import os
from copy import deepcopy

import numpy as np
import pytest
import torch as th
from stable_baselines3.common.identity_env import FakeImageEnv
from stable_baselines3.common.utils import zip_strict

from sb3_contrib import TQC


@pytest.mark.parametrize("model_class", [TQC])
def test_cnn(tmp_path, model_class):
    SAVE_NAME = "cnn_model.zip"
    # Fake grayscale with frameskip
    # Atari after preprocessing: 84x84x1, here we are using lower resolution
    # to check that the network handle it automatically
    env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=model_class not in {TQC})
    kwargs = {}
    if model_class in {TQC}:
        # Avoid memory error when using replay buffer
        # Reduce the size of the features
        kwargs = dict(buffer_size=250, policy_kwargs=dict(features_extractor_kwargs=dict(features_dim=32)))
    model = model_class("CnnPolicy", env, **kwargs).learn(250)

    obs = env.reset()

    action, _ = model.predict(obs, deterministic=True)

    model.save(tmp_path / SAVE_NAME)
    del model

    model = model_class.load(tmp_path / SAVE_NAME)

    # Check that the prediction is the same
    assert np.allclose(action, model.predict(obs, deterministic=True)[0])

    os.remove(str(tmp_path / SAVE_NAME))


def params_should_match(params, other_params):
    for param, other_param in zip_strict(params, other_params):
        assert th.allclose(param, other_param)


def params_should_differ(params, other_params):
    for param, other_param in zip_strict(params, other_params):
        assert not th.allclose(param, other_param)


@pytest.mark.parametrize("model_class", [TQC])
@pytest.mark.parametrize("share_features_extractor", [True, False])
def test_feature_extractor_target_net(model_class, share_features_extractor):
    env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=model_class not in {TQC})
    # Avoid memory error when using replay buffer
    # Reduce the size of the features
    kwargs = dict(
        buffer_size=250,
        learning_starts=100,
        policy_kwargs=dict(
            features_extractor_kwargs=dict(features_dim=32),
            share_features_extractor=share_features_extractor,
        ),
    )
    model = model_class("CnnPolicy", env, seed=0, **kwargs)

    if share_features_extractor:
        # Check that the objects are the same and not just copied
        assert id(model.policy.actor.features_extractor) == id(model.policy.critic.features_extractor)
    else:
        # Check that the objects differ
        assert id(model.policy.actor.features_extractor) != id(model.policy.critic.features_extractor)

    # Critic and target should be equal at the begginning of training
    params_should_match(model.critic.parameters(), model.critic_target.parameters())

    model.learn(200)

    # Critic and target should differ
    params_should_differ(model.critic.parameters(), model.critic_target.parameters())

    # Re-initialize and collect some random data (without doing gradient steps)
    model = model_class("CnnPolicy", env, seed=0, **kwargs).learn(10)

    original_param = deepcopy(list(model.critic.parameters()))
    original_target_param = deepcopy(list(model.critic_target.parameters()))

    # Deactivate copy to target
    model.tau = 0.0
    model.train(gradient_steps=1)

    # Target should be the same
    params_should_match(original_target_param, model.critic_target.parameters())

    # not the same for critic net (updated by gradient descent)
    params_should_differ(original_param, model.critic.parameters())

    # Update the reference as it should not change in the next step
    original_param = deepcopy(list(model.critic.parameters()))

    # Deactivate learning rate
    model.lr_schedule = lambda _: 0.0
    # Re-activate polyak update
    model.tau = 0.01

    model.train(gradient_steps=1)

    # Target should have changed now (due to polyak update)
    params_should_differ(original_target_param, model.critic_target.parameters())

    # Critic should be the same
    params_should_match(original_param, model.critic.parameters())