AVh1 dZddlmZddlmZddlmZddlmZddlmZddl m Z e ejejejejej ej"ej$gZe ddd g ej(dd Zej,d ej,d ej,dej,ddZddZe dddg ej(ddZe dddg ej(ddZe dddg ej(ddZy)zImplementation of tf.sets.)dtypes)ops) sparse_tensor) gen_set_ops)dispatch) tf_exportz sets.sizez sets.set_size)v1ctj|d}t|tjst d|z|j j jtvr*t d|j j dtdtj|j|j |j|S)aCompute number of unique elements along last dimension of `a`. Args: a: `SparseTensor`, with indices sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indices in `a`. Note that setting this to `false` allows for undefined behavior when calling this function with invalid indices. Returns: `int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the number of unique elements in the corresponding `[0...n-1]` dimension of `a`. Raises: TypeError: If `a` is an invalid types. anamez Expected `SparseTensor`, got %s.zInvalid dtype `` not in supported dtypes: ``.) r"convert_to_tensor_or_sparse_tensor isinstance SparseTensor TypeErrorvaluesdtype base_dtype _VALID_DTYPESrset_sizeindices dense_shape)r validate_indicess O/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/sets_impl.pyrrs&66qsC! A}11 2 6: ;;XX^^m3  !((..)* ?"     aii1==. 00SetSizeDenseToDenseSetOperationDenseToSparseSetOperationSparseToSparseSetOperationctj|d}|jjtvr t d|jdtdtj|d}|jj|jjk7r&t d|jd|jd t |tjrt |tjs||d fS||d fS) aConvert to tensor types, and flip order if necessary. Args: a: `Tensor` or `SparseTensor` of the same type as `b`. b: `Tensor` or `SparseTensor` of the same type as `a`. Returns: Tuple of `(a, b, flipped)`, where `a` and `b` have been converted to `Tensor` or `SparseTensor`, and `flipped` indicates whether the order has been flipped to make it dense,sparse instead of sparse,dense (since the set ops do not support the latter). r r z'a' has invalid dtype `rrbzTypes don't match, z vs .TF)rrrrrrrr)r r#s r%_convert_to_tensors_or_sparse_tensorsr%Ds66qsC!WW},  !!''+ ?"  66qsC!WW177--- aggqwwG HHM../ Q 22 3 a: Aurc Nt|tjrt|tjr]tj|j |j |j|j |j |j||\}}}n~tdt|tjr=tj||j |j |j||\}}}ntj||||\}}}tj|||S)aCompute set operation of elements in last dimension of `a` and `b`. All but the last dimension of `a` and `b` must match. Args: a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order. b: `Tensor` or `SparseTensor` of the same type as `a`. Must be `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be sorted in row-major order. set_operation: String indicating set operation. See SetOperationOp::SetOperationFromContext for valid values. validate_indices: Whether to validate the order and range of sparse indices in `a` and `b`. Returns: A `SparseTensor` with the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the results of the set operation. Raises: TypeError: If inputs are invalid types. ValueError: If `a` is sparse and `b` is dense. zYSparse,Dense is not supported, but Dense,Sparse is. Please flip the order of your inputs.) rrrrsparse_to_sparse_set_operationrrr ValueErrordense_to_sparse_set_operationdense_to_dense_set_operation)r r# set_operationrrrshapes r_set_operationr-_s2=--.!]//0*II ))QXXq}}aii --(8 :gvu ? @@!]//0(FF 199ahh }>NPGVU)EE 1m-/GVU  # #GVU ;;rzsets.intersectionzsets.set_intersectionc>t||\}}}t||d|S)aVCompute set intersection of elements in last dimension of `a` and `b`. All but the last dimension of `a` and `b` must match. Example: ```python import tensorflow as tf import collections # Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2]) # b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4]) # `tf.sets.intersection` is applied to each aligned pair of sets. tf.sets.intersection(a, b) # The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ]) ``` Args: a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order. b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indices in `a` and `b`. Returns: A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections. intersectionr%r-r r#r_s rset_intersectionr3s)F 2!Q 7'!Q 1n.> ??rzsets.differencezsets.set_differencecZt||\}}}|r| }t|||rd|Sd|S)aCompute set difference of elements in last dimension of `a` and `b`. All but the last dimension of `a` and `b` must match. Example: ```python import tensorflow as tf import collections # Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2]) # np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4]) # `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b) # The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ]) ``` Args: a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order. b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order. aminusb: Whether to subtract `b` from `a`, vs vice versa. validate_indices: Whether to validate the order and range of sparse indices in `a` and `b`. Returns: A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences. Raises: TypeError: If inputs are invalid types, or if `a` and `b` have different types. ValueError: If `a` is sparse and `b` is dense. errors_impl.InvalidArgumentError: If the shapes of `a` and `b` do not match in any dimension other than the last dimension. za-bzb-ar0)r r#aminusbrflippeds rset_differencer7s@V81=-!Q kG 1we;K LLE;K LLrz sets.unionzsets.set_unionc>t||\}}}t||d|S)aCompute set union of elements in last dimension of `a` and `b`. All but the last dimension of `a` and `b` must match. Example: ```python import tensorflow as tf import collections # [[{1, 2}, {3}], [{4}, {5, 6}]] a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2]) # [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]] b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4]) # `set_union` is applied to each aligned pair of sets. tf.sets.union(a, b) # The result will be a equivalent to either of: # # np.array([[{1, 2, 3}, {2, 3}], [{4, 5}, {5, 6, 7, 8}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((0, 0, 1), 2), # ((0, 0, 2), 3), # ((0, 1, 0), 2), # ((0, 1, 1), 3), # ((1, 0, 0), 4), # ((1, 0, 1), 5), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ((1, 1, 2), 7), # ((1, 1, 3), 8), # ]) ``` Args: a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order. b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indices in `a` and `b`. Returns: A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the unions. unionr0r1s r set_unionr:!s)V 2!Q 7'!Q 1g'7 88rN)T)TT)__doc__tensorflow.python.frameworkrrrtensorflow.python.opsrtensorflow.python.utilr tensorflow.python.util.tf_exportr frozensetint8int16int32int64uint8uint16stringradd_dispatch_supportrNotDifferentiabler%r-r3r7r:rrrKsw!.+5-+6 KKv||V\\6<< MM6==  ;K9: 0;0:i 0112236'J9r