import logging
import numpy as np
import time

from ray.rllib.models.jax.jax_modelv2 import JAXModelV2
from ray.rllib.models.jax.misc import SlimFC
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_jax

jax, flax = try_import_jax()

logger = logging.getLogger(__name__)


class FullyConnectedNetwork(JAXModelV2):
    """Generic fully connected network."""

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

        self.key = jax.random.PRNGKey(int(time.time()))

        activation = model_config.get("fcnet_activation")
        hiddens = model_config.get("fcnet_hiddens", [])
        no_final_linear = model_config.get("no_final_linear")
        self.vf_share_layers = model_config.get("vf_share_layers")
        self.free_log_std = model_config.get("free_log_std")

        # 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

        self._hidden_layers = []
        prev_layer_size = int(np.product(obs_space.shape))
        self._logits = None

        # Create layers 0 to second-last.
        for size in hiddens[:-1]:
            self._hidden_layers.append(
                SlimFC(
                    in_size=prev_layer_size,
                    out_size=size,
                    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:
            self._hidden_layers.append(
                SlimFC(
                    in_size=prev_layer_size,
                    out_size=num_outputs,
                    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:
                self._hidden_layers.append(
                    SlimFC(
                        in_size=prev_layer_size,
                        out_size=hiddens[-1],
                        activation_fn=activation))
                prev_layer_size = hiddens[-1]
            if num_outputs:
                self._logits = SlimFC(
                    in_size=prev_layer_size,
                    out_size=num_outputs,
                    activation_fn=None)
            else:
                self.num_outputs = (
                    [int(np.product(obs_space.shape))] + hiddens[-1:])[-1]

        # Layer to add the log std vars to the state-dependent means.
        if self.free_log_std and self._logits:
            raise ValueError("`free_log_std` not supported for JAX yet!")

        self._value_branch_separate = None
        if not self.vf_share_layers:
            # Build a parallel set of hidden layers for the value net.
            prev_vf_layer_size = int(np.product(obs_space.shape))
            vf_layers = []
            for size in hiddens:
                vf_layers.append(
                    SlimFC(
                        in_size=prev_vf_layer_size,
                        out_size=size,
                        activation_fn=activation,
                    ))
                prev_vf_layer_size = size
            self._value_branch_separate = vf_layers

        self._value_branch = SlimFC(
            in_size=prev_layer_size, out_size=1, activation_fn=None)
        # Holds the current "base" output (before logits layer).
        self._features = None
        # Holds the last input, in case value branch is separate.
        self._last_flat_in = None

    @override(JAXModelV2)
    def forward(self, input_dict, state, seq_lens):
        self._last_flat_in = input_dict["obs_flat"]
        x = self._last_flat_in
        for layer in self._hidden_layers:
            x = layer(x)
        self._features = x
        logits = self._logits(self._features) if self._logits else \
            self._features
        if self.free_log_std:
            logits = self._append_free_log_std(logits)
        return logits, state

    @override(JAXModelV2)
    def value_function(self):
        assert self._features is not None, "must call forward() first"
        if self._value_branch_separate:
            return self._value_branch(
                self._value_branch_separate(self._last_flat_in)).squeeze(1)
        else:
            return self._value_branch(self._features).squeeze(1)