import logging
import numpy as np

from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.misc import SlimFC, AppendBiasLayer, \
    normc_initializer
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import get_activation_fn
from ray.rllib.utils import try_import_torch

torch, nn = try_import_torch()

logger = logging.getLogger(__name__)


class FullyConnectedNetwork(TorchModelV2, nn.Module):
    """Generic fully connected network."""

    def __init__(self, obs_space, action_space, num_outputs, model_config,
                 name):
        TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
                              model_config, name)
        nn.Module.__init__(self)

        activation = get_activation_fn(
            model_config.get("fcnet_activation"), framework="torch")
        hiddens = model_config.get("fcnet_hiddens")
        no_final_linear = model_config.get("no_final_linear")
        self.free_log_std = model_config.get("free_log_std")

        # TODO(sven): implement case: vf_shared_layers = False.
        # vf_share_layers = model_config.get("vf_share_layers")

        logger.debug("Constructing fcnet {} {}".format(hiddens, activation))
        layers = []
        prev_layer_size = int(np.product(obs_space.shape))
        self._logits = None

        # Maybe generate free-floating bias variables for the second half of
        # the outputs.
        if self.free_log_std:
            assert num_outputs % 2 == 0, (
                "num_outputs must be divisible by two", num_outputs)
            num_outputs = num_outputs // 2

        # Create layers 0 to second-last.
        for size in hiddens[:-1]:
            layers.append(
                SlimFC(
                    in_size=prev_layer_size,
                    out_size=size,
                    initializer=normc_initializer(1.0),
                    activation_fn=activation))
            prev_layer_size = size

        # The last layer is adjusted to be of size num_outputs, but it's a
        # layer with activation.
        if no_final_linear and num_outputs:
            layers.append(
                SlimFC(
                    in_size=prev_layer_size,
                    out_size=num_outputs,
                    initializer=normc_initializer(1.0),
                    activation_fn=activation))
            prev_layer_size = num_outputs
        # Finish the layers with the provided sizes (`hiddens`), plus -
        # iff num_outputs > 0 - a last linear layer of size num_outputs.
        else:
            if len(hiddens) > 0:
                layers.append(
                    SlimFC(
                        in_size=prev_layer_size,
                        out_size=hiddens[-1],
                        initializer=normc_initializer(1.0),
                        activation_fn=activation))
                prev_layer_size = hiddens[-1]
            if num_outputs:
                self._logits = SlimFC(
                    in_size=prev_layer_size,
                    out_size=num_outputs,
                    initializer=normc_initializer(0.01),
                    activation_fn=None)
            else:
                self.num_outputs = (
                    [np.product(obs_space.shape)] + hiddens[-1:-1])[-1]

        # Layer to add the log std vars to the state-dependent means.
        if self.free_log_std:
            self._append_free_log_std = AppendBiasLayer(num_outputs)

        self._hidden_layers = nn.Sequential(*layers)

        # TODO(sven): Implement non-shared value branch.
        self._value_branch = SlimFC(
            in_size=prev_layer_size,
            out_size=1,
            initializer=normc_initializer(1.0),
            activation_fn=None)
        # Holds the current value output.
        self._cur_value = None

    @override(TorchModelV2)
    def forward(self, input_dict, state, seq_lens):
        obs = input_dict["obs_flat"].float()
        features = self._hidden_layers(obs.reshape(obs.shape[0], -1))
        logits = self._logits(features) if self._logits else features
        if self.free_log_std:
            logits = self._append_free_log_std(logits)
        self._cur_value = self._value_branch(features).squeeze(1)
        return logits, state

    @override(TorchModelV2)
    def value_function(self):
        assert self._cur_value is not None, "must call forward() first"
        return self._cur_value