TestBatsy2.py

import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import torch
import tensorflow as tf
import onnx
import openai  # Added the missing import

from transformers import AutoTokenizer
from safe_rlhf.models import AutoModelForScore

import discord
from discord.ext import commands

import gym

# Load the Transformers model and tokenizer
model = AutoModelForScore.from_pretrained('PKU-Alignment/beaver-7b-v1.0-reward', device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v1.0-reward', use_fast=False)

input_text = 'BEGINNING OF CONVERSATION: USER: hello ASSISTANT: Hello! How can I help you today?'

input_ids = tokenizer(input_text, return_tensors='pt')
output = model(**input_ids)
print(output)

# Load PyTorch and TensorFlow models
pytorch_model = torch.load('your_pytorch_model.pth')  # Replace with actual path
tf_model = tf.keras.models.load_model('your_tensorflow_model.h5')  # Replace with actual path

# Convert to ONNX
# Note: You need to provide the proper arguments for onnx.export
# For example: onnx.export(pytorch_model, 'your_onnx_model.onnx')

# Define the Discord bot
intents = discord.Intents.default()
intents.typing = True
intents.presences = True
client = commands.Bot(command_prefix="!", intents=intents)

@client.event
async def on_ready():
    print(f'We have logged in as {client.user}')

@client.event
async def on_message(message):
    if message.author == client.user:
        return

    if message.content.startswith('!hello'):
        await message.channel.send('Hello!')

# Replace 'YOUR_BOT_TOKEN' with your Discord bot token
client.run('YOUR_BOT_TOKEN')

# Define the environment
grid = np.array([[0, 0, 1, 0, 0],
                 [0, 0, 1, 0, 1],
                 [0, 0, 0, 0, 1],
                 [0, 1, 1, 0, 0],
                 [0, 0, 0, 0, 2]])

# Initialize Q-table
num_states = grid.size
num_actions = 4
q_table = np.zeros((num_states, num_actions))

# Q-learning parameters
learning_rate = 0.8
discount_factor = 0.95
exploration_prob = 0.2
episodes = 1000

# Helper function to get next state and reward
def get_next_state(current_state, action):
    # Implement your logic here
    pass

# Q-learning algorithm
for _ in range(episodes):
    current_state = (0, 0)

    while True:
        if np.random.uniform(0, 1) < exploration_prob:
            action = np.random.randint(num_actions)
        else:
            action = np.argmax(q_table[current_state[0] * grid.shape[1] + current_state[1]])

        next_state, reward = get_next_state(current_state, action)

        # Update Q-table
        # Implement your Q-table update logic here

        if reward == 2:  # Reached the goal
            break

        current_state = next_state

# Define a simple model with self-updating weights
class SelfUpdatingModel:
    def __init__(self):
        self.weights = [0.5, 0.3, -0.2]

    def predict(self, features):
        return sum(w * f for w, f in zip(self.weights, features))

    def update_weights(self, features, target, learning_rate):
        prediction = self.predict(features)
        error = target - prediction
        self.weights = [w + learning_rate * error * f for w, f in zip(self.weights, features)]

# Example usage of the SelfUpdatingModel
model = SelfUpdatingModel()
data = [([1, 2, 3], 7), ([2, 3, 4], 12), ([3, 4, 5], 17)]

for features, target in data:
    prediction = model.predict(features)
    print(f"Predicted: {prediction}, Actual: {target}")

    model.update_weights(features, target, learning_rate=0.1)
    print(f"Updated weights: {model.weights}")

# Generate sample data for linear regression
np.random.seed(0)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

# Train-test split
X_train, X_test = X[:80], X[80:]
y_train, y_test = y[:80], y[80:]

# Train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

# Visualize the results
plt.scatter(X_test, y_test, color='blue')
plt.plot(X_test, y_pred, color='red', linewidth=2)
plt.show()

def objective_function(x):
    return -(x ** 2)  # Negative because we want to find the maximum

def hill_climbing(starting_point, step_size, num_iterations):
    current_point = starting_point

    for _ in range(num_iterations):
        current_value = objective_function(current_point)

        # Evaluate neighboring points
        left_neighbor = current_point - step_size
        right_neighbor = current_point + step_size

        left_value = objective_function(left_neighbor)
        right_value = objective_function(right_neighbor)

        # Move to the neighbor with the higher value
        if left_value > current_value:
            current_point = left_neighbor
        elif right_value > current_value:
            current_point = right_neighbor

    return current_point, objective_function(current_point)

if __name__ == "__main__":
    starting_point = 2
    step_size = 0.1
    num_iterations = 100

    final_point, max_value = hill_climbing(starting_point, step_size, num_iterations)

    print(f"The maximum value is {max_value} at x = {final_point}")

    # Create a bot instance with a command prefix
bot = commands.Bot(command_prefix='!')

@bot.event
async def on_ready():
    print(f'Logged in as {bot.user.name}')

    # Initialize the Discord bot
intents = discord.Intents.default()
# 'max_features' is not defined. You may need to replace it with an actual value.
intents.typing = max_features
intents.presences = max_features
client = discord.Client(intents=intents)

# Set your OpenAI API key here
openai.api_key = "sk-n0w7IuoLWGJpoWB4FbzfT3BlbkF"