# 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
      self.assertAllEqual([3], src_len_v)
      self.assertAllEqual(
          [[4, 0, 1]],  # sos a b
          target_input_v)
      self.assertAllEqual(
          [[0, 1, 3]],  # a b eos
          target_output_v)
      self.assertAllEqual([3], tgt_len_v)

      with self.assertRaisesOpError("End of sequence"):
        sess.run(source)

  def testGetIteratorWithMaxLength(self):
    tf.set_random_seed(1)
    tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
        tf.constant(["a", "b", "c", "eos", "sos"]))
    src_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["f e a g", "c c a", "d", "c a"]))
    tgt_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["c c c c", "a b c c", "", "b c"]))
    hparams = tf.contrib.training.HParams(
        random_seed=3,
        num_buckets=5,
        eos="eos",
        sos="sos",
        context_feed="",
        server_mode=False)
    batch_size = 2
    # target length is effectively limited to 4 tokens (3 + sos/eos).
    max_len = 3

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

      self.assertAllEqual([3], src_len_v)
      self.assertAllEqual(
          [[4, 0, 1]],  # sos a b
          target_input_v)
      self.assertAllEqual(
          [[0, 1, 3]],  # a b eos
          target_output_v)
      self.assertAllEqual([3], tgt_len_v)

      with self.assertRaisesOpError("End of sequence"):
        sess.run(source)

  def testGetIteratorWithMaxLength(self):
    tf.set_random_seed(1)
    tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
        tf.constant(["a", "b", "c", "eos", "sos"]))
    src_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["f e a g", "c c a", "d", "c a"]))
    tgt_dataset = tf.data.Dataset.from_tensor_slices(
        tf.constant(["c c c c", "a b c c", "", "b c"]))
    hparams = tf.contrib.training.HParams(
        random_seed=3,
        num_buckets=5,
        eos="eos",
        sos="sos",
        context_feed="",
        server_mode=False)
    batch_size = 2
    # target length is effectively limited to 4 tokens (3 + sos/eos).
    max_len = 3

    iterator = iterator_utils.get_iterator(