# Copyright (c) 2021 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

from paddle.distributed.fleet.meta_optimizers.common import OpRole

from ..cost import (
    FillConstantBatchSizeLikeOpCost,
    build_comp_costs_from_descs,
    build_comp_desc_from_dist_op,
)
from ..utils import compute_compatible_and_update_dim_mapping
from .common import (
    DistributedOperatorImpl,
    DistributedOperatorImplContainer,
    register_distributed_operator_impl,
    register_distributed_operator_impl_container,
)
from .dist_default import DistributedDefaultImpl0


class DistributedFillConstantBatchSizeLike(DistributedOperatorImplContainer):
    def __init__(self, op_type):
        super().__init__(op_type)


register_distributed_operator_impl_container(
    DistributedFillConstantBatchSizeLike("fill_constant_batch_size_like")
)


class DistributedFillConstantBatchSizeLikeImpl0(DistributedOperatorImpl):
    def __init__(self, name):
        super().__init__(name)
        self._forward_implemented = True
        self._backward_implemented = True

    def calc_cost(self, op_role, dist_op, ctx, cluster):
        cost = None
        if int(op_role) == int(OpRole.Backward):
            raise ValueError(
                "The fill_constant_batch_size_like has no grad op."
            )
        else:
            cost = self.calc_fwd_cost(dist_op, ctx, cluster)
        assert cost is not None
        return cost

    def calc_fwd_cost(self, dist_op, ctx, cluster):
        # calc comp op cost
        desc_mapping = build_comp_desc_from_dist_op(
            dist_op=dist_op, dist_context=ctx
        )
        processes = dist_op.dist_attr.process_mesh.process_ids
        op_type = dist_op.serial_op.type
        cost_mapping = build_comp_costs_from_descs(
            FillConstantBatchSizeLikeOpCost,
            ctx,
            processes,
            desc_mapping,
            cluster,
        )

        res_cost = [cost_mapping]
        return res_cost

    def is_input_compatible(self, dist_op):

        return True

    def is_output_compatible(self, dist_op):
        op_desc = dist_op.serial_op.desc
        op_dist_attr = dist_op.dist_attr
        out_name = op_desc.output('Out')[0]
        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
        shape_list = op_desc.attr("shape")

        if len(shape_list) != len(out_dims_mapping):
            return False

        return True

    def is_auto_compatible(self, dist_op):
        if (not self.is_input_compatible(dist_op)) or (
            not self.is_output_compatible(dist_op)
        ):
            return False
        op_desc = dist_op.serial_op.desc
        op_dist_attr = dist_op.dist_attr
        out_name = op_desc.output('Out')[0]
        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
        in_name = op_desc.input('Input')[0]
        in_dims_mapping = op_dist_attr.get_input_dims_mapping(in_name)

        # the dim_mapping of batch dimension should be the same
        return out_dims_mapping[0] == in_dims_mapping[0]

    def update_dims_mapping(self, dist_op):
        changed = False
        op_desc = dist_op.serial_op.desc
        op_dist_attr = dist_op.dist_attr
        x_name = op_desc.input('Input')[0]
        out_name = op_desc.output('Out')[0]
        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)

        # only the batch size dimemsion of input and output are relative.
        dim_changed = compute_compatible_and_update_dim_mapping(
            [x_dims_mapping, out_dims_mapping], [0, 0]
        )
        if dim_changed:
            changed = True

        if changed:
            op_dist_attr.set_input_dims_mapping(x_name, x_dims_mapping)
            op_dist_attr.set_output_dims_mapping(out_name, out_dims_mapping)

        return changed

    @staticmethod
    def forward(ctx, *args, **kwargs):
        """
        kwargs: inputname_mapping & outputname_mapping
        """
        DistributedDefaultImpl0.forward(ctx, *args, **kwargs)

    @staticmethod
    def backward(ctx, *args, **kwargs):
        DistributedDefaultImpl0.backward(ctx, *args, **kwargs)


register_distributed_operator_impl(
    "fill_constant_batch_size_like",
    DistributedFillConstantBatchSizeLikeImpl0("fill_by_shape"),
)
