# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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"""CTC (Connectionist Temporal Classification) Operations."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor

from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_ctc_ops
from tensorflow.python.ops.nn_grad import _BroadcastMul
from tensorflow.python.util.tf_export import tf_export


# pylint: disable=protected-access, invalid-name
@tf_export("nn.ctc_loss")
def ctc_loss(labels, inputs, sequence_length,
             preprocess_collapse_repeated=False,
             ctc_merge_repeated=True,
             ignore_longer_outputs_than_inputs=False, time_major=True):
  """Computes the CTC (Connectionist Temporal Classification) Loss.

  This op implements the CTC loss as presented in the article:

  [A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
  Connectionist Temporal Classification: Labeling Unsegmented Sequence Data
  with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA,
  pp. 369-376.](http://www.cs.toronto.edu/~graves/icml_2006.pdf)

  Input requirements:

  ```
  sequence_length(b) <= time for all b

  max(labels.indices(labels.indices[:, 1] == b, 2))
    <= sequence_length(b) for all b.
  ```

  Notes:

  This class performs the softmax operation for you, so inputs should
  be e.g. linear projections of outputs by an LSTM.

  The `inputs` Tensor's innermost dimension size, `num_classes`, represents
  `num_labels + 1` classes, where num_labels is the number of true labels, and
  the largest value `(num_classes - 1)` is reserved for the blank label.

  For example, for a vocabulary containing 3 labels `[a, b, c]`,
  `num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.

  Regarding the arguments `preprocess_collapse_repeated` and
  `ctc_merge_repeated`:

  If `preprocess_collapse_repeated` is True, then a preprocessing step runs
  before loss calculation, wherein repeated labels passed to the loss
  are merged into single labels.  This is useful if the training labels come
  from, e.g., forced alignments and therefore have unnecessary repetitions.

  If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
  repeated non-blank labels will not be merged and are interpreted
  as individual labels.  This is a simplified (non-standard) version of CTC.

  Here is a table of the (roughly) expected first order behavior:

  * `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`

    Classical CTC behavior: Outputs true repeated classes with blanks in
    between, and can also output repeated classes with no blanks in
    between that need to be collapsed by the decoder.

  * `preprocess_collapse_repeated=True`, `ctc_merge_repeated=False`

    Never learns to output repeated classes, as they are collapsed
    in the input labels before training.

  * `preprocess_collapse_repeated=False`, `ctc_merge_repeated=False`

    Outputs repeated classes with blanks in between, but generally does not
    require the decoder to collapse/merge repeated classes.

  * `preprocess_collapse_repeated=True`, `ctc_merge_repeated=True`

    Untested.  Very likely will not learn to output repeated classes.

  The `ignore_longer_outputs_than_inputs` option allows to specify the behavior
  of the CTCLoss when dealing with sequences that have longer outputs than
  inputs. If true, the CTCLoss will simply return zero gradient for those
  items, otherwise an InvalidArgument error is returned, stopping training.

  Args:
    labels: An `int32` `SparseTensor`.
      `labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
      the id for (batch b, time t).
      `labels.values[i]` must take on values in `[0, num_labels)`.
      See `core/ops/ctc_ops.cc` for more details.
    inputs: 3-D `float` `Tensor`.
      If time_major == False, this will be a `Tensor` shaped:
        `[batch_size, max_time, num_classes]`.
      If time_major == True (default), this will be a `Tensor` shaped:
        `[max_time, batch_size, num_classes]`.
      The logits.
    sequence_length: 1-D `int32` vector, size `[batch_size]`.
      The sequence lengths.
    preprocess_collapse_repeated: Boolean.  Default: False.
      If True, repeated labels are collapsed prior to the CTC calculation.
    ctc_merge_repeated: Boolean.  Default: True.
    ignore_longer_outputs_than_inputs: Boolean. Default: False.
      If True, sequences with longer outputs than inputs will be ignored.
    time_major: The shape format of the `inputs` Tensors.
      If True, these `Tensors` must be shaped `[max_time, batch_size,
      num_classes]`.
      If False, these `Tensors` must be shaped `[batch_size, max_time,
      num_classes]`.
      Using `time_major = True` (default) is a bit more efficient because it
      avoids transposes at the beginning of the ctc_loss calculation.  However,
      most TensorFlow data is batch-major, so by this function also accepts
      inputs in batch-major form.

  Returns:
    A 1-D `float` `Tensor`, size `[batch]`, containing the negative log
      probabilities.

  Raises:
    TypeError: if labels is not a `SparseTensor`.
  """
  # The second, third, etc output tensors contain the gradients.  We use it in
  # _CTCLossGrad() below.
  if not isinstance(labels, sparse_tensor.SparseTensor):
    raise TypeError("Expected labels (first argument) to be a SparseTensor")

  # For internal calculations, we transpose to [time, batch, num_classes]
  if not time_major:
    inputs = array_ops.transpose(inputs, [1, 0, 2])  # (B,T,N) => (T,B,N)

  loss, _ = gen_ctc_ops.ctc_loss(
      inputs,
      labels.indices,
      labels.values,
      sequence_length,
      preprocess_collapse_repeated=preprocess_collapse_repeated,
      ctc_merge_repeated=ctc_merge_repeated,
      ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs)

  return loss


# pylint: disable=unused-argument
@ops.RegisterGradient("CTCLoss")
def _CTCLossGrad(op, grad_loss, _):
  """The derivative provided by CTC Loss.

  Args:
     op: the CTCLoss op.
     grad_loss: The backprop for cost.

  Returns:
     The CTC Loss gradient.
  """
  # Outputs are: loss, grad
  #
  # Currently there is no way to take the second derivative of this op
  # due to the fused implementation's interaction with tf.gradients(),
  # so we make sure we prevent silently incorrect results by raising
  # an error if the second derivative is requested via prevent_gradient.
  grad_without_gradient = array_ops.prevent_gradient(
      op.outputs[1], message="Currently there is no way to take the second "
      " derivative of ctc_loss due to the fused implementation's interaction "
      " with tf.gradients()")
  # Return gradient for inputs and None for
  # labels_indices, labels_values and sequence_length
  return [_BroadcastMul(grad_loss, grad_without_gradient), None, None, None]


@tf_export("nn.ctc_greedy_decoder")
def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
  """Performs greedy decoding on the logits given in input (best path).

  Note: Regardless of the value of merge_repeated, if the maximum index of a
  given time and batch corresponds to the blank index `(num_classes - 1)`, no
  new element is emitted.

  If `merge_repeated` is `True`, merge repeated classes in output.
  This means that if consecutive logits' maximum indices are the same,
  only the first of these is emitted.  The sequence `A B B * B * B` (where '*'
  is the blank label) becomes

    * `A B B B` if `merge_repeated=True`.
    * `A B B B B` if `merge_repeated=False`.

  Args:
    inputs: 3-D `float` `Tensor` sized
      `[max_time, batch_size, num_classes]`.  The logits.
    sequence_length: 1-D `int32` vector containing sequence lengths,
      having size `[batch_size]`.
    merge_repeated: Boolean.  Default: True.

  Returns:
    A tuple `(decoded, neg_sum_logits)` where
    decoded: A single-element list. `decoded[0]`
      is an `SparseTensor` containing the decoded outputs s.t.:
      `decoded.indices`: Indices matrix `(total_decoded_outputs, 2)`.
        The rows store: `[batch, time]`.
      `decoded.values`: Values vector, size `(total_decoded_outputs)`.
        The vector stores the decoded classes.
      `decoded.shape`: Shape vector, size `(2)`.
        The shape values are: `[batch_size, max_decoded_length]`
    neg_sum_logits: A `float` matrix `(batch_size x 1)` containing, for the
        sequence found, the negative of the sum of the greatest logit at each
        timeframe.
  """
  outputs = gen_ctc_ops.ctc_greedy_decoder(
      inputs, sequence_length, merge_repeated=merge_repeated)
  (decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs
  return ([sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)],
          log_probabilities)


@tf_export("nn.ctc_beam_search_decoder")
def ctc_beam_search_decoder(inputs, sequence_length, beam_width=100,
                            top_paths=1, merge_repeated=True):
  """Performs beam search decoding on the logits given in input.

  **Note** The `ctc_greedy_decoder` is a special case of the
  `ctc_beam_search_decoder` with `top_paths=1` and `beam_width=1` (but
  that decoder is faster for this special case).

  If `merge_repeated` is `True`, merge repeated classes in the output beams.
  This means that if consecutive entries in a beam are the same,
  only the first of these is emitted.  That is, when the top path
  is `A B B B B`, the return value is:

    * `A B` if `merge_repeated = True`.
    * `A B B B B` if `merge_repeated = False`.

  Args:
    inputs: 3-D `float` `Tensor`, size
      `[max_time x batch_size x num_classes]`.  The logits.
    sequence_length: 1-D `int32` vector containing sequence lengths,
      having size `[batch_size]`.
    beam_width: An int scalar >= 0 (beam search beam width).
    top_paths: An int scalar >= 0, <= beam_width (controls output size).
    merge_repeated: Boolean.  Default: True.

  Returns:
    A tuple `(decoded, log_probabilities)` where
    decoded: A list of length top_paths, where `decoded[j]`
      is a `SparseTensor` containing the decoded outputs:
      `decoded[j].indices`: Indices matrix `(total_decoded_outputs[j] x 2)`
        The rows store: [batch, time].
      `decoded[j].values`: Values vector, size `(total_decoded_outputs[j])`.
        The vector stores the decoded classes for beam j.
      `decoded[j].shape`: Shape vector, size `(2)`.
        The shape values are: `[batch_size, max_decoded_length[j]]`.
    log_probability: A `float` matrix `(batch_size x top_paths)` containing
        sequence log-probabilities.
  """

  decoded_ixs, decoded_vals, decoded_shapes, log_probabilities = (
      gen_ctc_ops.ctc_beam_search_decoder(
          inputs, sequence_length, beam_width=beam_width, top_paths=top_paths,
          merge_repeated=merge_repeated))

  return (
      [sparse_tensor.SparseTensor(ix, val, shape) for (ix, val, shape)
       in zip(decoded_ixs, decoded_vals, decoded_shapes)],
      log_probabilities)


ops.NotDifferentiable("CTCGreedyDecoder")


ops.NotDifferentiable("CTCBeamSearchDecoder")
