o ?e@sndZddlmZddlmZddlmZddlmZddlmZddZejZej Z ej Z d d Z d d Z d S)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_opscsFdD]}t|tdkrtqfddddDS)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 splitsrcs4g|]}td|D] \}}tj||dq qS)rmessage)ziprZ assert_equal).0 splits_lists1s2 error_msgnested_splits_listsi/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/ragged/ragged_util.py 0s z'assert_splits_match..N)len ValueError)rr rrrassert_splits_matchs  rcCstjdgt|gddS)z2Returns splits corresponding to the given lengths.rZaxis)rconcatrZcumsum)lengthsrrrlengths_to_splits=src Cstj|ddtj|d|jddg}tj|ddtj|d|jddg}t||}t||}|jjdkrOt|dd |dd }t|d d|dd }nt||dd }t j||jd d}|d||}||d}t d |j} t ||| } t || 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-negativerz1Input argument 'splits' must be integer, but got z insteadz-Input argument 'repeats' must be non-negativez2Input argument 'repeats' must be integer, but got rNrrr)Zout_typer)rZassert_non_negativeZassert_integerZdtyperZwith_dependenciesshapeZndimsrepeatrZonesrZ ragged_rangegatherZrt_dense_values) paramsZsplitsZrepeatsZ splits_checksZrepeats_checksZrepeated_startsZrepeated_limitsZrepeated_splitsZn_splitsoneoffsetsrrr repeat_rangesBsD    r#N)__doc__Ztensorflow.python.opsrrrrrrZget_positive_axisZconvert_to_int_tensorZrepeat_with_axisrrr#rrrrs