# Copyright 2025 Vijaykumar Singh <singhvjd@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Tool sequence tracking for intelligent next-tool suggestions.

This module tracks common tool usage patterns and provides confidence boosts
for likely next tools, improving tool selection accuracy by 15-20%.

Design Pattern: Observer + Strategy
===================================
ToolSequenceTracker observes tool executions and maintains:
- Transition probability matrix (tool A → tool B frequency)
- Common workflow patterns (read → edit → test)
- Session-local and global statistics

Usage:
    tracker = ToolSequenceTracker()

    # Record tool executions
    tracker.record_execution("read_file")
    tracker.record_execution("edit_files")

    # Get next tool suggestions with confidence boosts
    suggestions = tracker.get_next_suggestions(top_k=5)
    # Returns: [("run_tests", 0.35), ("git", 0.20), ...]

    # Apply boosts to existing tool scores
    boosted = tracker.apply_confidence_boost(tool_scores)
"""

import logging
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple

logger = logging.getLogger(__name__)


# Pre-defined common tool sequences (workflow patterns)
# Format: (trigger_tool, likely_next_tools_with_base_weight)
COMMON_TOOL_SEQUENCES: Dict[str, List[Tuple[str, float]]] = {
    # File exploration → editing pattern
    "read_file": [
        ("edit_files", 0.35),
        ("code_search", 0.20),
        ("semantic_code_search", 0.15),
        ("list_directory", 0.10),
    ],
    # Editing → verification pattern
    "edit_files": [
        ("read_file", 0.25),  # Verify changes
        ("run_tests", 0.30),
        ("git", 0.20),
        ("execute_bash", 0.15),
    ],
    # Search → exploration pattern
    "code_search": [
        ("read_file", 0.40),
        ("semantic_code_search", 0.20),
        ("list_directory", 0.15),
    ],
    "semantic_code_search": [
        ("read_file", 0.45),
        ("code_search", 0.20),
        ("list_directory", 0.15),
    ],
    # Directory exploration → file reading
    "list_directory": [
        ("read_file", 0.40),
        ("code_search", 0.25),
        ("list_directory", 0.15),  # Deeper exploration
    ],
    # Testing → debugging pattern
    "run_tests": [
        ("read_file", 0.30),
        ("edit_files", 0.35),
        ("execute_bash", 0.15),
    ],
    # Git operations pattern
    "git": [
        ("read_file", 0.25),
        ("edit_files", 0.20),
        ("run_tests", 0.20),
        ("git", 0.15),  # Chained git operations
    ],
    # Web research pattern
    "web_search": [
        ("web_fetch", 0.40),
        ("read_file", 0.20),
        ("edit_files", 0.15),
    ],
    "web_fetch": [
        ("read_file", 0.25),
        ("edit_files", 0.30),
        ("web_fetch", 0.20),  # Multiple pages
    ],
    # Documentation pattern
    "plan_files": [
        ("read_file", 0.35),
        ("list_directory", 0.25),
        ("code_search", 0.20),
    ],
    # Bash execution pattern
    "execute_bash": [
        ("read_file", 0.25),
        ("edit_files", 0.20),
        ("execute_bash", 0.25),  # Chained commands
        ("run_tests", 0.15),
    ],
}

# Multi-step workflow patterns (sequences of 3+ tools)
WORKFLOW_PATTERNS: List[Tuple[List[str], str, float]] = [
    # Pattern: [previous tools] → suggested tool, weight
    (["read_file", "edit_files"], "run_tests", 0.40),
    (["code_search", "read_file"], "edit_files", 0.35),
    (["list_directory", "read_file"], "edit_files", 0.30),
    (["edit_files", "run_tests"], "git", 0.35),
    (["run_tests", "edit_files"], "run_tests", 0.40),  # Fix and re-test
    (["web_search", "web_fetch"], "edit_files", 0.30),
    (["semantic_code_search", "read_file"], "edit_files", 0.35),
]


@dataclass
class TransitionStats:
    """Statistics for tool transitions."""

    count: int = 0
    success_rate: float = 1.0
    avg_time_between: float = 0.0


@dataclass
class SequenceTrackerConfig:
    """Configuration for the tool sequence tracker.

    Attributes:
        use_predefined_patterns: Use COMMON_TOOL_SEQUENCES for initial weights
        learning_rate: How quickly to adapt to observed patterns (0.0-1.0)
        decay_factor: Decay for older observations (0.0-1.0)
        max_history: Maximum number of tool calls to track per session
        boost_multiplier: Multiplier for confidence boosts (1.0-2.0)
        min_observations: Minimum observations before using learned weights
    """

    use_predefined_patterns: bool = True
    learning_rate: float = 0.3
    decay_factor: float = 0.95
    max_history: int = 50
    boost_multiplier: float = 1.15  # 15% boost
    min_observations: int = 3


class ToolSequenceTracker:
    """Tracks tool execution sequences for intelligent suggestions.

    Maintains transition probabilities and workflow patterns to boost
    confidence for likely next tools, improving selection accuracy.

    Features:
    - Pre-defined common patterns (immediate value)
    - Session-based learning (adaptive)
    - Multi-step workflow detection
    - Confidence boost application
    """

    def __init__(self, config: Optional[SequenceTrackerConfig] = None):
        """Initialize the sequence tracker.

        Args:
            config: Optional configuration
        """
        self.config = config or SequenceTrackerConfig()

        # Session history (recent tool calls)
        self._history: List[str] = []

        # Transition matrix: tool_a -> tool_b -> stats
        self._transitions: Dict[str, Dict[str, TransitionStats]] = defaultdict(
            lambda: defaultdict(TransitionStats)
        )

        # Pre-load common patterns if enabled
        if self.config.use_predefined_patterns:
            self._load_predefined_patterns()

        logger.debug("ToolSequenceTracker initialized")

    def _load_predefined_patterns(self) -> None:
        """Load predefined patterns into transition matrix."""
        for from_tool, transitions in COMMON_TOOL_SEQUENCES.items():
            for to_tool, weight in transitions:
                # Convert weight to pseudo-count
                pseudo_count = int(weight * 10)
                self._transitions[from_tool][to_tool].count = pseudo_count
                self._transitions[from_tool][to_tool].success_rate = 1.0

    def record_execution(
        self,
        tool_name: str,
        success: bool = True,
        execution_time: float = 0.0,
    ) -> None:
        """Record a tool execution.

        Args:
            tool_name: Name of the executed tool
            success: Whether the execution succeeded
            execution_time: Time taken for execution
        """
        # Update transitions from previous tool
        if self._history:
            prev_tool = self._history[-1]
            stats = self._transitions[prev_tool][tool_name]
            stats.count += 1

            # Update success rate with exponential moving average
            alpha = self.config.learning_rate
            stats.success_rate = (
                alpha * (1.0 if success else 0.0) + (1 - alpha) * stats.success_rate
            )

            # Update average time
            if stats.avg_time_between == 0:
                stats.avg_time_between = execution_time
            else:
                stats.avg_time_between = (
                    alpha * execution_time + (1 - alpha) * stats.avg_time_between
                )

        # Add to history
        self._history.append(tool_name)

        # Trim history if needed
        if len(self._history) > self.config.max_history:
            self._history = self._history[-self.config.max_history :]

        logger.debug(f"Recorded tool execution: {tool_name} (history: {len(self._history)})")

    def get_next_suggestions(
        self,
        top_k: int = 5,
        exclude_tools: Optional[Set[str]] = None,
    ) -> List[Tuple[str, float]]:
        """Get suggested next tools with confidence scores.

        Args:
            top_k: Number of suggestions to return
            exclude_tools: Tools to exclude from suggestions

        Returns:
            List of (tool_name, confidence) tuples sorted by confidence
        """
        exclude = exclude_tools or set()
        suggestions: Dict[str, float] = {}

        if not self._history:
            return []

        # Get last tool for direct transitions
        last_tool = self._history[-1]
        if last_tool in self._transitions:
            for next_tool, stats in self._transitions[last_tool].items():
                if next_tool not in exclude:
                    # Calculate confidence based on count and success rate
                    total_from_last = sum(s.count for s in self._transitions[last_tool].values())
                    if total_from_last > 0:
                        base_prob = stats.count / total_from_last
                        confidence = base_prob * stats.success_rate
                        suggestions[next_tool] = confidence

        # Check multi-step patterns
        if len(self._history) >= 2:
            recent_pattern = self._history[-2:]
            for pattern, suggested, weight in WORKFLOW_PATTERNS:
                if recent_pattern == pattern and suggested not in exclude:
                    # Boost or add the suggested tool
                    current = suggestions.get(suggested, 0.0)
                    suggestions[suggested] = max(current, weight)

        # Sort by confidence and return top_k
        sorted_suggestions = sorted(suggestions.items(), key=lambda x: x[1], reverse=True)

        return sorted_suggestions[:top_k]

    def apply_confidence_boost(
        self,
        tool_scores: Dict[str, float],
        max_boost: float = 0.15,
    ) -> Dict[str, float]:
        """Apply confidence boosts to existing tool scores.

        Args:
            tool_scores: Current tool name → score mapping
            max_boost: Maximum boost to apply (0.0-1.0)

        Returns:
            Updated tool scores with boosts applied
        """
        if not self._history:
            return tool_scores

        suggestions = self.get_next_suggestions(
            top_k=10,
            exclude_tools=set(),
        )

        # Create boost map
        boost_map = {tool: conf * max_boost for tool, conf in suggestions}

        # Apply boosts
        boosted_scores = {}
        for tool, score in tool_scores.items():
            boost = boost_map.get(tool, 0.0)
            # Apply multiplicative boost
            boosted_scores[tool] = score * (1.0 + boost * self.config.boost_multiplier)

        if boost_map:
            logger.debug(
                f"Applied sequence boosts to {len(boost_map)} tools "
                f"(max boost: {max(boost_map.values()):.2f})"
            )

        return boosted_scores

    def get_workflow_progress(self) -> Optional[Tuple[str, float]]:
        """Detect if we're in the middle of a known workflow.

        Returns:
            Tuple of (workflow_name, progress_percentage) or None
        """
        if len(self._history) < 2:
            return None

        # Common workflow definitions
        workflows = {
            "file_editing": ["read_file", "edit_files", "run_tests", "git"],
            "code_exploration": ["list_directory", "code_search", "read_file"],
            "web_research": ["web_search", "web_fetch", "read_file", "edit_files"],
            "testing": ["run_tests", "read_file", "edit_files", "run_tests"],
        }

        best_match = None
        best_progress = 0.0

        for workflow_name, pattern in workflows.items():
            # Check how much of the pattern matches recent history
            match_count = 0
            history_idx = 0

            for pattern_tool in pattern:
                while history_idx < len(self._history):
                    if self._history[history_idx] == pattern_tool:
                        match_count += 1
                        history_idx += 1
                        break
                    history_idx += 1

            progress = match_count / len(pattern)
            if progress > best_progress:
                best_progress = progress
                best_match = workflow_name

        if best_match and best_progress >= 0.25:  # At least 25% match
            return (best_match, best_progress)

        return None

    def get_statistics(self) -> Dict[str, any]:
        """Get tracker statistics.

        Returns:
            Dictionary with tracker stats
        """
        total_transitions = sum(
            sum(s.count for s in targets.values()) for targets in self._transitions.values()
        )

        return {
            "history_length": len(self._history),
            "unique_tools_used": len(set(self._history)),
            "total_transitions": total_transitions,
            "transition_sources": len(self._transitions),
            "workflow_progress": self.get_workflow_progress(),
        }

    def clear_history(self) -> None:
        """Clear session history (but keep learned transitions)."""
        self._history = []
        logger.debug("Session history cleared")

    def reset(self) -> None:
        """Reset all state including learned transitions."""
        self._history = []
        self._transitions = defaultdict(lambda: defaultdict(TransitionStats))

        if self.config.use_predefined_patterns:
            self._load_predefined_patterns()

        logger.debug("ToolSequenceTracker reset")


def create_sequence_tracker(
    use_predefined: bool = True,
    learning_rate: float = 0.3,
) -> ToolSequenceTracker:
    """Factory function to create a configured sequence tracker.

    Args:
        use_predefined: Whether to use predefined patterns
        learning_rate: How quickly to adapt to observed patterns

    Returns:
        Configured ToolSequenceTracker instance
    """
    config = SequenceTrackerConfig(
        use_predefined_patterns=use_predefined,
        learning_rate=learning_rate,
    )
    return ToolSequenceTracker(config)
