# 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

  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(
          ref_file, trans_file, metric, subword_option=subword_option)
      evaluation_scores[metric] = score
      utils.print_out("  %s %s: %.1f" % (metric, name, score))

  return evaluation_scores


def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
  """Given batch decoding outputs, select a sentence and turn to text."""
  if tgt_eos:
    tgt_eos = tgt_eos.encode("utf-8")

  # Select a sentence
  output = nmt_outputs[sent_id, :].tolist()

  # If there is an eos symbol in outputs, cut them at that point.
  if tgt_eos and tgt_eos in output:
    output = output[:output.index(tgt_eos)]

  if subword_option == "bpe":  # BPE
    translation = utils.format_bpe_text(output)
  elif subword_option == "spm":  # SPM
    translation = utils.format_spm_text(output)
  else:

# 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


  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(
          ref_file, trans_file, metric, subword_option=subword_option)
      evaluation_scores[metric] = score
      utils.print_out("  %s %s: %.1f" % (metric, name, score))

  return evaluation_scores


def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
  """Given batch decoding outputs, select a sentence and turn to text."""
  if tgt_eos:
    tgt_eos = tgt_eos.encode("utf-8")

  # Select a sentence
  output = nmt_outputs[sent_id, :].tolist()

  # If there is an eos symbol in outputs, cut them at that point.
  if tgt_eos and tgt_eos in output:
    output = output[:output.index(tgt_eos)]

  if subword_option == "bpe":  # BPE
    translation = utils.format_bpe_text(output)
  elif subword_option == "spm":  # SPM
    translation = utils.format_spm_text(output)
  else:

    translation = utils.format_text(output)