# Copyright (c) 2023 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
from collections import OrderedDict

from paddle.distributed.auto_parallel.static.dist_attribute import (
    DistTensorSpec,
    TensorDistAttr,
)
from paddle.distributed.fleet import auto
from paddle.framework import convert_nptype_to_datatype_or_vartype, core


class TestUniqueSPMDRule(unittest.TestCase):
    def setUp(self):
        self.rule = core.get_phi_spmd_rule("unique")
        x_shape = [4, 8]
        process_mesh = auto.ProcessMesh(mesh=[[0, 1], [2, 3]])

        x_tensor_dist_attr = TensorDistAttr()
        x_tensor_dist_attr.dims_mapping = [1, 0]
        x_tensor_dist_attr.process_mesh = process_mesh
        self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
        self.attrs = OrderedDict()
        self.attrs["return_index"] = True
        self.attrs["return_inverse"] = True
        self.attrs["return_counts"] = True
        self.attrs["axis"] = []
        self.attrs['dtype'] = convert_nptype_to_datatype_or_vartype("int32")

    def test_infer_forward(self):
        # return_index=True, return_inverse=True, return_counts=True, axis={}
        # [0, -1] --> [-1,-1], [-1], [-1], [-1], [-1]
        self.x_dist_tensor_spec.set_dims_mapping([0, -1])
        result_dist_attrs = self.rule.infer_forward(
            self.x_dist_tensor_spec,
            self.attrs["return_index"],
            self.attrs["return_inverse"],
            self.attrs["return_counts"],
            self.attrs["axis"],
            self.attrs['dtype'],
        )

        self.assertEqual(len(result_dist_attrs), 2)
        inferred_input_dist_attrs = result_dist_attrs[0]
        inferred_output_dist_attrs = result_dist_attrs[1]

        self.assertEqual(len(inferred_input_dist_attrs), 1)
        self.assertEqual(len(inferred_output_dist_attrs), 4)

        self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1])
        self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1])
        self.assertEqual(inferred_output_dist_attrs[1].dims_mapping, [-1])
        self.assertEqual(inferred_output_dist_attrs[2].dims_mapping, [-1])
        self.assertEqual(inferred_output_dist_attrs[3].dims_mapping, [-1])

        # return_index=True, return_inverse=True, return_counts=True, axis={0}
        # [0, -1] --> [-1,-1], [-1，-1], [-1], [-1], [-1]
        self.x_dist_tensor_spec.set_dims_mapping([0, -1])
        self.attrs["axis"] = [0]
        result_dist_attrs = self.rule.infer_forward(
            self.x_dist_tensor_spec,
            self.attrs["return_index"],
            self.attrs["return_inverse"],
            self.attrs["return_counts"],
            self.attrs["axis"],
            self.attrs['dtype'],
        )

        self.assertEqual(len(result_dist_attrs), 2)
        inferred_input_dist_attrs = result_dist_attrs[0]
        inferred_output_dist_attrs = result_dist_attrs[1]

        self.assertEqual(len(inferred_input_dist_attrs), 1)
        self.assertEqual(len(inferred_output_dist_attrs), 4)

        self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1])
        self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, -1])
        self.assertEqual(inferred_output_dist_attrs[1].dims_mapping, [-1])
        self.assertEqual(inferred_output_dist_attrs[2].dims_mapping, [-1])
        self.assertEqual(inferred_output_dist_attrs[3].dims_mapping, [-1])


if __name__ == "__main__":
    unittest.main()