Maith

import logging
import torch
import torch.nn as nn
import torch.optim as optim
import random
import numpy as np
import hypothetical_lora_adapter as lora  # Assuming you have a Lora adapter interface
from transformers import BertForSequenceClassification, BertTokenizer, AdamW
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, Lambda, Conv2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from typing import List

class DQN(nn.Module):
    def __init__(self, input_size, output_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_size, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class MathLLMAgent:
    EVALUATION_STRATEGY = "epoch"

    def __init__(self, num_iterations: int, learning_rate: float = 0.0001, discount_factor: float = 0.9):
        self.num_iterations = num_iterations
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.setup_bert_model()
        self.setup_logger()
        self.setup_optimizer()
        self.setup_siamese_network()
        self.setup_reinforcement_learning_agent()
        self.setup_lora_adapter()

    def setup_bert_model(self):
        self.bert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=1)
        self.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

    def setup_logger(self):
        self.logger = logging.getLogger(__name__)

    def setup_optimizer(self):
        self.optimizer = AdamW(self.bert_model.parameters(), lr=self.learning_rate)

    def setup_siamese_network(self):
        input_shape = (105, 105, 1)
        self.base_network = self.create_base_network(input_shape)

        input_a = Input(shape=input_shape)
        input_b = Input(shape=input_shape)

        processed_a = self.base_network(input_a)
        processed_b = self.base_network(input_b)

        distance = Lambda(self.euclidean_distance)([processed_a, processed_b])
        self.siamese_model = Model([input_a, input_b], distance)
        self.siamese_model.compile(loss=self.contrastive_loss, optimizer=Adam())

    def create_base_network(self, input_shape):
        input = Input(shape=input_shape)
        x = Conv2D(32, (5, 5), activation='relu')(input)
        x = Dropout(0.1)(x)
        x = Flatten()(x)
        x = Dense(128, activation='relu')(x)
        return Model(input, x)

    def euclidean_distance(self, vects):
        x, y = vects
        return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))

    def contrastive_loss(self, y_true, y_pred):
        margin = 1
        square_pred = K.square(y_pred)
        margin_square = K.square(K.maximum(margin - y_pred, 0))
        return K.mean(y_true * square_pred + (1 - y_true) * margin_square)

    def setup_reinforcement_learning_agent(self):
        self.rl_agent = ReinforcementLearningAgent(self.num_iterations, self.learning_rate, self.discount_factor)

    def setup_lora_adapter(self):
        self.lora_adapter = lora.Adapter()

    def choose_iteration(self):
        return np.random.choice(self.num_iterations)

    def update_q_values(self, chosen_iteration, reward):
        self.rl_agent.update_q_values(chosen_iteration, reward)

    def get_sentiment_score(self, text):
        return SentimentIntensityAnalyzer().polarity_scores(text)['compound']

    def extract_keywords(self, feedback):
        stop_words = set(stopwords.words('english'))
        tokens = word_tokenize(feedback)
        keywords = [word.lower() for word in tokens if word.isalnum() and word.lower() not in stop_words]
        return keywords

    def calculate_sentiment_reward(self, sentiment_score):
        return int(np.interp(sentiment_score, [-1, 1], [1, 5]))

    def calculate_keyword_reward(self, keywords):
        return len(keywords)

    def scale_and_combine_rewards(self, sentiment_reward, keyword_reward):
        return sentiment_reward + keyword_reward

    def fine_tune_bert(self, math_problems, labels):
        encodings = self.bert_tokenizer(math_problems, truncation=True, padding=True, return_tensors='pt', return_token_type_ids=False)
        labels = torch.tensor(labels, dtype=torch.float32).view(-1, 1)

        self.bert_model.train()
        for epoch in range(3):  # 3 epochs for fine-tuning
            outputs = self.bert_model(**encodings, labels=labels)
            loss = outputs.loss
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

        self.bert_model.eval()

    def train_siamese_network(self, pairs, labels):
        input_shape = (105, 105, 1)
        pairs_a = []
        pairs_b = []
        for pair in pairs:
            pair_a, pair_b = self.bert_tokenizer(pair[0], pair[1], padding=True, truncation=True, max_length=128, return_tensors='pt').values()
            pairs_a.append(pair_a)
            pairs_b.append(pair_b)
        pairs_a = torch.stack(pairs_a).reshape(-1, *input_shape)
        pairs_b = torch.stack(pairs_b).reshape(-1, *input_shape)
        labels = torch.tensor(labels, dtype=torch.float32).view(-1, 1)

        self.siamese_model.fit([pairs_a, pairs_b], labels, epochs=5, batch_size=64, validation_split=0.1)

    def generate_pairs(self, math_problems: List[str]):
        num_problems = len(math_problems)
        pairs = []
        labels = []
        for i in range(num_problems):
            for j in range(i+1, num_problems):
                pairs.append((math_problems[i], math_problems[j]))
                if i == j-1:
                    labels.append(0)
                else:
                    labels.append(1)
        return pairs, labels

    def get_similar_math_problem(self, math_problems: List[str], current_problem_index: int):
        pairs, _ = self.generate_pairs(math_problems)
        input_shape = (105, 105, 1)
        pairs_a = []
        pairs_b = []
        for pair in pairs:
            pair_a, pair_b = self.bert_tokenizer(pair[0], pair[1], padding=True, truncation=True, max_length=128, return_tensors='pt').values()
            pairs_a.append(pair_a)
            pairs_b.append(pair_b)
        pairs_a = torch.stack(pairs_a).reshape(-1, *input_shape)
        pairs_b = torch.stack(pairs_b).reshape(-1, *input_shape)

        similarities = self.siamese_model.predict([pairs_a, pairs_b]).reshape(-1)
        similarities[current_problem_index:] = -1  # Ignore similarities with the current problem

        most_similar_index = np.argmax(similarities)
        return math_problems[most_similar_index], most_similar_index

    def solve_math_problems(self, math_problems: List[str], initial_problem_index: int):
        current_problem_index = initial_problem_index
        for i in range(self.num_iterations):
            current_problem = math_problems[current_problem_index]
            sentiment_score = self.get_sentiment_score(current_problem)
            keywords = self.extract_keywords(current_problem)
            sentiment_reward = self.calculate_sentiment_reward(sentiment_score)
            keyword_reward = self.calculate_keyword_reward(keywords)
            reward = self.scale_and_combine_rewards(sentiment_reward, keyword_reward)
            self.update_q_values(current_problem_index, reward)

            similar_problem, similar_index = self.get_similar_math_problem(math_problems, current_problem_index)
            if self.rl_agent.q_values[similar_index] > self.rl_agent.q_values[current_problem_index]:
                current_problem_index = similar_index

        # Send solution over Lora
        solution = math_problems[current_problem_index]
        self.lora_adapter.send(solution)
class MathLLMAgent:
    # ... (previous code)

    def simulate_human_interaction(self, num_episodes):
        for episode in range(num_episodes):
            self.logger.info(f"Episode {episode + 1}")
            current_problem_index = self.choose_iteration()

            current_problem = math_problems[current_problem_index]
            self.logger.info(f"Current problem: {current_problem}")

            sentiment_score = self.get_sentiment_score(current_problem)
            self.logger.info(f"Sentiment score: {sentiment_score}")

            keywords = self.extract_keywords(current_problem)
            self.logger.info(f"Keywords: {keywords}")

            sentiment_reward = self.calculate_sentiment_reward(sentiment_score)
            keyword_reward = self.calculate_keyword_reward(keywords)

            reward = self.scale_and_combine_rewards(sentiment_reward, keyword_reward)
            self.logger.info(f"Reward: {reward}")

            self.update_q_values(current_problem_index, reward)

            similar_problem, similar_index = self.get_similar_math_problem(math_problems, current_problem_index)
            self.logger.info(f"Similar problem: {similar_problem}")

            if self.rl_agent.q_values[similar_index] > self.rl_agent.q_values[current_problem_index]:
                current_problem_index = similar_index
                self.logger.info("Moving to a similar problem")
            else:
                self.logger.info("Staying with the current problem")

        # Send the final solution over Lora
        final_solution = math_problems[current_problem_index]
        self.lora_adapter.send(final_solution)


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    num_iterations = 5
    math_llm_agent = MathLLMAgent(num_iterations)
    num_episodes = 10

    math_problems_for_fine_tuning = ["What is 2+2?", "Solve x for 2x = 6"]
    labels_for_fine_tuning = [4, 3]

    math_llm_agent.fine_tune_bert(math_problems_for_fine_tuning, labels_for_fine_tuning)

    math_llm_agent.simulate_human_interaction(num_episodes)

class ReinforcementLearningAgent:
    def __init__(self, num_iterations: int, learning_rate: float = 0.1, discount_factor: float = 0.9):
        self.num_iterations = num_iterations
        self.q_values = np.zeros(num_iterations)
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor

    def choose_iteration(self):
        epsilon = 0.1
        if np.random.uniform(0, 1) < epsilon:
            return np.random.choice(self.num_iterations)
        else:
            return np.argmax(self.q_values)

    def update_q_values(self, chosen_iteration, reward):
        old_q_value = self.q_values[chosen_iteration]
        new_q_value = old_q_value + self.learning_rate * (reward + self.discount_factor * np.max(self.q_values) - old_q_value)
        self.q_values[chosen_iteration] = new_q_value

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    num_iterations = 5
    math_llm_agent = MathLLMAgent(num_iterations)
    num_episodes = 10

    math_problems_for_fine_tuning = ["What is 2+2?", "Solve x for 2x = 6"]
    labels_for_fine_tuning = [4, 3]

    math_llm_agent.fine_tune_bert(math_problems_for_fine_tuning, labels_for_fine_tuning)

    math_llm_agent.simulate_human_interaction(num_episodes)