# Path: third_party/nmt/utils/evaluation_utils.py
# def evaluate(ref_file, trans_file, metric, subword_option=None):
# def _clean(sentence, subword_option):
# def _bleu(ref_file, trans_file, subword_option=None):
# def _rouge(ref_file, summarization_file, subword_option=None):
# def _accuracy(label_file, pred_file):
# def _word_accuracy(label_file, pred_file):
# def _moses_bleu(multi_bleu_script, tgt_test, trans_file, subword_option=None):
# 
# Path: third_party/nmt/utils/misc_utils.py
# def check_tensorflow_version():
# def safe_exp(value):
# def print_time(s, start_time):
# def print_out(s, f=None, new_line=True):
# def print_hparams(hparams, skip_patterns=None, header=None):
# def load_hparams(model_dir):
# def maybe_parse_standard_hparams(hparams, hparams_path):
# def save_hparams(out_dir, hparams):
# def debug_tensor(s, msg=None, summarize=10):
# def add_summary(summary_writer, global_step, tag, value):
# def get_config_proto(log_device_placement=False, allow_soft_placement=True,
#                      num_intra_threads=0, num_inter_threads=0):
# def format_text(words):
# def format_bpe_text(symbols, delimiter=b"@@"):
# def format_spm_text(symbols):


import time
import numpy as np
import tensorflow as tf
from third_party.nmt.utils import evaluation_utils
from third_party.nmt.utils import misc_utils as utils
                  tgt_eos=tgt_eos,
                  subword_option=subword_option)
              trans_f.write((translation + "\n").decode("utf-8"))

          if step % hparams.steps_per_stats == 0:
            # print_time does not print decimal places for time.
            utils.print_out("  external evaluation, step %d, time %.2f" %
                            (step, time.time() - start_time_step))
          step += 1
          start_time_step = time.time()
        except tf.errors.OutOfRangeError:
          utils.print_time(
              "  done, num sentences %d, num translations per input %d" %
              (num_sentences, num_translations_per_input), start_time)
          break

  # Evaluation
  evaluation_scores = {}

  # We treat F1 scores differently because they don't need ground truth
  # sentences and they are expensive to compute due to environment calls.
  if "f1" in metrics:
    f1_score = np.mean(all_rewards)
    evaluation_scores["f1"] = f1_score
    utils.print_out("  f1 %s: %.1f" % (name, f1_score))

  for metric in metrics:
    if metric != "f1" and ref_file:
      if not tf.gfile.Exists(trans_file):
        raise IOException("%s: translation file not found" % trans_file)

# Path: third_party/nmt/utils/evaluation_utils.py
# def evaluate(ref_file, trans_file, metric, subword_option=None):
# def _clean(sentence, subword_option):
# def _bleu(ref_file, trans_file, subword_option=None):
# def _rouge(ref_file, summarization_file, subword_option=None):
# def _accuracy(label_file, pred_file):
# def _word_accuracy(label_file, pred_file):
# def _moses_bleu(multi_bleu_script, tgt_test, trans_file, subword_option=None):
# 
# Path: third_party/nmt/utils/misc_utils.py
# def check_tensorflow_version():
# def safe_exp(value):
# def print_time(s, start_time):
# def print_out(s, f=None, new_line=True):
# def print_hparams(hparams, skip_patterns=None, header=None):
# def load_hparams(model_dir):
# def maybe_parse_standard_hparams(hparams, hparams_path):
# def save_hparams(out_dir, hparams):
# def debug_tensor(s, msg=None, summarize=10):
# def add_summary(summary_writer, global_step, tag, value):
# def get_config_proto(log_device_placement=False, allow_soft_placement=True,
#                      num_intra_threads=0, num_inter_threads=0):
# def format_text(words):
# def format_bpe_text(symbols, delimiter=b"@@"):
# def format_spm_text(symbols):


# Path: px/nmt/utils/nmt_utils.py
import time
import numpy as np
import tensorflow as tf
from third_party.nmt.utils import evaluation_utils
from third_party.nmt.utils import misc_utils as utils

                  tgt_eos=tgt_eos,
                  subword_option=subword_option)
              trans_f.write((translation + "\n").decode("utf-8"))

          if step % hparams.steps_per_stats == 0:
            # print_time does not print decimal places for time.
            utils.print_out("  external evaluation, step %d, time %.2f" %
                            (step, time.time() - start_time_step))
          step += 1
          start_time_step = time.time()
        except tf.errors.OutOfRangeError:
          utils.print_time(
              "  done, num sentences %d, num translations per input %d" %
              (num_sentences, num_translations_per_input), start_time)
          break

  # Evaluation
  evaluation_scores = {}

  # We treat F1 scores differently because they don't need ground truth
  # sentences and they are expensive to compute due to environment calls.
  if "f1" in metrics:
    f1_score = np.mean(all_rewards)
    evaluation_scores["f1"] = f1_score
    utils.print_out("  f1 %s: %.1f" % (name, f1_score))

  for metric in metrics:
    if metric != "f1" and ref_file:
      if not tf.gfile.Exists(trans_file):
        raise IOException("%s: translation file not found" % trans_file)

      score = evaluation_utils.evaluate(