AVhdZddlmZddlmZddlmZddlmZddlmZdZejZejZ ejZ dZ d Z y ) zrPrivate convenience functions for RaggedTensors. None of these methods are exposed in the main "ragged" package. ) array_ops) check_ops)control_flow_ops)gen_ragged_math_ops)math_opsc d}|D]'}t|t|dk7st||ddDcgc]1}t|d|D]\}}tj|||3c}}}Scc}}}w)a7Checks that the given splits lists are identical. Performs static tests to ensure that the given splits lists are identical, and returns a list of control dependency op tensors that check that they are fully identical. Args: nested_splits_lists: A list of nested_splits_lists, where each split_list is a list of `splits` tensors from a `RaggedTensor`, ordered from outermost ragged dimension to innermost ragged dimension. Returns: A list of control dependency op tensors. Raises: ValueError: If the splits are not identical. z(Inputs must have identical ragged splitsrNmessage)len ValueErrorzipr assert_equal)nested_splits_lists error_msg splits_lists1s2s X/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/ragged/ragged_util.pyassert_splits_matchrs"9)("k ;321566 y !!" -QR0   -a0+>  2rRY7 7  s6A3c\tjdgtj|gdS)z2Returns splits corresponding to the given lengths.raxis)rconcatrcumsum)lengthss rlengths_to_splitsr=s%   A3 89 CCctj|dtj|d|jdg}tj|dtj|d|jdg}t j ||}t j ||}|j jdk7r#t|dd |d }t|d d|d }n?t||d }tj ||j d}|d||z }||d}tjd |j} tj||| } tj|| jS)aRepeats each range of `params` (as specified by `splits`) `repeats` times. Let the `i`th range of `params` be defined as `params[splits[i]:splits[i + 1]]`. Then this function returns a tensor containing range 0 repeated `repeats[0]` times, followed by range 1 repeated `repeats[1]`, ..., followed by the last range repeated `repeats[-1]` times. Args: params: The `Tensor` whose values should be repeated. splits: A splits tensor indicating the ranges of `params` that should be repeated. Elements should be non-negative integers. repeats: The number of times each range should be repeated. Supports broadcasting from a scalar value. Elements should be non-negative integers. Returns: A `Tensor` with the same rank and type as `params`. #### Example: >>> print(repeat_ranges( ... params=tf.constant(['a', 'b', 'c']), ... splits=tf.constant([0, 2, 3]), ... repeats=tf.constant(3))) tf.Tensor([b'a' b'b' b'a' b'b' b'a' b'b' b'c' b'c' b'c'], shape=(9,), dtype=string) z,Input argument 'splits' must be non-negativer z1Input argument 'splits' must be integer, but got z insteadz-Input argument 'repeats' must be non-negativez2Input argument 'repeats' must be integer, but got rNrrr )out_type)rassert_non_negativeassert_integerdtyperwith_dependenciesshapendimsrepeatronesr ragged_rangegatherrt_dense_values) paramssplitsrepeats splits_checksrepeats_checksrepeated_startsrepeated_limitsrepeated_splitsn_splitsoneoffsetss r repeat_rangesr9Bs}<## H ,,x) -## J --* .  - -mV D&  . .~w G' ]]AVCR[':OVABZq9OVW15OGJH%&9x''9:O%gh/O r?001#  , , -'   &'"9"9 ::rN)__doc__tensorflow.python.opsrrrrrrget_positive_axisconvert_to_int_tensorrepeat_with_axisr)rr9r"rrr?sQ ,+25*://!77  # #D H;r