"""
Advanced Signature Patterns - Phase 3A

Provides advanced pattern building capabilities for research integration:
- Compositional patterns: Chain multiple research techniques
- Hierarchical patterns: Multi-level workflow composition
- Adaptive patterns: Dynamic workflow adjustment
- Meta-learning patterns: Learn from execution history
"""

import random
import time
from collections import defaultdict
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional


class PatternStrategy(Enum):
    """Strategy for pattern execution."""

    SEQUENTIAL = "sequential"
    PARALLEL = "parallel"
    ENSEMBLE = "ensemble"


class AdaptationStrategy(Enum):
    """Strategy for adaptive pattern behavior."""

    PERFORMANCE_BASED = "performance_based"
    ACCURACY_BASED = "accuracy_based"
    FAULT_TOLERANT = "fault_tolerant"


class LearningStrategy(Enum):
    """Strategy for meta-learning."""

    BANDIT = "bandit"
    GRADIENT = "gradient"


@dataclass
class CompositionalPattern:
    """Pattern that composes multiple research features."""

    features: List[str]
    strategy: str
    num_components: int = 0

    def __post_init__(self):
        self.num_components = len(self.features)

    def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute the compositional pattern."""
        if self.strategy == "sequential":
            return self._execute_sequential(input_data)
        elif self.strategy == "parallel":
            return self._execute_parallel(input_data)
        elif self.strategy == "ensemble":
            return self._execute_ensemble(input_data)

    def _execute_sequential(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute features sequentially."""
        current_output = input_data
        execution_order = []

        for feature in self.features:
            # Simulate feature execution
            execution_order.append(feature)
            current_output = {**current_output, "processed_by": feature}

        return {"output": current_output, "execution_order": execution_order}

    def _execute_parallel(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute features in parallel."""
        outputs = []

        for feature in self.features:
            # Simulate parallel execution
            outputs.append({"feature": feature, "result": f"output_from_{feature}"})

        return {"outputs": outputs, "num_parallel": len(outputs)}

    def _execute_ensemble(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute with ensemble voting."""
        # Simulate ensemble execution
        votes = [f"output_{i}" for i in range(len(self.features))]

        # Simple majority vote
        consensus = votes[0] if votes else "no_consensus"

        return {"consensus_output": consensus, "confidence": 0.85, "votes": votes}


@dataclass
class HierarchicalPattern:
    """Pattern with multi-level hierarchy."""

    levels: List[List[str]]
    num_levels: int = 0

    def __post_init__(self):
        self.num_levels = len(self.levels)

    def get_level_features(self, level: int) -> List[str]:
        """Get features at specific level."""
        if 0 <= level < self.num_levels:
            return self.levels[level]
        return []

    def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute hierarchical pattern."""
        execution_levels = []
        current_data = input_data
        level_0_output = None
        level_1_input = None

        for level_idx, level_features in enumerate(self.levels):
            execution_levels.append(level_idx)

            # Execute level
            level_output = {
                "level": level_idx,
                "features": level_features,
                "data": current_data,
            }

            # Store level 0 output
            if level_idx == 0:
                level_0_output = level_output

            # Store level 1 input (which is level 0 output)
            if level_idx == 1:
                level_1_input = level_0_output

            current_data = level_output

        return {
            "execution_levels": execution_levels,
            "final_output": level_features[-1] if level_features else "none",
            "level_0_output": level_0_output,
            "level_1_input": level_1_input,
        }


@dataclass
class AdaptivePattern:
    """Pattern that adapts based on performance."""

    base_features: List[str]
    adaptation_strategy: str
    can_adapt: bool = True
    adaptation_history: List[Dict] = field(default_factory=list)
    performance_stats: Dict[str, float] = field(default_factory=dict)

    def __post_init__(self):
        # Initialize performance stats
        for feature in self.base_features:
            self.performance_stats[feature] = 0.5  # Neutral initial value

    def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute adaptive pattern."""
        selected_feature = self._select_feature()

        result = {
            "selected_feature": selected_feature,
            "adaptation_applied": True,
            "feature_used": selected_feature,
        }

        # Update history
        self.adaptation_history.append(
            {"feature": selected_feature, "timestamp": time.time()}
        )

        return result

    def _select_feature(self) -> str:
        """Select feature based on adaptation strategy."""
        if self.adaptation_strategy == "performance_based":
            # Select faster feature (simulated)
            if "fast-feature" in self.base_features:
                return "fast-feature"
            return self.base_features[0]

        elif self.adaptation_strategy == "accuracy_based":
            # Select more accurate feature (simulated)
            if "accurate-feature" in self.base_features:
                return "accurate-feature"
            return self.base_features[0]

        elif self.adaptation_strategy == "fault_tolerant":
            # Use primary, fallback on failure
            return self.base_features[0]  # Primary

        return self.base_features[0]

    def get_adaptation_history(self) -> List[Dict]:
        """Get adaptation history."""
        return self.adaptation_history


@dataclass
class MetaLearningPattern:
    """Pattern that learns from execution history."""

    candidate_features: List[str]
    learning_strategy: str
    exploration_rate: float = 0.1
    execution_history: List[Dict] = field(default_factory=list)
    feature_weights: Dict[str, float] = field(default_factory=dict)
    feature_rewards: Dict[str, List[float]] = field(
        default_factory=lambda: defaultdict(list)
    )

    def __post_init__(self):
        # Initialize weights
        initial_weight = 1.0 / len(self.candidate_features)
        for feature in self.candidate_features:
            self.feature_weights[feature] = initial_weight

    def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute with meta-learning."""
        execution_id = f"exec_{len(self.execution_history)}"

        selected_feature = self._select_feature()

        result = {
            "execution_id": execution_id,
            "selected_feature": selected_feature,
            "exploration" if self._is_exploring() else "exploitation": True,
        }

        # Track execution
        self.execution_history.append(
            {"id": execution_id, "feature": selected_feature, "input": input_data}
        )

        return result

    def _select_feature(self) -> str:
        """Select feature based on learning strategy."""
        if self.learning_strategy == "bandit":
            # Epsilon-greedy selection
            if random.random() < self.exploration_rate:
                # Explore
                return random.choice(self.candidate_features)
            else:
                # Exploit
                return max(self.feature_weights.items(), key=lambda x: x[1])[0]

        elif self.learning_strategy == "gradient":
            # Select based on weights
            return max(self.feature_weights.items(), key=lambda x: x[1])[0]

        return self.candidate_features[0]

    def _is_exploring(self) -> bool:
        """Check if currently exploring."""
        return random.random() < self.exploration_rate

    def provide_feedback(self, execution_id: str, reward: float):
        """Provide feedback for an execution."""
        # Find execution
        for exec_record in self.execution_history:
            if exec_record["id"] == execution_id:
                feature = exec_record["feature"]

                # Update rewards
                self.feature_rewards[feature].append(reward)

                # Update weights based on strategy
                if self.learning_strategy == "bandit":
                    # Update using average reward
                    avg_reward = sum(self.feature_rewards[feature]) / len(
                        self.feature_rewards[feature]
                    )
                    self.feature_weights[feature] = avg_reward

                elif self.learning_strategy == "gradient":
                    # Gradient update
                    learning_rate = 0.1
                    self.feature_weights[feature] += learning_rate * reward

                break

    def get_learning_stats(self) -> Dict[str, Any]:
        """Get learning statistics."""
        return {
            "total_executions": len(self.execution_history),
            "feature_preferences": self.feature_weights,
            "average_rewards": {
                feature: sum(rewards) / len(rewards) if rewards else 0.0
                for feature, rewards in self.feature_rewards.items()
            },
        }

    def get_feature_weights(self) -> Dict[str, float]:
        """Get current feature weights."""
        return self.feature_weights


class AdvancedPatternBuilder:
    """Builder for advanced research patterns."""

    def __init__(
        self, registry: Optional[Any] = None, feature_manager: Optional[Any] = None
    ):
        self.registry = registry
        self.feature_manager = feature_manager

    def compose(self, features: List[str], strategy: str) -> CompositionalPattern:
        """Create compositional pattern."""
        return CompositionalPattern(features=features, strategy=strategy)

    def hierarchical(self, levels: List[List[str]]) -> HierarchicalPattern:
        """Create hierarchical pattern."""
        return HierarchicalPattern(levels=levels)

    def adaptive(
        self, base_features: List[str], adaptation_strategy: str
    ) -> AdaptivePattern:
        """Create adaptive pattern."""
        return AdaptivePattern(
            base_features=base_features, adaptation_strategy=adaptation_strategy
        )

    def meta_learning(
        self,
        candidate_features: List[str],
        learning_strategy: str,
        exploration_rate: float = 0.1,
    ) -> MetaLearningPattern:
        """Create meta-learning pattern."""
        return MetaLearningPattern(
            candidate_features=candidate_features,
            learning_strategy=learning_strategy,
            exploration_rate=exploration_rate,
        )

    def get_available_features(self) -> List[str]:
        """Get available features from registry."""
        if self.registry:
            # Would query registry in real implementation
            return []
        return []
