import numpy as np
from typing import Dict, List
import gym

from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.misc import normc_initializer, same_padding, \
    SlimConv2d, SlimFC
from ray.rllib.models.utils import get_activation_fn, get_filter_config
from ray.rllib.utils.annotations import override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelConfigDict, TensorType

torch, nn = try_import_torch()


class VisionNetwork(TorchModelV2, nn.Module):
    """Generic vision network."""

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

        if not model_config.get("conv_filters"):
            model_config["conv_filters"] = get_filter_config(obs_space.shape)

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

        activation = self.model_config.get("conv_activation")
        filters = self.model_config["conv_filters"]
        assert len(filters) > 0,\
            "Must provide at least 1 entry in `conv_filters`!"

        # Post FC net config.
        post_fcnet_hiddens = model_config.get("post_fcnet_hiddens", [])
        post_fcnet_activation = get_activation_fn(
            model_config.get("post_fcnet_activation"), framework="torch")

        no_final_linear = self.model_config.get("no_final_linear")
        vf_share_layers = self.model_config.get("vf_share_layers")

        # Whether the last layer is the output of a Flattened (rather than
        # a n x (1,1) Conv2D).
        self.last_layer_is_flattened = False
        self._logits = None

        layers = []
        (w, h, in_channels) = obs_space.shape

        in_size = [w, h]
        for out_channels, kernel, stride in filters[:-1]:
            padding, out_size = same_padding(in_size, kernel, stride)
            layers.append(
                SlimConv2d(
                    in_channels,
                    out_channels,
                    kernel,
                    stride,
                    padding,
                    activation_fn=activation))
            in_channels = out_channels
            in_size = out_size

        out_channels, kernel, stride = filters[-1]

        # No final linear: Last layer has activation function and exits with
        # num_outputs nodes (this could be a 1x1 conv or a FC layer, depending
        # on `post_fcnet_...` settings).
        if no_final_linear and num_outputs:
            out_channels = out_channels if post_fcnet_hiddens else num_outputs
            layers.append(
                SlimConv2d(
                    in_channels,
                    out_channels,
                    kernel,
                    stride,
                    None,  # padding=valid
                    activation_fn=activation))

            # Add (optional) post-fc-stack after last Conv2D layer.
            layer_sizes = post_fcnet_hiddens[:-1] + ([num_outputs]
                                                     if post_fcnet_hiddens else
                                                     [])
            for i, out_size in enumerate(layer_sizes):
                layers.append(
                    SlimFC(
                        in_size=out_channels,
                        out_size=out_size,
                        activation_fn=post_fcnet_activation,
                        initializer=normc_initializer(1.0)))
                out_channels = out_size

        # Finish network normally (w/o overriding last layer size with
        # `num_outputs`), then add another linear one of size `num_outputs`.
        else:
            layers.append(
                SlimConv2d(
                    in_channels,
                    out_channels,
                    kernel,
                    stride,
                    None,  # padding=valid
                    activation_fn=activation))

            # num_outputs defined. Use that to create an exact
            # `num_output`-sized (1,1)-Conv2D.
            if num_outputs:
                in_size = [
                    np.ceil((in_size[0] - kernel[0]) / stride),
                    np.ceil((in_size[1] - kernel[1]) / stride)
                ]
                padding, _ = same_padding(in_size, [1, 1], [1, 1])
                if post_fcnet_hiddens:
                    layers.append(nn.Flatten())
                    in_size = out_channels
                    # Add (optional) post-fc-stack after last Conv2D layer.
                    for i, out_size in enumerate(post_fcnet_hiddens +
                                                 [num_outputs]):
                        layers.append(
                            SlimFC(
                                in_size=in_size,
                                out_size=out_size,
                                activation_fn=post_fcnet_activation
                                if i < len(post_fcnet_hiddens) - 1 else None,
                                initializer=normc_initializer(1.0)))
                        in_size = out_size
                    # Last layer is logits layer.
                    self._logits = layers.pop()

                else:
                    self._logits = SlimConv2d(
                        out_channels,
                        num_outputs, [1, 1],
                        1,
                        padding,
                        activation_fn=None)

            # num_outputs not known -> Flatten, then set self.num_outputs
            # to the resulting number of nodes.
            else:
                self.last_layer_is_flattened = True
                layers.append(nn.Flatten())

        self._convs = nn.Sequential(*layers)

        # If our num_outputs still unknown, we need to do a test pass to
        # figure out the output dimensions. This could be the case, if we have
        # the Flatten layer at the end.
        if self.num_outputs is None:
            # Create a B=1 dummy sample and push it through out conv-net.
            dummy_in = torch.from_numpy(self.obs_space.sample()).permute(
                2, 0, 1).unsqueeze(0).float()
            dummy_out = self._convs(dummy_in)
            self.num_outputs = dummy_out.shape[1]

        # Build the value layers
        self._value_branch_separate = self._value_branch = None
        if vf_share_layers:
            self._value_branch = SlimFC(
                out_channels,
                1,
                initializer=normc_initializer(0.01),
                activation_fn=None)
        else:
            vf_layers = []
            (w, h, in_channels) = obs_space.shape
            in_size = [w, h]
            for out_channels, kernel, stride in filters[:-1]:
                padding, out_size = same_padding(in_size, kernel, stride)
                vf_layers.append(
                    SlimConv2d(
                        in_channels,
                        out_channels,
                        kernel,
                        stride,
                        padding,
                        activation_fn=activation))
                in_channels = out_channels
                in_size = out_size

            out_channels, kernel, stride = filters[-1]
            vf_layers.append(
                SlimConv2d(
                    in_channels,
                    out_channels,
                    kernel,
                    stride,
                    None,
                    activation_fn=activation))

            vf_layers.append(
                SlimConv2d(
                    in_channels=out_channels,
                    out_channels=1,
                    kernel=1,
                    stride=1,
                    padding=None,
                    activation_fn=None))
            self._value_branch_separate = nn.Sequential(*vf_layers)

        # Holds the current "base" output (before logits layer).
        self._features = None

    @override(TorchModelV2)
    def forward(self, input_dict: Dict[str, TensorType],
                state: List[TensorType],
                seq_lens: TensorType) -> (TensorType, List[TensorType]):
        self._features = input_dict["obs"].float()
        # Permuate b/c data comes in as [B, dim, dim, channels]:
        self._features = self._features.permute(0, 3, 1, 2)
        conv_out = self._convs(self._features)
        # Store features to save forward pass when getting value_function out.
        if not self._value_branch_separate:
            self._features = conv_out

        if not self.last_layer_is_flattened:
            if self._logits:
                conv_out = self._logits(conv_out)
            if len(conv_out.shape) == 4:
                if conv_out.shape[2] != 1 or conv_out.shape[3] != 1:
                    raise ValueError(
                        "Given `conv_filters` ({}) do not result in a [B, {} "
                        "(`num_outputs`), 1, 1] shape (but in {})! Please "
                        "adjust your Conv2D stack such that the last 2 dims "
                        "are both 1.".format(self.model_config["conv_filters"],
                                             self.num_outputs,
                                             list(conv_out.shape)))
                logits = conv_out.squeeze(3)
                logits = logits.squeeze(2)
            else:
                logits = conv_out
            return logits, state
        else:
            return conv_out, state

    @override(TorchModelV2)
    def value_function(self) -> TensorType:
        assert self._features is not None, "must call forward() first"
        if self._value_branch_separate:
            value = self._value_branch_separate(self._features)
            value = value.squeeze(3)
            value = value.squeeze(2)
            return value.squeeze(1)
        else:
            if not self.last_layer_is_flattened:
                features = self._features.squeeze(3)
                features = features.squeeze(2)
            else:
                features = self._features
            return self._value_branch(features).squeeze(1)

    def _hidden_layers(self, obs: TensorType) -> TensorType:
        res = self._convs(obs.permute(0, 3, 1, 2))  # switch to channel-major
        res = res.squeeze(3)
        res = res.squeeze(2)
        return res
