# 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 .common import DistributedOperatorImplContainer
from .common import DistributedOperatorImpl
from .common import register_distributed_operator_impl_container
from .common import register_distributed_operator_impl
from .common import is_parameter_related
from ..utils import compute_compatible_and_update_dim_mapping
from .dist_default import DistributedDefaultImpl0
from ..cost import Transpose2OpCost, Transpose2GradOpCost
from ..cost import build_comp_desc_from_dist_op, build_dp_costs
from ..cost import build_comp_costs_from_descs
from paddle.distributed.fleet.meta_optimizers.common import OpRole


class DistributedTranspose2(DistributedOperatorImplContainer):
    def __init__(self, op_type):
        super().__init__(op_type)


register_distributed_operator_impl_container(
    DistributedTranspose2("transpose2")
)


class DistributedTranspose2Impl(DistributedOperatorImpl):
    def __init__(self, name):
        super().__init__(name)
        self._forward_implemented = False
        self._backward_implemented = False

    def is_input_compatible(self, dist_op):
        return True

    def is_output_compatible(self, dist_op):
        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
        perm = op_desc.attr('axis')
        x_name = op_desc.input('X')[0]
        out_name = op_desc.output('Out')[0]
        x_shape_name = op_desc.output('XShape')[0]
        x_shape_dims_mapping = op_dist_attr.get_output_dims_mapping(
            x_shape_name
        )
        x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
        out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
        new_dims_mapping = [-1 for i in range(len(x_dims_mapping))]
        for i in range(len(x_dims_mapping)):
            new_dims_mapping[i] = x_dims_mapping[perm[i]]

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

        if new_dims_mapping != out_dims_mapping:
            return False

        if x_shape_dims_mapping[0] != -1:
            return False

        if x_shape_dims_mapping[1:] != x_dims_mapping[:]:
            return False

        return True

    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('X')[0]
        out_name = op_desc.output('Out')[0]
        x_shape_name = op_desc.output('XShape')[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)
        x_shape_dims_mapping = op_dist_attr.get_output_dims_mapping(
            x_shape_name
        )
        perm = op_desc.attr('axis')

        assert len(x_dims_mapping) == len(perm)

        new_dims_mapping = [-1 for i in range(len(x_dims_mapping))]
        for i in range(len(x_dims_mapping)):
            new_dims_mapping[i] = x_dims_mapping[perm[i]]

        for i in range(len(out_dims_mapping)):
            dim_changed = compute_compatible_and_update_dim_mapping(
                [new_dims_mapping, out_dims_mapping], [i, i]
            )
            if dim_changed:
                changed = True

        for i in range(len(x_dims_mapping)):
            if x_dims_mapping[perm[i]] != new_dims_mapping[i]:
                x_dims_mapping[perm[i]] = new_dims_mapping[i]
                changed = True

        for i in range(len(x_dims_mapping)):
            x_shape_dims_mapping[i + 1] = x_dims_mapping[i]

        return changed

    def calc_cost(self, op_role, dist_op, ctx, cluster):
        cost = None
        if int(op_role) == int(OpRole.Backward):
            cost = self.calc_bwd_cost(dist_op, ctx, cluster)
        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.processes
        op_type = dist_op.serial_op.type
        cost_mapping = build_comp_costs_from_descs(
            Transpose2OpCost, ctx, processes, desc_mapping, cluster
        )

        res_cost = [cost_mapping]
        return res_cost

    def calc_bwd_cost(self, dist_op, ctx, cluster):
        # calc comp op cost
        res = []
        desc_mapping = build_comp_desc_from_dist_op(
            dist_op=dist_op, dist_context=ctx
        )
        dist_attr = dist_op.dist_attr
        process_mesh = dist_attr.process_mesh
        processes = process_mesh.processes
        op_type = dist_op.serial_op.type
        cost_mapping = build_comp_costs_from_descs(
            Transpose2GradOpCost, ctx, processes, desc_mapping, cluster
        )
        res.append(cost_mapping)

        backward_op = dist_op.serial_op
        main_block = backward_op.block
        need_gradient_allreduce = False
        vars = main_block.vars
        for input_name in backward_op.desc.input_names():
            for varname in backward_op.desc.input(input_name):
                if "@GRAD" not in varname and is_parameter_related(
                    varname, main_block
                ):
                    # NOTE input var's dim_mapping of backward op should be the same with input var instead of corresponding varname of forward op
                    var_dim_mapping = dist_attr.get_input_dims_mapping(varname)

                    mesh_shape = process_mesh.topology
                    batch_size_axis = var_dim_mapping[0]
                    if batch_size_axis > -1 and mesh_shape[batch_size_axis] > 1:
                        parallel_axis = batch_size_axis
                        attrs = {"use_calc_stream": True}
                        var_names = [varname + "@GRAD"]
                        build_dp_costs(
                            res,
                            dist_op,
                            ctx,
                            var_names,
                            attrs,
                            parallel_axis,
                            cluster,
                        )
        return res

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

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


register_distributed_operator_impl(
    "transpose2", DistributedTranspose2Impl("same_mapping_transpose")
)