import gymnasium as gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(
    repo_id="sb3/demo-hf-CartPole-v1",
    filename="ppo-CartPole-v1.zip",
)
model = PPO.load(checkpoint)

# Evaluate the agent and watch it
eval_env = gym.make("CartPole-v1")
mean_reward, std_reward = evaluate_policy(
    model, eval_env, render=False, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

from huggingface_hub import notebook_login
notebook_login()

import gymnasium as gym

from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import package_to_hub

# Create the environment
env_id = "LunarLander-v2"
env = make_vec_env(env_id, n_envs=1)

# Create the evaluation env
eval_env = make_vec_env(env_id, n_envs=1)

# Instantiate the agent
model = PPO("MlpPolicy", env, verbose=1)

# Train the agent
model.learn(total_timesteps=int(5000))

# This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
package_to_hub(model=model, 
               model_name="ppo-LunarLander-v2",
               model_architecture="PPO",
               env_id=env_id,
               eval_env=eval_env,
               repo_id="ThomasSimonini/ppo-LunarLander-v2",
               commit_message="Test commit")

import gymnasium as gym

from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import push_to_hub

# Create the environment
env_id = "LunarLander-v2"
env = make_vec_env(env_id, n_envs=1)

# Instantiate the agent
model = PPO("MlpPolicy", env, verbose=1)

# Train it for 10000 timesteps
model.learn(total_timesteps=10_000)

# Save the model
model.save("ppo-LunarLander-v2")

# Push this saved model .zip file to the hf repo
# If this repo does not exists it will be created
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename: the name of the file == "name" inside model.save("ppo-LunarLander-v2")
push_to_hub(
    repo_id="ThomasSimonini/ppo-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip",
    commit_message="Added LunarLander-v2 model trained with PPO",
)

import gymnasium as gym
from huggingface_sb3.naming_schemes import EnvironmentName, ModelName, ModelRepoId

env_name = EnvironmentName("seals/Walker2d-v0")
model_name = ModelName("ppo", env_name)
repo_id = ModelRepoId("YourOrganization", model_name)

# prints 'seals-Walker2d-v0'. Notice how the slash is removed so you can use it to 
# construct file paths if you like.
print(env_name)

# you can still access the original gym id if needed
env = gym.make(env_name.gym_id)  

# prints `ppo-seals-Walker2d-v0`
print(model_name)  

# prints: `ppo-seals-Walker2d-v0.zip`. 
# This is where `model.save(model_name)` will place the model file
print(model_name.filename)  

# prints: `YourOrganization/ppo-seals-Walker2d-v0`
print(repo_id)