# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name
    def forward(self, x):
        '''
            Fetch and return the word embeddings given word IDs x

            Inputs
            ------

            x           : a theano array of integer IDs


            Outputs
            -------

            a theano matrix of word embeddings
        '''
        return self.embeddings[x]

    @property
    def params(self):
        return [ self.embeddings_trainable ]

    @params.setter
    def params(self, param_list):
        self.embeddings.set_value(param_list[0].get_value())


class LSTM(Layer):
    '''
        LSTM implementation.
    '''

# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


# Path: code/nn/basic.py
import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name

    def forward(self, x):
        '''
            Fetch and return the word embeddings given word IDs x

            Inputs
            ------

            x           : a theano array of integer IDs


            Outputs
            -------

            a theano matrix of word embeddings
        '''
        return self.embeddings[x]

    @property
    def params(self):
        return [ self.embeddings_trainable ]

    @params.setter
    def params(self, param_list):
        self.embeddings.set_value(param_list[0].get_value())


class LSTM(Layer):
    '''
        LSTM implementation.
    '''

    def __init__(self, n_in, n_out, activation=tanh,