import numpy as np

from ray.rllib.models.modelv2 import ModelV2, restore_original_dimensions
from ray.rllib.models.tf.tf_action_dist import Categorical
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
from ray.rllib.models.torch.torch_action_dist import TorchCategorical
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.offline import JsonReader

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


class CustomLossModel(TFModelV2):
    """Custom model that adds an imitation loss on top of the policy loss."""

    def __init__(self, obs_space, action_space, num_outputs, model_config,
                 name):
        super().__init__(obs_space, action_space, num_outputs, model_config,
                         name)

        self.fcnet = FullyConnectedNetwork(
            self.obs_space,
            self.action_space,
            num_outputs,
            model_config,
            name="fcnet")

    @override(ModelV2)
    def forward(self, input_dict, state, seq_lens):
        # Delegate to our FCNet.
        return self.fcnet(input_dict, state, seq_lens)

    @override(ModelV2)
    def value_function(self):
        # Delegate to our FCNet.
        return self.fcnet.value_function()

    @override(ModelV2)
    def custom_loss(self, policy_loss, loss_inputs):
        # Create a new input reader per worker.
        reader = JsonReader(
            self.model_config["custom_model_config"]["input_files"])
        input_ops = reader.tf_input_ops()

        # Define a secondary loss by building a graph copy with weight sharing.
        obs = restore_original_dimensions(
            tf.cast(input_ops["obs"], tf.float32), self.obs_space)
        logits, _ = self.forward({"obs": obs}, [], None)

        # You can also add self-supervised losses easily by referencing tensors
        # created during _build_layers_v2(). For example, an autoencoder-style
        # loss can be added as follows:
        # ae_loss = squared_diff(
        #     loss_inputs["obs"], Decoder(self.fcnet.last_layer))
        print("FYI: You can also use these tensors: {}, ".format(loss_inputs))

        # Compute the IL loss.
        action_dist = Categorical(logits, self.model_config)
        self.policy_loss = policy_loss
        self.imitation_loss = tf.reduce_mean(
            -action_dist.logp(input_ops["actions"]))
        return policy_loss + 10 * self.imitation_loss

    def metrics(self):
        return {
            "policy_loss": self.policy_loss,
            "imitation_loss": self.imitation_loss,
        }


class TorchCustomLossModel(TorchModelV2, nn.Module):
    """PyTorch version of the CustomLossModel above."""

    def __init__(self, obs_space, action_space, num_outputs, model_config,
                 name, input_files):
        super().__init__(obs_space, action_space, num_outputs, model_config,
                         name)
        nn.Module.__init__(self)

        self.input_files = input_files
        # Create a new input reader per worker.
        self.reader = JsonReader(self.input_files)
        self.fcnet = TorchFC(
            self.obs_space,
            self.action_space,
            num_outputs,
            model_config,
            name="fcnet")

    @override(ModelV2)
    def forward(self, input_dict, state, seq_lens):
        # Delegate to our FCNet.
        return self.fcnet(input_dict, state, seq_lens)

    @override(ModelV2)
    def value_function(self):
        # Delegate to our FCNet.
        return self.fcnet.value_function()

    @override(ModelV2)
    def custom_loss(self, policy_loss, loss_inputs):
        """Calculates a custom loss on top of the given policy_loss(es).

        Args:
            policy_loss (List[TensorType]): The list of already calculated
                policy losses (as many as there are optimizers).
            loss_inputs (TensorStruct): Struct of np.ndarrays holding the
                entire train batch.

        Returns:
            List[TensorType]: The altered list of policy losses. In case the
                custom loss should have its own optimizer, make sure the
                returned list is one larger than the incoming policy_loss list.
                In case you simply want to mix in the custom loss into the
                already calculated policy losses, return a list of altered
                policy losses (as done in this example below).
        """
        # Get the next batch from our input files.
        batch = self.reader.next()

        # Define a secondary loss by building a graph copy with weight sharing.
        obs = restore_original_dimensions(
            torch.from_numpy(batch["obs"]).float().to(policy_loss[0].device),
            self.obs_space,
            tensorlib="torch")
        logits, _ = self.forward({"obs": obs}, [], None)

        # You can also add self-supervised losses easily by referencing tensors
        # created during _build_layers_v2(). For example, an autoencoder-style
        # loss can be added as follows:
        # ae_loss = squared_diff(
        #     loss_inputs["obs"], Decoder(self.fcnet.last_layer))
        print("FYI: You can also use these tensors: {}, ".format(loss_inputs))

        # Compute the IL loss.
        action_dist = TorchCategorical(logits, self.model_config)
        imitation_loss = torch.mean(-action_dist.logp(
            torch.from_numpy(batch["actions"]).to(policy_loss[0].device)))
        self.imitation_loss_metric = imitation_loss.item()
        self.policy_loss_metric = np.mean(
            [loss.item() for loss in policy_loss])

        # Add the imitation loss to each already calculated policy loss term.
        # Alternatively (if custom loss has its own optimizer):
        # return policy_loss + [10 * self.imitation_loss]
        return [loss_ + 10 * imitation_loss for loss_ in policy_loss]

    def metrics(self):
        return {
            "policy_loss": self.policy_loss_metric,
            "imitation_loss": self.imitation_loss_metric,
        }
