import os
from copy import deepcopy

import numpy as np
import pytest
import torch as th
from gymnasium import spaces

from stable_baselines3 import A2C, DQN, PPO, SAC, TD3, RecurrentPPO
from stable_baselines3.common.envs import FakeImageEnv
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.preprocessing import (
    is_image_space,
    is_image_space_channels_first,
)
from stable_baselines3.common.utils import zip_strict
from stable_baselines3.common.vec_env import (
    DummyVecEnv,
    VecFrameStack,
    VecNormalize,
    VecTransposeImage,
    is_vecenv_wrapped,
)


@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3, DQN])
@pytest.mark.parametrize("share_features_extractor", [True, False])
def test_cnn(tmp_path, model_class, share_features_extractor):
    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 {SAC, TD3})
    if model_class in {A2C, PPO}:
        kwargs = dict(
            n_steps=64,
            policy_kwargs=dict(
                share_features_extractor=share_features_extractor,
            ),
        )
    else:
        # share_features_extractor is checked later for offpolicy algorithms
        if share_features_extractor:
            return

        # 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)),
            seed=1,
        )
    model = model_class("CnnPolicy", env, **kwargs).learn(250)

    # FakeImageEnv is channel last by default and should be wrapped
    assert is_vecenv_wrapped(model.get_env(), VecTransposeImage)
    # Need to use the vec_env
    env = model.get_env()

    obs = env.reset()

    # Test stochastic predict with channel last input
    if model_class == DQN:
        model.exploration_rate = 0.9

    for _ in range(10):
        model.predict(obs, deterministic=False)

    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))


@pytest.mark.parametrize("model_class", [A2C])
def test_vec_transpose_skip(tmp_path, model_class):
    # Fake grayscale with frameskip
    env = FakeImageEnv(
        screen_height=41, screen_width=40, n_channels=10, discrete=model_class not in {SAC, TD3}, channel_first=True
    )
    env = DummyVecEnv([lambda: env])
    # Stack 5 frames so the observation is now (50, 40, 40) but the env is still channel first
    env = VecFrameStack(env, 5, channels_order="first")
    obs_shape_before = env.reset().shape
    # The observation space should be different as the heuristic thinks it is channel last
    assert not np.allclose(obs_shape_before, VecTransposeImage(env).reset().shape)
    env = VecTransposeImage(env, skip=True)
    # The observation space should be the same as we skip the VecTransposeImage
    assert np.allclose(obs_shape_before, env.reset().shape)

    kwargs = dict(
        n_steps=64,
        policy_kwargs=dict(features_extractor_kwargs=dict(features_dim=32)),
        seed=1,
    )
    model = model_class("CnnPolicy", env, **kwargs).learn(250)

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


def patch_dqn_names_(model):
    # Small hack to make the test work with DQN
    if isinstance(model, DQN):
        model.critic = model.q_net
        model.critic_target = model.q_net_target


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)


def check_td3_feature_extractor_match(model):
    for (key, actor_param), critic_param in zip(model.actor_target.named_parameters(), model.critic_target.parameters()):
        if "features_extractor" in key:
            assert th.allclose(actor_param, critic_param), key


def check_td3_feature_extractor_differ(model):
    for (key, actor_param), critic_param in zip(model.actor_target.named_parameters(), model.critic_target.parameters()):
        if "features_extractor" in key:
            assert not th.allclose(actor_param, critic_param), key


@pytest.mark.parametrize("model_class", [SAC, TD3, DQN])
@pytest.mark.parametrize("share_features_extractor", [True, False])
def test_features_extractor_target_net(model_class, share_features_extractor):
    if model_class == DQN and share_features_extractor:
        pytest.skip()

    env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=model_class not in {SAC, TD3})
    # 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)))
    if model_class != DQN:
        kwargs["policy_kwargs"]["share_features_extractor"] = share_features_extractor

    # No delay for TD3 (changes when the actor and polyak update take place)
    if model_class == TD3:
        kwargs["policy_delay"] = 1

    model = model_class("CnnPolicy", env, seed=0, **kwargs)

    patch_dqn_names_(model)

    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)
        if model_class == TD3:
            assert id(model.policy.actor_target.features_extractor) == id(model.policy.critic_target.features_extractor)
        # Actor and critic features extractor should be the same
        td3_features_extractor_check = check_td3_feature_extractor_match
    else:
        # Actor and critic features extractor should differ same
        td3_features_extractor_check = check_td3_feature_extractor_differ
        # Check that the object differ
        if model_class != DQN:
            assert id(model.policy.actor.features_extractor) != id(model.policy.critic.features_extractor)

        if model_class == TD3:
            assert id(model.policy.actor_target.features_extractor) != id(model.policy.critic_target.features_extractor)

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

    # TD3 has also a target actor net
    if model_class == TD3:
        params_should_match(model.actor.parameters(), model.actor_target.parameters())

    model.learn(200)

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

    if model_class == TD3:
        params_should_differ(model.actor.parameters(), model.actor_target.parameters())
        td3_features_extractor_check(model)

    # Re-initialize and collect some random data (without doing gradient steps,
    # since 10 < learning_starts = 100)
    model = model_class("CnnPolicy", env, seed=0, **kwargs).learn(10)

    patch_dqn_names_(model)

    original_param = deepcopy(list(model.critic.parameters()))
    original_target_param = deepcopy(list(model.critic_target.parameters()))
    if model_class == TD3:
        original_actor_target_param = deepcopy(list(model.actor_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())

    if model_class == TD3:
        params_should_match(original_actor_target_param, model.actor_target.parameters())
        td3_features_extractor_check(model)

    # 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()))

    if model_class == TD3:
        original_actor_param = deepcopy(list(model.actor.parameters()))

    # Deactivate learning rate
    model.lr_schedule = lambda _: 0.0
    # Re-activate polyak update
    model.tau = 0.01
    # Special case for DQN: target net is updated in the `collect_rollouts()`
    # not the `train()` method
    if model_class == DQN:
        model.target_update_interval = 1
        model._on_step()

    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())

    if model_class == TD3:
        params_should_differ(original_actor_target_param, model.actor_target.parameters())

        params_should_match(original_actor_param, model.actor.parameters())

        td3_features_extractor_check(model)


def test_channel_first_env(tmp_path):
    # test_cnn uses environment with HxWxC setup that is transposed, but we
    # also want to work with CxHxW envs directly without transposing wrapper.
    SAVE_NAME = "cnn_model.zip"

    # Create environment with transposed images (CxHxW).
    # If underlying CNN processes the data in wrong format,
    # it will raise an error of negative dimension sizes while creating convolutions
    env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=True, channel_first=True)

    model = A2C("CnnPolicy", env, n_steps=100).learn(250)

    assert not is_vecenv_wrapped(model.get_env(), VecTransposeImage)

    obs, _ = env.reset()

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

    model.save(tmp_path / SAVE_NAME)
    del model

    model = A2C.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 test_image_space_checks():
    not_image_space = spaces.Box(0, 1, shape=(10,))
    assert not is_image_space(not_image_space)

    # Not uint8
    not_image_space = spaces.Box(0, 255, shape=(10, 10, 3))
    assert not is_image_space(not_image_space)

    # Not correct shape
    not_image_space = spaces.Box(0, 255, shape=(10, 10), dtype=np.uint8)
    assert not is_image_space(not_image_space)

    # Not correct low/high
    not_image_space = spaces.Box(0, 10, shape=(10, 10, 3), dtype=np.uint8)
    assert not is_image_space(not_image_space)

    # Deactivate dtype and bound checking
    normalized_image = spaces.Box(0, 1, shape=(10, 10, 3), dtype=np.float32)
    assert is_image_space(normalized_image, normalized_image=True)

    # Not correct space
    not_image_space = spaces.Discrete(n=10)
    assert not is_image_space(not_image_space)

    an_image_space = spaces.Box(0, 255, shape=(10, 10, 3), dtype=np.uint8)
    assert is_image_space(an_image_space, check_channels=False)
    assert is_image_space(an_image_space, check_channels=True)

    channel_first_image_space = spaces.Box(0, 255, shape=(3, 10, 10), dtype=np.uint8)
    assert is_image_space(channel_first_image_space, check_channels=False)
    assert is_image_space(channel_first_image_space, check_channels=True)

    an_image_space_with_odd_channels = spaces.Box(0, 255, shape=(10, 10, 5), dtype=np.uint8)
    assert is_image_space(an_image_space_with_odd_channels)
    # Should not pass if we check if channels are valid for an image
    assert not is_image_space(an_image_space_with_odd_channels, check_channels=True)

    # Test if channel-check works
    channel_first_space = spaces.Box(0, 255, shape=(3, 10, 10), dtype=np.uint8)
    assert is_image_space_channels_first(channel_first_space)

    channel_last_space = spaces.Box(0, 255, shape=(10, 10, 3), dtype=np.uint8)
    assert not is_image_space_channels_first(channel_last_space)

    channel_mid_space = spaces.Box(0, 255, shape=(10, 3, 10), dtype=np.uint8)
    # Should raise a warning
    with pytest.warns(Warning):
        assert not is_image_space_channels_first(channel_mid_space)


@pytest.mark.parametrize("model_class", [A2C, PPO, DQN, SAC, TD3, RecurrentPPO])
@pytest.mark.parametrize("normalize_images", [True, False])
def test_image_like_input(model_class, normalize_images):
    """
    Check that we can handle image-like input (3D tensor)
    when normalize_images=False
    """
    # 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=36,
        screen_width=36,
        n_channels=1,
        channel_first=True,
        discrete=model_class not in {SAC, TD3},
    )
    vec_env = VecNormalize(DummyVecEnv([lambda: env]))
    # Reduce the size of the features
    # deactivate normalization
    kwargs = dict(
        policy_kwargs=dict(
            normalize_images=normalize_images,
            features_extractor_kwargs=dict(features_dim=32),
        ),
        seed=1,
    )
    if issubclass(model_class, OnPolicyAlgorithm):
        kwargs.update(dict(n_steps=64))
    else:
        # Avoid memory error when using replay buffer
        # Reduce the size of the features
        kwargs.update(dict(buffer_size=250))
    if normalize_images:
        with pytest.raises(AssertionError):
            model_class("CnnPolicy", vec_env, **kwargs).learn(128)
    else:
        model_class("CnnPolicy", vec_env, **kwargs).learn(128)
