#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)

import paddle
from paddle.base import core


def compute_segment_sum(x, segment_ids):
    length = segment_ids[-1] + 1
    target_shape = list(x.shape)
    target_shape[0] = length
    results = np.zeros(target_shape, dtype=x.dtype)
    for index, ids in enumerate(segment_ids):
        results[ids, :] += x[index, :]
    return results


def compute_segment_mean(x, segment_ids):
    length = segment_ids[-1] + 1
    target_shape = list(x.shape)
    target_shape[0] = length
    results = np.zeros(target_shape, dtype=x.dtype)
    count = np.zeros(length, dtype=x.dtype) + 1e-8
    for index, ids in enumerate(segment_ids):
        results[ids, :] += x[index, :]
        count[ids] += 1
    results = results / count.reshape([-1, 1])
    return results


def compute_segment_min_max(x, segment_ids, pooltype="MAX"):
    length = segment_ids[-1] + 1
    target_shape = list(x.shape)
    target_shape[0] = length
    gradient = np.zeros_like(x)
    results = np.zeros(target_shape, dtype=x.dtype)
    last_idx = 0
    current_id = segment_ids[0]
    for idx in range(1, len(segment_ids) + 1):
        if idx < len(segment_ids):
            if segment_ids[idx] == current_id:
                continue
        sub_x = x[last_idx:idx, :]
        if pooltype == "MAX":
            results[current_id] = np.amax(sub_x, axis=0)
        elif pooltype == "MIN":
            results[current_id] = np.amin(sub_x, axis=0)
        else:
            raise ValueError("Invalid pooltype, only MAX, MIN supported!")
        gradient[last_idx:idx, :][sub_x == results[current_id]] = 1
        last_idx = idx
        if idx < len(segment_ids):
            current_id = segment_ids[idx]

    return results, gradient / results.size


def segment_pool_split(X, SegmentIds, pooltype):
    if pooltype == "SUM":
        return paddle.geometric.segment_sum(X, SegmentIds)
    elif pooltype == "MEAN":
        return paddle.geometric.segment_mean(X, SegmentIds)
    elif pooltype == "MIN":
        return paddle.geometric.segment_min(X, SegmentIds)
    elif pooltype == "MAX":
        return paddle.geometric.segment_max(X, SegmentIds)


class TestSegmentOps(OpTest):
    def set_data(self):
        if self.dtype == np.uint16:
            x = np.random.uniform(-1, 1, self.shape).astype(self.np_dtype)
        else:
            x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        segment_ids = self.set_segment(len(x), len(x) // 5 + 1)
        return x, segment_ids

    def set_segment(self, origin_len, reduce_len):
        segment = np.zeros(reduce_len, dtype='int64')
        segment = np.random.randint(0, reduce_len, size=[origin_len])
        segment = np.sort(segment)
        return segment.astype('int64')

    def compute(self, x, segment_ids):
        return compute_segment_sum(x, segment_ids)

    def prepare(self):
        self.op_type = "segment_pool"
        self.python_api = segment_pool_split
        self.python_out_sig = ["Out"]
        self.dtype = np.float64
        self.shape = [30, 15]
        self.attrs = {"pooltype": "SUM"}

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {
            'X': x,
            'SegmentIds': segment_ids.astype(np.int64),
        }
        if self.dtype == np.uint16:
            self.outputs = {'Out': result.astype(self.np_dtype)}
        else:
            self.outputs = {'Out': result.astype(self.dtype)}
        self.convert_bf16()

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad(self):
        self.check_grad(["X"], "Out", check_pir=True)

    def convert_bf16(self):
        if self.dtype == np.uint16:
            self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
            self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
            self.place = get_device_place()


class TestSegmentSum2(TestSegmentOps):
    def prepare(self):
        super().prepare()
        self.shape = [40, 20]
        self.dtype = np.float32

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {
            'X': x.astype(self.dtype),
            'SegmentIds': segment_ids.astype(np.int32),
        }
        self.outputs = {'Out': result.astype(self.dtype)}


class TestSegmentMax(TestSegmentOps):
    def compute(self, x, segment_ids):
        result, self.gradient = compute_segment_min_max(
            x, segment_ids, pooltype="MAX"
        )
        return result

    def prepare(self):
        super().prepare()
        self.shape = [40, 20]
        self.attrs = {'pooltype': "MAX"}

    def test_check_grad(self):
        self.check_grad(
            ["X"], "Out", user_defined_grads=[self.gradient], check_pir=True
        )


class TestSegmentMax2(TestSegmentMax):
    def prepare(self):
        super().prepare()
        self.dtype = np.float32


class TestSegmentMin(TestSegmentMax):
    def compute(self, x, segment_ids):
        result, self.gradient = compute_segment_min_max(
            x, segment_ids, pooltype="MIN"
        )
        return result

    def prepare(self):
        super().prepare()
        self.attrs = {'pooltype': "MIN"}


class TestSegmentMin2(TestSegmentMin):
    def prepare(self):
        super().prepare()
        self.dtype = np.float32


class TestSegmentMean(TestSegmentOps):
    def compute(self, x, segment_ids):
        return compute_segment_mean(x, segment_ids)

    def prepare(self):
        super().prepare()
        self.shape = [40, 20]
        self.attrs = {'pooltype': "MEAN"}

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {'X': x, 'SegmentIds': segment_ids}
        if self.dtype == np.uint16:
            astype = self.np_dtype
        else:
            astype = self.dtype
        self.outputs = {
            'Out': result,
            'SummedIds': compute_segment_sum(
                np.ones([len(x), 1]).astype(astype), segment_ids
            ),
        }
        self.convert_bf16()

    def test_check_output(self):
        if core.is_compiled_with_cuda() or is_custom_device():
            self.check_output_with_place(
                get_device_place(), check_pir=True, check_symbol_infer=False
            )
        # due to CPU kernel not implement calculate 'SummedIds'
        # so cannot check 'SummedIds'
        del self.outputs['SummedIds']
        self.check_output_with_place(
            core.CPUPlace(), check_pir=True, check_symbol_infer=False
        )


class TestSegmentMean2(TestSegmentMean):
    def prepare(self):
        super().prepare()
        self.dtype = np.float32
        self.shape = [30, 20]
        self.attrs = {'pooltype': "MEAN"}


class TestSegmentSumFP16Op(TestSegmentOps):
    def prepare(self):
        super().prepare()
        self.dtype = np.float16


class TestSegmentMaxFP16Op(TestSegmentMax):
    def prepare(self):
        super().prepare()
        self.dtype = np.float16


class TestSegmentMinFP16Op(TestSegmentMin):
    def prepare(self):
        super().prepare()
        self.dtype = np.float16


class TestSegmentMeanFP16Op(TestSegmentMean):
    def prepare(self):
        super().prepare()
        self.dtype = np.float16


class TestSegmentMaxFP16OddNumel(TestSegmentMax):
    """Test MAX+float16 with odd output numel to exercise 4-byte alignment
    padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
    35*2=70 bytes, padded to 72."""

    def prepare(self):
        super().prepare()
        self.dtype = np.float16
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int64',
            )
        )


class TestSegmentMinFP16OddNumel(TestSegmentMin):
    """Test MIN+float16 with odd output numel to exercise 4-byte alignment
    padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
    35*2=70 bytes, padded to 72."""

    def prepare(self):
        super().prepare()
        self.dtype = np.float16
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int64',
            )
        )


class TestSegmentMaxFP16OddNumelInt32(TestSegmentMax):
    """Test MAX+float16+int32 segment_ids with odd output numel."""

    def prepare(self):
        super().prepare()
        self.dtype = np.float16
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int32',
            )
        )

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {
            'X': x.astype(self.dtype),
            'SegmentIds': segment_ids.astype(np.int32),
        }
        self.outputs = {'Out': result.astype(self.dtype)}


class TestSegmentMinFP16OddNumelInt32(TestSegmentMin):
    """Test MIN+float16+int32 segment_ids with odd output numel."""

    def prepare(self):
        super().prepare()
        self.dtype = np.float16
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int32',
            )
        )

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {
            'X': x.astype(self.dtype),
            'SegmentIds': segment_ids.astype(np.int32),
        }
        self.outputs = {'Out': result.astype(self.dtype)}


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentSumBF16Op(TestSegmentOps):
    def prepare(self):
        super().prepare()
        self.dtype = np.uint16
        self.np_dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(
            self.place, check_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ["X"], "Out", check_pir=True)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentMaxBF16Op(TestSegmentMax):
    def prepare(self):
        super().prepare()
        self.dtype = np.uint16
        self.np_dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(
            self.place, check_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad_with_place(
            self.place,
            ["X"],
            "Out",
            user_defined_grads=[self.gradient],
            check_pir=True,
        )


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentMinBF16Op(TestSegmentMin):
    def prepare(self):
        super().prepare()
        self.dtype = np.uint16
        self.np_dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(
            self.place, check_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad_with_place(
            self.place,
            ["X"],
            "Out",
            user_defined_grads=[self.gradient],
            check_pir=True,
        )


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentMeanBF16Op(TestSegmentMean):
    def prepare(self):
        super().prepare()
        self.dtype = np.uint16
        self.np_dtype = np.float32

    def test_check_output(self):
        self.check_output_with_place(
            self.place, check_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ["X"], "Out", check_pir=True)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentMaxBF16OddNumel(TestSegmentMaxBF16Op):
    """Test MAX+bfloat16 with odd output numel to exercise 4-byte alignment
    padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
    35*2=70 bytes, padded to 72."""

    def prepare(self):
        super().prepare()
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int64',
            )
        )


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestSegmentMinBF16OddNumel(TestSegmentMinBF16Op):
    """Test MIN+bfloat16 with odd output numel to exercise 4-byte alignment
    padding. Input [21,5], 7 segments -> output [7,5]=35 numel (odd),
    35*2=70 bytes, padded to 72."""

    def prepare(self):
        super().prepare()
        self.shape = [21, 5]

    def set_segment(self, origin_len, reduce_len):
        return np.sort(
            np.array(
                [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
                dtype='int64',
            )
        )


# default SUM
class TestSegmentOps_ZeroSize(OpTest):
    def set_data(self):
        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        segment_ids = self.set_segment(self.shape[0])
        return x, segment_ids

    def set_segment(self, len):
        segment = np.random.randint(0, len, size=[len])
        segment = np.sort(segment)
        return segment.astype('int64')

    def compute(self, x, segment_ids):
        return compute_segment_sum(x, segment_ids)

    def prepare(self):
        self.op_type = "segment_pool"
        self.python_api = segment_pool_split
        self.python_out_sig = ["Out"]
        self.dtype = np.float64
        self.shape = [30, 0]
        self.attrs = {"pooltype": "SUM"}

    def setUp(self):
        self.prepare()
        x, segment_ids = self.set_data()
        result = self.compute(x, segment_ids)
        self.inputs = {
            'X': x,
            'SegmentIds': segment_ids.astype(np.int64),
        }
        self.outputs = {'Out': result.astype(self.dtype)}

    def test_check_output(self):
        self.check_output(check_pir=True, check_symbol_infer=False)

    def test_check_grad(self):
        self.check_grad(["X"], "Out", check_pir=True)


class TestSegmentOps_Min_ZeroSize(TestSegmentOps_ZeroSize):
    def compute(self, x, segment_ids):
        result, self.gradient = compute_segment_min_max(
            x, segment_ids, pooltype="MIN"
        )
        return result

    def prepare(self):
        super().prepare()
        self.attrs = {'pooltype': "MIN"}


class TestSegmentOps_Max_ZeroSize(TestSegmentOps_ZeroSize):
    def compute(self, x, segment_ids):
        result, self.gradient = compute_segment_min_max(
            x, segment_ids, pooltype="MAX"
        )
        return result

    def prepare(self):
        super().prepare()
        self.attrs = {'pooltype': "MAX"}


class TestSegmentOps_Mean_ZeroSize(TestSegmentOps_ZeroSize):
    def compute(self, x, segment_ids):
        return compute_segment_mean(x, segment_ids)

    def prepare(self):
        super().prepare()
        self.attrs = {'pooltype': "MEAN"}


class API_SegmentOpsTest(unittest.TestCase):
    def test_static(self):
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
            y = paddle.static.data(name='y', shape=[3], dtype='int32')

            res_sum = paddle.incubate.segment_sum(x, y)
            res_mean = paddle.incubate.segment_mean(x, y)
            res_max = paddle.incubate.segment_max(x, y)
            res_min = paddle.incubate.segment_min(x, y)

            exe = paddle.static.Executor(paddle.CPUPlace())
            data1 = np.array([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
            data2 = np.array([0, 0, 1], dtype="int32")

            np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
            np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
            np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
            np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")

            ret = exe.run(
                feed={'x': data1, 'y': data2},
                fetch_list=[res_sum, res_mean, res_max, res_min],
            )

        for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
            np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)

    def test_dygraph(self):
        device = paddle.CPUPlace()
        with paddle.base.dygraph.guard(device):
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
            )
            y = paddle.to_tensor([0, 0, 1], dtype="int32")
            res_sum = paddle.incubate.segment_sum(x, y)
            res_mean = paddle.incubate.segment_mean(x, y)
            res_max = paddle.incubate.segment_max(x, y)
            res_min = paddle.incubate.segment_min(x, y)

            np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
            np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
            np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
            np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")

            ret = [res_sum, res_mean, res_max, res_min]

        for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
            np.testing.assert_allclose(
                np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
            )


class API_GeometricSegmentOpsTest(unittest.TestCase):
    def test_static(self):
        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(name="x", shape=[3, 3], dtype="float32")
            y = paddle.static.data(name='y', shape=[3], dtype='int32')

            res_sum = paddle.geometric.segment_sum(x, y)
            res_mean = paddle.geometric.segment_mean(x, y)
            res_max = paddle.geometric.segment_max(x, y)
            res_min = paddle.geometric.segment_min(x, y)

            exe = paddle.static.Executor(paddle.CPUPlace())
            data1 = np.array([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
            data2 = np.array([0, 0, 1], dtype="int32")

            np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
            np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
            np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
            np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")

            ret = exe.run(
                feed={'x': data1, 'y': data2},
                fetch_list=[res_sum, res_mean, res_max, res_min],
            )

        for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
            np.testing.assert_allclose(np_res, ret_res, rtol=1e-05, atol=1e-06)

    def test_dygraph(self):
        device = paddle.CPUPlace()
        with paddle.base.dygraph.guard(device):
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
            )
            y = paddle.to_tensor([0, 0, 1], dtype="int32")
            res_sum = paddle.geometric.segment_sum(x, y)
            res_mean = paddle.geometric.segment_mean(x, y)
            res_max = paddle.geometric.segment_max(x, y)
            res_min = paddle.geometric.segment_min(x, y)

            np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float32")
            np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float32")
            np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float32")
            np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float32")

            ret = [res_sum, res_mean, res_max, res_min]

        for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
            np.testing.assert_allclose(
                np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
            )

    def test_dygraph_cpu_float16(self):
        device = paddle.CPUPlace()
        with paddle.base.dygraph.guard(device):
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float16'
            )
            y = paddle.to_tensor([0, 0, 1], dtype="int32")
            res_sum = paddle.geometric.segment_sum(x, y)
            res_mean = paddle.geometric.segment_mean(x, y)
            res_max = paddle.geometric.segment_max(x, y)
            res_min = paddle.geometric.segment_min(x, y)

            np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float16")
            np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float16")
            np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float16")
            np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float16")

            ret = [res_sum, res_mean, res_max, res_min]
        for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
            np.testing.assert_allclose(
                np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
            )

    def test_dygraph_cuda_float16(self):
        if core.is_compiled_with_cuda() or is_custom_device():
            device = get_device_place()
            with paddle.base.dygraph.guard(device):
                x = paddle.to_tensor(
                    [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float16'
                )
                y = paddle.to_tensor([0, 0, 1], dtype="int32")
                res_sum = paddle.geometric.segment_sum(x, y)
                res_mean = paddle.geometric.segment_mean(x, y)
                res_max = paddle.geometric.segment_max(x, y)
                res_min = paddle.geometric.segment_min(x, y)

                np_sum = np.array([[4, 4, 4], [4, 5, 6]], dtype="float16")
                np_mean = np.array([[2, 2, 2], [4, 5, 6]], dtype="float16")
                np_max = np.array([[3, 2, 3], [4, 5, 6]], dtype="float16")
                np_min = np.array([[1, 2, 1], [4, 5, 6]], dtype="float16")

                ret = [res_sum, res_mean, res_max, res_min]

            for np_res, ret_res in zip([np_sum, np_mean, np_max, np_min], ret):
                np.testing.assert_allclose(
                    np_res, ret_res.numpy(), rtol=1e-05, atol=1e-06
                )

    def test_infermeta_dimension_validation(self):
        """Test InferMeta dimension validation for both dynamic and static graph modes.

        This test covers the dimension checking logic added to InferMeta:
        - config.is_runtime || !contain_unknown_dim
        - Should check in dynamic mode (is_runtime=true)
        - Should check in static mode when dimensions are known (!contain_unknown_dim)
        - Should skip in static mode when dimensions contain unknown (-1)
        """

        device = paddle.CPUPlace()

        # ========== Dynamic Graph Tests ==========
        with paddle.base.dygraph.guard(device):
            # Test 1: Normal case - dimensions match, should pass
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([0, 0, 1], dtype='int32')  # [3]
            res_sum = paddle.geometric.segment_sum(x, y)
            self.assertEqual(res_sum.shape[0], 2)  # max segment id is 1

            # Test 2: Error case - segment_ids is shorter
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([0, 1], dtype='int32')  # [2] - 2 vs 3
            with self.assertRaises(ValueError) as cm:
                paddle.geometric.segment_sum(x, y)
            self.assertIn("same size as dimension 0", str(cm.exception))

            # Test 3: Error case - segment_ids is longer
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([0, 0, 1, 2], dtype='int32')  # [4] - 4 vs 3
            with self.assertRaises(ValueError) as cm:
                paddle.geometric.segment_sum(x, y)
            self.assertIn("same size as dimension 0", str(cm.exception))

            # Test 4: Error case - segment_ids is multi-dimensional
            x = paddle.to_tensor(
                [[1, 2, 3], [3, 2, 1]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([[0, 0], [1, 1]], dtype='int32')  # [2, 2]
            with self.assertRaises(ValueError) as cm:
                paddle.geometric.segment_sum(x, y)
            self.assertIn("1-D tensor", str(cm.exception))

            # Test 5: Error case - segment_ids shorter, verify error message
            x = paddle.to_tensor(
                [[1, 2, 3], [2, 1, 3], [3, 4, 5]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([0, 1], dtype='int32')  # [2]
            with self.assertRaises(ValueError) as cm:
                paddle.geometric.segment_mean(x, y)
            self.assertIn("same size as dimension 0", str(cm.exception))

            # Test 6: Error case - segment_ids longer, verify error message
            x = paddle.to_tensor(
                [[1, 2, 3], [2, 1, 3], [3, 4, 5]], dtype='float32'
            )  # [3, 3]
            y = paddle.to_tensor([0, 0, 1, 2], dtype='int32')  # [4]
            with self.assertRaises(ValueError) as cm:
                paddle.geometric.segment_min(x, y)
            self.assertIn("same size as dimension 0", str(cm.exception))

        # ========== Static Graph Tests ==========
        in_dygraph = paddle.in_dynamic_mode()
        paddle.enable_static()

        # Test 7: Static graph - known dimensions, should check at compile time
        x = paddle.static.data(name='x', shape=[3, 3], dtype='float32')
        y = paddle.static.data(name='y', shape=[3], dtype='int32')
        # This should trigger InferMeta check during graph construction
        # if dimensions are known at compile time
        # Note: Actual check happens at compile time for static graph
        # The error message might differ from dynamic graph

        # Test 8: Static graph - unknown dimensions (-1), should NOT check at compile time
        # Dimensions with -1 are deferred to runtime
        x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
        y = paddle.static.data(name='y', shape=[-1], dtype='int32')
        # Should NOT trigger InferMeta check during graph construction
        # because contain_unknown_dim will be true
        # Check happens at runtime when executor runs

        if in_dygraph:
            paddle.disable_static()


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()