# Path: seya/utils.py
# def theano_rng(seed=123):
#     return MRG_RandomStreams(seed=seed)
# 
# Path: seya/regularizers.py
# class GaussianKL(Regularizer):
# class ExponentialKL(Regularizer):
# class LambdaRegularizer(Regularizer):
# class CorrentropyActivityRegularizer(Regularizer):
# class CorrentropyWeightRegularizer(Regularizer):
#     def __init__(self, mean, logsigma, prior_mean=0, prior_logsigma=0,
#                  regularizer_scale=1):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, _lambda, prior_lambda=1.,
#                  regularizer_scale=1):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, cost):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, scale, sigma=1.):
#     def set_layer(self, layer):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, scale, sigma=1):
#     def set_param(self, p):
#     def __call__(self, loss):
#     def get_config(self):
# def correntropy(x, sigma):


import theano.tensor as T
from theano import scan
from keras.layers.recurrent import GRU, Recurrent, LSTM
from keras.utils.theano_utils import shared_zeros  # , alloc_zeros_matrix
from ..utils import theano_rng
from ..regularizers import SimpleCost




class DRAW(Recurrent):
    '''DRAW

    Parameters:
    ===========
    output_dim : encoder/decoder dimension
    code_dim : random sample dimension (reparametrization trick output)
    input_shape : (n_channels, rows, cols)
    N_enc : Size of the encoder's filter bank (MNIST default: 2)
    N_dec : Size of the decoder's filter bank (MNIST default: 5)
    n_steps : number of sampling steps (or how long it takes to draw, default 64)
    inner_rnn : str with rnn type ('gru' default)
    truncate_gradient : int (-1 default)
    return_sequences : bool (False default)
    '''

# Path: seya/utils.py
# def theano_rng(seed=123):
#     return MRG_RandomStreams(seed=seed)
# 
# Path: seya/regularizers.py
# class GaussianKL(Regularizer):
# class ExponentialKL(Regularizer):
# class LambdaRegularizer(Regularizer):
# class CorrentropyActivityRegularizer(Regularizer):
# class CorrentropyWeightRegularizer(Regularizer):
#     def __init__(self, mean, logsigma, prior_mean=0, prior_logsigma=0,
#                  regularizer_scale=1):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, _lambda, prior_lambda=1.,
#                  regularizer_scale=1):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, cost):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, scale, sigma=1.):
#     def set_layer(self, layer):
#     def __call__(self, loss):
#     def get_config(self):
#     def __init__(self, scale, sigma=1):
#     def set_param(self, p):
#     def __call__(self, loss):
#     def get_config(self):
# def correntropy(x, sigma):


# Path: seya/layers/draw.py
import theano.tensor as T
from theano import scan
from keras.layers.recurrent import GRU, Recurrent, LSTM
from keras.utils.theano_utils import shared_zeros  # , alloc_zeros_matrix
from ..utils import theano_rng
from ..regularizers import SimpleCost





class DRAW(Recurrent):
    '''DRAW

    Parameters:
    ===========
    output_dim : encoder/decoder dimension
    code_dim : random sample dimension (reparametrization trick output)
    input_shape : (n_channels, rows, cols)
    N_enc : Size of the encoder's filter bank (MNIST default: 2)
    N_dec : Size of the decoder's filter bank (MNIST default: 5)
    n_steps : number of sampling steps (or how long it takes to draw, default 64)
    inner_rnn : str with rnn type ('gru' default)
    truncate_gradient : int (-1 default)
    return_sequences : bool (False default)
    '''

    theano_rng = theano_rng()