
from pycamia import info_manager

__info__ = info_manager(
    project = "PyCAMIA",
    package = "batorch",
    fileinfo = "batorch version of `torch.nn`",
    requires = "torch"
)

from . import Module
from ..parameter import Parameter
from ...tensor import Tensor
import batorch as torch
from .. import functional as F
import torch.nn.init as init

class Linear(Module):

    __constants__ = ['in_features', 'out_features']
    in_features: int
    out_features: int
    weight: Tensor

    def __init__(self, in_features: int, out_features: int, bias: bool = True, activation=None) -> None:
        super(Linear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.zeros(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.zeros(out_features))
        else:
            self.register_parameter('bias', None)
        # print(self.weight)
        self.reset_parameters()
        # print(self.weight)
        self.activation = activation

    def reset_parameters(self) -> None:
        init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='relu')
        # if get_setting("basic_torch"):
        #     init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        #     if self.bias is not None:
        #         fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
        #         bound = 1 / math.sqrt(fan_in)
        #         init.uniform_(self.bias, -bound, bound)
        # else:
        #     # init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        #     init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='relu')
        #     # if self.bias is not None:
        #     #     fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
        #     #     bound = 1 / math.sqrt(fan_in)
        #     #     init.uniform_(self.bias, -bound, bound)
        #     # TODO: different initialization method based on different activation function

    def forward(self, input: Tensor) -> Tensor:
        if self.activation is None:
            return F.linear(input, self.weight, self.bias)
        else:
            return self.activation(F.linear(input, self.weight, self.bias))

    def extra_repr(self) -> str:
        if self.activation is None:
            return 'in_features={}, out_features={}, bias={}'.format(self.in_features, self.out_features, self.bias is not None)
        else:
            return 'in_features={}, out_features={}, bias={}, activation={}'.format(self.in_features, self.out_features, self.bias is not None, self.activation)

class Identity(Module):
    r"""A placeholder identity operator that is argument-insensitive.

    Args:
        args: any argument (unused)
        kwargs: any keyword argument (unused)

    Examples::

        >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False)
        >>> input = torch.randn(128, 20)
        >>> output = m(input)
        >>> print(output.size())
        torch.Size([128, 20])

    """
    def __init__(self, *args, **kwargs):
        super(Identity, self).__init__()

    def forward(self, input: Tensor) -> Tensor:
        return input

class Bilinear(Module):
    r"""Applies a bilinear transformation to the incoming data:
    :math:`y = x_1^T A x_2 + b`

    Args:
        in1_features: size of each first input sample
        in2_features: size of each second input sample
        out_features: size of each output sample
        bias: If set to False, the layer will not learn an additive bias.
            Default: ``True``

    Shape:
        - Input1: :math:`(N, *, H_{in1})` where :math:`H_{in1}=\text{in1\_features}` and
          :math:`*` means any number of additional dimensions. All but the last dimension
          of the inputs should be the same.
        - Input2: :math:`(N, *, H_{in2})` where :math:`H_{in2}=\text{in2\_features}`.
        - Output: :math:`(N, *, H_{out})` where :math:`H_{out}=\text{out\_features}`
          and all but the last dimension are the same shape as the input.

    Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`.
            The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
            :math:`k = \frac{1}{\text{in1\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
                :math:`k = \frac{1}{\text{in1\_features}}`

    Examples::

        >>> m = nn.Bilinear(20, 30, 40)
        >>> input1 = torch.randn(128, 20)
        >>> input2 = torch.randn(128, 30)
        >>> output = m(input1, input2)
        >>> print(output.size())
        torch.Size([128, 40])
    """
    __constants__ = ['in1_features', 'in2_features', 'out_features']
    in1_features: int
    in2_features: int
    out_features: int
    weight: Tensor

    def __init__(self, in1_features: int, in2_features: int, out_features: int, bias: bool = True) -> None:
        super(Bilinear, self).__init__()
        self.in1_features = in1_features
        self.in2_features = in2_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_features))

        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) -> None:
        bound = 1 / math.sqrt(self.weight.size(1))
        init.uniform_(self.weight, -bound, bound)
        if self.bias is not None:
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
        return F.bilinear(input1, input2, self.weight, self.bias)

    def extra_repr(self) -> str:
        return 'in1_features={}, in2_features={}, out_features={}, bias={}'.format(
            self.in1_features, self.in2_features, self.out_features, self.bias is not None
        )
