import gym
import unittest

from ray.rllib.models.catalog import ModelCatalog, MODEL_DEFAULTS
from ray.rllib.models.tf.visionnet import VisionNetwork
from ray.rllib.models.torch.visionnet import VisionNetwork as TorchVision
from ray.rllib.utils.framework import try_import_torch, try_import_tf
from ray.rllib.utils.test_utils import framework_iterator

torch, nn = try_import_torch()
tf1, tf, tfv = try_import_tf()


class TestConv2DDefaultStacks(unittest.TestCase):
    """Tests our ConvTranspose2D Stack modules/layers."""

    def test_conv2d_default_stacks(self):
        """Tests, whether conv2d defaults are available for img obs spaces.
        """
        action_space = gym.spaces.Discrete(2)

        shapes = [
            (480, 640, 3),
            (240, 320, 3),
            (96, 96, 3),
            (84, 84, 3),
            (42, 42, 3),
            (10, 10, 3),
        ]
        for shape in shapes:
            print(f"shape={shape}")
            obs_space = gym.spaces.Box(-1.0, 1.0, shape=shape)
            for fw in framework_iterator():
                model = ModelCatalog.get_model_v2(
                    obs_space,
                    action_space,
                    2,
                    MODEL_DEFAULTS.copy(),
                    framework=fw)
                self.assertTrue(
                    isinstance(model, (VisionNetwork, TorchVision)))
                if fw == "torch":
                    output, _ = model({
                        "obs": torch.from_numpy(obs_space.sample()[None])
                    })
                else:
                    output, _ = model({"obs": obs_space.sample()[None]})
                # B x [action logits]
                self.assertTrue(output.shape == (1, 2))
                print("ok")


if __name__ == "__main__":
    import pytest
    import sys
    sys.exit(pytest.main(["-v", __file__]))