# flake8: noqa
import numpy as np

from typing import Dict
from scipy.ndimage import measurements
from scipy.special import expit

initial_range = 0.1


class Codec(object):
    """Translate between integer codes and characters."""
    def init(self, charset):
        charset = sorted(list(set(charset)))
        self.code2char = {}  # type: Dict[int, str]
        self.char2code = {}  # type:  Dict[str, int]
        for code,char in enumerate(charset):
            self.code2char[code] = char
            self.char2code[char] = code
        return self
    def size(self):
        """The total number of codes (use this for the number of output
        classes when training a classifier."""
        return len(list(self.code2char.keys()))
    def encode(self, s):
        "Encode the string `s` into a code sequence."
        tab = self.char2code
        dflt = self.char2code["~"]
        return [self.char2code.get(c,dflt) for c in s]
    def decode(self, l):
        "Decode a code sequence into a string."
        s = [self.code2char.get(c,"~") for c in l]
        return s

class Network:
    def predict(self,xs):
        """Prediction is the same as forward propagation."""
        return self.forward(xs)

class Softmax(Network):
    """A logistic regression network."""
    def __init__(self,Nh,No,initial_range=0.1,rand=None):
        pass
    def ninputs(self):
        pass
    def noutputs(self):
        pass
    def forward(self,ys):
        pass
    def backward(self,deltas):
        pass


class LSTM(Network):
    """A standard LSTM network. This is a direct implementation of all the forward
    and backward propagation formulas, mainly for speed. (There is another, more
    abstract implementation as well, but that's significantly slower in Python
    due to function call overhead.)"""
    def __init__(self,ni,ns,initial=0.1,maxlen=5000):
        pass

    def init_weights(self,initial):
        pass

    def allocate(self,n):
        pass

    def reset(self,n):
        pass

    def forward(self,xs):
        pass

################################################################
# combination classifiers
################################################################

class Stacked(Network):
    """Stack two networks on top of each other."""
    def __init__(self,nets):
        self.nets = nets
    def forward(self,xs):
        pass

class Reversed(Network):
    """Run a network on the time-reversed input."""
    def __init__(self,net):
        self.net = net
    def forward(self,xs):
        pass

class Parallel(Network):
    """Run multiple networks in parallel on the same input."""
    def __init__(self,*nets):
        self.nets = nets
    def forward(self,xs):
        pass

def BIDILSTM(Ni,Ns,No):
    """A bidirectional LSTM, constructed from regular and reversed LSTMs."""
    lstm1 = LSTM(Ni,Ns)
    lstm2 = Reversed(LSTM(Ni,Ns))
    bidi = Parallel(lstm1,lstm2)
    logreg = Softmax(2*Ns,No)
    stacked = Stacked([bidi,logreg])
    return stacked


class SeqRecognizer(Network):
    """Perform sequence recognition using BIDILSTM and alignment."""
    def __init__(self,ninput,nstates,noutput=-1,codec=None,normalize=None):
        self.Ni = ninput
        if codec: noutput = codec.size()
        self.No = noutput
        self.lstm = BIDILSTM(ninput,nstates,noutput)
        self.codec = codec
    def translate_back(self, output):
        pass
    def translate_back_locations(self, output):
        pass
    def predictSequence(self,xs):
        "Predict an integer sequence of codes."
        pass
    def l2s(self,l):
        "Convert a code sequence into a unicode string after recognition."
        l = self.codec.decode(l)
        return u"".join(l)
    def predictString(self,xs):
        "Predict output as a string. This uses codec and normalizer."
        pass
