import {
    MemoryUnit,
    MemoryType,
    WorkingMemory,
    MemoryRetrieval,
    MemoryConsolidation,
    DatabaseMemory,
    EmotionalState,
    LLMWorkingMemory,
    LLMContextMemory,
    EmbeddingMemory,
    PromptMemory,
    TemporalProperties,
    MemoryFormationContext,
    EmotionalProperties
} from '../../types/fusemind/memory';

class MemoryService {
    private workingMemory: LLMWorkingMemory;
    private emotionalState: EmotionalState;
    private activeLifeEvents: Map<string, TemporalProperties['lifeEvents'][0]>;

    constructor() {
        this.workingMemory = {
            activeUnits: [],
            focus: {
                primary: '',
                secondary: [],
                attentionWeights: {}
            },
            context: {
                currentTask: '',
                relevantMemories: [],
                emotionalState: {
                    mood: 0,
                    energy: 1,
                    stress: 0,
                    focus: 1
                }
            },
            llmContext: {
                currentModel: 'gpt-4',
                contextWindow: {
                    used: 0,
                    available: 8192
                },
                recentPrompts: [],
                temperature: 0.7,
                systemPrompt: 'You are a helpful AI assistant.'
            },
            embeddings: {
                model: 'text-embedding-ada-002',
                cache: new Map(),
                similarityThreshold: 0.8
            }
        };
        this.emotionalState = this.workingMemory.context.emotionalState;
        this.activeLifeEvents = new Map();
    }

    // Memory Operations
    async storeMemory(memory: MemoryUnit, formationContext?: MemoryFormationContext): Promise<string> {
        // Add life event context if available
        if (formationContext?.temporalContext.lifeEvent) {
            const event = this.activeLifeEvents.get(formationContext.temporalContext.lifeEvent);
            if (event) {
                memory.temporal.lifeEvents.push(event);
                memory.temporal.temporalContext = {
                    period: formationContext.temporalContext.period,
                    moodState: this.getMoodState(),
                    cognitiveBias: this.getActiveBiases()
                };

                // Apply emotional biases
                memory.emotional.triggers.push(...formationContext.emotionalInfluence.biasEffects.map(bias => ({
                    type: 'contextual' as const,
                    source: 'life_event',
                    intensity: bias.strength,
                    associatedEvent: formationContext.temporalContext.lifeEvent,
                    biasEffect: {
                        type: bias.type as 'amplification' | 'distortion' | 'suppression',
                        strength: bias.strength,
                        duration: event.emotionalImpact.persistence
                    }
                })));
            }
        }

        // Convert to database format
        const dbMemory: DatabaseMemory = {
            id: memory.id,
            type: memory.type,
            content: JSON.stringify(memory.content),
            temporal: JSON.stringify(memory.temporal),
            emotional: JSON.stringify(memory.emotional),
            connections: JSON.stringify(memory.connections),
            created_at: new Date(),
            updated_at: new Date()
        };

        // TODO: Implement database storage
        // await db.memories.insert(dbMemory);

        // Update working memory if relevant
        this.updateWorkingMemory(memory);

        return memory.id;
    }

    async retrieveMemory(query: MemoryRetrieval): Promise<MemoryUnit[]> {
        const memories = await this.retrieveMemoryFromStorage(query);

        // Apply emotional and temporal context filters
        return memories.filter(memory => {
            // Check if memory is relevant to current life events
            const isRelevantToCurrentEvents = memory.temporal.lifeEvents.some(event =>
                this.activeLifeEvents.has(event.eventId)
            );

            // Check emotional triggers
            const hasActiveTriggers = memory.emotional.triggers.some(trigger =>
                trigger.associatedEvent && this.activeLifeEvents.has(trigger.associatedEvent)
            );

            return isRelevantToCurrentEvents || hasActiveTriggers;
        });
    }

    private async retrieveMemoryFromStorage(query: MemoryRetrieval): Promise<MemoryUnit[]> {
        // TODO: Implement actual storage retrieval
        return [];
    }

    async consolidateMemories(consolidation: MemoryConsolidation): Promise<MemoryUnit> {
        const memories = await Promise.all(
            consolidation.sourceMemories.map(id => this.retrieveMemory({
                query: id,
                context: {},
                filters: {}
            }))
        ).then(results => results.flat());

        // Calculate emotional influence
        const emotionalInfluence = this.calculateEmotionalInfluence(memories);

        // Create new memory with emotional context
        const newMemory: MemoryUnit = {
            id: this.generateId(),
            type: 'semantic',
            content: this.mergeMemoryContents(memories),
            temporal: {
                ...this.calculateTemporalProperties(memories),
                lifeEvents: memories.flatMap(m => m.temporal.lifeEvents),
                temporalContext: this.getCurrentTemporalContext()
            },
            emotional: {
                ...this.calculateEmotionalProperties(memories),
                triggers: emotionalInfluence.triggers,
                emotionalState: {
                    baseline: emotionalInfluence.baseline,
                    current: this.emotionalState.mood,
                    volatility: emotionalInfluence.volatility
                }
            },
            connections: this.buildMemoryConnections(memories)
        };

        await this.storeMemory(newMemory);
        return newMemory;
    }

    // Working Memory Management
    private updateWorkingMemory(memory: MemoryUnit) {
        // Add to active units if not already present
        if (!this.workingMemory.activeUnits.find(u => u.id === memory.id)) {
            this.workingMemory.activeUnits.push(memory);
        }

        // Update focus based on memory importance
        this.updateFocus(memory);
    }

    private updateFocus(memory: MemoryUnit) {
        const importance = memory.temporal.importance;

        if (importance > 0.8) {
            // High importance memories become primary focus
            this.workingMemory.focus.primary = memory.id;
        } else if (importance > 0.5) {
            // Medium importance memories become secondary focus
            if (!this.workingMemory.focus.secondary.includes(memory.id)) {
                this.workingMemory.focus.secondary.push(memory.id);
            }
        }

        // Update attention weights
        this.workingMemory.focus.attentionWeights[memory.id] = importance;
    }

    // Helper Methods
    private matchesQuery(memory: MemoryUnit, query: MemoryRetrieval): boolean {
        // Implement query matching logic
        return true; // Placeholder
    }

    private generateId(): string {
        return Math.random().toString(36).substring(2, 15);
    }

    private mergeMemoryContents(memories: MemoryUnit[]): any {
        // Implement memory content merging logic
        return {}; // Placeholder
    }

    private calculateTemporalProperties(memories: MemoryUnit[]): any {
        // Implement temporal properties calculation
        return {}; // Placeholder
    }

    private calculateEmotionalProperties(memories: MemoryUnit[]): any {
        // Implement emotional properties calculation
        return {}; // Placeholder
    }

    private buildMemoryConnections(memories: MemoryUnit[]): any[] {
        // Implement memory connections building
        return []; // Placeholder
    }

    // Emotional State Management
    updateEmotionalState(update: Partial<EmotionalState>) {
        this.emotionalState = {
            ...this.emotionalState,
            ...update
        };
        this.workingMemory.context.emotionalState = this.emotionalState;
    }

    getCurrentEmotionalState(): EmotionalState {
        return this.emotionalState;
    }

    // LLM-specific operations
    async storeLLMContext(context: LLMContextMemory): Promise<string> {
        // Update working memory context
        this.workingMemory.llmContext = {
            ...this.workingMemory.llmContext,
            currentModel: context.content.data.model,
            contextWindow: {
                used: context.content.data.tokens.total,
                available: context.content.data.contextWindow
            },
            temperature: context.content.data.parameters.temperature
        };

        return this.storeMemory(context);
    }

    async storeEmbedding(embedding: EmbeddingMemory): Promise<string> {
        // Cache the embedding
        this.workingMemory.embeddings.cache.set(
            embedding.content.data.text,
            embedding.content.data.embedding
        );

        return this.storeMemory(embedding);
    }

    async storePrompt(prompt: PromptMemory): Promise<string> {
        // Update recent prompts
        this.workingMemory.llmContext.recentPrompts.push(
            prompt.content.data.template
        );

        // Keep only last 10 prompts
        if (this.workingMemory.llmContext.recentPrompts.length > 10) {
            this.workingMemory.llmContext.recentPrompts.shift();
        }

        return this.storeMemory(prompt);
    }

    async retrieveSimilarMemories(query: string, threshold: number = 0.8): Promise<MemoryUnit[]> {
        // Get embedding for query
        const queryEmbedding = await this.getEmbedding(query);

        // Find similar memories
        const similarMemories: MemoryUnit[] = [];

        for (const [text, embedding] of this.workingMemory.embeddings.cache) {
            const similarity = this.calculateSimilarity(queryEmbedding, embedding);
            if (similarity >= threshold) {
                const memories = await this.retrieveMemory({
                    query: text,
                    context: {},
                    filters: {}
                });
                similarMemories.push(...memories);
            }
        }

        return similarMemories;
    }

    // Helper methods for LLM operations
    private async getEmbedding(text: string): Promise<number[]> {
        // Check cache first
        if (this.workingMemory.embeddings.cache.has(text)) {
            return this.workingMemory.embeddings.cache.get(text)!;
        }

        // TODO: Implement actual embedding generation
        // const embedding = await openai.embeddings.create({
        //     model: this.workingMemory.embeddings.model,
        //     input: text
        // });

        // For now, return placeholder
        return new Array(1536).fill(0).map(() => Math.random());
    }

    private calculateSimilarity(embedding1: number[], embedding2: number[]): number {
        // Implement cosine similarity
        const dotProduct = embedding1.reduce((sum, val, i) => sum + val * embedding2[i], 0);
        const norm1 = Math.sqrt(embedding1.reduce((sum, val) => sum + val * val, 0));
        const norm2 = Math.sqrt(embedding2.reduce((sum, val) => sum + val * val, 0));

        return dotProduct / (norm1 * norm2);
    }

    // Enhanced memory consolidation for LLM context
    async consolidateLLMMemories(
        sourceMemories: string[],
        llmContext: Partial<LLMContextMemory['content']>
    ): Promise<MemoryUnit> {
        const memories = await Promise.all(
            sourceMemories.map(id => this.retrieveMemory({
                query: id,
                context: {},
                filters: {}
            }))
        ).then(results => results.flat());

        // Create new consolidated memory with LLM context
        const newMemory: MemoryUnit = {
            id: this.generateId(),
            type: 'llm_context',
            content: {
                ...this.mergeMemoryContents(memories),
                ...llmContext
            },
            temporal: this.calculateTemporalProperties(memories),
            emotional: this.calculateEmotionalProperties(memories),
            connections: this.buildMemoryConnections(memories)
        };

        await this.storeMemory(newMemory);
        return newMemory;
    }

    // Life event management
    async registerLifeEvent(event: TemporalProperties['lifeEvents'][0]) {
        this.activeLifeEvents.set(event.eventId, event);

        // Update emotional state based on event
        this.updateEmotionalState({
            mood: event.emotionalImpact.valence,
            energy: event.emotionalImpact.arousal,
            stress: 1 - event.emotionalImpact.persistence,
            focus: 0.5 // Reduced focus during significant events
        });
    }

    async endLifeEvent(eventId: string) {
        const event = this.activeLifeEvents.get(eventId);
        if (event) {
            event.endDate = new Date();
            this.activeLifeEvents.delete(eventId);

            // Gradually return to baseline emotional state
            this.updateEmotionalState({
                mood: 0, // Neutral
                energy: 1,
                stress: 0,
                focus: 1
            });
        }
    }

    // Helper methods
    private calculateEmotionalInfluence(memories: MemoryUnit[]): {
        triggers: EmotionalProperties['triggers'];
        baseline: number;
        volatility: number;
    } {
        const triggers: EmotionalProperties['triggers'] = [];
        let totalValence = 0;
        let totalArousal = 0;
        let count = 0;

        memories.forEach(memory => {
            // Collect triggers
            triggers.push(...memory.emotional.triggers);

            // Calculate average emotional state
            totalValence += memory.emotional.valence;
            totalArousal += memory.emotional.arousal;
            count++;
        });

        return {
            triggers,
            baseline: count > 0 ? totalValence / count : 0,
            volatility: count > 0 ? totalArousal / count : 0
        };
    }

    private getCurrentTemporalContext(): TemporalProperties['temporalContext'] {
        const activeEvents = Array.from(this.activeLifeEvents.values());
        return {
            period: activeEvents.length > 0 ?
                activeEvents.map(e => e.type).join('-') :
                'normal',
            moodState: this.getMoodState(),
            cognitiveBias: this.getActiveBiases()
        };
    }

    private getMoodState(): string {
        const mood = this.emotionalState.mood;
        if (mood > 0.7) return 'euphoric';
        if (mood > 0.3) return 'positive';
        if (mood < -0.7) return 'depressed';
        if (mood < -0.3) return 'negative';
        return 'neutral';
    }

    private getActiveBiases(): string[] {
        const biases: string[] = [];
        const mood = this.emotionalState.mood;

        if (mood > 0.5) {
            biases.push('optimism', 'overestimation');
        } else if (mood < -0.5) {
            biases.push('pessimism', 'catastrophizing');
        }

        if (this.emotionalState.stress > 0.7) {
            biases.push('anxiety', 'hypervigilance');
        }

        return biases;
    }
}

export const memoryService = new MemoryService(); 