# 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 numpy as np
import paddle
import paddle.distributed as dist
import test_collective_api_base as test_collective_base


class StreamSendRecvTestCase():

    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"]:
            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 = 0
        dst_rank = 1

        rank = dist.get_rank()
        tensor = paddle.to_tensor(test_data_list[rank])
        if rank == 0:
            task = dist.stream.send(tensor,
                                    dst=dst_rank,
                                    sync_op=self._sync_op,
                                    use_calc_stream=self._use_calc_stream)
        else:
            task = dist.stream.recv(tensor,
                                    src=src_rank,
                                    sync_op=self._sync_op,
                                    use_calc_stream=self._use_calc_stream)
        if not self._sync_op:
            task.wait()

        result = test_data_list[src_rank]
        assert np.allclose(tensor, result, rtol=1e-05, atol=1e-05)


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