from torch.nn.modules.module import Module
import torch.nn.functional as F

from isrlu import ISRLU, ISRU_sigmoid, ISRU_tanh
from bipolar import Bipolar
from softexp import SoftExp
from ternary import TernaryTanh
activations = [ISRLU, ISRU_sigmoid, ISRU_tanh, Bipolar, SoftExp, TernaryTanh]


# see: https://github.com/jpeg729/pytorch_bits/tree/master/nn         ----------------------------- #
class RNNCellBase(Module):
    def __repr__(self):
        s = '{name}({input_size}, {hidden_size}'
        if 'bias' in self.__dict__ and self.bias is not True:
            s += ', bias={bias}'
        if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh":
            s += ', nonlinearity={nonlinearity}'
        s += ')'
        return s.format(name=self.__class__.__name__, **self.__dict__)

    def get_activation(self, activation):
        if hasattr(activations, activation):
            return getattr(activations, activation)
        return getattr(F, activation)

    def check_forward_input(self, input_data):
        if input_data.size(1) != self.input_size:
            raise RuntimeError(
                "input has inconsistent input_size: got {}, expected {}".format(
                    input_data.size(1), self.input_size))

    def check_forward_hidden(self, input_data, hx, hidden_label=''):
        if input_data.size(0) != hx.size(0):
            raise RuntimeError(
                "Input batch size {} doesn't match hidden{} batch size {}".format(
                    input_data.size(0), hidden_label, hx.size(0)))

        if hx.size(1) != self.hidden_size:
            raise RuntimeError(
                "hidden{} has inconsistent hidden_size: got {}, expected {}".format(
                    hidden_label, input_data.size(1), self.input_size))
# ------------------------------------------------------------------------------------------------- #