import gym
from typing import Dict, List, Union

from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override, PublicAPI
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelConfigDict, TensorType

_, nn = try_import_torch()


@PublicAPI
class TorchModelV2(ModelV2):
    """Torch version of ModelV2.

    Note that this class by itself is not a valid model unless you
    inherit from nn.Module and implement forward() in a subclass."""

    def __init__(self, obs_space: gym.spaces.Space,
                 action_space: gym.spaces.Space, num_outputs: int,
                 model_config: ModelConfigDict, name: str):
        """Initialize a TorchModelV2.

        Here is an example implementation for a subclass
        ``MyModelClass(TorchModelV2, nn.Module)``::

            def __init__(self, *args, **kwargs):
                TorchModelV2.__init__(self, *args, **kwargs)
                nn.Module.__init__(self)
                self._hidden_layers = nn.Sequential(...)
                self._logits = ...
                self._value_branch = ...
        """

        if not isinstance(self, nn.Module):
            raise ValueError(
                "Subclasses of TorchModelV2 must also inherit from "
                "nn.Module, e.g., MyModel(TorchModelV2, nn.Module)")

        ModelV2.__init__(
            self,
            obs_space,
            action_space,
            num_outputs,
            model_config,
            name,
            framework="torch")

        # Dict to store per multi-gpu tower stats into.
        # In PyTorch multi-GPU, we use a single TorchPolicy and copy
        # it's Model(s) n times (1 copy for each GPU). When computing the loss
        # on each tower, we cannot store the stats (e.g. `entropy`) inside the
        # policy object as this would lead to race conditions between the
        # different towers all accessing the same property at the same time.
        self.tower_stats = {}

    @override(ModelV2)
    def variables(self, as_dict: bool = False) -> \
            Union[List[TensorType], Dict[str, TensorType]]:
        p = list(self.parameters())
        if as_dict:
            return {k: p[i] for i, k in enumerate(self.state_dict().keys())}
        return p

    @override(ModelV2)
    def trainable_variables(self, as_dict: bool = False) -> \
            Union[List[TensorType], Dict[str, TensorType]]:
        if as_dict:
            return {
                k: v
                for k, v in self.variables(as_dict=True).items()
                if v.requires_grad
            }
        return [v for v in self.variables() if v.requires_grad]
