from mlagents.torch_utils import torch
import abc
from typing import Tuple
from enum import Enum


class Swish(torch.nn.Module):
    def forward(self, data: torch.Tensor) -> torch.Tensor:
        return torch.mul(data, torch.sigmoid(data))


class Initialization(Enum):
    Zero = 0
    XavierGlorotNormal = 1
    XavierGlorotUniform = 2
    KaimingHeNormal = 3  # also known as Variance scaling
    KaimingHeUniform = 4
    Normal = 5


_init_methods = {
    Initialization.Zero: torch.zero_,
    Initialization.XavierGlorotNormal: torch.nn.init.xavier_normal_,
    Initialization.XavierGlorotUniform: torch.nn.init.xavier_uniform_,
    Initialization.KaimingHeNormal: torch.nn.init.kaiming_normal_,
    Initialization.KaimingHeUniform: torch.nn.init.kaiming_uniform_,
    Initialization.Normal: torch.nn.init.normal_,
}


def linear_layer(
    input_size: int,
    output_size: int,
    kernel_init: Initialization = Initialization.XavierGlorotUniform,
    kernel_gain: float = 1.0,
    bias_init: Initialization = Initialization.Zero,
) -> torch.nn.Module:
    """
    Creates a torch.nn.Linear module and initializes its weights.
    :param input_size: The size of the input tensor
    :param output_size: The size of the output tensor
    :param kernel_init: The Initialization to use for the weights of the layer
    :param kernel_gain: The multiplier for the weights of the kernel. Note that in
    TensorFlow, the gain is square-rooted. Therefore calling  with scale 0.01 is equivalent to calling
        KaimingHeNormal with kernel_gain of 0.1
    :param bias_init: The Initialization to use for the weights of the bias layer
    """
    layer = torch.nn.Linear(input_size, output_size)
    if (
        kernel_init == Initialization.KaimingHeNormal
        or kernel_init == Initialization.KaimingHeUniform
    ):
        _init_methods[kernel_init](layer.weight.data, nonlinearity="linear")
    else:
        _init_methods[kernel_init](layer.weight.data)
    layer.weight.data *= kernel_gain
    _init_methods[bias_init](layer.bias.data)
    return layer


def lstm_layer(
    input_size: int,
    hidden_size: int,
    num_layers: int = 1,
    batch_first: bool = True,
    forget_bias: float = 1.0,
    kernel_init: Initialization = Initialization.XavierGlorotUniform,
    bias_init: Initialization = Initialization.Zero,
) -> torch.nn.Module:
    """
    Creates a torch.nn.LSTM and initializes its weights and biases. Provides a
    forget_bias offset like is done in TensorFlow.
    """
    lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=batch_first)
    # Add forget_bias to forget gate bias
    for name, param in lstm.named_parameters():
        # Each weight and bias is a concatenation of 4 matrices
        if "weight" in name:
            for idx in range(4):
                block_size = param.shape[0] // 4
                _init_methods[kernel_init](
                    param.data[idx * block_size : (idx + 1) * block_size]
                )
        if "bias" in name:
            for idx in range(4):
                block_size = param.shape[0] // 4
                _init_methods[bias_init](
                    param.data[idx * block_size : (idx + 1) * block_size]
                )
                if idx == 1:
                    param.data[idx * block_size : (idx + 1) * block_size].add_(
                        forget_bias
                    )
    return lstm


class MemoryModule(torch.nn.Module):
    @abc.abstractproperty
    def memory_size(self) -> int:
        """
        Size of memory that is required at the start of a sequence.
        """
        pass

    @abc.abstractmethod
    def forward(
        self, input_tensor: torch.Tensor, memories: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Pass a sequence to the memory module.
        :input_tensor: Tensor of shape (batch_size, seq_length, size) that represents the input.
        :memories: Tensor of initial memories.
        :return: Tuple of output, final memories.
        """
        pass


class LinearEncoder(torch.nn.Module):
    """
    Linear layers.
    """

    def __init__(self, input_size: int, num_layers: int, hidden_size: int):
        super().__init__()
        self.layers = [
            linear_layer(
                input_size,
                hidden_size,
                kernel_init=Initialization.KaimingHeNormal,
                kernel_gain=1.0,
            )
        ]
        self.layers.append(Swish())
        for _ in range(num_layers - 1):
            self.layers.append(
                linear_layer(
                    hidden_size,
                    hidden_size,
                    kernel_init=Initialization.KaimingHeNormal,
                    kernel_gain=1.0,
                )
            )
            self.layers.append(Swish())
        self.seq_layers = torch.nn.Sequential(*self.layers)

    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
        return self.seq_layers(input_tensor)


class LSTM(MemoryModule):
    """
    Memory module that implements LSTM.
    """

    def __init__(
        self,
        input_size: int,
        memory_size: int,
        num_layers: int = 1,
        forget_bias: float = 1.0,
        kernel_init: Initialization = Initialization.XavierGlorotUniform,
        bias_init: Initialization = Initialization.Zero,
    ):
        super().__init__()
        # We set hidden size to half of memory_size since the initial memory
        # will be divided between the hidden state and initial cell state.
        self.hidden_size = memory_size // 2
        self.lstm = lstm_layer(
            input_size,
            self.hidden_size,
            num_layers,
            True,
            forget_bias,
            kernel_init,
            bias_init,
        )

    @property
    def memory_size(self) -> int:
        return 2 * self.hidden_size

    def forward(
        self, input_tensor: torch.Tensor, memories: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # We don't use torch.split here since it is not supported by Barracuda
        h0 = memories[:, :, : self.hidden_size].contiguous()
        c0 = memories[:, :, self.hidden_size :].contiguous()
        hidden = (h0, c0)
        lstm_out, hidden_out = self.lstm(input_tensor, hidden)
        output_mem = torch.cat(hidden_out, dim=-1)
        return lstm_out, output_mem