# 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
      graph=graph,
      model=model,
      iterator=iterator,
      src_placeholder=src_placeholder,
      tgt_placeholder=tgt_placeholder,
      annot_placeholder=annot_placeholder,
      skip_count_placeholder=skip_count_placeholder)


class TrainModelForServer(
    collections.namedtuple(
        "TrainModelForServer",
        ("graph", "model", "iterator", "src_placeholder", "tgt_placeholder",
         "wgt_placeholder", "batch_size_placeholder", "skip_count_placeholder"))
):
  pass


def create_train_model_for_server(model_creator,
                                  hparams,
                                  scope=None,
                                  num_workers=1,
                                  jobid=0,
                                  graph=None,
                                  extra_args=None,
                                  trie=None):
  """Create graph, model, and iterator when running the NMT in server mode.

  This is different from the standard training model, because the input arrives
  via RPC and thus has to be fed using placeholders."""

# 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

      graph=graph,
      model=model,
      iterator=iterator,
      src_placeholder=src_placeholder,
      tgt_placeholder=tgt_placeholder,
      annot_placeholder=annot_placeholder,
      skip_count_placeholder=skip_count_placeholder)


class TrainModelForServer(
    collections.namedtuple(
        "TrainModelForServer",
        ("graph", "model", "iterator", "src_placeholder", "tgt_placeholder",
         "wgt_placeholder", "batch_size_placeholder", "skip_count_placeholder"))
):
  pass


def create_train_model_for_server(model_creator,
                                  hparams,
                                  scope=None,
                                  num_workers=1,
                                  jobid=0,
                                  graph=None,
                                  extra_args=None,
                                  trie=None):
  """Create graph, model, and iterator when running the NMT in server mode.

  This is different from the standard training model, because the input arrives
  via RPC and thus has to be fed using placeholders."""

  assert hparams.num_buckets == 1, "No bucketing when in server mode."