import torch
import torch.nn as nn

class EmojiGenerator(nn.Module):
    def __init__(self, latent_dim=100, num_classes=10):
        super(EmojiGenerator, self).__init__()
        
        self.label_embedding = nn.Embedding(num_classes, num_classes)
        
        self.model = nn.Sequential(
            nn.Linear(latent_dim + num_classes, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, 32*32*3),
            nn.Tanh()
        )
    
    def forward(self, noise, labels):
        label_embedding = self.label_embedding(labels)
        gen_input = torch.cat([noise, label_embedding], -1)
        emoji = self.model(gen_input)
        emoji = emoji.view(emoji.size(0), 3, 32, 32)
        return emoji

class EmojiVAE(nn.Module):
    def __init__(self, input_dim=3072, hidden_dim=512, latent_dim=64):
        super(EmojiVAE, self).__init__()
        
        # Encoder
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim//2),
            nn.ReLU()
        )
        
        self.mu = nn.Linear(hidden_dim//2, latent_dim)
        self.logvar = nn.Linear(hidden_dim//2, latent_dim)
        
        # Decoder
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, hidden_dim//2),
            nn.ReLU(),
            nn.Linear(hidden_dim//2, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
            nn.Sigmoid()
        )
    
    def encode(self, x):
        h = self.encoder(x)
        return self.mu(h), self.logvar(h)
    
    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std
    
    def decode(self, z):
        return self.decoder(z)
    
    def forward(self, x):
        mu, logvar = self.encode(x.view(-1, 3072))
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

from transformers import CLIPModel, CLIPTokenizer
import torch.nn.functional as F

class TextToEmojiGenerator:
    def __init__(self, model_name="openai/clip-vit-base-patch32"):
        self.clip_model = CLIPModel.from_pretrained(model_name)
        self.tokenizer = CLIPTokenizer.from_pretrained(model_name)
        self.emoji_generator = EmojiGenerator()
        
    def generate_from_text(self, text_description, num_samples=1):
        # Encode text description
        inputs = self.tokenizer(text_description, 
                               return_tensors="pt", 
                               padding=True, 
                               truncation=True)
        
        text_features = self.clip_model.get_text_features(**inputs)
        
        # Generate emojis conditioned on text features
        noise = torch.randn(num_samples, 100)
        generated_emojis = self.emoji_generator(noise, text_features)
        
        return generated_emojis

import numpy as np
from sklearn.decomposition import NMF

class EmojiCollaborativeFilter:
    def __init__(self, n_components=50):
        self.model = NMF(n_components=n_components, init='random', random_state=0)
        self.user_emoji_matrix = None
        self.emoji_features = None
        self.user_features = None
        
    def fit(self, user_emoji_interactions):
        """
        user_emoji_interactions: sparse matrix where rows are users,
        columns are emojis, and values are interaction frequencies
        """
        self.user_emoji_matrix = user_emoji_interactions
        self.user_features = self.model.fit_transform(user_emoji_interactions)
        self.emoji_features = self.model.components_
        
    def recommend_emojis(self, user_id, n_recommendations=10):
        if user_id >= len(self.user_features):
            return self._recommend_popular_emojis(n_recommendations)
            
        # Reconstruct user preferences
        user_profile = self.user_features[user_id]
        predicted_scores = np.dot(user_profile, self.emoji_features)
        
        # Get emojis user hasn't used much
        used_emojis = self.user_emoji_matrix[user_id].nonzero()[1]
        predicted_scores[used_emojis] *= 0.1  # Reduce scores for already used emojis
        
        # Get top recommendations
        top_emoji_indices = np.argsort(predicted_scores)[-n_recommendations:][::-1]
        return top_emoji_indices

from transformers import BertTokenizer, BertModel
import torch

class SemanticEmojiRecommender:
    def __init__(self):
        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        self.bert_model = BertModel.from_pretrained('bert-base-uncased')
        self.emoji_embeddings = {}  # Pre-computed emoji embeddings
        
    def compute_message_embedding(self, message_text):
        inputs = self.tokenizer(message_text, 
                               return_tensors='pt', 
                               truncation=True, 
                               padding=True)
        
        with torch.no_grad():
            outputs = self.bert_model(**inputs)
            # Use CLS token embedding
            message_embedding = outputs.last_hidden_state[:, 0, :].squeeze()
            
        return message_embedding
    
    def find_similar_emojis(self, message_text, n_recommendations=5):
        message_embedding = self.compute_message_embedding(message_text)
        
        similarities = {}
        for emoji_id, emoji_embedding in self.emoji_embeddings.items():
            similarity = F.cosine_similarity(
                message_embedding.unsqueeze(0), 
                emoji_embedding.unsqueeze(0)
            ).item()
            similarities[emoji_id] = similarity
            
        # Sort by similarity and return top recommendations
        sorted_emojis = sorted(similarities.items(), 
                             key=lambda x: x[1], 
                             reverse=True)
        
        return [emoji_id for emoji_id, _ in sorted_emojis[:n_recommendations]]

class HybridEmojiRecommender:
    def __init__(self, collaborative_weight=0.4, content_weight=0.4, popularity_weight=0.2):
        self.collaborative_filter = EmojiCollaborativeFilter()
        self.semantic_recommender = SemanticEmojiRecommender()
        self.weights = {
            'collaborative': collaborative_weight,
            'content': content_weight,
            'popularity': popularity_weight
        }
        
    def get_recommendations(self, user_id, message_text, n_recommendations=10):
        # Get recommendations from each approach
        collab_recs = self.collaborative_filter.recommend_emojis(user_id, n_recommendations*2)
        content_recs = self.semantic_recommender.find_similar_emojis(message_text, n_recommendations*2)
        popular_recs = self.get_trending_emojis(n_recommendations*2)
        
        # Combine scores with weighted approach
        combined_scores = {}
        
        # Add collaborative filtering scores
        for i, emoji_id in enumerate(collab_recs):
            score = (len(collab_recs) - i) / len(collab_recs)
            combined_scores[emoji_id] = combined_scores.get(emoji_id, 0) + \
                                      self.weights['collaborative'] * score
        
        # Add content-based scores
        for i, emoji_id in enumerate(content_recs):
            score = (len(content_recs) - i) / len(content_recs)
            combined_scores[emoji_id] = combined_scores.get(emoji_id, 0) + \
                                      self.weights['content'] * score
        
        # Add popularity scores
        for i, emoji_id in enumerate(popular_recs):
            score = (len(popular_recs) - i) / len(popular_recs)
            combined_scores[emoji_id] = combined_scores.get(emoji_id, 0) + \
                                      self.weights['popularity'] * score
        
        # Sort and return top recommendations
        sorted_recommendations = sorted(combined_scores.items(), 
                                      key=lambda x: x[1], 
                                      reverse=True)
        
        return [emoji_id for emoji_id, _ in sorted_recommendations[:n_recommendations]]

    def get_trending_emojis(self, n_recommendations):
        # Implementation for retrieving trending emojis based on usage data
        pass

from transformers import pipeline
import spacy

class ContextualEmojiSuggester:
    def __init__(self):
        self.sentiment_analyzer = pipeline("sentiment-analysis")
        self.emotion_classifier = pipeline("text-classification", 
                                         model="j-hartmann/emotion-english-distilroberta-base")
        self.nlp = spacy.load("en_core_web_sm")
        
    def analyze_context(self, message_text, conversation_history=None):
        # Sentiment analysis
        sentiment = self.sentiment_analyzer(message_text)[0]
        
        # Emotion detection
        emotions = self.emotion_classifier(message_text)
        
        # Entity recognition
        doc = self.nlp(message_text)
        entities = [(ent.text, ent.label_) for ent in doc.ents]
        
        # Topic modeling on conversation history
        topics = []
        if conversation_history:
            topics = self.extract_topics(conversation_history)
        
        context = {
            'sentiment': sentiment,
            'emotions': emotions,
            'entities': entities,
            'topics': topics,
            'message_length': len(message_text.split()),
            'question_marks': message_text.count('?'),
            'exclamation_marks': message_text.count('!')
        }
        
        return context
    
    def suggest_contextual_emojis(self, message_text, conversation_history=None):
        context = self.analyze_context(message_text, conversation_history)
        
        emoji_suggestions = []
        
        # Sentiment-based suggestions
        if context['sentiment']['label'] == 'POSITIVE':
            if context['sentiment']['score'] > 0.8:
                emoji_suggestions.extend(['😊', '😄', '🎉', '👍'])
        elif context['sentiment']['label'] == 'NEGATIVE':
            if context['sentiment']['score'] > 0.8:
                emoji_suggestions.extend(['😢', '😔', '💔', '😞'])
                
        # Emotion-based suggestions
        emotion_emoji_map = {
            'joy': ['😊', '😄', '🎉', '😆'],
            'sadness': ['😢', '😔', '💔', '😞'],
            'anger': ['😠', '😡', '🤬', '💢'],
            'fear': ['😰', '😱', '😨', '😧'],
            'surprise': ['😮', '😯', '🤯', '😲'],
            'love': ['❤️', '💕', '💖', '😍']
        }
        
        for emotion_result in context['emotions']:
            emotion = emotion_result['label'].lower()
            if emotion in emotion_emoji_map:
                emoji_suggestions.extend(emotion_emoji_map[emotion])
        
        # Entity-based suggestions
        entity_emoji_map = {
            'PERSON': ['👨', '👩', '🧑', '👤'],
            'ORG': ['🏢', '🏪', '🏛️', '🏭'],
            'GPE': ['🌍', '🗺️', '🏙️', '🌆'],
            'MONEY': ['💰', '💵', '💸', '💳'],
            'TIME': ['⏰', '⏳', '⏱️', '📅'],
            'EVENT': ['🎊', '🎉', '🎈', '🎆']
        }
        
        for entity_text, entity_type in context['entities']:
            if entity_type in entity_emoji_map:
                emoji_suggestions.extend(entity_emoji_map[entity_type])
        
        # Remove duplicates and return top suggestions
        unique_suggestions = list(dict.fromkeys(emoji_suggestions))
        return unique_suggestions[:10]

class ConversationFlowAnalyzer:
    def __init__(self):
        self.conversation_states = {
            'greeting': ['👋', '😊', '🙂', '😄'],
            'question': ['🤔', '❓', '🤷', '🧐'],
            'agreement': ['👍', '✅', '💯', '😊'],
            'disagreement': ['👎', '❌', '🤷', '😐'],
            'farewell': ['👋', '😊', '🙂', '💙'],
            'excitement': ['🎉', '😆', '🤩', '🚀'],
            'concern': ['😟', '🤔', '😔', '💭']
        }
        
    def detect_conversation_state(self, message_text, position_in_conversation):
        # Implement conversation state detection logic
        # This could use pattern matching, ML classification, or rule-based approaches
        
        greeting_patterns = ['hello', 'hi', 'hey', 'good morning', 'good afternoon']
        farewell_patterns = ['bye', 'goodbye', 'see you', 'talk later', 'take care']
        question_patterns = ['what', 'how', 'when', 'where', 'why', 'which', 'who']
        
        message_lower = message_text.lower()
        
        if position_in_conversation == 'start':
            if any(pattern in message_lower for pattern in greeting_patterns):
                return 'greeting'
        
        if position_in_conversation == 'end':
            if any(pattern in message_lower for pattern in farewell_patterns):
                return 'farewell'
        
        if any(pattern in message_lower for pattern in question_patterns):
            return 'question'
        
        # Additional state detection logic...
        
        return 'neutral'
    
    def get_flow_based_suggestions(self, message_text, conversation_position):
        state = self.detect_conversation_state(message_text, conversation_position)
        return self.conversation_states.get(state, ['😊', '🙂', '👍'])