AVhq0dZddlmZddlmZddlmZddlmZddlmZddl m Z dZ d Z dd Z dd Zd ZdZej"ej$_e ej$_ej*ej$_eej$_e ej$_ej2ej$_ej6ej$_ej:ej$_eej$_eeej$_ ejBej$_"eejFej$_$ejFej$_%ejLej$_'eejLej$_(e ej$_)ejTej$_+eejTej$_,ejZej$_.eejZej$_/ej`ej$_1eej`ej$_2ejfej$_4eejfej$_5ejlej$_7eejlej$_8ejrej$_:ejvej$_<eejvej$_=ej|ej$_?eej|ej$_@ejej$_Beejej$_CdZDeDej$_EeDej$_Fy )z&Operator overloads for `RaggedTensor`.)ops)tensor)math_ops)ragged_getitem) ragged_tensor) tf_decoratorc.tj||S)a#Returns result of elementwise `==` or False if not broadcast-compatible. Compares two ragged tensors elemewise for equality if they are broadcast-compatible; or returns False if they are not [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). Note that this behavior differs from `tf.math.equal`, which raises an exception if the two ragged tensors are not broadcast-compatible. For example: >>> rt1 = tf.ragged.constant([[1, 2], [3]]) >>> rt1 == rt1 >>> rt2 = tf.ragged.constant([[1, 2], [4]]) >>> rt1 == rt2 >>> rt3 = tf.ragged.constant([[1, 2], [3, 4]]) >>> # rt1 and rt3 are not broadcast-compatible. >>> rt1 == rt3 False >>> # You can also compare a `tf.RaggedTensor` to a `tf.Tensor`. >>> t = tf.constant([[1, 2], [3, 4]]) >>> rt1 == t False >>> t == rt1 False >>> rt4 = tf.ragged.constant([[1, 2], [3, 4]]) >>> rt4 == t >>> t == rt4 Args: other: The right-hand side of the `==` operator. Returns: The ragged tensor result of the elementwise `==` operation, or `False` if the arguments are not broadcast-compatible. )r tensor_equalsselfothers ]/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/ragged/ragged_operators.py ragged_eqrsX   e ,,c.tj||S)aElementwise `>=` comparison of two convertible-to-ragged-tensor values. Computes the elemewise `>=` comparison of two values that are convertible to ragged tenors, with [broadcasting] (http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) support. Raises an exception if two values are not broadcast-compatible. For example: >>> rt1 = tf.ragged.constant([[1, 2], [3]]) >>> rt1 >= rt1 >>> rt2 = tf.ragged.constant([[2, 1], [3]]) >>> rt1 >= rt2 >>> rt3 = tf.ragged.constant([[1, 2], [3, 4]]) >>> # rt1 and rt3 are not broadcast-compatible. >>> rt1 >= rt3 Traceback (most recent call last): ... InvalidArgumentError: ... >>> # You can also compare a `tf.RaggedTensor` to a `tf.Tensor`. >>> rt4 = tf.ragged.constant([[1, 2],[3, 4]]) >>> t1 = tf.constant([[2, 1], [4, 3]]) >>> rt4 >= t1 >>> t1 >= rt4 >>> # Compares a `tf.RaggedTensor` to a `tf.Tensor` with broadcasting. >>> t2 = tf.constant([[2]]) >>> rt4 >= t2 >>> t2 >= rt4 Args: other: The right-hand side of the `>=` operator. Returns: A `tf.RaggedTensor` of dtype `tf.bool` with the shape that `self` and `other` broadcast to. Raises: InvalidArgumentError: If `self` and `other` are not broadcast-compatible. )r greater_equalr s r ragged_gerNsl   e ,,rNc0tj||S)aComputes the absolute value of a ragged tensor. Given a ragged tensor of integer or floating-point values, this operation returns a ragged tensor of the same type, where each element contains the absolute value of the corresponding element in the input. Given a ragged tensor `x` of complex numbers, this operation returns a tensor of type `float32` or `float64` that is the absolute value of each element in `x`. For a complex number \\(a + bj\\), its absolute value is computed as \\(\sqrt{a^2 + b^2}\\). For example: >>> # real number >>> x = tf.ragged.constant([[-2.2, 3.2], [-4.2]]) >>> tf.abs(x) >>> # complex number >>> x = tf.ragged.constant([[-2.2 + 4.7j], [-3.2 + 5.7j], [-4.2 + 6.7j]]) >>> tf.abs(x) Args: name: A name for the operation (optional). Returns: A `RaggedTensor` of the same size and type as `x`, with absolute values. Note, for `complex64` or `complex128` input, the returned `RaggedTensor` will be of type `float32` or `float64`, respectively. )name)rabs)r rs r ragged_absrsD d &&rc0tj|||S)a Returns the truth value of elementwise `x & y`. Logical AND function. Requires that `x` and `y` have the same shape or have [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) shapes. For example, `y` can be: - A single Python boolean, where the result will be calculated by applying logical AND with the single element to each element in `x`. - A `tf.Tensor` object of dtype `tf.bool` of the same shape or [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) shape. In this case, the result will be the element-wise logical AND of `x` and `y`. - A `tf.RaggedTensor` object of dtype `tf.bool` of the same shape or [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) shape. In this case, the result will be the element-wise logical AND of `x` and `y`. For example: >>> # `y` is a Python boolean >>> x = tf.ragged.constant([[True, False], [True]]) >>> y = True >>> x & y >>> tf.math.logical_and(x, y) # Equivalent of x & y >>> y & x >>> tf.math.reduce_all(x & y) # Reduce to a scalar bool Tensor. >>> # `y` is a tf.Tensor of the same shape. >>> x = tf.ragged.constant([[True, False], [True, False]]) >>> y = tf.constant([[True, False], [False, True]]) >>> x & y >>> # `y` is a tf.Tensor of a broadcast-compatible shape. >>> x = tf.ragged.constant([[True, False], [True]]) >>> y = tf.constant([[True], [False]]) >>> x & y >>> # `y` is a `tf.RaggedTensor` of the same shape. >>> x = tf.ragged.constant([[True, False], [True]]) >>> y = tf.ragged.constant([[False, True], [True]]) >>> x & y >>> # `y` is a `tf.RaggedTensor` of a broadcast-compatible shape. >>> x = tf.ragged.constant([[[True, True, False]], [[]], [[True, False]]]) >>> y = tf.ragged.constant([[[True]], [[True]], [[False]]], ragged_rank=1) >>> x & y Args: y: A Python boolean or a `tf.Tensor` or `tf.RaggedTensor` of dtype `tf.bool`. name: A name for the operation (optional). Returns: A `tf.RaggedTensor` of dtype `tf.bool` with the shape that `x` and `y` broadcast to. )r logical_and)r yrs r ragged_andrsF   dAt ,,rc6tjfdS)z7Right-handed version of an operator: swap args x and y.c||SN)rxoperators rz_right..s HQNr)rmake_decorator)r!s`r_rightr$s  $ $X/J KKrct|jdd}tjjr-t j r| |jr tdt|S)z>The operation invoked by the `RaggedTensor.__hash__` operator.graphNzRaggedTensor is unhashable.) getattr row_splitsrTensor _USE_EQUALITYr#executing_eagerly_outside_functionsbuilding_function TypeErrorid)r gs r ragged_hashr0sQ doow-! mm!! 1 1 3 9++ 1 22 d8Orctd)aRaises TypeError when a RaggedTensor is used as a Python bool. To prevent RaggedTensor from being used as a bool, this function always raise TypeError when being called. For example: >>> x = tf.ragged.constant([[1, 2], [3]]) >>> result = True if x else False # Evaluate x as a bool value. Traceback (most recent call last): ... TypeError: RaggedTensor may not be used as a boolean. >>> x = tf.ragged.constant([[1]]) >>> r = (x == 1) # tf.RaggedTensor [[True]] >>> if r: # Evaluate r as a bool value. ... pass Traceback (most recent call last): ... 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