""" Code adapted from https://github.com/ikostrikov/pytorch-a3c"""
import numpy as np
from typing import Union, Tuple, Any, List

from ray.rllib.models.utils import get_activation_fn
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import TensorType

torch, nn = try_import_torch()


def normc_initializer(std: float = 1.0) -> Any:
    def initializer(tensor):
        tensor.data.normal_(0, 1)
        tensor.data *= std / torch.sqrt(
            tensor.data.pow(2).sum(1, keepdim=True))

    return initializer


def same_padding(in_size: Tuple[int, int], filter_size: Tuple[int, int],
                 stride_size: Union[int, Tuple[int, int]]
                 ) -> (Union[int, Tuple[int, int]], Tuple[int, int]):
    """Note: Padding is added to match TF conv2d `same` padding. See
    www.tensorflow.org/versions/r0.12/api_docs/python/nn/convolution

    Args:
        in_size (tuple): Rows (Height), Column (Width) for input
        stride_size (Union[int,Tuple[int, int]]): Rows (Height), column (Width)
            for stride. If int, height == width.
        filter_size (tuple): Rows (Height), column (Width) for filter

    Returns:
        padding (tuple): For input into torch.nn.ZeroPad2d.
        output (tuple): Output shape after padding and convolution.
    """
    in_height, in_width = in_size
    if isinstance(filter_size, int):
        filter_height, filter_width = filter_size, filter_size
    else:
        filter_height, filter_width = filter_size
    if isinstance(stride_size, (int, float)):
        stride_height, stride_width = int(stride_size), int(stride_size)
    else:
        stride_height, stride_width = int(stride_size[0]), int(stride_size[1])

    out_height = np.ceil(float(in_height) / float(stride_height))
    out_width = np.ceil(float(in_width) / float(stride_width))

    pad_along_height = int(
        ((out_height - 1) * stride_height + filter_height - in_height))
    pad_along_width = int(
        ((out_width - 1) * stride_width + filter_width - in_width))
    pad_top = pad_along_height // 2
    pad_bottom = pad_along_height - pad_top
    pad_left = pad_along_width // 2
    pad_right = pad_along_width - pad_left
    padding = (pad_left, pad_right, pad_top, pad_bottom)
    output = (out_height, out_width)
    return padding, output


class SlimConv2d(nn.Module):
    """Simple mock of tf.slim Conv2d"""

    def __init__(
            self,
            in_channels: int,
            out_channels: int,
            kernel: Union[int, Tuple[int, int]],
            stride: Union[int, Tuple[int, int]],
            padding: Union[int, Tuple[int, int]],
            # Defaulting these to nn.[..] will break soft torch import.
            initializer: Any = "default",
            activation_fn: Any = "default",
            bias_init: float = 0):
        """Creates a standard Conv2d layer, similar to torch.nn.Conv2d

            Args:
                in_channels(int): Number of input channels
                out_channels (int): Number of output channels
                kernel (Union[int, Tuple[int, int]]): If int, the kernel is
                    a tuple(x,x). Elsewise, the tuple can be specified
                stride (Union[int, Tuple[int, int]]): Controls the stride
                    for the cross-correlation. If int, the stride is a
                    tuple(x,x). Elsewise, the tuple can be specified
                padding (Union[int, Tuple[int, int]]): Controls the amount
                    of implicit zero-paddings during the conv operation
                initializer (Any): Initializer function for kernel weights
                activation_fn (Any): Activation function at the end of layer
                bias_init (float): Initalize bias weights to bias_init const
        """
        super(SlimConv2d, self).__init__()
        layers = []
        # Padding layer.
        if padding:
            layers.append(nn.ZeroPad2d(padding))
        # Actual Conv2D layer (including correct initialization logic).
        conv = nn.Conv2d(in_channels, out_channels, kernel, stride)
        if initializer:
            if initializer == "default":
                initializer = nn.init.xavier_uniform_
            initializer(conv.weight)
        nn.init.constant_(conv.bias, bias_init)
        layers.append(conv)
        # Activation function (if any; default=ReLu).
        if isinstance(activation_fn, str):
            if activation_fn == "default":
                activation_fn = nn.ReLU
            else:
                activation_fn = get_activation_fn(activation_fn, "torch")
        if activation_fn is not None:
            layers.append(activation_fn())
        # Put everything in sequence.
        self._model = nn.Sequential(*layers)

    def forward(self, x: TensorType) -> TensorType:
        return self._model(x)


class SlimFC(nn.Module):
    """Simple PyTorch version of `linear` function"""

    def __init__(self,
                 in_size: int,
                 out_size: int,
                 initializer: Any = None,
                 activation_fn: Any = None,
                 use_bias: bool = True,
                 bias_init: float = 0.0):
        """Creates a standard FC layer, similar to torch.nn.Linear

        Args:
            in_size(int): Input size for FC Layer
            out_size (int): Output size for FC Layer
            initializer (Any): Initializer function for FC layer weights
            activation_fn (Any): Activation function at the end of layer
            use_bias (bool): Whether to add bias weights or not
            bias_init (float): Initalize bias weights to bias_init const
        """
        super(SlimFC, self).__init__()
        layers = []
        # Actual nn.Linear layer (including correct initialization logic).
        linear = nn.Linear(in_size, out_size, bias=use_bias)
        if initializer is None:
            initializer = nn.init.xavier_uniform_
        initializer(linear.weight)
        if use_bias is True:
            nn.init.constant_(linear.bias, bias_init)
        layers.append(linear)
        # Activation function (if any; default=None (linear)).
        if isinstance(activation_fn, str):
            activation_fn = get_activation_fn(activation_fn, "torch")
        if activation_fn is not None:
            layers.append(activation_fn())
        # Put everything in sequence.
        self._model = nn.Sequential(*layers)

    def forward(self, x: TensorType) -> TensorType:
        return self._model(x)


class AppendBiasLayer(nn.Module):
    """Simple bias appending layer for free_log_std."""

    def __init__(self, num_bias_vars: int):
        super().__init__()
        self.log_std = torch.nn.Parameter(
            torch.as_tensor([0.0] * num_bias_vars))
        self.register_parameter("log_std", self.log_std)

    def forward(self, x: TensorType) -> TensorType:
        out = torch.cat(
            [x, self.log_std.unsqueeze(0).repeat([len(x), 1])], axis=1)
        return out


class Reshape(nn.Module):
    """Standard module that reshapes/views a tensor
    """

    def __init__(self, shape: List):
        super().__init__()
        self.shape = shape

    def forward(self, x):
        return x.view(*self.shape)