# 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 unittest

import paddle
from paddle.distributed.fleet import auto

paddle.enable_static()


def make_program():
    main_program = paddle.fluid.Program()
    start_program = paddle.fluid.Program()
    with paddle.static.program_guard(main_program, start_program):

        x = paddle.static.data(name='x', shape=[4, 4, 8], dtype='float32')
        y = paddle.static.data(name='y', shape=[4, 4, 8], dtype='float32')
        auto.shard_tensor(
            x, auto.ProcessMesh([0, 1], dim_names=["d"]), [None, "d", None]
        )

        z = paddle.add(x, y)
        paddle.assign(x, output=z)

    return main_program, start_program


def parallelizer(program_func, rank):
    from paddle.distributed.auto_parallel.completion import Completer
    from paddle.distributed.auto_parallel.dist_context import DistributedContext
    from paddle.distributed.auto_parallel.partitioner import Partitioner

    main_program, start_program = program_func()

    dist_context = DistributedContext()
    completer = Completer(dist_context)
    completer.complete_forward_annotation(main_program)
    dist_context.block_state.parse_forward_blocks(main_program)

    partitioner = Partitioner(dist_context, rank)
    dist_main_prog, _, _ = partitioner.partition(
        main_program, start_program, []
    )

    return dist_main_prog, dist_context


class TestDistAssign(unittest.TestCase):
    def test_dist_assign(self):

        dist_main_prog, dist_context = parallelizer(make_program, 0)
        ops = dist_main_prog.global_block().ops
        for op in ops:
            if op.type == "assign":
                dist_op = dist_context.get_dist_op_for_program(op)
                dist_op.dist_attr.impl_type == "assign"
                dist_op.dist_attr.impl_idx == 0

                x_name = op.input_arg_names[0]
                out_name = op.output_arg_names[0]
                out_var = dist_main_prog.global_block().vars[out_name]
                dist_out = dist_context.get_dist_tensor_for_program(out_var)

                x_dims_mapping = dist_op.dist_attr.get_input_dims_mapping(
                    x_name
                )
                out_dims_mapping = dist_op.dist_attr.get_output_dims_mapping(
                    out_name
                )

                assert x_dims_mapping == out_dims_mapping
                assert out_dims_mapping == dist_out.dist_attr.dims_mapping


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