content: # -*- coding: utf-8 -*-
  # Copyright 2017 Google Inc.
  #
  # 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.
  """Collection of MetricSpecs for training and evaluation
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  from pydoc import locate
  import abc
  
  import numpy as np
  import six
  
  import tensorflow as tf
  from tensorflow.contrib import metrics
  from tensorflow.contrib.learn import MetricSpec
  
  from seq2seq.data import postproc
  from seq2seq.configurable import Configurable
  from seq2seq.metrics import rouge
  from seq2seq.metrics import bleu
  
  
  def accumulate_strings(values, name="strings"):
    """Accumulates strings into a vector.
  
    Args:
      values: A 1-d string tensor that contains values to add to the accumulator.
  
    Returns:
      A tuple (value_tensor, update_op).
    """
    tf.assert_type(values, tf.string)
    strings = tf.Variable(
        name=name,
        initial_value=[],
        dtype=tf.string,
        trainable=False,
        collections=[],
        validate_shape=True)
    value_tensor = tf.identity(strings)
    update_op = tf.assign(
        ref=strings, value=tf.concat([strings, values], 0), validate_shape=False)
    return value_tensor, update_op
  
  
  @six.add_metaclass(abc.ABCMeta)
  class TextMetricSpec(Configurable, MetricSpec):
    """Abstract class for text-based metrics calculated based on
    hypotheses and references. Subclasses must implement `metric_fn`.
  
    Args:
      name: A name for the metric
      separator: A separator used to join predicted tokens. Default to space.
      eos_token: A string token used to find the end of a sequence. Hypotheses
        and references will be slcied until this token is found.
    """
  
    def __init__(self, params, name):
      # We don't call the super constructor on purpose
      #pylint: disable=W0231
      """Initializer"""
      Configurable.__init__(self, params, tf.contrib.learn.ModeKeys.EVAL)
      self._name = name
      self._eos_token = self.params["eos_token"]
      self._sos_token = self.params["sos_token"]
      self._separator = self.params["separator"]
      self._postproc_fn = None
      if self.params["postproc_fn"]:
        self._postproc_fn = locate(self.params["postproc_fn"])
        if self._postproc_fn is None:
          raise ValueError("postproc_fn not found: {}".format(
              self.params["postproc_fn"]))
  
    @property
    def name(self):
      """Name of the metric"""
      return self._name
  
    @staticmethod
    def default_params():
      return {
          "sos_token": "SEQUENCE_START",
          "eos_token": "SEQUENCE_END",
          "separator": " ",
          "postproc_fn": "",
      }
  
    def create_metric_ops(self, _inputs, labels, predictions):
      """Creates (value, update_op) tensors
      """
      with tf.variable_scope(self._name):
  
        # Join tokens into single strings
        predictions_flat = tf.reduce_join(
            predictions["predicted_tokens"], 1, separator=self._separator)
        labels_flat = tf.reduce_join(
            labels["target_tokens"], 1, separator=self._separator)
  
        sources_value, sources_update = accumulate_strings(
            values=predictions_flat, name="sources")
        targets_value, targets_update = accumulate_strings(
            values=labels_flat, name="targets")
  
        metric_value = tf.py_func(
            func=self._py_func,
            inp=[sources_value, targets_value],
            Tout=tf.float32,
            name="value")
  
      with tf.control_dependencies([sources_update, targets_update]):
        update_op = tf.identity(metric_value, name="update_op")
  
      return metric_value, update_op
  
    def _py_func(self, hypotheses, references):
      """Wrapper function that converts tensors to unicode and slices
        them until the EOS token is found.
      """
      # Deal with byte chars
      if hypotheses.dtype.kind == np.dtype("U"):
        hypotheses = np.char.encode(hypotheses, "utf-8")
      if references.dtype.kind == np.dtype("U"):
        references = np.char.encode(references, "utf-8")
  
      # Convert back to unicode object
      hypotheses = [_.decode("utf-8") for _ in hypotheses]
      references = [_.decode("utf-8") for _ in references]
  
      # Slice all hypotheses and references up to SOS -> EOS
      sliced_hypotheses = [postproc.slice_text(
          _, self._eos_token, self._sos_token) for _ in hypotheses]
      sliced_references = [postproc.slice_text(
          _, self._eos_token, self._sos_token) for _ in references]
  
      # Apply postprocessing function
      if self._postproc_fn:
        sliced_hypotheses = [self._postproc_fn(_) for _ in sliced_hypotheses]
        sliced_references = [self._postproc_fn(_) for _ in sliced_references]
  
      return self.metric_fn(sliced_hypotheses, sliced_references) #pylint: disable=E1102
  
    def metric_fn(self, hypotheses, references):
      """Calculates the value of the metric.
  
      Args:
        hypotheses: A python list of strings, each corresponding to a
          single hypothesis/example.
        references: A python list of strings, each corresponds to a single
          reference. Must have the same number of elements of `hypotheses`.
  
      Returns:
        A float value.
      """
      raise NotImplementedError()
  
  
  class BleuMetricSpec(TextMetricSpec):
    """Calculates BLEU score using the Moses multi-bleu.perl script.
    """
  
    def __init__(self, params):
      super(BleuMetricSpec, self).__init__(params, "bleu")
  
    def metric_fn(self, hypotheses, references):
      return bleu.moses_multi_bleu(hypotheses, references, lowercase=False)
  
  
  class RougeMetricSpec(TextMetricSpec):
    """Calculates BLEU score using the Moses multi-bleu.perl script.
    """
  
    def __init__(self, params, **kwargs):
      if not params["rouge_type"]:
        raise ValueError("You must provide a rouge_type for ROUGE")
      super(RougeMetricSpec, self).__init__(
          params, params["rouge_type"], **kwargs)
      self._rouge_type = self.params["rouge_type"]
  
    @staticmethod
    def default_params():
      params = TextMetricSpec.default_params()
      params.update({
          "rouge_type": "",
      })
      return params
  
    def metric_fn(self, hypotheses, references):
      if not hypotheses or not references:
        return np.float32(0.0)
      return np.float32(rouge.rouge(hypotheses, references)[self._rouge_type])
  
  
  class LogPerplexityMetricSpec(MetricSpec, Configurable):
    """A MetricSpec to calculate straming log perplexity"""
  
    def __init__(self, params):
      """Initializer"""
      # We don't call the super constructor on purpose
      #pylint: disable=W0231
      Configurable.__init__(self, params, tf.contrib.learn.ModeKeys.EVAL)
  
    @staticmethod
    def default_params():
      return {}
  
    @property
    def name(self):
      """Name of the metric"""
      return "log_perplexity"
  
    def create_metric_ops(self, _inputs, labels, predictions):
      """Creates the metric op"""
      loss_mask = tf.sequence_mask(
          lengths=tf.to_int32(labels["target_len"] - 1),
          maxlen=tf.to_int32(tf.shape(predictions["losses"])[1]))
      return metrics.streaming_mean(predictions["losses"], loss_mask)
  