from theano import tensor, scan

from blocks.bricks import Brick

# T: INPUT_SEQUENCE_LENGTH
# B: BATCH_SIZE
# L: OUTPUT_SEQUENCE_LENGTH
# C: NUM_CLASSES
class CTC(Brick):
    def apply(l, probs, l_len=None, probs_mask=None):
        """
        Numeration:
            Characters 0 to C-1 are true characters
            Character C is the blank character
        Inputs:
            l : L x B : the sequence labelling
            probs : T x B x C+1 : the probabilities output by the RNN
            l_len : B : the length of each labelling sequence
            probs_mask : T x B
        Output: the B probabilities of the labelling sequences
        Steps:
            - Calculate y' the labelling sequence with blanks
            - Calculate the recurrence relationship for the alphas
            - Calculate the sequence of the alphas
            - Return the probability found at the end of that sequence
        """
        T = probs.shape[0]
        C = probs.shape[2]-1
        L = l.shape[0]
        S = 2*L+1
        B = l.shape[1]
        
        # l_blk = l with interleaved blanks
        l_blk = C * tensor.ones((S, B))
        l_blk = tensor.set_subtensor(l_blk[1::2,:],l)
        l_blk = l_blk.T     # now l_blk is B x S

        # dimension of alpha (corresponds to alpha hat in the paper) :
        #   T x B x S
        # dimension of c :
        #   T x B
        # first value of alpha (size B x S)
        alpha0 = tensor.concatenate([
                                        probs[0, :, C],
                                        probs[0][tensor.arange(B), l[0]],
                                        tensor.zeros((B, S-2))
                                    ], axis=1)
        c0 = alpha0.sum(axis=1)
        alpha0 = alpha0 / c0[:,None]

        # recursion
        l_blk_2 = tensor.concatenate([-tensor.ones((B,2)), l_blk[:,:-2]], axis=1)
        l_case2 = tensor.ne(l_blk, numpy.float32(C)) * tensor.ne(l_blk, l_blk_2)
        # l_case2 is B x S

        def recursion(p, p_mask, prev_alpha, prev_c):
            prev_alpha = prev_alpha[-1]
            # p is B x C+1
            # prev_alpha is B x S 
            prev_alpha_1 = tensor.concatenate([tensor.zeros((B,1)),prev_alpha[:,:-1]], axis=1)
            prev_alpha_2 = tensor.concatenate([tensor.zeros((B,2)),prev_alpha[:,:-2]], axis=1)

            alphabar = prev_alpha + prev_alpha1
            alphabar = tensor.switch(l_case2, alphabar + prev_alpha2, alphabar)
            next_alpha = alpha_bar * p[tensor.arange(B)[:,None].repeat(S,axis=1).flatten(), l_blk.flatten()].reshape((B,S))
            next_alpha = tensor.switch(p_mask[:,None], next_alpha, prev_alpha]
            next_c = next_alpha.sum(axis=1)
            
            return next_alpha / next_c[:, None], next_c

        # apply the recursion with scan
        alpha, c = tensor.scan(fn=recursion,
                               sequences=[probs, probs_mask],
                               outputs_info=[alpha0, c0])

        # return the log probability of the labellings
        return tensor.log(c).sum(axis=0)

    
    def best_path_decoding(y_hat, y_hat_mask=None):
        # Easy one !
        pass

    def prefix_search(y_hat, y_hat_mask=None):
        # Hard one...
        pass
        
        
 
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