# Path: third_party/bi_att_flow/my/tensorflow/general.py
# def grouper(iterable, n, fillvalue=None, shorten=False, num_groups=None):
#   args = [iter(iterable)] * n
#   out = zip_longest(*args, fillvalue=fillvalue)
#   out = list(out)
#   if num_groups is not None:
#     default = (fillvalue,) * n
#     assert isinstance(num_groups, int)
#     out = list(
#         each
#         for each, _ in zip_longest(out, range(num_groups), fillvalue=default))
#   if shorten:
#     assert fillvalue is None
#     out = (tuple(e for e in each if e is not None) for each in out)
#   return out
# 
# Path: third_party/bi_att_flow/my/utils.py
# def index(l, i):
#   return index(l[i[0]], i[1:]) if len(i) > 1 else l[i[0]]


import json
import os
import random
import itertools
import math
import numpy as np
import tensorflow as tf
from collections import defaultdict
from third_party.bi_att_flow.my.tensorflow.general import grouper
from third_party.bi_att_flow.my.utils import index
      out = defaultdict(list)
      for key, val in self.data.items():
        out[key].extend(val[idx] for idx in idxs)
      return out
    elif isinstance(self.data, Data):
      return self.data.get_by_idxs(idxs)
    raise Exception()

  def get_batches(self,
                  batch_size,
                  num_batches=None,
                  shuffle=False,
                  cluster=False):
    """
        :param batch_size:
        :param num_batches:
        :param shuffle:
        :param cluster: cluster examples by their lengths; this might give
        performance boost (i.e. faster training).
        :return:
    """
    num_batches_per_epoch = int(math.ceil(self.num_examples / batch_size))
    if num_batches is None:
      num_batches = num_batches_per_epoch
    num_epochs = int(math.ceil(num_batches / num_batches_per_epoch))

    if shuffle:
      random_idxs = random.sample(self.valid_idxs, len(self.valid_idxs))
      if cluster:
        sorted_idxs = sorted(random_idxs, key=self._sort_key)

# Path: third_party/bi_att_flow/my/tensorflow/general.py
# def grouper(iterable, n, fillvalue=None, shorten=False, num_groups=None):
#   args = [iter(iterable)] * n
#   out = zip_longest(*args, fillvalue=fillvalue)
#   out = list(out)
#   if num_groups is not None:
#     default = (fillvalue,) * n
#     assert isinstance(num_groups, int)
#     out = list(
#         each
#         for each, _ in zip_longest(out, range(num_groups), fillvalue=default))
#   if shorten:
#     assert fillvalue is None
#     out = (tuple(e for e in each if e is not None) for each in out)
#   return out
# 
# Path: third_party/bi_att_flow/my/utils.py
# def index(l, i):
#   return index(l[i[0]], i[1:]) if len(i) > 1 else l[i[0]]


# Path: third_party/bi_att_flow/basic/read_data.py
import json
import os
import random
import itertools
import math
import numpy as np
import tensorflow as tf
from collections import defaultdict
from third_party.bi_att_flow.my.tensorflow.general import grouper
from third_party.bi_att_flow.my.utils import index

      out = defaultdict(list)
      for key, val in self.data.items():
        out[key].extend(val[idx] for idx in idxs)
      return out
    elif isinstance(self.data, Data):
      return self.data.get_by_idxs(idxs)
    raise Exception()

  def get_batches(self,
                  batch_size,
                  num_batches=None,
                  shuffle=False,
                  cluster=False):
    """
        :param batch_size:
        :param num_batches:
        :param shuffle:
        :param cluster: cluster examples by their lengths; this might give
        performance boost (i.e. faster training).
        :return:
    """
    num_batches_per_epoch = int(math.ceil(self.num_examples / batch_size))
    if num_batches is None:
      num_batches = num_batches_per_epoch
    num_epochs = int(math.ceil(num_batches / num_batches_per_epoch))

    if shuffle:
      random_idxs = random.sample(self.valid_idxs, len(self.valid_idxs))
      if cluster:
        sorted_idxs = sorted(random_idxs, key=self._sort_key)

        sorted_grouped = lambda: list(grouper(sorted_idxs, batch_size))