#   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 __future__ import annotations

import copy
import logging
from typing import TYPE_CHECKING, Any, SupportsIndex

import numpy as np

import paddle
from paddle.distributed import fleet
from paddle.distributed.collective import _get_group_map
from paddle.distributed.communication.group import is_initialized
from paddle.framework import core

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
    from collections.abc import Iterable, Sequence
    from types import TracebackType

    import numpy.typing as npt

    from paddle._typing import NestedNumericSequence

    _NumpyShapeLike = SupportsIndex | Sequence[SupportsIndex]


# Use to store the previous and current process mesh
_g_previous_process_mesh = None
_g_current_process_mesh = None
# {shape_process_ids : unique_id}
_g_unique_process_mesh_map = {}
_g_group_map = {}


def get_current_process_mesh():
    global _g_current_process_mesh
    return _g_current_process_mesh


def set_current_process_mesh(process_mesh):
    global _g_previous_process_mesh
    global _g_current_process_mesh
    _g_previous_process_mesh = _g_current_process_mesh
    _g_current_process_mesh = process_mesh


def reset_current_process_mesh():
    global _g_previous_process_mesh
    global _g_current_process_mesh
    _g_current_process_mesh = _g_previous_process_mesh


def get_unique_id_for_process_mesh(shape, process_ids):
    key = f"shape {shape}, process_ids {process_ids}"
    global _g_unique_process_mesh_map
    if key in _g_unique_process_mesh_map:
        unique_id = _g_unique_process_mesh_map[key]
    else:
        unique_id = len(_g_unique_process_mesh_map) + 1
        _g_unique_process_mesh_map[key] = unique_id

    return unique_id


def retrieve_unique_id_for_process_mesh(shape, process_ids):
    key = f"shape {shape}, process_ids {process_ids}"
    global _g_unique_process_mesh_map
    assert key in _g_unique_process_mesh_map
    return _g_unique_process_mesh_map[key]


def get_unique_process_mesh_map():
    global _g_unique_process_mesh_map
    return _g_unique_process_mesh_map


def init_group_by_process_mesh(dim_names):
    global _g_group_map
    if dim_names is None:
        dim_names = []
    assert isinstance(dim_names, list), "dim_names must be a list."
    for dim_name in dim_names:
        if dim_name in _g_group_map:
            continue
        _g_group_map[dim_name] = {}


def get_group_map_by_dim_name(dim_name):
    global _g_group_map
    if dim_name not in _g_group_map:
        raise RuntimeError(f'No group found for dim_name {dim_name}')
    return _g_group_map[dim_name]


class ProcessMesh(core.ProcessMesh):
    """
    The `ProcessMesh` object describes the Cartesian topology of the used processes.

    Args:
        mesh (list|numpy.array): an n-dimensional array describes the topology
            of the processes.
        dim_names (list, optional): the i-th element of this list gives the name of the
            i-th dimension of the mesh.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.distributed as dist

            >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"])
            >>> assert mesh.shape == [2, 3]
            >>> assert mesh.process_ids == [2, 4, 5, 0, 1, 3]

    """

    shape: list[int]
    process_ids: list[int]

    def __init__(
        self,
        mesh: npt.NDArray[Any] | NestedNumericSequence | None = None,
        dim_names: list[str] | None = None,
        shape: _NumpyShapeLike | None = None,
        process_ids: Iterable[Any] | None = None,
    ) -> None:
        paddle.base.framework.global_var._in_auto_parallel_ = True

        # Use shape and process_ids just for compatibility
        # Users should not use these directly
        if mesh is None:
            assert shape is not None
            assert process_ids is not None
            mesh = np.array(process_ids).reshape(shape)

        if not isinstance(mesh, list) and not isinstance(mesh, np.ndarray):
            raise ValueError(
                'The mesh must be an instance of list or np.ndarray.'
            )
        if isinstance(mesh, list):
            mesh = np.array(mesh)

        if dim_names is not None and not isinstance(dim_names, list):
            raise ValueError('The dim_names must be an instance of list.')

        self._mesh = mesh
        self._shape = list(self._mesh.shape)
        self._process_ids = self._mesh.flatten().tolist()

        assert all(isinstance(p, int) for p in self._process_ids), (
            "All elements of the mesh must be integer"
        )
        assert min(self._process_ids) >= 0, (
            'All elements of the mesh must be >= 0.'
        )
        unique_process_ids = set(self._process_ids)
        assert len(unique_process_ids) == len(self._process_ids), (
            'All elements of the mesh must be unique.'
        )

        if dim_names is not None:
            assert len(dim_names) == len(self._shape), (
                "The length of dims_names must be same as the shape of the mesh."
            )
            self._dim_names = copy.deepcopy(dim_names)
        else:
            self._dim_names = ["d" + str(i) for i in range(len(self._shape))]
        unique_dim_names = set(self._dim_names)
        assert len(unique_dim_names) == len(self._dim_names), (
            f'All dim_names {dim_names} must be unique.'
        )

        # Follow the requirement for using pybind11
        core.ProcessMesh.__init__(
            self, self._shape, self._process_ids, self._dim_names
        )

        # Store all process meshes
        from .static.dist_context import get_default_distributed_context

        default_dist_cxt = get_default_distributed_context()
        default_dist_cxt.add_process_mesh(self)
        # Add new processes to process group 0
        from .static.process_group import get_process_group

        pg0 = get_process_group(0)
        pg0.add_ranks(self.process_ids)

        # Unique Mesh Id
        self._unique_id = get_unique_id_for_process_mesh(
            self._shape, self._process_ids
        )
        init_group_by_process_mesh(self._dim_names)

    @property
    def mesh(self) -> npt.NDArray[Any]:
        """
        Get the underlying mesh of ProcessMesh.
        """
        return self._mesh

    @property
    def dim_names(self) -> list[str]:
        """
        Get the underlying dimension names of ProcessMesh.
        """
        return self._dim_names

    @property
    def unique_id(self) -> int:
        """
        Get the unique id of ProcessMesh.
        NOTE
        Unique id only take process_ids and shape into account.
        Different ProcessMesh with same process_ids and shape have same unique id.
        """
        return self._unique_id

    def __getitem__(
        self, index: slice | tuple[slice, ...] | str | SupportsIndex
    ) -> ProcessMesh:
        if isinstance(index, tuple):
            new_dim_names = []
            for i, item in enumerate(index):
                if isinstance(item, slice):
                    new_dim_names.append(self._dim_names[i])
            new_mesh = self._mesh[index]
            if new_mesh.shape:
                return ProcessMesh(new_mesh, new_dim_names)
            else:
                # Wrap a scalar into a list but without dim_names
                return ProcessMesh([new_mesh])
        elif isinstance(index, slice):
            new_mesh = self._mesh[index]
            new_dim_names = self._dim_names
            return ProcessMesh(new_mesh, new_dim_names)
        elif isinstance(index, str):
            return self.get_submesh_with_dim(index)
        else:
            new_mesh = self._mesh[index]
            new_dim_names = self._dim_names[1:]
            if new_mesh.shape:
                return ProcessMesh(new_mesh, new_dim_names)
            else:
                return ProcessMesh([new_mesh])

    def get_rank_by_dim_and_process_id(
        self, dim: str | int, process_id: int
    ) -> int:
        # do some check
        if process_id not in self._process_ids:
            # -1 means invalid rank
            return -1

        if dim is None:
            # if dim is None, all process's rank is 0
            return 0

        if isinstance(dim, int):
            dim_name = self._dim_names[dim]
        elif isinstance(dim, str):
            dim_name = dim
        else:
            raise ValueError("dim must be a string or an integer.")
        dim_name_index = self._dim_names.index(dim_name)
        rank_index = np.where(self._mesh == process_id)[dim_name_index]
        return int(rank_index.item())

    def get_dim_size(self, dim: str | int) -> int:
        if dim is None:
            return 1

        if isinstance(dim, int):
            dim_name = self._dim_names[dim]
        elif isinstance(dim, str):
            dim_name = dim
        else:
            raise ValueError("dim must be a string or an integer.")
        assert dim_name in self._dim_names
        return self._shape[self._dim_names.index(dim_name)]

    def get_mesh_with_dim(
        self,
        dim_name: str,
        index: slice | tuple[slice, ...] | SupportsIndex | None = None,
    ) -> ProcessMesh:
        assert dim_name in self._dim_names, (
            f'{dim_name} is not a valid dim name.'
        )
        index_axis = self._dim_names.index(dim_name)
        new_order = [index_axis] + [
            i for i in range(len(self._dim_names)) if i != index_axis
        ]
        new_dim_names = [dim_name] + [
            dim for dim in self._dim_names if dim != dim_name
        ]
        new_mesh = self._mesh.transpose(new_order)

        if index is not None:
            if len(new_dim_names[1:]) > 0:
                return ProcessMesh(new_mesh[index], new_dim_names[1:])
            # satisfy the single dimension mesh case
            else:
                return ProcessMesh([new_mesh[index]], new_dim_names)
        return ProcessMesh(new_mesh, new_dim_names)

    def get_submesh_with_dim(
        self,
        dim_name: str,
    ) -> ProcessMesh:
        """
        Slice the current ProcessMesh based on the dim_name given to create a submesh with single dimension remained.

        Args:
            dim_name (str): the name of the mesh dimension of the ProcessMesh to create the submesh for.
        Returns:
            A :class:`ProcessMesh` object

        Examples:
            .. code-block:: pycon

                >>> import paddle
                >>> import paddle.distributed as dist

                >>> dist.init_parallel_env()
                >>> mesh_2d = dist.ProcessMesh([[0, 1, 2, 3], [4, 5, 6, 7]], dim_names=["dp", "tp"])

                >>> dp_mesh = mesh_2d.get_submesh_with_dim("dp")
                >>> # ProcessMesh:([0, 4]) on rank 0, 4
                >>> # ProcessMesh:([1, 5]) on rank 1, 5
                >>> # ProcessMesh:([2, 6]) on rank 2, 6
                >>> # ProcessMesh:([3, 7]) on rank 3, 7

                >>> tp_mesh = mesh_2d.get_submesh_with_dim("tp")
                >>> # ProcessMesh:([0, 1, 2, 3]) on rank 0, 1, 2, 3
                >>> # ProcessMesh:([4, 5, 6, 7]) on rank 4, 5, 6, 7

                >>> mesh_3d = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["pp", "dp", "tp"])

                >>> pp_mesh = mesh_3d.get_submesh_with_dim("pp")
                >>> # ProcessMesh:([0, 4]) on rank 0, 4
                >>> # ProcessMesh:([1, 5]) on rank 1, 5
                >>> # ProcessMesh:([2, 6]) on rank 2, 6
                >>> # ProcessMesh:([3, 7]) on rank 3, 7

                >>> dp_mesh = mesh_3d.get_submesh_with_dim("dp")
                >>> # ProcessMesh:([0, 2]) on rank 0, 2
                >>> # ProcessMesh:([1, 3]) on rank 1, 3
                >>> # ProcessMesh:([4, 6]) on rank 4, 6
                >>> # ProcessMesh:([5, 7]) on rank 5, 7

                >>> tp_mesh = mesh_3d.get_submesh_with_dim("tp")
                >>> # ProcessMesh:([0, 1]) on rank 0, 1
                >>> # ProcessMesh:([2, 3]) on rank 2, 3
                >>> # ProcessMesh:([4, 5]) on rank 4, 5
                >>> # ProcessMesh:([6, 7]) on rank 6, 7
        """

        reorder_mesh = self.get_mesh_with_dim(dim_name)._mesh.reshape(
            self.get_dim_size(dim_name), -1
        )
        curr_rank = paddle.distributed.get_rank()
        if curr_rank not in self._process_ids:
            logger.warning(
                f"Rank {curr_rank} is not in the process mesh, just return None"
            )
            return None
        # find curr_rank in reorder_mesh, get the column index
        col_idx = np.argmax(reorder_mesh == curr_rank) % reorder_mesh.shape[-1]
        sub_mesh = ProcessMesh(reorder_mesh[:, col_idx], [dim_name])
        return sub_mesh

    def _get_group(
        self,
        dim_name: str | None = None,
    ) -> paddle.distributed.communication.group.Group:
        """ """
        assert is_initialized(), (
            "When you want to get a group from the ProcessMesh."
            " Call paddle.distributed.init_parallel_env first "
            "to initialize the distributed environment."
        )
        if len(self._dim_names) > 1 and dim_name is None:
            raise ValueError(
                "You should specify the dim_name when the ProcessMesh has more than one dimensions."
            )
        reorder_mesh = self.get_mesh_with_dim(dim_name)._mesh.reshape(
            self.get_dim_size(dim_name), -1
        )
        curr_rank = paddle.distributed.get_rank()
        groups = get_group_map_by_dim_name(dim_name)

        for rank in self._process_ids:
            col_idx = np.argmax(reorder_mesh == rank) % reorder_mesh.shape[-1]
            if col_idx in groups:
                continue
            pg = paddle.distributed.new_group(reorder_mesh[:, col_idx])
            groups[col_idx] = pg

        cur_col_idx = (
            np.argmax(reorder_mesh == curr_rank) % reorder_mesh.shape[-1]
        )
        return groups[cur_col_idx]

    def get_group(
        self,
        dim_name: str | None = None,
    ) -> paddle.distributed.communication.group.Group:
        """
        Convert single dimension ProcessMesh to the corresponding Group.

        Args:
            dim_name (str, optional): it can be the name of the mesh dimension. Default is None.

        Returns:
            A :class:`Group` object.
        """

        # check parallel environment whether ready or not
        assert is_initialized(), (
            "When you want to get a group from the ProcessMesh."
            " Call paddle.distributed.init_parallel_env first "
            "to initialize the distributed environment."
        )
        if len(self._dim_names) > 1 and dim_name is None:
            raise ValueError(
                "You should specify the dim_name when the ProcessMesh has more than one dimensions."
            )
        if len(self._dim_names) == 1:
            if dim_name is not None and dim_name not in self._dim_names:
                raise ValueError(
                    f"{dim_name} not in the dimension names {self._dim_names}"
                )
            else:
                if hasattr(fleet.fleet, "_hcg"):
                    hcg = fleet.get_hybrid_communicate_group()
                    if hcg is not None:
                        parallel_group_map = {
                            "pp": hcg.get_pipe_parallel_group,
                            "dp": hcg.get_data_parallel_group,
                            "mp": hcg.get_model_parallel_group,
                            "sep": hcg.get_sep_parallel_group,
                            "sharding": hcg.get_sharding_parallel_group,
                        }

                        if dim_name not in parallel_group_map:
                            raise ValueError(
                                f"{dim_name} is not a valid dim name."
                            )

                        return parallel_group_map[dim_name]()
                group_map = _get_group_map()
                for group in group_map.values():
                    if set(group.ranks) == set(self._process_ids):
                        return group
                return paddle.distributed.new_group(self._process_ids)
        else:
            if dim_name not in self._dim_names:
                raise ValueError(
                    f"{dim_name} not in the dimension names {self._dim_names}"
                )
            sub_mesh = self.get_submesh_with_dim(dim_name)
            return sub_mesh.get_group(dim_name)

    def __enter__(self) -> None:
        set_current_process_mesh(self)
        default_prog = paddle.static.default_main_program()
        cur_block = default_prog.current_block()
        self._old_var_names = list(cur_block.vars.keys())
        self._old_op_size = len(cur_block.ops)

    def __exit__(
        self,
        exc_type: type[BaseException] | None,
        exc_value: BaseException | None,
        traceback: TracebackType | None,
    ) -> None:
        from .static.dist_op import DistributedOperator
        from .static.dist_tensor import DistributedTensor

        default_prog = paddle.static.default_main_program()
        cur_block = default_prog.current_block()
        new_var_names = list(cur_block.vars.keys())
        new_op_size = len(cur_block.ops)
        from .static.dist_context import get_default_distributed_context

        default_dist_ctx = get_default_distributed_context()
        for name in new_var_names:
            if name not in self._old_var_names:
                tensor = cur_block.vars[name]
                dist_tensor = default_dist_ctx.get_dist_tensor_for_program(
                    tensor
                )
                if dist_tensor is None:
                    dist_tensor = DistributedTensor(cur_block.vars[name])
                    dist_tensor.dist_attr.process_mesh = self
                    dist_tensor.dist_attr.mark_annotated("process_mesh")
                    default_dist_ctx.add_dist_tensor_for_program(dist_tensor)
                else:
                    if dist_tensor.dist_attr.process_mesh is None:
                        dist_tensor.dist_attr.process_mesh = self
                        dist_tensor.dist_attr.mark_annotated("process_mesh")

        for idx in range(self._old_op_size, new_op_size):
            op = cur_block.ops[idx]
            dist_op = default_dist_ctx.get_dist_op_for_program(op)
            if dist_op is None:
                dist_op = DistributedOperator(op)
                dist_op.dist_attr.process_mesh = self
                dist_op.dist_attr.mark_annotated("process_mesh")
                default_dist_ctx.add_dist_op_for_program(dist_op)
            else:
                if dist_op.dist_attr.process_mesh is None:
                    dist_op.dist_attr.process_mesh = self
                    dist_op.dist_attr.mark_annotated("process_mesh")
        reset_current_process_mesh()

    def __deepcopy__(self, memo: Any) -> ProcessMesh:
        if id(self) in memo:
            return memo[id(self)]
        new_process_mesh = ProcessMesh(np.array(self.mesh), self.dim_names)
        memo[id(self)] = new_process_mesh
        return new_process_mesh

    def __eq__(self, other: ProcessMesh | core.ProcessMesh) -> bool:
        if not isinstance(other, (ProcessMesh, core.ProcessMesh)):
            return False
        if self.shape != other.shape or self.process_ids != other.process_ids:
            return False
        return True

    def __ne__(self, other: ProcessMesh | core.ProcessMesh) -> None:
        return not self.__eq__(other)

    def __str__(self) -> str:
        str = f"shape {self.shape}, process_ids {self.process_ids}, dim_names {self.dim_names}"
        return str

    def __hash__(self) -> int:
        return super().__hash__()


def compute_compatible_process_mesh(process_mesh_list):
    """Compute the compatible process mesh given a list of process meshes."""
    if not process_mesh_list:
        return None

    def _compute_compatible_process_mesh_of_two(pm1, pm2):
        if pm1 is None:
            return True, pm2
        if pm2 is None:
            return True, pm1
        if pm1 == pm2:
            return True, pm1
        if pm1.process_ids == pm2.process_ids:
            if len(pm1.shape) >= len(pm2.shape):
                return True, pm1
            else:
                return True, pm2
        process_set1 = set(pm1.process_ids)
        process_set2 = set(pm2.process_ids)
        if process_set1.issubset(process_set2):
            return True, pm2
        if process_set2.issubset(process_set1):
            return True, pm1
        return False, None

    compatible_result = None
    for process_mesh in process_mesh_list:
        compatible, compatible_result = _compute_compatible_process_mesh_of_two(
            compatible_result, process_mesh
        )
        if not compatible:
            return None
    return copy.deepcopy(compatible_result)


def merge_process_meshes(process_meshes):
    """Merge a list of process meshes."""
    merged_process_mesh = None
    merged_process_ids = set()
    for process_mesh in process_meshes:
        if process_mesh is not None:
            process_ids = set(process_mesh.process_ids)
            merged_process_ids = merged_process_ids.union(process_ids)
    if len(merged_process_ids) != 0:
        merged_process_mesh = ProcessMesh(list(merged_process_ids))
    return merged_process_mesh