import tensorflow as tf

from onnx_tf.handlers.backend_handler import BackendHandler
from onnx_tf.handlers.handler import onnx_op


@onnx_op("SequenceAt")
class SequenceAt(BackendHandler):

  @classmethod
  def chk_pos_in_bounds(cls, input_seq, pos):
    """
    Check the position is in-bounds with respect to the sequence.
    Accepted range for 'position' is in [-n, n - 1], where n is the
    number of tensors in 'input_sequence'.

    :param input_seq: input sequence
    :param pos: position of the output tensor

    :return: True if position is in-bounds or input length is dynamic.
    """
    seq_length = input_seq.shape[0].value
    if seq_length is None:
      return True

    seq_length = tf.cast(seq_length, pos.dtype)

    cond1 = tf.greater_equal(pos, tf.negative(seq_length))
    cond2 = tf.less_equal(pos, seq_length - 1)

    # pos >= -n and pos < n
    return tf.reduce_all(tf.logical_and(cond1, cond2))

  @classmethod
  def version_11(cls, node, **kwargs):
    tensor_dict = kwargs["tensor_dict"]
    input_sequence = tensor_dict[node.inputs[0]]
    position = tensor_dict[node.inputs[1]]

    # check whether position is in-bounds and assert if not
    result = cls.chk_pos_in_bounds(input_sequence, position)
    assert_pos = tf.Assert(tf.equal(result, True), [result])

    with tf.control_dependencies([assert_pos]):
      return [input_sequence[position]]