x-jm(FddlmZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z erddlmZgZe d d d d  dd dZe d dd d  dd dZe d dd d  dd dZe d dd d  dd dZdS)!) annotations) TYPE_CHECKING)_C_ops)check_variable_and_dtype) LayerHelper)in_dynamic_or_pir_mode) deprecated)Tensorz2.4.0zpaddle.geometric.segment_sumz5paddle.incubate.segment_sum will be removed in future)since update_tolevelreasonNdatar segment_idsname str | Nonereturncttrtj||dSt|dddt|dddt dit }||j}||j}|d||d ||d d di |S)a  Segment Sum Operator. Sum the elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that .. math:: out_i = \sum_{j \in \{segment\_ids_j == i \} } data_{j} where sum is over j such that `segment_ids[j] == i`. Args: data (Tensor): A tensor, available data type float32, float64, int32, int64. segment_ids (Tensor): A 1-D tensor, which have the same size with the first dimension of input data. Available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the Segment Sum result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.incubate.segment_sum(data, segment_ids) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[4., 4., 4.], [4., 5., 6.]]) SUMXfloat32float64int32int64 segment_pool SegmentIdsrr segment_sumdtyperrOut SummedIdspooltypetypeinputsoutputsattrsN)r rrrrrlocals"create_variable_for_type_inferencer" append_oprrrhelperout summed_idss [/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/paddle/incubate/tensor/math.pyr r s^"4e<<< #?    ! '9>   77fhh7777dj7II>>TZ>PP [99j99u%     zpaddle.geometric.segment_meanz6paddle.incubate.segment_mean will be removed in futurecttrtj||dSt|dddt|dddt d it }||j}||j}|d||d ||d d di |S)an Segment Mean Operator. Ihis operator calculate the mean value of input `data` which with the same index in `segment_ids`. It computes a tensor such that .. math:: out_i = \mathop{mean}_{j \in \{segment\_ids_j == i \} } data_{j} where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number of all index 'segment_ids[j] == i'. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the Segment Mean result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.incubate.segment_mean(data, segment_ids) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[2., 2., 2.], [4., 5., 6.]]) MEANrrrrr segment_meanr!r#r$r'r()r9r-r1s r5r9r9dsb>"4f=== c;^\#5~ 4 4688 4 4F  3 3$* 3 E EC:::LLJ  55*556"  Jr6zpaddle.geometric.segment_minz5paddle.incubate.segment_min will be removed in futurecttrtj||dSt|dddt|dddt d it }||j}||j}|d||d ||d d di |S)a Segment min operator. Calculate the minimum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that .. math:: out_i = \min_{j \in \{segment\_ids_j == i \} } data_{j} where min is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the minimum result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.incubate.segment_min(data, segment_ids) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 1.], [4., 5., 6.]]) MINrrrrr segment_minr!r#r$r'r()r<r-r1s r5r<r<s`="4e<<< c;^\#5~ 3 3&(( 3 3F  3 3$* 3 E EC:::LLJ  55*555!  Jr6zpaddle.geometric.segment_maxz5paddle.incubate.segment_max will be removed in futurecxtrtj||d}|St|dddt|dddt d it }||j}||j}|d||d ||d d di |S)a Segment max operator. Calculate the maximum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that .. math:: out_i = \max_{j \in \{segment\_ids_j == i \} } data_{j} where max is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the maximum result. Examples: .. code-block:: python >>> import paddle >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') >>> out = paddle.incubate.segment_max(data, segment_ids) >>> print(out) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[3., 2., 3.], [4., 5., 6.]]) MAXrrrrr segment_maxr!r#r$r'r()r?r-)rrrr3r2r4s r5r?r?s`!$ U;;  c;^\#5~ 3 3&(( 3 3F  3 3$* 3 E EC:::LLJ  55*555!  Jr6)N)rr rr rrrr ) __future__rtypingrpaddlerpaddle.base.data_feederrpaddle.base.layer_helperrpaddle.frameworkr paddle.utilsr r __all__r r9r<r?r6r5rIs#""""" <<<<<<000000333333######   , B ;?<<<<  <~  - C ;?>>>>  >B  , B ;?====  =@  , B ;?>>>>  >>>r6