# 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/iterator_utils.py
# class BatchedInput(
#     collections.namedtuple(
#         "BatchedInput", ("initializer", "source_string", "source",
#                          "target_input", "target_output", "weights", "context",
#                          "annotation", "trie_exclude", "source_sequence_length",
#                          "target_sequence_length", "context_sequence_length"))):
# def get_infer_iterator(hparams,
#                        src_dataset,
#                        src_vocab_table,
#                        batch_size,
#                        eos,
#                        ctx_dataset=None,
#                        annot_dataset=None,
#                        trie_exclude_dataset=None,
#                        src_max_len=None,
#                        tgt_vocab_table=None):
#   def batching_func(x):
# def get_iterator(hparams,
#                  src_dataset,
#                  tgt_dataset,
#                  src_vocab_table,
#                  tgt_vocab_table,
#                  batch_size,
#                  sos,
#                  eos,
#                  random_seed,
#                  num_buckets,
#                  wgt_dataset=None,
#                  ctx_dataset=None,
#                  annot_dataset=None,
#                  src_max_len=None,
#                  tgt_max_len=None,
#                  num_parallel_calls=4,
#                  output_buffer_size=None,
#                  skip_count=None,
#                  num_shards=1,
#                  shard_index=0,
#                  reshuffle_each_iteration=True):
#   def batching_func(x):
#     def key_func(unused_1, unused_2, unused_3, unused_4, unused_5, unused_6,
#                  unused_7, unused_8, src_len, tgt_len, unused_9):
#     def reduce_func(unused_key, windowed_data):
# 
# Path: px/nmt/utils/vocab_utils.py
# UNK = "<unk>"
# SOS = "<s>"
# EOS = "</s>"
# UNK_ID = 0
# def load_vocab(vocab_file):
# def check_vocab(vocab_file,
#                 out_dir,
#                 check_special_token=True,
#                 sos=None,
#                 eos=None,
#                 unk=None,
#                 context_delimiter=None):
# def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
# def load_embed_txt(embed_file):


import collections
import os
import six
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from third_party.nmt.utils import misc_utils as utils
from px.nmt.utils import iterator_utils
from px.nmt.utils import vocab_utils
            "embedding_encoder", src_vocab_file, src_embed_file, src_vocab_size,
            src_embed_size, dtype)

      with tf.variable_scope(
          "decoder", partitioner=partitioner, reuse=tf.AUTO_REUSE):
        embedding_decoder = _create_or_load_embed(
            "embedding_decoder", tgt_vocab_file, tgt_embed_file, tgt_vocab_size,
            tgt_embed_size, dtype)

  return embedding_encoder, embedding_decoder


def _single_cell(unit_type,
                 num_units,
                 forget_bias,
                 dropout,
                 mode,
                 residual_connection=False,
                 device_str=None,
                 residual_fn=None):
  """Create an instance of a single RNN cell."""
  # dropout (= 1 - keep_prob) is set to 0 during eval and infer
  dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0

  # Cell Type
  if unit_type == "lstm":
    utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
    single_cell = tf.contrib.rnn.BasicLSTMCell(
        num_units, forget_bias=forget_bias)
  elif unit_type == "gru":

# 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/iterator_utils.py
# class BatchedInput(
#     collections.namedtuple(
#         "BatchedInput", ("initializer", "source_string", "source",
#                          "target_input", "target_output", "weights", "context",
#                          "annotation", "trie_exclude", "source_sequence_length",
#                          "target_sequence_length", "context_sequence_length"))):
# def get_infer_iterator(hparams,
#                        src_dataset,
#                        src_vocab_table,
#                        batch_size,
#                        eos,
#                        ctx_dataset=None,
#                        annot_dataset=None,
#                        trie_exclude_dataset=None,
#                        src_max_len=None,
#                        tgt_vocab_table=None):
#   def batching_func(x):
# def get_iterator(hparams,
#                  src_dataset,
#                  tgt_dataset,
#                  src_vocab_table,
#                  tgt_vocab_table,
#                  batch_size,
#                  sos,
#                  eos,
#                  random_seed,
#                  num_buckets,
#                  wgt_dataset=None,
#                  ctx_dataset=None,
#                  annot_dataset=None,
#                  src_max_len=None,
#                  tgt_max_len=None,
#                  num_parallel_calls=4,
#                  output_buffer_size=None,
#                  skip_count=None,
#                  num_shards=1,
#                  shard_index=0,
#                  reshuffle_each_iteration=True):
#   def batching_func(x):
#     def key_func(unused_1, unused_2, unused_3, unused_4, unused_5, unused_6,
#                  unused_7, unused_8, src_len, tgt_len, unused_9):
#     def reduce_func(unused_key, windowed_data):
# 
# Path: px/nmt/utils/vocab_utils.py
# UNK = "<unk>"
# SOS = "<s>"
# EOS = "</s>"
# UNK_ID = 0
# def load_vocab(vocab_file):
# def check_vocab(vocab_file,
#                 out_dir,
#                 check_special_token=True,
#                 sos=None,
#                 eos=None,
#                 unk=None,
#                 context_delimiter=None):
# def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
# def load_embed_txt(embed_file):


# Path: px/nmt/model_helper.py
import collections
import os
import six
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from third_party.nmt.utils import misc_utils as utils
from px.nmt.utils import iterator_utils
from px.nmt.utils import vocab_utils

            "embedding_encoder", src_vocab_file, src_embed_file, src_vocab_size,
            src_embed_size, dtype)

      with tf.variable_scope(
          "decoder", partitioner=partitioner, reuse=tf.AUTO_REUSE):
        embedding_decoder = _create_or_load_embed(
            "embedding_decoder", tgt_vocab_file, tgt_embed_file, tgt_vocab_size,
            tgt_embed_size, dtype)

  return embedding_encoder, embedding_decoder


def _single_cell(unit_type,
                 num_units,
                 forget_bias,
                 dropout,
                 mode,
                 residual_connection=False,
                 device_str=None,
                 residual_fn=None):
  """Create an instance of a single RNN cell."""
  # dropout (= 1 - keep_prob) is set to 0 during eval and infer
  dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0

  # Cell Type
  if unit_type == "lstm":
    utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
    single_cell = tf.contrib.rnn.BasicLSTMCell(
        num_units, forget_bias=forget_bias)
  elif unit_type == "gru":

    utils.print_out("  GRU", new_line=False)