# Path: bionlp/utils/utils.py
# def theano_logsumexp(x, axis=None):
#     """
#     Compute log(sum(exp(x), axis=axis) in a numerically stable
#     fashion.
#     Parameters
#     ----------
#     x : tensor_like
#         A Theano tensor (any dimension will do).
#     axis : int or symbolic integer scalar, or None
#         Axis over which to perform the summation. `None`, the
#         default, performs over all axes.
#     Returns
#     -------
#     result : ndarray or scalar
#         The result of the log(sum(exp(...))) operation.
#     """
#     xmax = T.max(x,axis=axis, keepdims=True)
#     xmax_ = T.max(x,axis=axis)
#     return xmax_ + T.log(T.exp(x - xmax).sum(axis=axis))


import lasagne,sys
import logging,theano
import theano.tensor as T
from bionlp.utils.utils import theano_logsumexp

    pairwise_sequence=lasagne.layers.get_output(pairwise,deterministic = False)
    pairwise_sequence=pairwise_sequence.dimshuffle(1,0,2)
    pair_shape=pairwise_sequence.shape
    pairwise_potential=T.reshape(pairwise_sequence,(pair_shape[0],pair_shape[1],numTags,numTags))
    unary_potential = lasagne.layers.get_output(Inference_Layer,deterministic = False,unary=True)
    unnormalized_log = lasagne.layers.get_output(Inference_Layer,deterministic = False,unary=False)

    mask_out=lasagne.layers.get_output(l_mask)
    #Wp=Inference_Layer.get_CRF_params()
    logger.info('Initializing CRF structured loss')
    unary_score=T.sum(unary_potential*t_out*mask_out.dimshuffle(0,1,'x'),axis=[1,2])
    unary_sequence = t_out.dimshuffle(1,0,2)    #Reshuffling the batched unary potential shape so that it can be used for word level iterations in theano.scan
    mask_out=mask_out.dimshuffle(1,0)
    m_out_f=theano.function([l_mask.input_var],mask_out)
    print("rearranged output shape for mask",m_out_f(mask_in.astype('float32')).shape)

    def pairwise_collector(yprev,ycurrent,Wp):
        yip = yprev.dimshuffle(0,1,'x')*Wp
        yip = yip*ycurrent.dimshuffle(0,'x',1)
        yip = T.sum(yip,axis=[1,2])
        return yip


    pairwise_score,_=theano.scan(fn=pairwise_collector,sequences=[dict(input=unary_sequence,taps=[-1,0]),dict(input=pairwise_potential,taps=[0])],non_sequences=None)
    pairwise_score_result = theano.function([t_out,l_in.input_var,l_mask.input_var],pairwise_score)
    logger.debug('Scan pairwise score is calculated.{0}'.format(pairwise_score_result(y_in.astype('float32'),x_in.astype('int32'),mask_in.astype('float32')).shape))
    pairwise_score=T.sum(pairwise_score*mask_out[1:,:],axis=[0])
    total_score=theano.function([t_out,l_in.input_var,l_mask.input_var],pairwise_score+unary_score)
    zee_ = theano.function([l_in.input_var,l_mask.input_var],theano_logsumexp(unnormalized_log,axis=2)[:,0])

# Path: bionlp/utils/utils.py
# def theano_logsumexp(x, axis=None):
#     """
#     Compute log(sum(exp(x), axis=axis) in a numerically stable
#     fashion.
#     Parameters
#     ----------
#     x : tensor_like
#         A Theano tensor (any dimension will do).
#     axis : int or symbolic integer scalar, or None
#         Axis over which to perform the summation. `None`, the
#         default, performs over all axes.
#     Returns
#     -------
#     result : ndarray or scalar
#         The result of the log(sum(exp(...))) operation.
#     """
#     xmax = T.max(x,axis=axis, keepdims=True)
#     xmax_ = T.max(x,axis=axis)
#     return xmax_ + T.log(T.exp(x - xmax).sum(axis=axis))


# Path: bionlp/taggers/rnn_feature/networks/crf_approx_layer.py
import lasagne,sys
import logging,theano
import theano.tensor as T
from bionlp.utils.utils import theano_logsumexp


    pairwise_sequence=lasagne.layers.get_output(pairwise,deterministic = False)
    pairwise_sequence=pairwise_sequence.dimshuffle(1,0,2)
    pair_shape=pairwise_sequence.shape
    pairwise_potential=T.reshape(pairwise_sequence,(pair_shape[0],pair_shape[1],numTags,numTags))
    unary_potential = lasagne.layers.get_output(Inference_Layer,deterministic = False,unary=True)
    unnormalized_log = lasagne.layers.get_output(Inference_Layer,deterministic = False,unary=False)

    mask_out=lasagne.layers.get_output(l_mask)
    #Wp=Inference_Layer.get_CRF_params()
    logger.info('Initializing CRF structured loss')
    unary_score=T.sum(unary_potential*t_out*mask_out.dimshuffle(0,1,'x'),axis=[1,2])
    unary_sequence = t_out.dimshuffle(1,0,2)    #Reshuffling the batched unary potential shape so that it can be used for word level iterations in theano.scan
    mask_out=mask_out.dimshuffle(1,0)
    m_out_f=theano.function([l_mask.input_var],mask_out)
    print("rearranged output shape for mask",m_out_f(mask_in.astype('float32')).shape)

    def pairwise_collector(yprev,ycurrent,Wp):
        yip = yprev.dimshuffle(0,1,'x')*Wp
        yip = yip*ycurrent.dimshuffle(0,'x',1)
        yip = T.sum(yip,axis=[1,2])
        return yip


    pairwise_score,_=theano.scan(fn=pairwise_collector,sequences=[dict(input=unary_sequence,taps=[-1,0]),dict(input=pairwise_potential,taps=[0])],non_sequences=None)
    pairwise_score_result = theano.function([t_out,l_in.input_var,l_mask.input_var],pairwise_score)
    logger.debug('Scan pairwise score is calculated.{0}'.format(pairwise_score_result(y_in.astype('float32'),x_in.astype('int32'),mask_in.astype('float32')).shape))
    pairwise_score=T.sum(pairwise_score*mask_out[1:,:],axis=[0])
    total_score=theano.function([t_out,l_in.input_var,l_mask.input_var],pairwise_score+unary_score)
    zee_ = theano.function([l_in.input_var,l_mask.input_var],theano_logsumexp(unnormalized_log,axis=2)[:,0])

    cost_ = theano.function([t_out,l_in.input_var,l_mask.input_var],theano_logsumexp(unnormalized_log,axis=2)[:,0]-(pairwise_score+unary_score))