require 'nn'
require 'ctc_log'

torch.setdefaulttensortype('torch.DoubleTensor')

outputTable = torch.Tensor{
	{0.00851561,0.00838812,0.00831823,0.00827574,0.00824642,0.00822375,0.00820343,0.00819058,0.00818666,0.00818973,0.0081931,0.00819045,0.0081746,0.00815639,0.00813955,0.00813717,0.00814291,0.00815282,0.00816507,0.00818201,0.00819585,0.00820183,0.0082032,0.00820701,0.00821897,0.00823849,0.00827241,0.00831417,0.00834877,0.00838797},
	{0.00832554,0.0083547,0.00836662,0.0083672,0.00835925,0.00834443,0.00832733,0.00830804,0.00829558,0.00828797,0.00826878,0.00823972,0.00820705,0.00817149,0.00815277,0.00815138,0.00816869,0.00819128,0.00820619,0.00821728,0.00822713,0.00822409,0.00820866,0.00819896,0.00819222,0.00818621,0.00817647,0.00817657,0.00819549,0.0082471},
	{0.00804687,0.00828849,0.00844932,0.00854689,0.00860203,0.00863111,0.00864463,0.00864913,0.00864941,0.00864568,0.00864145,0.00864273,0.00864651,0.00864103,0.00862982,0.00861546,0.00860792,0.0086006,0.00859696,0.00859797,0.00860569,0.00861662,0.00862803,0.00864035,0.00864985,0.00866094,0.00866769,0.00867244,0.00866648,0.00860276},
	{0.0071507,0.00694943,0.00682586,0.00674856,0.0067018,0.00667533,0.00666434,0.00666094,0.00665562,0.00664758,0.00663679,0.00662265,0.00661063,0.00661422,0.00662202,0.00662611,0.00662495,0.00662458,0.00662262,0.00661391,0.00660734,0.00661144,0.00662187,0.0066317,0.00663687,0.00663818,0.00663882,0.00664628,0.00667254,0.00675394},
	{0.00754264,0.00749866,0.00748816,0.00748909,0.00748963,0.00748719,0.00748255,0.00747492,0.00747167,0.00747423,0.0074759,0.00747402,0.0074708,0.00746449,0.00746391,0.00747165,0.00748495,0.00749894,0.00751074,0.00751627,0.007513,0.00749486,0.00747238,0.00745048,0.00743328,0.00741408,0.0073878,0.00735739,0.00732742,0.00732945},
	{0.0073096,0.00723635,0.00719494,0.00716805,0.00715027,0.00713952,0.00713509,0.00712752,0.00711783,0.00711011,0.00710192,0.00708911,0.00708077,0.00708461,0.00708462,0.00707723,0.00707139,0.00706921,0.00706241,0.00705613,0.00706148,0.00707487,0.00708709,0.00709222,0.00708823,0.00708249,0.00708064,0.00708988,0.00710951,0.00714969},
	{0.00830771,0.00823418,0.00816136,0.00809586,0.00804167,0.00799979,0.00796758,0.00794364,0.00793313,0.00792998,0.00792908,0.00792848,0.00792855,0.00792866,0.00792361,0.00791404,0.00790926,0.00790524,0.00789946,0.00789935,0.00789862,0.0078946,0.00788624,0.00787816,0.00786684,0.00784255,0.00779934,0.00772262,0.00759376,0.00739087},
	{0.00727931,0.00734384,0.00737194,0.00738059,0.00737914,0.00737259,0.00736598,0.00737079,0.00738365,0.00739321,0.00740285,0.0074161,0.00742545,0.00742927,0.00743403,0.00744413,0.00745141,0.00745337,0.00746366,0.00746699,0.00745354,0.00743827,0.00743547,0.00744751,0.00748458,0.00755231,0.00764996,0.00777761,0.00796437,0.00824862},
	{0.00759865,0.00760253,0.007619,0.00763628,0.00764887,0.00765355,0.00765037,0.00764366,0.0076389,0.00763273,0.00762202,0.00761542,0.00761321,0.00761493,0.00761815,0.00761988,0.00762385,0.00761992,0.00761883,0.0076235,0.00763021,0.00763094,0.00762206,0.00760918,0.00758451,0.00755949,0.00753664,0.00751976,0.00751458,0.00752432},
	{0.00807587,0.00800675,0.00794393,0.00790045,0.00787595,0.0078656,0.00786785,0.00787848,0.00788506,0.00788517,0.00788813,0.00789203,0.00789427,0.00790812,0.00792374,0.00793619,0.00793606,0.00793511,0.00793498,0.00792428,0.00790891,0.00790654,0.00791503,0.0079268,0.00794357,0.00796792,0.00800614,0.00807022,0.00817937,0.00835883},
	{0.00902721,0.00923254,0.00935981,0.00942818,0.00945831,0.00946226,0.00945235,0.00943923,0.00943316,0.00942081,0.00939779,0.00937562,0.00936163,0.00935968,0.0093776,0.0094018,0.00943297,0.00944957,0.00945726,0.00946422,0.00946807,0.00945142,0.00941994,0.00939279,0.00935696,0.00931577,0.00925278,0.0091793,0.00908306,0.00896482},
	{0.00817932,0.00824187,0.00828341,0.00831163,0.00833313,0.00835258,0.00837072,0.00838791,0.00839768,0.00840187,0.00840477,0.00840498,0.00839802,0.00839423,0.0083998,0.0084094,0.00840665,0.00839996,0.00839527,0.00838367,0.00837028,0.0083724,0.00839625,0.00843072,0.00846929,0.00850286,0.0085422,0.00858495,0.00866,0.00877247},
	{0.00765023,0.00748373,0.00738717,0.00733217,0.00730354,0.00729194,0.00729252,0.00729467,0.00729358,0.00729548,0.00730373,0.00730991,0.00731084,0.0073158,0.00731402,0.00730786,0.00729353,0.00728685,0.00728254,0.00727386,0.00727117,0.00728254,0.00729897,0.00730843,0.00731725,0.0073286,0.0073535,0.00738615,0.00742871,0.00748284},
	{0.00675057,0.0066293,0.00654755,0.0064934,0.00645808,0.0064347,0.00641783,0.00640552,0.0063952,0.00638518,0.00637609,0.00637163,0.00637494,0.00638893,0.00641063,0.00643255,0.00644374,0.00644555,0.00644544,0.00644605,0.00644555,0.00644547,0.0064497,0.00646288,0.00648797,0.00653274,0.00661251,0.00673724,0.00692173,0.00718667},
	{0.00918335,0.00914577,0.00911366,0.00909089,0.0090778,0.00907167,0.00907246,0.00908205,0.00908736,0.00908863,0.00909847,0.00911673,0.00913672,0.00915208,0.0091591,0.00915776,0.00914384,0.00912792,0.00912464,0.00911718,0.00910646,0.00910203,0.00910907,0.00911052,0.00910743,0.00909927,0.00907481,0.00902575,0.00894358,0.00878026},
	{0.00804851,0.00801377,0.00798571,0.00796503,0.00794885,0.00793622,0.00792406,0.00790549,0.00788894,0.00788346,0.00788096,0.0078791,0.00788259,0.00788236,0.00787847,0.00787234,0.00786953,0.00787329,0.00787384,0.00788077,0.00789635,0.00790469,0.00790562,0.00790307,0.00790476,0.00791308,0.00793394,0.00797176,0.0080062,0.00802328},
	{0.00804263,0.00804189,0.00801113,0.00797653,0.00794703,0.00792481,0.00790932,0.00789841,0.00788874,0.00788058,0.0078668,0.00784778,0.00783389,0.00783016,0.00784207,0.00785935,0.00787323,0.00787845,0.00787904,0.00788003,0.0078825,0.00788204,0.00788413,0.00788901,0.00789046,0.00788951,0.00788775,0.00788351,0.00788097,0.00789731},
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}

target = {74, 119, 118, 81, 117, 95, 7}

fvs = torch.Tensor{
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}

bvs = torch.Tensor{
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}

grad_contrast = torch.Tensor{
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}
function toMatrix(outputTable)
	local net = nn.Sequential()
	net:add(nn.JoinTable(1))
	net:add(nn.Reshape(#outputTable, outputTable[1]:size(1)))
	return net:forward(outputTable)
end

-- outputTable = nn.Log():forward(outputTable:t())

T = outputTable:size(1)
nrow = outputTable:size(2)

splitedOutputTable = nn.SplitTable(1):forward(outputTable:t())

c_pzx, c_grad = ctc.getCTCCostAndGrad(splitedOutputTable, target)

c_m = toMatrix(c_grad):float()

-- print(c_m:t())

-- print(torch.dist(c_m:t(), grad_contrast:float()))



eps = 1e-5
ctc_lua = false

est_grad = torch.Tensor(nrow)

for t = 1, T do
	for i = 1, nrow do
		outputTable[t][i] = outputTable[t][i] + eps

		splitedOutputTable = nn.SplitTable(1):forward(outputTable:t())
		loss1, _ = ctc.getCTCCostAndGrad(splitedOutputTable, target)

		outputTable[t][i] = outputTable[t][i] - 2 * eps
		splitedOutputTable = nn.SplitTable(1):forward(outputTable:t())
		loss2, _ = ctc.getCTCCostAndGrad(splitedOutputTable, target)
		

		outputTable[t][i] = outputTable[t][i] + eps

		est_grad[i] = (loss1 - loss2) / (2 * eps)

		print(est_grad[i], grad_contrast[t][i])
	end
end


