content: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """
  IMPORTANT: This code is taken directly from Tensorflow
  (https://github.com/tensorflow/tensorflow) and is copied temporarily
  until it is available in a packaged Tensorflow version on pypi.
  
  TODO(dennybritz): Delete this code when it becomes available in TF.
  
  A library of helpers for use with SamplingDecoders.
  """
  
  # pylint: skip-file
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import abc
  
  import six
  
  from tensorflow.contrib.distributions.python.ops import bernoulli
  from tensorflow.contrib.distributions.python.ops import categorical
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.layers import base as layers_base
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.util import nest
  
  from seq2seq.contrib.seq2seq import decoder
  
  __all__ = [
      "Helper",
      "TrainingHelper",
      "GreedyEmbeddingHelper",
      "CustomHelper",
      "ScheduledEmbeddingTrainingHelper",
      "ScheduledOutputTrainingHelper",
  ]
  
  _transpose_batch_time = decoder._transpose_batch_time  # pylint: disable=protected-access
  
  
  def _unstack_ta(inp):
    return tensor_array_ops.TensorArray(
        dtype=inp.dtype, size=array_ops.shape(inp)[0],
        element_shape=inp.get_shape()[1:]).unstack(inp)
  
  
  @six.add_metaclass(abc.ABCMeta)
  class Helper(object):
    """Helper interface.  Helper instances are used by SamplingDecoder."""
  
    @abc.abstractproperty
    def batch_size(self):
      """Returns a scalar int32 tensor."""
      raise NotImplementedError("batch_size has not been implemented")
  
    @abc.abstractmethod
    def initialize(self, name=None):
      """Returns `(initial_finished, initial_inputs)`."""
      pass
  
    @abc.abstractmethod
    def sample(self, time, outputs, state, name=None):
      """Returns `sample_ids`."""
      pass
  
    @abc.abstractmethod
    def next_inputs(self, time, outputs, state, sample_ids, name=None):
      """Returns `(finished, next_inputs, next_state)`."""
      pass
  
  
  class CustomHelper(Helper):
    """Base abstract class that allows the user to customize sampling."""
  
    def __init__(self, initialize_fn, sample_fn, next_inputs_fn):
      """Initializer.
  
      Args:
        initialize_fn: callable that returns `(finished, next_inputs)`
          for the first iteration.
        sample_fn: callable that takes `(time, outputs, state)`
          and emits tensor `sample_ids`.
        next_inputs_fn: callable that takes `(time, outputs, state, sample_ids)`
          and emits `(finished, next_inputs, next_state)`.
      """
      self._initialize_fn = initialize_fn
      self._sample_fn = sample_fn
      self._next_inputs_fn = next_inputs_fn
      self._batch_size = None
  
    @property
    def batch_size(self):
      if self._batch_size is None:
        raise ValueError("batch_size accessed before initialize was called")
      return self._batch_size
  
    def initialize(self, name=None):
      with ops.name_scope(name, "%sInitialize" % type(self).__name__):
        (finished, next_inputs) = self._initialize_fn()
        if self._batch_size is None:
          self._batch_size = array_ops.size(finished)
      return (finished, next_inputs)
  
    def sample(self, time, outputs, state, name=None):
      with ops.name_scope(
          name, "%sSample" % type(self).__name__, (time, outputs, state)):
        return self._sample_fn(time=time, outputs=outputs, state=state)
  
    def next_inputs(self, time, outputs, state, sample_ids, name=None):
      with ops.name_scope(
          name, "%sNextInputs" % type(self).__name__, (time, outputs, state)):
        return self._next_inputs_fn(
            time=time, outputs=outputs, state=state, sample_ids=sample_ids)
  
  
  class TrainingHelper(Helper):
    """A helper for use during training.  Only reads inputs.
  
    Returned sample_ids are the argmax of the RNN output logits.
    """
  
    def __init__(self, inputs, sequence_length, time_major=False, name=None):
      """Initializer.
  
      Args:
        inputs: A (structure of) input tensors.
        sequence_length: An int32 vector tensor.
        time_major: Python bool.  Whether the tensors in `inputs` are time major.
          If `False` (default), they are assumed to be batch major.
        name: Name scope for any created operations.
  
      Raises:
        ValueError: if `sequence_length` is not a 1D tensor.
      """
      with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]):
        inputs = ops.convert_to_tensor(inputs, name="inputs")
        if not time_major:
          inputs = nest.map_structure(_transpose_batch_time, inputs)
  
        self._input_tas = nest.map_structure(_unstack_ta, inputs)
        self._sequence_length = ops.convert_to_tensor(
            sequence_length, name="sequence_length")
        if self._sequence_length.get_shape().ndims != 1:
          raise ValueError(
              "Expected sequence_length to be a vector, but received shape: %s" %
              self._sequence_length.get_shape())
  
        self._zero_inputs = nest.map_structure(
            lambda inp: array_ops.zeros_like(inp[0, :]), inputs)
  
        self._batch_size = array_ops.size(sequence_length)
  
    @property
    def batch_size(self):
      return self._batch_size
  
    def initialize(self, name=None):
      with ops.name_scope(name, "TrainingHelperInitialize"):
        finished = math_ops.equal(0, self._sequence_length)
        all_finished = math_ops.reduce_all(finished)
        next_inputs = control_flow_ops.cond(
            all_finished, lambda: self._zero_inputs,
            lambda: nest.map_structure(lambda inp: inp.read(0), self._input_tas))
        return (finished, next_inputs)
  
    def sample(self, time, outputs, name=None, **unused_kwargs):
      with ops.name_scope(name, "TrainingHelperSample", [time, outputs]):
        sample_ids = math_ops.cast(
            math_ops.argmax(outputs, axis=-1), dtypes.int32)
        return sample_ids
  
    def next_inputs(self, time, outputs, state, name=None, **unused_kwargs):
      """next_inputs_fn for TrainingHelper."""
      with ops.name_scope(name, "TrainingHelperNextInputs",
                          [time, outputs, state]):
        next_time = time + 1
        finished = (next_time >= self._sequence_length)
        all_finished = math_ops.reduce_all(finished)
        def read_from_ta(inp):
          return inp.read(next_time)
        next_inputs = control_flow_ops.cond(
            all_finished, lambda: self._zero_inputs,
            lambda: nest.map_structure(read_from_ta, self._input_tas))
        return (finished, next_inputs, state)
  
  
  class ScheduledEmbeddingTrainingHelper(TrainingHelper):
    """A training helper that adds scheduled sampling.
  
    Returns -1s for sample_ids where no sampling took place; valid sample id
    values elsewhere.
    """
  
    def __init__(self, inputs, sequence_length, embedding, sampling_probability,
                 time_major=False, seed=None, scheduling_seed=None, name=None):
      """Initializer.
  
      Args:
        inputs: A (structure of) input tensors.
        sequence_length: An int32 vector tensor.
        embedding: A callable that takes a vector tensor of `ids` (argmax ids),
          or the `params` argument for `embedding_lookup`.
        sampling_probability: A 0D `float32` tensor: the probability of sampling
          categorically from the output ids instead of reading directly from the
          inputs.
        time_major: Python bool.  Whether the tensors in `inputs` are time major.
          If `False` (default), they are assumed to be batch major.
        seed: The sampling seed.
        scheduling_seed: The schedule decision rule sampling seed.
        name: Name scope for any created operations.
  
      Raises:
        ValueError: if `sampling_probability` is not a scalar or vector.
      """
      with ops.name_scope(name, "ScheduledEmbeddingSamplingWrapper",
                          [embedding, sampling_probability]):
        if callable(embedding):
          self._embedding_fn = embedding
        else:
          self._embedding_fn = (
              lambda ids: embedding_ops.embedding_lookup(embedding, ids))
        self._sampling_probability = ops.convert_to_tensor(
            sampling_probability, name="sampling_probability")
        if self._sampling_probability.get_shape().ndims not in (0, 1):
          raise ValueError(
              "sampling_probability must be either a scalar or a vector. "
              "saw shape: %s" % (self._sampling_probability.get_shape()))
        self._seed = seed
        self._scheduling_seed = scheduling_seed
        super(ScheduledEmbeddingTrainingHelper, self).__init__(
            inputs=inputs,
            sequence_length=sequence_length,
            time_major=time_major,
            name=name)
  
    def initialize(self, name=None):
      return super(ScheduledEmbeddingTrainingHelper, self).initialize(name=name)
  
    def sample(self, time, outputs, state, name=None):
      with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
                          [time, outputs, state]):
        # Return -1s where we did not sample, and sample_ids elsewhere
        select_sample_noise = random_ops.random_uniform(
            [self.batch_size], seed=self._scheduling_seed)
        select_sample = (self._sampling_probability > select_sample_noise)
        sample_id_sampler = categorical.Categorical(logits=outputs)
        return array_ops.where(
            select_sample,
            sample_id_sampler.sample(seed=self._seed),
            array_ops.tile([-1], [self.batch_size]))
  
    def next_inputs(self, time, outputs, state, sample_ids, name=None):
      with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
                          [time, outputs, state, sample_ids]):
        (finished, base_next_inputs, state) = (
            super(ScheduledEmbeddingTrainingHelper, self).next_inputs(
                time=time,
                outputs=outputs,
                state=state,
                sample_ids=sample_ids,
                name=name))
  
        def maybe_sample():
          """Perform scheduled sampling."""
          where_sampling = math_ops.cast(
              array_ops.where(sample_ids > -1), dtypes.int32)
          where_not_sampling = math_ops.cast(
              array_ops.where(sample_ids <= -1), dtypes.int32)
          where_sampling_flat = array_ops.reshape(where_sampling, [-1])
          where_not_sampling_flat = array_ops.reshape(where_not_sampling, [-1])
          sample_ids_sampling = array_ops.gather(sample_ids, where_sampling_flat)
          inputs_not_sampling = array_ops.gather(
              base_next_inputs, where_not_sampling_flat)
          sampled_next_inputs = self._embedding_fn(sample_ids_sampling)
          base_shape = array_ops.shape(base_next_inputs)
          return (array_ops.scatter_nd(indices=where_sampling,
                                       updates=sampled_next_inputs,
                                       shape=base_shape)
                  + array_ops.scatter_nd(indices=where_not_sampling,
                                         updates=inputs_not_sampling,
                                         shape=base_shape))
  
        all_finished = math_ops.reduce_all(finished)
        next_inputs = control_flow_ops.cond(
            all_finished, lambda: base_next_inputs, maybe_sample)
        return (finished, next_inputs, state)
  
  
  class ScheduledOutputTrainingHelper(TrainingHelper):
    """A training helper that adds scheduled sampling directly to outputs.
  
    Returns False for sample_ids where no sampling took place; True elsewhere.
    """
  
    def __init__(self, inputs, sequence_length, sampling_probability,
                 time_major=False, seed=None, next_input_layer=None,
                 auxiliary_inputs=None, name=None):
      """Initializer.
  
      Args:
        inputs: A (structure) of input tensors.
        sequence_length: An int32 vector tensor.
        sampling_probability: A 0D `float32` tensor: the probability of sampling
          from the outputs instead of reading directly from the inputs.
        time_major: Python bool.  Whether the tensors in `inputs` are time major.
          If `False` (default), they are assumed to be batch major.
        seed: The sampling seed.
        next_input_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
          `tf.layers.Dense`.  Optional layer to apply to the RNN output to create
          the next input.
        auxiliary_inputs: An optional (structure of) auxiliary input tensors with
          a shape that matches `inputs` in all but (potentially) the final
          dimension. These tensors will be concatenated to the sampled output or
          the `inputs` when not sampling for use as the next input.
        name: Name scope for any created operations.
  
      Raises:
        ValueError: if `sampling_probability` is not a scalar or vector.
      """
      with ops.name_scope(name, "ScheduledOutputTrainingHelper",
                          [inputs, auxiliary_inputs, sampling_probability]):
        self._sampling_probability = ops.convert_to_tensor(
            sampling_probability, name="sampling_probability")
        if self._sampling_probability.get_shape().ndims not in (0, 1):
          raise ValueError(
              "sampling_probability must be either a scalar or a vector. "
              "saw shape: %s" % (self._sampling_probability.get_shape()))
  
        if auxiliary_inputs is None:
          maybe_concatenated_inputs = inputs
        else:
          inputs = ops.convert_to_tensor(inputs, name="inputs")
          auxiliary_inputs = ops.convert_to_tensor(
              auxiliary_inputs, name="auxiliary_inputs")
          maybe_concatenated_inputs = nest.map_structure(
              lambda x, y: array_ops.concat((x, y), -1),
              inputs, auxiliary_inputs)
          if not time_major:
            auxiliary_inputs = nest.map_structure(
                _transpose_batch_time, auxiliary_inputs)
  
        self._auxiliary_input_tas = (
            nest.map_structure(_unstack_ta, auxiliary_inputs)
            if auxiliary_inputs is not None else None)
  
        self._seed = seed
  
        if (next_input_layer is not None and not isinstance(next_input_layer,
                                                            layers_base._Layer)):  # pylint: disable=protected-access
          raise TypeError("next_input_layer must be a Layer, received: %s" %
                          type(next_input_layer))
        self._next_input_layer = next_input_layer
  
        super(ScheduledOutputTrainingHelper, self).__init__(
            inputs=maybe_concatenated_inputs,
            sequence_length=sequence_length,
            time_major=time_major,
            name=name)
  
    def initialize(self, name=None):
      return super(ScheduledOutputTrainingHelper, self).initialize(name=name)
  
    def sample(self, time, outputs, state, name=None):
      with ops.name_scope(name, "ScheduledOutputTrainingHelperSample",
                          [time, outputs, state]):
        sampler = bernoulli.Bernoulli(probs=self._sampling_probability)
        return math_ops.cast(
            sampler.sample(sample_shape=self.batch_size, seed=self._seed),
            dtypes.bool)
  
    def next_inputs(self, time, outputs, state, sample_ids, name=None):
      with ops.name_scope(name, "ScheduledOutputTrainingHelperNextInputs",
                          [time, outputs, state, sample_ids]):
        (finished, base_next_inputs, state) = (
            super(ScheduledOutputTrainingHelper, self).next_inputs(
                time=time,
                outputs=outputs,
                state=state,
                sample_ids=sample_ids,
                name=name))
  
        def maybe_sample():
          """Perform scheduled sampling."""
  
          def maybe_concatenate_auxiliary_inputs(outputs_, indices=None):
            """Concatenate outputs with auxiliary inputs, if they exist."""
            if self._auxiliary_input_tas is None:
              return outputs_
  
            next_time = time + 1
            auxiliary_inputs = nest.map_structure(
                lambda ta: ta.read(next_time), self._auxiliary_input_tas)
            if indices is not None:
              auxiliary_inputs = array_ops.gather_nd(auxiliary_inputs, indices)
            return nest.map_structure(
                lambda x, y: array_ops.concat((x, y), -1),
                outputs_, auxiliary_inputs)
  
          if self._next_input_layer is None:
            return array_ops.where(
                sample_ids, maybe_concatenate_auxiliary_inputs(outputs),
                base_next_inputs)
  
          where_sampling = math_ops.cast(
              array_ops.where(sample_ids), dtypes.int32)
          where_not_sampling = math_ops.cast(
              array_ops.where(math_ops.logical_not(sample_ids)), dtypes.int32)
          outputs_sampling = array_ops.gather_nd(outputs, where_sampling)
          inputs_not_sampling = array_ops.gather_nd(base_next_inputs,
                                                    where_not_sampling)
          sampled_next_inputs = maybe_concatenate_auxiliary_inputs(
              self._next_input_layer(outputs_sampling), where_sampling)
  
          base_shape = array_ops.shape(base_next_inputs)
          return (array_ops.scatter_nd(indices=where_sampling,
                                       updates=sampled_next_inputs,
                                       shape=base_shape)
                  + array_ops.scatter_nd(indices=where_not_sampling,
                                         updates=inputs_not_sampling,
                                         shape=base_shape))
  
        all_finished = math_ops.reduce_all(finished)
        next_inputs = control_flow_ops.cond(
            all_finished, lambda: base_next_inputs, maybe_sample)
        return (finished, next_inputs, state)
  
  
  class GreedyEmbeddingHelper(Helper):
    """A helper for use during inference.
  
    Uses the argmax of the output (treated as logits) and passes the
    result through an embedding layer to get the next input.
    """
  
    def __init__(self, embedding, start_tokens, end_token):
      """Initializer.
  
      Args:
        embedding: A callable that takes a vector tensor of `ids` (argmax ids),
          or the `params` argument for `embedding_lookup`.
        start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
        end_token: `int32` scalar, the token that marks end of decoding.
  
      Raises:
        ValueError: if `sequence_length` is not a 1D tensor.
      """
      if callable(embedding):
        self._embedding_fn = embedding
      else:
        self._embedding_fn = (
            lambda ids: embedding_ops.embedding_lookup(embedding, ids))
  
      self._start_tokens = ops.convert_to_tensor(
          start_tokens, dtype=dtypes.int32, name="start_tokens")
      self._end_token = ops.convert_to_tensor(
          end_token, dtype=dtypes.int32, name="end_token")
      if self._start_tokens.get_shape().ndims != 1:
        raise ValueError("start_tokens must be a vector")
      self._batch_size = array_ops.size(start_tokens)
      if self._end_token.get_shape().ndims != 0:
        raise ValueError("end_token must be a scalar")
      self._start_inputs = self._embedding_fn(self._start_tokens)
  
    @property
    def batch_size(self):
      return self._batch_size
  
    def initialize(self, name=None):
      finished = array_ops.tile([False], [self._batch_size])
      return (finished, self._start_inputs)
  
    def sample(self, time, outputs, state, name=None):
      """sample for GreedyEmbeddingHelper."""
      del time, state  # unused by sample_fn
      # Outputs are logits, use argmax to get the most probable id
      if not isinstance(outputs, ops.Tensor):
        raise TypeError("Expected outputs to be a single Tensor, got: %s" %
                        type(outputs))
      sample_ids = math_ops.cast(
          math_ops.argmax(outputs, axis=-1), dtypes.int32)
      return sample_ids
  
    def next_inputs(self, time, outputs, state, sample_ids, name=None):
      """next_inputs_fn for GreedyEmbeddingHelper."""
      del time, outputs  # unused by next_inputs_fn
      finished = math_ops.equal(sample_ids, self._end_token)
      all_finished = math_ops.reduce_all(finished)
      next_inputs = control_flow_ops.cond(
          all_finished,
          # If we're finished, the next_inputs value doesn't matter
          lambda: self._start_inputs,
          lambda: self._embedding_fn(sample_ids))
      return (finished, next_inputs, state)
  