# Copyright (c) 2022 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 legacy_test.test_collective_api_base as test_collective_base
import numpy as np

import paddle
import paddle.distributed as dist


class StreamScatterTestCase:
    def __init__(self):
        self._sync_op = eval(os.getenv("sync_op"))
        self._use_calc_stream = eval(os.getenv("use_calc_stream"))
        self._backend = os.getenv("backend")
        self._shape = eval(os.getenv("shape"))
        self._dtype = os.getenv("dtype")
        self._seeds = eval(os.getenv("seeds"))
        if self._backend not in ["nccl", "gloo", "flagcx"]:
            raise NotImplementedError(
                "Only support nccl and gloo as the backend for now."
            )
        os.environ["PADDLE_DISTRI_BACKEND"] = self._backend

    def run_test_case(self):
        dist.init_parallel_env()

        test_data_list = []
        for seed in self._seeds:
            test_data_list.append(
                test_collective_base.create_test_data(
                    shape=self._shape, dtype=self._dtype, seed=seed
                )
            )

        src_rank = 1
        src_data = test_data_list[src_rank]
        result1 = src_data[0 : src_data.shape[0] // 2]
        result2 = src_data[src_data.shape[0] // 2 :]

        rank = dist.get_rank()

        # case 1: pass a pre-sized tensor list
        tensor = paddle.to_tensor(test_data_list[rank])
        t1, t2 = paddle.split(tensor, 2, axis=0)
        task = dist.stream.scatter(
            t1,
            [t1, t2],
            src=src_rank,
            sync_op=self._sync_op,
            use_calc_stream=self._use_calc_stream,
        )
        if not self._sync_op:
            task.wait()
        if rank == src_rank:
            np.testing.assert_allclose(t1, result2, rtol=1e-05, atol=1e-05)
        else:
            np.testing.assert_allclose(t1, result1, rtol=1e-05, atol=1e-05)

        # case 2: pass a pre-sized tensor
        tensor = paddle.to_tensor(src_data)
        t1 = paddle.empty_like(t1)
        task = dist.stream.scatter(
            t1,
            tensor,
            src=src_rank,
            sync_op=self._sync_op,
            use_calc_stream=self._use_calc_stream,
        )
        if not self._sync_op:
            task.wait()
        if rank == src_rank:
            np.testing.assert_allclose(t1, result2, rtol=1e-05, atol=1e-05)
        else:
            np.testing.assert_allclose(t1, result1, rtol=1e-05, atol=1e-05)


if __name__ == "__main__":
    StreamScatterTestCase().run_test_case()