count_here = 0
                i += 1
                while i < len(predicted) and not predicted[i].startswith("Chain: "):
                    t = predicted[i]
                    if idx < need and t == canonical[idx]:
                        count_here += 1
                        idx += 1
                    i += 1
                if count_here > 0:
                    group_counts.append(count_here)
            else:
                if s == "Chain: format_output":
                    break
                i += 1

        if idx != need or sum(group_counts) != need:
            return None

        allowed = {
            (1, 1, 1, 1),
            (2, 1, 1),
            (1, 2, 1),
            (1, 1, 2),
            (3, 1),
            (1, 3),
            (4,),
        }
        if tuple(group_counts) not in allowed:
            return None
        return group_counts

    def _split_review_book_stage(
        self, stage3: List[str]
    ) -> Tuple[List[str], List[List[str]], List[str]]:
        """Split Stage3 into header, loops (up to 4), and footer."""
        if len(stage3) < 4 + 5 + 4:
            return stage3, [], []
        header = stage3[:4]
        loops: List[List[str]] = []
        i = 4
        while i + 4 < len(stage3) and len(loops) < 4:
            a, b, c, d, e = stage3[i : i + 5]
            if not a.startswith("AGENT: "):
                break
            if b != "LLM: *" or c != "Chain: _should_continue" or d != "Chain: tools":
                break
            if not e.startswith("Tool: "):
                break
            loops.append(stage3[i : i + 5])
            i += 5
        footer = stage3[i:]
        return header, loops, footer

    def _build_dynamic_review_book_reference(
        self, reference: List[str], pattern: List[int]
    ) -> List[str]:
        try:
            start = reference.index("SPAN: review_book")
        except ValueError:
            return reference
        before = reference[:start]
        stage3 = reference[start:]
        header, loops, footer = self._split_review_book_stage(stage3)
        if not loops:
            return reference
        total = len(loops)
        if sum(pattern) != total:
            return reference

        new_stage3: List[str] = header.copy()
        idx = 0
        for cnt in pattern:
            first = loops[idx]
            if cnt == 1:
                new_stage3.extend(first)
            else:
                prefix = first[:-1]  # exclude Tool
                tools = [seg[-1] for seg in loops[idx : idx + cnt]]
                new_stage3.extend(prefix + tools)
            idx += cnt
        new_stage3.extend(footer)
        return before + new_stage3

    # ---------- AutoGen outline dynamic pattern ----------

    @staticmethod
    def detect_autogen_outline_pattern(predicted: List[str]) -> str:
        """Detect the LLM/Tool pattern for AutoGen outline generation (researcher).

        Returns:
        - "compact": LLM → Tool1 → Tool2 → LLM (adjacent tools)
        - "interleaved": LLM → Tool1 → LLM → Tool2 → LLM (one LLM between tools)
        - "unknown": cannot be recognized or does not follow the rules

        Rule: must start and end with LLM; between the two tools there can be at most one LLM.
        """
        # Find the position of the researcher agent
        start_idx = -1
        for i, s in enumerate(predicted):
            if s == "AGENT: invoke_agent researcher":
                start_idx = i
                break

        if start_idx == -1:
            return "unknown"

        # Extract the LLM/Tool sequence under the researcher agent
        sequence = []
        i = start_idx + 1

        # Find the next AGENT (end of researcher stage, then outliner)
        while i < len(predicted):
            s = predicted[i]
            if s.startswith("AGENT: "):
                break

            if s.startswith("LLM: "):
                sequence.append("LLM")
            elif s.startswith("Tool: execute_tool bocha_websearch_tool"):
                sequence.append("Tool1")
            elif s.startswith("Tool: execute_tool extract_keywords"):
                sequence.append("Tool2")

            i += 1

        # Validate the sequence
        if not sequence or len(sequence) < 3:
            return "unknown"

        # Must start and end with LLM
        if sequence[0] != "LLM" or sequence[-1] != "LLM":
            return "unknown"

        # The middle must include Tool1 and Tool2
        middle = sequence[1:-1]
        if "Tool1" not in middle or "Tool2" not in middle:
            return "unknown"

        # Determine the specific pattern
        # Pattern A: LLM → Tool1 → Tool2 → LLM (adjacent tools)
        if sequence == ["LLM", "Tool1", "Tool2", "LLM"]:
            return "compact"

        # Pattern B: LLM → Tool1 → LLM → Tool2 → LLM (one LLM between tools)
        if sequence == ["LLM", "Tool1", "LLM", "Tool2", "LLM"]:
            return "interleaved"

        # Check for invalid patterns (more than one LLM between the two tools)
        # Extract the elements between Tool1 and Tool2
        try:
            tool1_idx = middle.index("Tool1")
            tool2_idx = middle.index("Tool2")

            if tool1_idx < tool2_idx:
                between = middle[tool1_idx + 1 : tool2_idx]
            else:
                # Tool2 appears before Tool1 (reversed order is accepted)
                between = middle[tool2_idx + 1 : tool1_idx]

            # At most one LLM in-between
            llm_count = between.count("LLM")
            if llm_count <= 1:
                if llm_count == 0:
                    return "compact"
                else:
                    return "interleaved"
        except (ValueError, IndexError):
            pass

        return "unknown"

    def _build_dynamic_autogen_outline_reference(
        self, reference: List[str], pattern: str
    ) -> List[str]:
        """Build the reference trajectory variant for AutoGen outline generation.

        Args:
            reference: base reference trajectory
            pattern: pattern type ("compact" or "interleaved")

        Returns:
            adjusted full reference trajectory
        """
        if pattern == "compact":
            # Already compact
            return reference

        if pattern != "interleaved":
            # Unknown pattern
            return reference

        try:
            # Locate the researcher agent
            start_idx = reference.index("AGENT: invoke_agent researcher")

            # Locate the outliner agent (end of researcher stage)
            end_idx = -1
            for i in range(start_idx + 1, len(reference)):
                if reference[i] == "AGENT: invoke_agent outliner":
                    end_idx = i
                    break

            if end_idx == -1:
                return reference

            # Split into 3 parts: prefix, researcher, suffix (starting from outliner)
            before = reference[
                : start_idx + 1
            ]  # includes AGENT: invoke_agent researcher
            after = reference[end_idx:]  # starts from outliner

            # Build the researcher segment for the interleaved pattern
            researcher_part = [
                "LLM: *",
                "Tool: execute_tool bocha_websearch_tool",
                "LLM: *",  # insert an LLM between two tools
                "Tool: execute_tool extract_keywords",
                "LLM: *",
            ]

            # Compose the full trajectory
            return before + researcher_part + after

        except (ValueError, IndexError):
            # If parsing fails, return the original reference
            return reference

    # ---------- Matching and 6 metrics ----------

    def _match_action(self, p: str, r: str) -> bool:
        if p == r:
            return True
        if r == "LLM: *" and p.startswith("LLM: "):
            return True
        return False

    def exact_match(self, predicted: List[str]) -> int:
        if len(predicted) != len(self.reference):
            return 0
        for a, b in zip(predicted, self.reference):
            if not self._match_action(a, b):
                return 0
        return 1

    def in_order_match(self, predicted: List[str]) -> int:
        if not self.reference:
            return 1
        ref_idx = 0
        for s in predicted:
            if ref_idx < len(self.reference) and self._match_action(
                s, self.reference[ref_idx]
            ):
                ref_idx += 1
        return 1 if ref_idx == len(self.reference) else 0

    def any_order_match(self, predicted: List[str]) -> int:
        if not self.reference:
            return 1
        diagnosis = self.diagnose_any_order_match_failure(predicted)
        return 1 if diagnosis["match"] else 0

    def diagnose_any_order_match_failure(
        self, predicted: List[str]
    ) -> Dict[str, object]:
        """Diagnose why `any_order_match` failed (simple overall match check)."""
        if not self.reference:
            return {
                "match": True,
                "failure_stage": None,
                "missing_steps": [],
                "missing_details": "",
            }

        pred_remaining = predicted.copy()
        missing_steps: List[str] = []

        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)
                    matched = True
                    break
            if not matched:
                missing_steps.append(ref_action)

        if missing_steps:
            return {
                "match": False,
                "failure_stage": "simple_match",
                "missing_steps": missing_steps,
                "missing_details": f"Missing {len(missing_steps)} required steps",
            }

        return {
            "match": True,
            "failure_stage": None,
            "missing_steps": [],
            "missing_details": "",
        }

    def precision(self, predicted: List[str]) -> float:
        if not predicted:
            return 1.0
        if not self.reference:
            return 0.0
        ref_rem = self.reference.copy()
        tp = 0
        for p in predicted:
            for i, r in enumerate(ref_rem):
                if self._match_action(p, r):
                    tp += 1
                    ref_rem.pop(i)
                    break
        fp = len(predicted) - tp
        return tp / (tp + fp) if tp + fp > 0 else 0.0

    def recall(self, predicted: List[str]) -> float:
        if not self.reference:
            return 1.0
        if not predicted:
            return 0.0
        pred_rem = predicted.copy()
        tp = 0
        for r in self.reference:
            for i, p in enumerate(pred_rem):
                if self._match_action(p, r):
                    tp += 1
                    pred_rem.pop(i)
                    break
        fn = len(self.reference) - tp
        return tp / (tp + fn) if tp + fn > 0 else 0.0

    def single_tool_use(self, predicted: List[str], tool_name: str) -> int:
        for p in predicted:
            if self._match_action(p, tool_name):
                return 1
        return 0

    def evaluate_all(
        self, predicted: List[str], target_tools: List[str]
    ) -> Dict[str, float]:
        res = {
            "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),
        }
        if target_tools:
            used = sum(self.single_tool_use(predicted, t) for t in target_tools)
            res["single_tool_use"] = used / len(target_tools)
        return res


# ======================  Dataset Evaluation  ======================


class DatasetEvaluator:
    def __init__(self, config_file: str) -> None:
        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", "BookWriter-H_A2A")
        self.extract_types = self.config.get("extract_types", ["Tool"])
        self.repeatable_patterns = self.config.get("repeatable_patterns", [])
        # Permutable tool group configuration (used to generate dynamic reference trajectories)
        self.permutable_tool_groups = self.config.get("permutable_tool_groups", {})

    def _load_config(self) -> Dict:
        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_for_mix(
        self, base_trajectory: List[str]
    ) -> List[List[str]]:
        """
        Generate all possible tool-permuted trajectories based on `permutable_tool_groups`
        (specialized version).

        Special handling:
        1. CrewAI Chapter Writer: permute tools together with their surrounding LLM steps
        2. LangGraph Book Reviewer: permute the entire loop block
           (5 steps: AGENT → LLM → Chain → Chain tools → Tool)

        Args:
            base_trajectory: base reference trajectory

        Returns:
            A list of all possible permuted trajectories
        """
        if not self.permutable_tool_groups:
            return [base_trajectory]

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

        for group_name, tools in self.permutable_tool_groups.items():
            if "chapter_writer" in group_name:
                # CrewAI Chapter Writer: simple permutation (Tool + surrounding LLM)
                # Pattern: LLM → Tool → LLM
                positions_blocks = []
                for tool in tools:
                    # Find all occurrences of the tool in the trajectory
                    for i, action in enumerate(base_trajectory):
                        if action == tool:
                            # For a tool occurrence, look for an LLM before and after
                            if i > 0 and i < len(base_trajectory) - 1:
                                if (
                                    base_trajectory[i - 1] == "LLM: *"
                                    and base_trajectory[i + 1] == "LLM: *"
                                ):
                                    # Found the full block: LLM → Tool → LLM
                                    positions_blocks.append((i - 1, i + 1, tool))

                if len(positions_blocks) == len(tools):
                    tool_groups_positions.append(
                        (group_name, "chapter_writer", positions_blocks)
                    )

            elif "book_reviewer" in group_name:
                # LangGraph Book Reviewer: permute the entire loop block
                # Pattern: AGENT: agent → LLM: * → Chain: _should_continue → Chain: tools → Tool: xxx
                positions_blocks = []
                for tool in tools:
                    # Find tool occurrences
                    for i, action in enumerate(base_trajectory):
                        if action == tool:
                            # Check whether it matches the LangGraph loop block pattern
                            if i >= 4:
                                block_start = i - 4
                                if (
                                    base_trajectory[block_start] == "AGENT: agent"
                                    and base_trajectory[block_start + 1] == "LLM: *"
                                    and base_trajectory[block_start + 2]
                                    == "Chain: _should_continue"
                                    and base_trajectory[block_start + 3]
                                    == "Chain: tools"
                                    and base_trajectory[block_start + 4] == tool
                                ):
                                    # Found the full loop block (5 steps)
                                    positions_blocks.append((block_start, i, tool))

                if len(positions_blocks) == len(tools):
                    tool_groups_positions.append(
                        (group_name, "book_reviewer", positions_blocks)
                    )

        if not tool_groups_positions:
            return [base_trajectory]

        # Generate all permutation combinations
        all_trajectories = []

        # Generate all permutations for each tool group
        group_permutations = []
        for group_name, group_type, blocks in tool_groups_positions:
            # Extract tool order
            tools_order = [tool for _, _, tool in blocks]
            # Generate all permutations
            perms = list(permutations(tools_order))
            group_permutations.append([(blocks, perm, group_type) for perm in perms])

        # Cartesian product: combine permutations across all tool 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 blocks, perm, group_type in combination:
                if group_type == "chapter_writer":
                    # CrewAI: swap LLM → Tool → LLM blocks
                    # blocks: [(start, end, tool), ...]
                    # perm: new tool order
                    old_blocks = []
                    for start, end, _ in blocks:
                        # Extract the full block (3 steps)
                        old_blocks.append(base_trajectory[start : end + 1])

                    # Reorder blocks based on the new order
                    old_tools = [tool for _, _, tool in blocks]
                    tool_to_block = dict(zip(old_tools, old_blocks))

                    # Replace blocks in the trajectory
                    for i, (start, end, old_tool) in enumerate(blocks):
                        new_tool = perm[i]
                        new_block = tool_to_block[new_tool].copy()
                        # Update the tool name inside the block (the middle element)
                        new_block[1] = new_tool
                        new_trajectory[start : end + 1] = new_block

                elif group_type == "book_reviewer":
                    # LangGraph: swap entire loop blocks (5 steps)
                    # blocks: [(start, end, tool), ...]
                    # perm: new tool order
                    old_blocks = []
                    for start, end, _ in blocks:
                        # Extract the full block (5 steps)
                        old_blocks.append(base_trajectory[start : end + 1])

                    # Reorder blocks based on the new order
                    old_tools = [tool for _, _, tool in blocks]
                    tool_to_block = dict(zip(old_tools, old_blocks))

                    # Replace blocks in the trajectory
                    for i, (start, end, old_tool) in enumerate(blocks):
                        new_tool = perm[i]
                        new_block = tool_to_block[new_tool].copy()
                        # Update the tool name inside the block (the last element)
                        new_block[4] = new_tool
                        new_trajectory[start : end + 1] = new_block

            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.

        Strategy: compute match scores between each candidate reference and the predicted
        trajectory using a weighted sum:
        exact_match * 3 + in_order_match * 2 + any_order_match * 1

        Args:
            predicted: predicted trajectory
            candidate_references: candidate reference trajectories

        Returns:
            (best reference trajectory, matching metrics for that best reference)
        """
        best_reference = candidate_references[0]
        best_score = -1
        best_metrics = {}

        for ref_trajectory in candidate_references:
            evaluator = TrajectoryEvaluator(ref_trajectory)

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

            # Composite score: exact_match has the highest weight, 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]]]:
        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 []
        out: List[Tuple[str, List[str]]] = []
        for session_dir in sorted(model_dir.iterdir()):
            if not session_dir.is_dir():
                continue
            md_file = session_dir / "execution_path.md"
            if not md_file.exists():
                continue
            parser = TrajectoryParser(str(md_file), extract_types=self.extract_types)
            traj = parser.parse()
            out.append((session_dir.name, traj))
        return out

    def evaluate_model(
        self, model_name: str, base_dir: str, collect_failure_reasons: bool = False
    ) -> Dict[str, float]:
        trajs = self.collect_execution_paths(model_name, base_dir)
        if not trajs:
            print(f"⚠️  No trajectory data found for model {model_name}")
            return {}

        all_metrics: Dict[str, List[float]] = defaultdict(list)

        if collect_failure_reasons and not hasattr(self, "failure_reasons"):
            self.failure_reasons = []

        patterns: List[List[int]] = [
            [1, 1, 1, 1],
            [2, 1, 1],
            [1, 2, 1],
            [1, 1, 2],
            [3, 1],
            [1, 3],
            [4],
        ]

        for session_id, predicted in trajs:
            chapter_count = TrajectoryEvaluator.detect_chapter_count(predicted)

            # Detect AutoGen outline pattern
            autogen_pattern_detected = (
                TrajectoryEvaluator.detect_autogen_outline_pattern(predicted)
            )

            best_metrics: Optional[Dict[str, float]] = None
            best_score: int = -1
            best_evaluator: Optional[TrajectoryEvaluator] = None

            # All possible AutoGen patterns
            if autogen_pattern_detected and autogen_pattern_detected != "unknown":
                autogen_patterns = ["compact", "interleaved"]
            else:
                autogen_patterns = [None]  # no change

            # Enumerate all combinations (AutoGen pattern × LangGraph pattern × tool permutations)
            for autogen_pat in autogen_patterns:
                for langgraph_pat in patterns:
                    # Step 1: create a base evaluator (apply chapter count, AutoGen pattern, LangGraph grouping)
                    base_evaluator = TrajectoryEvaluator(
                        self.reference_trajectory,
                        self.repeatable_patterns,
                        actual_chapter_count=chapter_count,
                        review_book_pattern=langgraph_pat,
                        autogen_outline_pattern=autogen_pat,
                    )

                    # Step 2: generate reference trajectories with tool permutations
                    if self.permutable_tool_groups:
                        candidate_refs = self._generate_permuted_trajectories_for_mix(
                            base_evaluator.reference
                        )
                    else:
                        candidate_refs = [base_evaluator.reference]

                    # Step 3: evaluate each permutation and pick the best
                    for candidate_ref in candidate_refs:
                        evaluator = TrajectoryEvaluator(candidate_ref)
                        m = evaluator.evaluate_all(predicted, self.target_tools)
                        # Use a weighted strategy: exact*3 + in_order*2 + any_order*1
                        score = (
                            int(m.get("exact_match", 0)) * 3
                            + int(m.get("in_order_match", 0)) * 2
                            + int(m.get("any_order_match", 0))
                        )
                        if score > best_score:
                            best_score = score
                            best_metrics = m
                            best_evaluator = evaluator

            if best_metrics is None or best_evaluator is None:
                evaluator = TrajectoryEvaluator(
                    self.reference_trajectory,
                    self.repeatable_patterns,
                    actual_chapter_count=chapter_count,
                    review_book_pattern=None,
                    autogen_outline_pattern=None,
                )
                best_metrics = best_evaluator.evaluate_all(predicted, self.target_tools)

            metrics = best_metrics

            for k, v in metrics.items():
                all_metrics[k].append(v)

            if collect_failure_reasons and metrics.get("any_order_match", 0) == 0:
                diagnosis = best_evaluator.diagnose_any_order_match_failure(predicted)
                missing_steps = diagnosis.get("missing_steps", [])
                self.failure_reasons.append(
                    {
                        "model": model_name,
                        "session": session_id,
                        "chapter_count": chapter_count,
                        "failure_stage": diagnosis.get("failure_stage"),
                        "missing_steps_count": len(missing_steps),
                        "missing_details": diagnosis.get("missing_details", ""),
                        "first_missing_step": (
                            missing_steps[0] if missing_steps else "N/A"
                        ),
                    }
                )

        avg: Dict[str, float] = {}
        for k, vs in all_metrics.items():
            avg[k] = sum(vs) / len(vs) if vs else 0.0

        num_samples = len(trajs)
        if num_samples > 0:
            path_counter: Dict[Tuple[str, ...], int] = defaultdict(int)
            for _, pred in trajs:
                path_counter[tuple(pred)] += 1
            unique_paths = len(path_counter)
            avg["unique_path_ratio"] = unique_paths / num_samples
            probs = [c / num_samples for c in path_counter.values()]
            H = -sum(p * math.log(p) for p in probs if p > 0)
            avg["path_entropy"] = H / math.log(len(probs)) if len(probs) > 1 else 0.0
        else:
            avg["unique_path_ratio"] = 0.0
            avg["path_entropy"] = 0.0

        avg["num_samples"] = num_samples
        return avg

    def evaluate_all_models(
        self, base_dir: Optional[str] = None, collect_failure_reasons: bool = False
    ) -> pd.DataFrame:
        if base_dir is None:
            # Default RESULTS directory:
            # This script is located at RESULTS/RQ1/BookWriter-H_A2A,
            # so three levels up is RESULTS.
            base_dir = str(Path(__file__).parent.parent.parent)
        results = []
        for model in self.models:
            print(f"\n📊 Evaluating model: {model}")
            metrics = self.evaluate_model(
                model, base_dir, collect_failure_reasons=collect_failure_reasons
            )
            if metrics:
                metrics["model"] = model
                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()
        df = pd.DataFrame(results)
        cols = [
            "model",
            "num_samples",
            "exact_match",
            "in_order_match",
            "any_order_match",
            "precision",
            "recall",
            "single_tool_use",
            "unique_path_ratio",
            "path_entropy",
        ]
        cols = [c for c in cols if c in df.columns]
        return df[cols]

    def save_failure_reasons(self, output_file: str = "any_order_match_failures.csv"):
        if not hasattr(self, "failure_reasons") or not self.failure_reasons:
            print("\n⚠️  No failure reason data collected")
            return
        output_path = Path(__file__).parent / output_file
        df = pd.DataFrame(self.failure_reasons)
        df.to_csv(output_path, index=False, encoding="utf-8")
        print(f"\n✅ any_order_match failure reasons saved: {output_path}")


# ======================  CLI  ======================


def main() -> None:
    import argparse

    parser = argparse.ArgumentParser(
        description="Evaluate trajectory metrics for the BookWriter-H_A2A project"
    )
    parser.add_argument(
        "--config",
        type=str,
        default="reference_trajectory.yaml",
        help="Path to the reference trajectory config file (YAML)",
    )
    parser.add_argument(
        "--base-dir",
        type=str,
        default=None,
        help="Path to the RESULTS directory (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"],
        default="csv",
        help="Output format (Markdown output is disabled)",
    )
    parser.add_argument(
        "--diagnose-failures",
        action="store_true",
        help="Diagnose any_order_match failures and generate a CSV",
    )

    args = parser.parse_args()

    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 - BookWriter-H_A2A")
    print("=" * 80)
    print(f"\n📁 Config file: {config_path}")

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

    if args.diagnose_failures:
        print("🔍 Failure reason diagnosis enabled")

    df = evaluator.evaluate_all_models(
        args.base_dir, collect_failure_reasons=args.diagnose_failures
    )
    if df.empty:
        print("\n❌ Evaluation failed: no data")
        return

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

    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))

    out_dir = os.path.dirname(args.output) or "."
    os.makedirs(out_dir, exist_ok=True)

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

    if args.diagnose_failures:
        evaluator.save_failure_reasons()

    print("\n" + "=" * 80)
    print("✅ Done")
    print("=" * 80)


if __name__ == "__main__":  # pragma: no cover
    main()