text: import math
  import unittest
  
  import numpy
  
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  
  
  class CTCTestBase(object):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, (4, 2, 3)).astype(numpy.float32)
          self.t = numpy.array([[0, 1], [1, 0]]).astype(numpy.int32)
          self.l = numpy.array([[2, 0, 2, 1, 2],
                                [2, 1, 2, 0, 2]]).astype(numpy.int32)
          self.blank_symbol = 2
          self.x_length = numpy.full((len(self.x[0]),), len(self.x), dtype='i')
          self.l_length = numpy.full((len(self.t),), len(self.t[0]), dtype='i')
          self.use_length = True
          if self.reduce == 'mean':
              self.gy = numpy.random.uniform(-1, 1, ()).astype(numpy.float32)
          else:
              self.gy = numpy.random.uniform(-1, 1, (2,)).astype(numpy.float32)
  
      # recursive forward computation.
      def alpha(self, x, l, t, u):
          if u < 0:
              return 0.0
          if t == 0:
              if u == 0:
                  return x[0][self.blank_symbol]
              elif u == 1:
                  return x[0][l[1]]
              else:
                  return 0.0
          elif l[u] == self.blank_symbol or l[u] == l[u - 2]:
              return (x[t][l[u]] *
                      (self.alpha(x, l, t - 1, u - 1) +
                       self.alpha(x, l, t - 1, u)))
          else:
              return (x[t][l[u]] *
                      (self.alpha(x, l, t - 1, u - 2) +
                       self.alpha(x, l, t - 1, u - 1) +
                       self.alpha(x, l, t - 1, u)))
  
      def check_forward(self, t_data, xs_data, l_length, x_length,
                        wrap_variable=True):
          if wrap_variable:
              x = tuple(chainer.Variable(x_data) for x_data in xs_data)
              t = chainer.Variable(t_data)
          else:
              x = xs_data
              t = t_data
  
          args = (x, t, self.blank_symbol)
          if self.use_length:
              if wrap_variable:
                  args += (chainer.Variable(x_length),
                           chainer.Variable(l_length))
              else:
                  args += (x_length, l_length)
          loss = functions.connectionist_temporal_classification(
              *args, reduce=self.reduce).data
  
          # compute expected value by recursive computation.
          xp = backend.get_array_module(self.x)
          xt = xp.swapaxes(self.x, 0, 1)
          for b in range(xt.shape[0]):
              for t in range(xt.shape[1]):
                  xt[b][t] = numpy.exp(xt[b][t]) / numpy.sum(numpy.exp(xt[b][t]))
          batch_size = xt.shape[0]
          path_length = 2 * l_length + 1
          loss_expect = xp.zeros((batch_size,), dtype=xp.float32)
          for i in range(batch_size):
              xtb, lb, xlb, plb = xt[i], self.l[i], x_length[i], path_length[i]
              loss_expect[i] = -math.log(
                  self.alpha(xtb, lb, int(xlb - 1), int(plb - 1)) +
                  self.alpha(xtb, lb, int(xlb - 1), int(plb - 2)))
          if self.reduce == 'mean':
              loss_expect = xp.mean(loss_expect)
          testing.assert_allclose(loss_expect, loss)
  
      def test_forward_cpu(self):
          self.check_forward(self.t, tuple(self.x),
                             self.l_length, self.x_length)
  
      def test_forward_without_wrap_cpu(self):
          self.check_forward(self.t, tuple(self.x),
                             self.l_length, self.x_length, wrap_variable=False)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.t),
                             tuple(cuda.to_gpu(x_data) for x_data in self.x),
                             cuda.to_gpu(self.l_length),
                             cuda.to_gpu(self.x_length))
  
      @attr.gpu
      def test_forward_without_wrap_gpu(self):
          self.check_forward(cuda.to_gpu(self.t),
                             tuple(cuda.to_gpu(x_data) for x_data in self.x),
                             cuda.to_gpu(self.l_length),
                             cuda.to_gpu(self.x_length),
                             wrap_variable=False)
  
      # expected value(via numerical differentiation) from t_data
      def check_backward(self, t_data, xs_data, l_length, x_length, gy_data):
          def f(input_length, label_length, t, *x):
              return functions.connectionist_temporal_classification(
                  x, t, self.blank_symbol, x_length, l_length,
                  reduce=self.reduce)
  
          gradient_check.check_backward(
              f, (x_length, l_length, t_data) + xs_data, gy_data,
              eps=1e-2, atol=1e-4)
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.t, tuple(self.x),
                              self.l_length, self.x_length,
                              self.gy)
  
      @condition.retry(3)
      @attr.gpu
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.t),
                              tuple(cuda.to_gpu(x_data) for x_data in self.x),
                              cuda.to_gpu(self.l_length),
                              cuda.to_gpu(self.x_length),
                              cuda.to_gpu(self.gy))
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTC(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCWithoutLength(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.use_length = False
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCWithLabelPadding(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.l_length[0] = 1
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCWithInputPadding(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.x_length[0] = 3
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCWithAllPadding(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.x_length[...] = 3
          self.l_length[...] = 1
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCWithRepeatedLabel(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.t = numpy.array([[0, 1, 1], [0, 1, 0]]).astype(numpy.int32)
          self.l = numpy.array([[2, 0, 2, 1, 2, 1, 2],
                                [2, 0, 2, 1, 2, 0, 2]]).astype(numpy.int32)
          self.l_length = numpy.full((len(self.t),), len(self.t[0]), dtype='i')
  
  
  @testing.parameterize(
      {'reduce': 'mean'},
      {'reduce': 'no'}
  )
  class TestCTCBlankSymbol(unittest.TestCase, CTCTestBase):
  
      def setUp(self):
          CTCTestBase.setUp(self)
          self.x = numpy.random.uniform(-1, 1, (4, 2, 4)).astype(numpy.float32)
          self.l = numpy.array([[3, 0, 3, 1, 3],
                                [3, 1, 3, 0, 3]]).astype(numpy.int32)
          self.blank_symbol = 3
  
  
  class TestCTCUseNoBackpropMode(unittest.TestCase):
  
      def test_no_backprop_mode(self):
          xs_data = numpy.random.uniform(-1, 1, (4, 2, 3)).astype(numpy.float32)
          t_data = numpy.array([[0, 1], [1, 0]]).astype(numpy.int32)
          with chainer.no_backprop_mode():
              x = [chainer.Variable(x_data) for x_data in xs_data]
              t = chainer.Variable(t_data)
              functions.connectionist_temporal_classification(x, t, 2)
  
  
  class TestCTCError(unittest.TestCase):
  
      def test_not_iterable(self):
          x = chainer.Variable(numpy.zeros((4, 2, 3), numpy.float32))
          t = chainer.Variable(numpy.zeros((2, 2), numpy.int32))
          with self.assertRaises(TypeError):
              functions.connectionist_temporal_classification(x, t, 0)
  
  
  class TestCTCInvalidReductionOption(unittest.TestCase):
  
      def test_not_iterable(self):
          x = chainer.Variable(numpy.zeros((4, 2, 3), numpy.float32))
          t = chainer.Variable(numpy.zeros((2, 2), numpy.int32))
          with self.assertRaises(ValueError):
              functions.connectionist_temporal_classification(
                  tuple(x), t, 0, reduce='invalid_option')
  
  
  testing.run_module(__name__, __file__)
  