# 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 os
import unittest

import numpy as np

import paddle
import paddle.distributed as dist
from paddle.base import core
from paddle.distributed.auto_parallel.placement_type import (
    check_placements_equal,
)


class TestDistTensorSRP(unittest.TestCase):
    def setUp(self):
        self._shape = eval(os.getenv("shape"))
        self._dtype = os.getenv("dtype")
        self._seed = 2023
        self._backend = os.getenv("backend")
        self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
        paddle.seed(self._seed)
        np.random.seed(self._seed)

    def run_test_placements(self):
        self.placements = [core.Replicate(), core.Replicate()]
        shard = core.Shard(1)
        replicate = core.Replicate()
        partial = core.Partial()

        self.assertEqual(shard.get_dim(), 1)
        self.assertEqual(shard.is_shard(), True)
        self.assertEqual(shard.is_replicated(), False)
        self.assertEqual(shard.is_partial(), False)
        self.assertEqual(str(shard), "Shard(dim=1)")

        self.assertEqual(replicate.is_shard(), False)
        self.assertEqual(replicate.is_replicated(), True)
        self.assertEqual(replicate.is_partial(), False)
        self.assertEqual(str(replicate), "Replicate()")

        self.assertEqual(partial.is_shard(), False)
        self.assertEqual(partial.is_replicated(), False)
        self.assertEqual(partial.is_partial(), True)
        self.assertEqual(str(partial), "Partial(reduce_type=SUM)")

        shard_1 = core.Shard(1)
        replicate_1 = core.Replicate()
        partial_1 = core.Partial()

        self.assertEqual(shard_1, shard)
        self.assertEqual(replicate_1, replicate)
        self.assertEqual(partial_1, partial)
        self.assertNotEqual(shard_1, replicate)
        self.assertNotEqual(shard_1, partial)

        self.assertEqual(hash(shard_1), hash(shard))
        self.assertEqual(hash(replicate_1), hash(replicate))
        self.assertEqual(hash(partial_1), hash(partial))

    def run_test_check_placements_equal(self):
        this_placements = [dist.Shard(1), dist.Replicate()]
        that_placements = [dist.Shard(1)]
        self.assertTrue(
            check_placements_equal(this_placements, that_placements)
        )
        self.assertTrue(
            check_placements_equal(that_placements, this_placements)
        )
        that_placements = [dist.Shard(1), dist.Replicate(), dist.Shard(0)]
        self.assertFalse(
            check_placements_equal(this_placements, that_placements)
        )
        that_placements = [dist.Shard(0), dist.Replicate()]
        self.assertFalse(
            check_placements_equal(this_placements, that_placements)
        )
        that_placements = [dist.Replicate(), dist.Shard(1)]
        self.assertFalse(
            check_placements_equal(this_placements, that_placements)
        )

    def run_test_dist_tensor(self):
        if self._backend == "cpu":
            paddle.set_device("cpu")
            place = paddle.CPUPlace()
        elif self._backend == "gpu":
            place = paddle.CUDAPlace(dist.get_rank())

        tensor = paddle.rand([2, 10])
        dist_tensor = paddle.Tensor(
            tensor, process_mesh=self._mesh, placements=[dist.Shard(0)]
        )

        self.assertEqual(dist_tensor.num_shard, 2)

        np.testing.assert_equal(
            dist_tensor.numpy(),
            tensor.numpy(),
        )

    def run_test_dist_tensor_with_local_tensor(self):
        if self._backend == "cpu":
            paddle.set_device("cpu")
            place = paddle.CPUPlace()
        elif self._backend == "gpu":
            place = paddle.CUDAPlace(dist.get_rank())

        tensor = paddle.rand([2, 10])
        global_dims = [4, 10]
        dist_tensor = paddle.Tensor(
            tensor,
            dims=global_dims,
            process_mesh=self._mesh,
            placements=core.Shard(0),
        )

        np.testing.assert_equal(
            dist_tensor._local_value().numpy(),
            tensor.numpy(),
        )

    def test_case(self):
        self.run_test_placements()
        self.run_test_check_placements_equal()
        self.run_test_dist_tensor()
        self.run_test_dist_tensor_with_local_tensor()


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