# 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):


import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from px.nmt.utils import iterator_utils

  src_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant(["a a b b c", "a b b"]))

  ctx_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant(["c b c b a", "b c b a"]))

  trie_excludes = trie_excludes or []
  trie_excludes = " {} ".format(hparams.eos).join(trie_excludes)
  tex_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant([trie_excludes, trie_excludes]))

  if mode != tf.contrib.learn.ModeKeys.INFER:
    tgt_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["a b c b c", "a b c b"]))
    return (iterator_utils.get_iterator(
        hparams=hparams,
        src_dataset=src_dataset,
        tgt_dataset=tgt_dataset,
        ctx_dataset=ctx_dataset,
        annot_dataset=None,
        src_vocab_table=src_vocab_table,
        tgt_vocab_table=tgt_vocab_table,
        batch_size=hparams.batch_size,
        sos=hparams.sos,
        eos=hparams.eos,
        random_seed=hparams.random_seed,
        num_buckets=hparams.num_buckets), src_vocab_table, tgt_vocab_table,
            reverse_tgt_vocab_table)
  else:

# 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/common_test_utils.py
import tensorflow as tf
from tensorflow.python.ops import lookup_ops
from px.nmt.utils import iterator_utils


  src_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant(["a a b b c", "a b b"]))

  ctx_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant(["c b c b a", "b c b a"]))

  trie_excludes = trie_excludes or []
  trie_excludes = " {} ".format(hparams.eos).join(trie_excludes)
  tex_dataset = tf.data.Dataset.from_tensor_slices(
      tf.constant([trie_excludes, trie_excludes]))

  if mode != tf.contrib.learn.ModeKeys.INFER:
    tgt_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["a b c b c", "a b c b"]))
    return (iterator_utils.get_iterator(
        hparams=hparams,
        src_dataset=src_dataset,
        tgt_dataset=tgt_dataset,
        ctx_dataset=ctx_dataset,
        annot_dataset=None,
        src_vocab_table=src_vocab_table,
        tgt_vocab_table=tgt_vocab_table,
        batch_size=hparams.batch_size,
        sos=hparams.sos,
        eos=hparams.eos,
        random_seed=hparams.random_seed,
        num_buckets=hparams.num_buckets), src_vocab_table, tgt_vocab_table,
            reverse_tgt_vocab_table)
  else:

    return (iterator_utils.get_infer_iterator(