"""
Process Optimization Module for BleuJS
Provides intelligent workflow analysis and optimization capabilities.
"""

import logging
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import networkx as nx
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from scipy.optimize import differential_evolution, linear_sum_assignment, minimize
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler


@dataclass
class ProcessMetrics:
    """Process performance metrics."""

    throughput: float
    cycle_time: float
    resource_utilization: float
    quality_score: float
    cost_per_unit: float
    bottleneck_score: float


@dataclass
class OptimizationConstraints:
    """Optimization constraints configuration."""

    max_resources: int
    min_quality_score: float
    max_cost_per_unit: float
    max_cycle_time: Optional[float] = None
    min_throughput: Optional[float] = None


class ProcessOptimizationNN(nn.Module):
    """Neural network for process optimization."""

    def __init__(self, input_dim: int, hidden_dim: int = 128):
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid(),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.network(x)


class ProcessOptimizer:
    """Intelligent process optimization engine."""

    def __init__(
        self,
        workflow_type: str,
        optimization_goals: List[str],
        constraints: Dict[str, float],
        use_ml: bool = True,
        parallel_processing: bool = True,
    ):
        """
        Initialize the process optimizer.

        Args:
            workflow_type: Type of workflow ('manufacturing', 'service', 'software', etc.)
            optimization_goals: List of optimization objectives
            constraints: Dictionary of optimization constraints
            use_ml: Whether to use machine learning for optimization
            parallel_processing: Whether to enable parallel processing
        """
        self.workflow_type = workflow_type
        self.optimization_goals = optimization_goals
        self.constraints = OptimizationConstraints(**constraints)
        self.use_ml = use_ml
        self.parallel_processing = parallel_processing
        self.logger = logging.getLogger(__name__)

        # Initialize optimization components
        self.process_graph = nx.DiGraph()
        self.metrics_history = []
        self.bottleneck_cache = {}
        self._initialize_optimization_engine()

    def _initialize_optimization_engine(self) -> None:
        """Initialize the optimization engine components."""
        self.scaler = StandardScaler()
        self.kmeans = KMeans(n_clusters=3, random_state=42)
        self.executor = (
            ThreadPoolExecutor(max_workers=4) if self.parallel_processing else None
        )

    def analyze_workflow(
        self, process_data: Dict
    ) -> Dict[str, Union[float, List[str]]]:
        """
        Analyze current workflow and identify optimization opportunities.

        Args:
            process_data: Dictionary containing process performance data

        Returns:
            Dictionary containing analysis results
        """
        try:
            # Build process graph
            self._build_process_graph(process_data)

            # Calculate current metrics
            current_metrics = self._calculate_process_metrics(process_data)

            # Identify bottlenecks
            bottlenecks = self._identify_bottlenecks()

            # Analyze resource utilization
            resource_analysis = self._analyze_resource_utilization(process_data)

            # Perform quality analysis
            quality_issues = self._analyze_quality_metrics(process_data)

            # Calculate optimization potential
            optimization_potential = self._calculate_optimization_potential(
                current_metrics, bottlenecks, resource_analysis
            )

            return {
                "current_metrics": current_metrics.__dict__,
                "bottlenecks": bottlenecks,
                "resource_analysis": resource_analysis,
                "quality_issues": quality_issues,
                "optimization_potential": optimization_potential,
            }

        except Exception as e:
            self.logger.error(f"Error analyzing workflow: {str(e)}")
            raise

    def get_recommendations(self) -> List[Dict[str, Union[str, float, List[str]]]]:
        """
        Generate optimization recommendations based on analysis.

        Returns:
            List of recommendation dictionaries
        """
        recommendations = []

        try:
            # Resource optimization recommendations
            resource_recs = self._generate_resource_recommendations()
            recommendations.extend(resource_recs)

            # Process flow optimization
            flow_recs = self._generate_flow_recommendations()
            recommendations.extend(flow_recs)

            # Quality improvement recommendations
            quality_recs = self._generate_quality_recommendations()
            recommendations.extend(quality_recs)

            # Cost optimization recommendations
            cost_recs = self._generate_cost_recommendations()
            recommendations.extend(cost_recs)

            # Sort recommendations by impact
            recommendations.sort(key=lambda x: x["impact_score"], reverse=True)

            return recommendations

        except Exception as e:
            self.logger.error(f"Error generating recommendations: {str(e)}")
            raise

    def _build_process_graph(self, process_data: Dict) -> None:
        """Build directed graph representation of the process."""
        steps = process_data.get("steps", [])
        dependencies = process_data.get("dependencies", [])

        for step in steps:
            self.process_graph.add_node(
                step["id"],
                name=step["name"],
                duration=step["duration"],
                resources=step["resources"],
            )

        for dep in dependencies:
            self.process_graph.add_edge(dep["from"], dep["to"])

    def _calculate_process_metrics(self, process_data: Dict) -> ProcessMetrics:
        """Calculate current process performance metrics."""
        throughput = self._calculate_throughput(process_data)
        cycle_time = self._calculate_cycle_time()
        resource_util = self._calculate_resource_utilization(process_data)
        quality_score = self._calculate_quality_score(process_data)
        cost_per_unit = self._calculate_cost_per_unit(process_data)
        bottleneck_score = self._calculate_bottleneck_score()

        return ProcessMetrics(
            throughput=throughput,
            cycle_time=cycle_time,
            resource_utilization=resource_util,
            quality_score=quality_score,
            cost_per_unit=cost_per_unit,
            bottleneck_score=bottleneck_score,
        )

    def _identify_bottlenecks(self) -> List[str]:
        """Identify process bottlenecks using critical path analysis."""
        critical_path = nx.dag_longest_path(self.process_graph, weight="duration")
        bottlenecks = []

        for node in critical_path:
            node_data = self.process_graph.nodes[node]
            if node_data["duration"] > np.mean(
                [
                    self.process_graph.nodes[n]["duration"]
                    for n in self.process_graph.nodes
                ]
            ):
                bottlenecks.append(node)

        return bottlenecks

    def _analyze_resource_utilization(self, process_data: Dict) -> Dict[str, float]:
        """Analyze resource utilization patterns."""
        resource_usage = {}
        total_time = sum(step["duration"] for step in process_data["steps"])

        for step in process_data["steps"]:
            for resource in step["resources"]:
                if resource not in resource_usage:
                    resource_usage[resource] = 0
                resource_usage[resource] += step["duration"]

        return {
            resource: usage / total_time for resource, usage in resource_usage.items()
        }

    def _analyze_quality_metrics(self, process_data: Dict) -> List[Dict]:
        """Analyze quality-related metrics and issues."""
        quality_issues = []

        if "quality_data" in process_data:
            for metric, value in process_data["quality_data"].items():
                if value < self.constraints.min_quality_score:
                    quality_issues.append(
                        {
                            "metric": metric,
                            "current_value": value,
                            "target": self.constraints.min_quality_score,
                            "gap": self.constraints.min_quality_score - value,
                        }
                    )

        return quality_issues

    def _calculate_optimization_potential(
        self,
        current_metrics: ProcessMetrics,
        bottlenecks: List[str],
        resource_analysis: Dict[str, float],
    ) -> float:
        """Calculate overall optimization potential."""
        # Weighted scoring of different factors
        bottleneck_impact = len(bottlenecks) * 0.3
        resource_impact = (1 - np.mean(list(resource_analysis.values()))) * 0.3
        quality_impact = (1 - current_metrics.quality_score) * 0.2
        cost_impact = (
            current_metrics.cost_per_unit / self.constraints.max_cost_per_unit
        ) * 0.2

        return bottleneck_impact + resource_impact + quality_impact + cost_impact

    def _generate_resource_recommendations(self) -> List[Dict]:
        """Generate resource optimization recommendations."""
        recommendations = []

        # Resource reallocation recommendations
        underutilized = []
        overutilized = []

        for resource, utilization in self._analyze_resource_utilization({}).items():
            if utilization < 0.5:
                underutilized.append(resource)
            elif utilization > 0.8:
                overutilized.append(resource)

        if underutilized and overutilized:
            recommendations.append(
                {
                    "type": "resource_reallocation",
                    "description": "Reallocate resources from underutilized to overutilized areas",
                    "from_resources": underutilized,
                    "to_resources": overutilized,
                    "impact_score": 0.8,
                }
            )

        return recommendations

    def _generate_flow_recommendations(self) -> List[Dict]:
        """Generate process flow optimization recommendations."""
        recommendations = []

        # Parallel processing recommendations
        independent_paths = list(nx.all_simple_paths(self.process_graph))
        if len(independent_paths) > 1:
            recommendations.append(
                {
                    "type": "parallel_processing",
                    "description": "Implement parallel processing for independent paths",
                    "paths": independent_paths,
                    "impact_score": 0.7,
                }
            )

        return recommendations

    def _generate_quality_recommendations(self) -> List[Dict]:
        """Generate quality improvement recommendations."""
        recommendations = []

        quality_issues = self._analyze_quality_metrics({})
        if quality_issues:
            recommendations.append(
                {
                    "type": "quality_improvement",
                    "description": "Implement additional quality control measures",
                    "target_metrics": [issue["metric"] for issue in quality_issues],
                    "impact_score": 0.6,
                }
            )

        return recommendations

    def _generate_cost_recommendations(self) -> List[Dict]:
        """Generate cost optimization recommendations."""
        recommendations = []

        # Cost reduction through automation
        manual_steps = [
            node
            for node in self.process_graph.nodes
            if self.process_graph.nodes[node].get("automation_potential", 0) > 0.7
        ]

        if manual_steps:
            recommendations.append(
                {
                    "type": "automation",
                    "description": "Automate high-potential manual steps",
                    "target_steps": manual_steps,
                    "impact_score": 0.75,
                }
            )

        return recommendations

    def _calculate_throughput(self, process_data: Dict) -> float:
        """Calculate process throughput."""
        if "throughput_data" in process_data:
            return np.mean(process_data["throughput_data"])
        return 0.0

    def _calculate_cycle_time(self) -> float:
        """Calculate process cycle time using critical path."""
        critical_path = nx.dag_longest_path(self.process_graph, weight="duration")
        return sum(self.process_graph.nodes[node]["duration"] for node in critical_path)

    def _calculate_resource_utilization(self, process_data: Dict) -> float:
        """Calculate overall resource utilization."""
        utilization_values = list(
            self._analyze_resource_utilization(process_data).values()
        )
        return np.mean(utilization_values) if utilization_values else 0.0

    def _calculate_quality_score(self, process_data: Dict) -> float:
        """Calculate overall quality score."""
        if "quality_data" in process_data:
            return np.mean(list(process_data["quality_data"].values()))
        return 0.0

    def _calculate_cost_per_unit(self, process_data: Dict) -> float:
        """Calculate cost per unit of output."""
        if "cost_data" in process_data:
            total_cost = sum(process_data["cost_data"].values())
            total_units = process_data.get("total_units", 1)
            return total_cost / total_units
        return 0.0

    def _calculate_bottleneck_score(self) -> float:
        """Calculate overall bottleneck impact score."""
        bottlenecks = self._identify_bottlenecks()
        if not bottlenecks:
            return 0.0

        total_nodes = len(self.process_graph.nodes)
        return len(bottlenecks) / total_nodes


class AdvancedProcessOptimizer(ProcessOptimizer):
    """Enhanced process optimizer with advanced algorithms."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.optimization_model = None
        self.genetic_population_size = 100
        self.genetic_generations = 50
        self.rl_episodes = 1000

    def _initialize_optimization_engine(self) -> None:
        """Initialize enhanced optimization components."""
        super()._initialize_optimization_engine()

        # Initialize advanced components
        self.rf_model = RandomForestRegressor(
            n_estimators=100, max_depth=10, random_state=42
        )

        # Initialize neural network if using ML
        if self.use_ml:
            self.optimization_model = ProcessOptimizationNN(
                input_dim=len(self.optimization_goals)
            )
            self.optimizer = optim.Adam(
                self.optimization_model.parameters(), lr=0.001, weight_decay=0.01
            )

    def optimize_using_genetic_algorithm(
        self,
        process_data: Dict[str, Any],
        population_size: int = None,
        generations: int = None,
    ) -> Dict[str, Any]:
        """
        Optimize process using genetic algorithm.

        Args:
            process_data: Current process data
            population_size: Size of genetic population
            generations: Number of generations to evolve

        Returns:
            Optimized process configuration
        """
        pop_size = population_size or self.genetic_population_size
        gens = generations or self.genetic_generations

        def fitness_function(x):
            """Calculate fitness of a solution."""
            config = self._decode_solution(x)
            metrics = self._calculate_process_metrics(config)
            return (
                metrics.throughput * 0.3
                + (1 - metrics.cycle_time) * 0.2
                + metrics.resource_utilization * 0.2
                + metrics.quality_score * 0.2
                + (1 - metrics.cost_per_unit) * 0.1
            )

        bounds = self._get_optimization_bounds(process_data)
        result = differential_evolution(
            fitness_function,
            bounds,
            popsize=pop_size,
            maxiter=gens,
            workers=self.executor._max_workers if self.executor else 1,
        )

        return self._decode_solution(result.x)

    def optimize_using_reinforcement_learning(
        self, process_data: Dict[str, Any], episodes: int = None
    ) -> Dict[str, Any]:
        """
        Optimize process using reinforcement learning.

        Args:
            process_data: Current process data
            episodes: Number of training episodes

        Returns:
            Optimized process configuration
        """
        num_episodes = episodes or self.rl_episodes

        # Initialize state space
        state = self._encode_process_state(process_data)
        best_reward = float("-inf")
        best_config = None

        for _ in range(num_episodes):
            current_state = state.clone()
            total_reward = 0

            for _ in range(100):  # Max steps per episode
                # Get action from policy
                action = self._get_rl_action(current_state)

                # Apply action and get reward
                next_state, reward, done = self._apply_rl_action(
                    current_state, action, process_data
                )

                # Update policy
                self._update_rl_policy(reward, next_state, done)

                total_reward += reward
                current_state = next_state

                if done:
                    break

            if total_reward > best_reward:
                best_reward = total_reward
                best_config = self._decode_process_state(current_state)

        return best_config

    def _encode_process_state(self, process_data: Dict) -> torch.Tensor:
        """Encode process state for RL."""
        features = []

        # Extract relevant features
        features.extend(
            [
                len(process_data.get("steps", [])),
                len(process_data.get("dependencies", [])),
                self._calculate_throughput(process_data),
                self._calculate_cycle_time(),
                self._calculate_resource_utilization(process_data),
                self._calculate_quality_score(process_data),
                self._calculate_cost_per_unit(process_data),
            ]
        )

        return torch.tensor(features, dtype=torch.float32)

    def _get_rl_action(self, state: torch.Tensor) -> torch.Tensor:
        """Get action from current policy."""
        if self.optimization_model is None:
            return torch.zeros_like(state)
        with torch.no_grad():
            return self.optimization_model(state)

    def _apply_rl_action(
        self, state: torch.Tensor, action: torch.Tensor, process_data: Dict
    ) -> Tuple[torch.Tensor, float, bool]:
        """Apply action and get reward."""
        # Apply action to process
        new_config = self._apply_optimization_action(action.numpy(), process_data)

        # Calculate new state and reward
        new_state = self._encode_process_state(new_config)
        metrics = self._calculate_process_metrics(new_config)
        reward = (
            metrics.throughput * 0.3
            + (1 - metrics.cycle_time) * 0.2
            + metrics.resource_utilization * 0.2
            + metrics.quality_score * 0.2
            + (1 - metrics.cost_per_unit) * 0.1
        )

        # Check if optimization is complete
        done = reward > 0.95 or self._check_convergence(state, new_state)

        return new_state, reward, done

    def _update_rl_policy(
        self, reward: float, next_state: torch.Tensor, done: bool
    ) -> None:
        """Update RL policy using experience."""
        if self.optimization_model is None:
            return

        # Calculate loss
        value = self.optimization_model(next_state)
        target = reward + (0.99 * value * (1 - done))
        loss = nn.MSELoss()(value, target)

        # Update model
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def _check_convergence(
        self, state: torch.Tensor, new_state: torch.Tensor, threshold: float = 1e-6
    ) -> bool:
        """Check if optimization has converged."""
        return torch.norm(new_state - state, dim=0) < threshold

    def _get_optimization_bounds(self, process_data: Dict) -> List[Tuple[float, float]]:
        """Get bounds for optimization variables."""
        bounds = []

        # Add bounds for each optimization variable
        for _ in range(len(process_data.get("steps", []))):
            bounds.append((0.0, 1.0))  # Resource allocation
            bounds.append((0.1, 10.0))  # Processing time
            bounds.append((0.0, 1.0))  # Quality threshold

        return bounds

    def _decode_solution(self, solution: np.ndarray) -> Dict[str, Any]:
        """Decode optimization solution to process configuration."""
        config = {}
        idx = 0

        # Decode resource allocation
        config["resource_allocation"] = solution[
            idx : idx + len(self.process_graph.nodes)
        ]
        idx += len(self.process_graph.nodes)

        # Decode processing times
        config["processing_times"] = solution[idx : idx + len(self.process_graph.nodes)]
        idx += len(self.process_graph.nodes)

        # Decode quality thresholds
        config["quality_thresholds"] = solution[
            idx : idx + len(self.process_graph.nodes)
        ]

        return config

    def _apply_optimization_action(
        self, action: np.ndarray, process_data: Dict
    ) -> Dict[str, Any]:
        """Apply optimization action to process configuration."""
        new_config = process_data.copy()

        # Update resource allocation
        for i, step in enumerate(new_config.get("steps", [])):
            step["resources"] = self._adjust_resources(step["resources"])

        return new_config

    def _adjust_resources(self, current_resources: List[str]) -> List[str]:
        """Adjust resource allocation based on optimization."""
        # Implementation depends on specific resource types
        return current_resources
