# Path: brainforge/atomic/activation_op.py
# class ActivationFunction:
# class Sigmoid(ActivationFunction):
# class HardSigmoid(ActivationFunction):
# class Tanh(ActivationFunction):
# class Sqrt(ActivationFunction):
# class Linear(ActivationFunction):
# class ReLU(ActivationFunction):
# class LeakyReLU(ActivationFunction):
# class SoftMax(ActivationFunction):
# class OnePlus(ActivationFunction):
#     def forward(self, Z: np.ndarray) -> np.ndarray:
#     def __str__(self):
#     def backward(self, Z: np.ndarray) -> np.ndarray:
#     def forward(self, Z: np.ndarray):
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z: np.ndarray):
#     def backward(self, A: np.ndarray):
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, Z) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A) -> np.ndarray:
#     def __init__(self, alpha=0.3):
#     def forward(self, Z: np.ndarray) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def __init__(self, temperature=1.):
#     def tn(self, Z):
#     def t1(Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def true_backward(A: np.ndarray):
#     def forward(self, Z):
#     def backward(self, Z: np.ndarray):
#         J = np.ones_like(A)
#         J[A <= 0.] = self.alpha
#         I = np.eye(A.shape[1], dtype=floatX)
# 
# Path: brainforge/util/typing.py
# def zX(*dims, dtype=floatX):
#     return np.zeros(dims, dtype=dtype)
# 
# def zX_like(array, dtype=floatX):
#     return zX(*array.shape, dtype=dtype)
# 
# def scalX(scalar, dtype=floatX):
#     return np.asscalar(np.array([scalar], dtype=dtype))


import numpy as np
from .activation_op import activations
from ..util.typing import zX, zX_like, scalX


sigmoid = activations["sigmoid"]()
s0 = scalX(0.)


class RecurrentOp:

    def __init__(self, activation):
        self.actfn = activations[activation]()

    def forward(self, X, W, b):
        outdim = W.shape[-1]
        time, batch, indim = X.shape


# Path: brainforge/atomic/activation_op.py
# class ActivationFunction:
# class Sigmoid(ActivationFunction):
# class HardSigmoid(ActivationFunction):
# class Tanh(ActivationFunction):
# class Sqrt(ActivationFunction):
# class Linear(ActivationFunction):
# class ReLU(ActivationFunction):
# class LeakyReLU(ActivationFunction):
# class SoftMax(ActivationFunction):
# class OnePlus(ActivationFunction):
#     def forward(self, Z: np.ndarray) -> np.ndarray:
#     def __str__(self):
#     def backward(self, Z: np.ndarray) -> np.ndarray:
#     def forward(self, Z: np.ndarray):
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z: np.ndarray):
#     def backward(self, A: np.ndarray):
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, Z) -> np.ndarray:
#     def forward(self, Z) -> np.ndarray:
#     def backward(self, A) -> np.ndarray:
#     def __init__(self, alpha=0.3):
#     def forward(self, Z: np.ndarray) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def __init__(self, temperature=1.):
#     def tn(self, Z):
#     def t1(Z) -> np.ndarray:
#     def backward(self, A: np.ndarray) -> np.ndarray:
#     def true_backward(A: np.ndarray):
#     def forward(self, Z):
#     def backward(self, Z: np.ndarray):
#         J = np.ones_like(A)
#         J[A <= 0.] = self.alpha
#         I = np.eye(A.shape[1], dtype=floatX)
# 
# Path: brainforge/util/typing.py
# def zX(*dims, dtype=floatX):
#     return np.zeros(dims, dtype=dtype)
# 
# def zX_like(array, dtype=floatX):
#     return zX(*array.shape, dtype=dtype)
# 
# def scalX(scalar, dtype=floatX):
#     return np.asscalar(np.array([scalar], dtype=dtype))


# Path: brainforge/atomic/recurrent_op.py
import numpy as np
from .activation_op import activations
from ..util.typing import zX, zX_like, scalX



sigmoid = activations["sigmoid"]()
s0 = scalX(0.)


class RecurrentOp:

    def __init__(self, activation):
        self.actfn = activations[activation]()

    def forward(self, X, W, b):
        outdim = W.shape[-1]
        time, batch, indim = X.shape


        Z = zX(time, batch, indim+outdim)