# 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 codecs
import os
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from third_party.nmt.utils import misc_utils as utils
from __future__ import print_function




UNK = "<unk>"
SOS = "<s>"
EOS = "</s>"
UNK_ID = 0


def load_vocab(vocab_file):
  vocab = []
  with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
    vocab_size = 0
    for word in f:
      vocab_size += 1
      vocab.append(word.strip())
  return vocab, vocab_size


def check_vocab(vocab_file,
                out_dir,
                check_special_token=True,
                sos=None,
                eos=None,
                unk=None,
                context_delimiter=None):
  """Check if vocab_file doesn't exist, create from corpus_file."""
  if tf.gfile.Exists(vocab_file):

# 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/vocab_utils.py
import codecs
import os
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from third_party.nmt.utils import misc_utils as utils

from __future__ import print_function




UNK = "<unk>"
SOS = "<s>"
EOS = "</s>"
UNK_ID = 0


def load_vocab(vocab_file):
  vocab = []
  with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
    vocab_size = 0
    for word in f:
      vocab_size += 1
      vocab.append(word.strip())
  return vocab, vocab_size


def check_vocab(vocab_file,
                out_dir,
                check_special_token=True,
                sos=None,
                eos=None,
                unk=None,
                context_delimiter=None):
  """Check if vocab_file doesn't exist, create from corpus_file."""
  if tf.gfile.Exists(vocab_file):

    utils.print_out("# Vocab file %s exists" % vocab_file)