#   Copyright (c) 2019 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 print_function

__all__ = [
    'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker',
    'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker'
]

import os


class Role:
    WORKER = 1
    SERVER = 2


class RoleMakerBase(object):
    """
    RoleMakerBase is a base class for assigning a role to current process
    in distributed training.
    A paddle developer can implement RoleMakerBase to design a role maker
    for worker or pserver assignment.
    """

    def __init__(self):
        self._worker_endpoints = []
        self._server_endpoints = []
        self._role_is_generated = False
        self._role = None
        self._current_id = -1

    def is_worker(self):
        """
        return is_worker() of current process
        """
        raise NotImplementedError("Please implement this method in child class")

    def is_server(self):
        """
        return is_server() of current process
        """
        raise NotImplementedError("Please implement this method in child class")

    def is_first_worker(self):
        """
        Check whether the node is the first instance of worker.
        Returns:
            bool: True if this is the first node of worker,
                  False if not.
        """
        raise NotImplementedError("Please implement this method in child class")

    def worker_num(self):
        """
        Get current total worker number.

        Returns:
            int: worker number
        """
        raise NotImplementedError("Please implement this method in child class")

    def worker_index(self):
        """
        Get current worker id.

        Returns:
            int: node id
        """
        raise NotImplementedError("Please implement this method in child class")

    def server_index(self):
        """
        Get current server id.

        Returns:
            int: node id
        """
        raise NotImplementedError("Please implement this method in child class")

    def get_trainer_endpoints(self):
        """
        return trainer endpoints
        """
        return self._worker_endpoints

    def get_pserver_endpoints(self):
        """
        return pserver endpoints
        """
        return self._server_endpoints

    def to_string(self):
        return "role: {}, current_id: {}, worker_endpoints: {}, server_endpoints: {}".format(
            self._role, self._current_id, self._worker_endpoints,
            self._server_endpoints)


class MPIRoleMaker(RoleMakerBase):
    """
    MPIRoleMaker is a MPI-API based role maker which is a counter-part of K8SRoleMaker
    mpi4py will be used if a developer inherits MPIRoleMaker
    """

    def __init__(self):
        super(MPIRoleMaker, self).__init__()
        from mpi4py import MPI
        self.MPI = MPI
        self._comm = MPI.COMM_WORLD
        self._node_type_comm = None
        self._ips = None
        self._ip = None

    def _get_rank(self):
        """
        return rank
        """
        self._rank = self._comm.Get_rank()
        return self._rank

    def _get_size(self):
        """
        return size
        """
        self._size = self._comm.Get_size()
        return self._size

    def _all_gather(self, obj):
        """
        all_gather(obj) will call MPI's allgather function
        """
        self._barrier_all()
        return self._comm.allgather(obj)

    def _worker_gather(self, obj):
        """
        worker_gather(obj) will call MPI's allgather function
        """
        if self.is_worker():
            self._node_type_comm.barrier()
            return self._node_type_comm.allgather(obj)
        return None

    def _barrier_all(self):
        """
        barrier_all() will call MPI's barrier_all function
        """
        self._comm.barrier()

    def _finalize(self):
        """
        finalize the current MPI instance.
        """
        self.MPI.Finalize()

    def _get_ips(self):
        """
        collect current distributed job's ip list
        """
        if not self._ips:
            self._ips = self._comm.allgather(self.get_local_ip())
        return self._ips

    def get_local_ip(self):
        """
        return get local ip
        """
        import socket
        self._ip = socket.gethostbyname(socket.gethostname())
        return self._ip

    def generate_role(self):
        """
        generate_role() should be called to identify current process's role
        """
        raise NotImplementedError("Please implement this method in child class")


class MPISymetricRoleMaker(MPIRoleMaker):
    """
    MPISymetricRoleMaker is designed for worker and server assignment
    under MPI. Typically, a worker and a server node will be appointed
    on each physical node. This role maker can be only used under MPI.
    """

    def __init__(self):
        super(MPISymetricRoleMaker, self).__init__()
        self._node_type = None
        self._proc_per_node = 2
        self._pserver_rand_port = 0

    def _check_role_generation(self):
        if not self._role_is_generated:
            raise NameError("generate_role() should be called first")
        return True

    def is_first_worker(self):
        """
        return whether current process is the first worker assigned by role maker
        """
        if self._check_role_generation():
            return self.is_worker() and 0 == self.worker_index()
        return False

    def get_pserver_endpoints(self):
        if self._pserver_rand_port <= 0:
            import random
            random.seed(self._server_num())
            # port will be randomly generated from 60001 to 63999
            # random seed is server num so that all nodes will get
            # the same port
            self._pserver_rand_port = random.randint(60001, 64000)
        endpoints = [
            x + ":" + str(self._pserver_rand_port)
            for x in self._server_endpoints
        ]
        return endpoints

    def worker_num(self):
        return self._worker_num()

    def is_worker(self):
        """
        return whether current process is worker assigned by role maker
        """
        if self._check_role_generation():
            return self._node_type == 1
        return False

    def is_server(self):
        """
        return whether current process is server assigned by role maker
        """
        if self._check_role_generation():
            return self._node_type == 0
        return False

    def _worker_num(self):
        """
        return the current number of worker
        """
        if self._check_role_generation():
            if self.is_worker():
                return self._get_size() / self._proc_per_node
        return 0

    def _server_num(self):
        """
        return the current number of server
        """
        if self._check_role_generation():
            return self._get_size() / self._proc_per_node
        else:
            self.generate_role()
            return self._get_size() / self._proc_per_node

    def worker_index(self):
        """
        return the index of worker
        """
        if self._check_role_generation():
            return self._rank / self._proc_per_node
        else:
            self.generate_role()
            return self._get_size() / 2

    def server_index(self):
        """
        return the index of server
        """
        if self._check_role_generation():
            return self._rank / self._proc_per_node
        else:
            self.generate_role()
            return self._get_size() / self._proc_per_node

    def _barrier_worker(self):
        """
        barrier all workers in current distributed job
        """
        if self._check_role_generation():
            if self.is_worker():
                self._node_type_comm.barrier()
        else:
            raise Exception("You should check role generation first")

    def _barrier_server(self):
        """
        barrier all servers in current distributed job
        """
        if self._check_role_generation():
            if self.is_server():
                self._node_type_comm.barrier()
        else:
            raise Exception("You should check role generation first")

    def generate_role(self):
        """
        generate currently process's role
        """
        if not self._role_is_generated:
            # TODO(guru4elephant): only allow to be called once
            self._worker_endpoints = self._get_ips()[1::2]
            self._server_endpoints = self._get_ips()[::2]

            if 0 == self._get_rank() % self._proc_per_node % 2:
                self._node_type = 0
            else:
                self._node_type = 1
            self._node_type_comm = self._comm.Split(self._node_type)
            self._role_is_generated = True
        else:
            raise Exception("You should check role generation first")


class PaddleCloudRoleMaker(RoleMakerBase):
    def __init__(self, is_collective=False):
        super(PaddleCloudRoleMaker, self).__init__()
        self._role_is_generated = False
        self._is_collective = is_collective

    def generate_role(self):
        if not self._role_is_generated:
            if not self._is_collective:
                try:
                    port = os.environ["PADDLE_PORT"]
                    pserver_ips = os.environ["PADDLE_PSERVERS"].split(",")
                    if "," in port:
                        ports = port.split(",")
                    else:
                        ports = [port] * len(pserver_ips)
                    eplist = []
                    # note that, we usually assign the same port to different ips
                    # if we run parameter server training in local mode
                    # port should be different in environment variables
                    for i, ip in enumerate(pserver_ips):
                        eplist.append(':'.join([ip, ports[i]]))

                    trainers_num = int(os.environ["PADDLE_TRAINERS_NUM"])
                    training_role = os.environ["TRAINING_ROLE"]

                    if training_role not in ["TRAINER", "PSERVER"]:
                        raise ValueError(
                            "TRAINING_ROLE must be PSERVER or TRAINER")

                    if training_role == "TRAINER":
                        role = Role.WORKER
                        current_id = int(os.environ["PADDLE_TRAINER_ID"])
                    elif training_role == "PSERVER":
                        role = Role.SERVER
                        cur_ip = os.environ["POD_IP"]
                        cur_idx = pserver_ips.index(cur_ip)
                        current_id = eplist.index(":".join(
                            [cur_ip, ports[cur_idx]]))
                    else:
                        raise ValueError(
                            "TRAINING_ROLE must be PSERVER or TRAINER")
                except ValueError as ve:
                    raise ValueError(
                        "something wrong with PaddleCloud, please check environment"
                    )

                self._trainers_num = trainers_num
                self._server_endpoints = eplist
                self._role = role
                self._current_id = current_id
            else:
                self._current_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
                self._training_role = os.getenv("PADDLE_TRAINING_ROLE",
                                                "TRAINER")
                assert (self._training_role == "TRAINER")
                self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
                self._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
                assert self._worker_endpoints is not None, "can't find PADDLE_TRAINER_ENDPOINTS"
                self._worker_endpoints = self._worker_endpoints.split(",")
                self._trainers_num = len(self._worker_endpoints)

            self._role_is_generated = True

    def get_pserver_endpoints(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._server_endpoints

    def is_worker(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._role == Role.WORKER

    def is_server(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._role == Role.SERVER

    def is_first_worker(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._role == Role.WORKER and self._current_id == 0

    def worker_index(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._current_id

    def server_index(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._current_id

    def worker_num(self):
        if not self._role_is_generated:
            self.generate_role()
        return self._trainers_num


class UserDefinedRoleMaker(RoleMakerBase):
    def __init__(self,
                 current_id=0,
                 role=Role.WORKER,
                 worker_num=0,
                 server_endpoints=None):
        """
        UserDefinedRoleMaker is designed for worker and server assignment
        under manual. Typically, a worker and a server node will be appointed
        on each physical node, It can be assign by user.
        """
        super(UserDefinedRoleMaker, self).__init__()

        if not isinstance(server_endpoints, list):
            raise TypeError("server_endpoints must be as string list")
        elif len(server_endpoints) <= 0:
            raise ValueError(
                "the length of server_endpoints list must be greater than 0")
        elif len(server_endpoints) != len(set(server_endpoints)):
            raise ValueError("server_endpoints can't have duplicate elements")
        else:
            for server_endpoint in server_endpoints:
                if not isinstance(server_endpoint, str):
                    raise TypeError(
                        "every element in server_endpoints list must be as string"
                    )
            self._server_endpoints = server_endpoints

        if role != Role.WORKER and role != Role.SERVER:
            raise TypeError("role must be as Role")
        else:
            self._role = role

        if not isinstance(current_id, int):
            raise TypeError("current_id must be as int")
        else:
            if current_id < 0:
                raise ValueError(
                    "current_id must be greater than or equal to 0")
            elif self._role == Role.SERVER and current_id >= len(
                    server_endpoints):
                raise ValueError(
                    "if role is Role.SERVER, current_id must be less than or equal to len(server_endpoints) - 1"
                )
            self._current_id = current_id

        if not isinstance(worker_num, int):
            raise TypeError("worker_num must be as int")
        else:
            if worker_num <= 0:
                raise ValueError("worker_num must be greater than 0")
            self._worker_num = worker_num

    def generate_role(self):
        self._role_is_generated = True

    def is_worker(self):
        return self._role == Role.WORKER

    def is_server(self):
        return self._role == Role.SERVER

    def is_first_worker(self):
        return self._role == Role.WORKER and self._current_id == 0

    def worker_index(self):
        return self._current_id

    def server_index(self):
        return self._current_id

    def worker_num(self):
        return self._worker_num


class UserDefinedCollectiveRoleMaker(RoleMakerBase):
    def __init__(self, current_id=0, worker_endpoints=None):
        """
        UserDefinedCollectiveRoleMaker is designed for worker assignment
        under manual for collective mode.
        """
        super(UserDefinedCollectiveRoleMaker, self).__init__()

        if not isinstance(worker_endpoints, list):
            raise TypeError("worker_endpoints must be as string list")
        elif len(worker_endpoints) <= 0:
            raise ValueError(
                "the length of worker_endpoints list must be greater than 0")
        elif len(worker_endpoints) != len(set(worker_endpoints)):
            raise ValueError("worker_endpoints can't have duplicate elements")
        else:
            for worker_endpoint in worker_endpoints:
                if not isinstance(worker_endpoint, str):
                    raise TypeError(
                        "every element in worker_endpoints list must be as string"
                    )
            self._worker_endpoints = worker_endpoints

        if not isinstance(current_id, int):
            raise TypeError("current_id must be as int")
        else:
            if current_id < 0:
                raise ValueError(
                    "current_id must be greater than or equal to 0")
            elif current_id >= len(worker_endpoints):
                raise ValueError(
                    "current_id must be less than or equal to len(worker_endpoints) - 1"
                )
            self._current_id = current_id

        self._worker_num = len(self._worker_endpoints)

    def generate_role(self):
        self._role_is_generated = True

    def is_worker(self):
        return True

    def is_first_worker(self):
        return self._current_id == 0

    def worker_index(self):
        return self._current_id

    def worker_num(self):
        return self._worker_num
