#   Copyright (c) 2018 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 multiprocessing
import os
import six
import sys
from .. import compat as cpt

from . import core

ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy


def _place_obj(place):
    p = core.Place()
    p.set_place(place)
    return p


class CompiledProgram(object):
    """
    Compiles a Program for execution.

    The CompiledProgram is used to transform a program for various
    optimizations, for example.
      * Pre-compute some logic once so that each run is faster.
      * Transform the program so that it can run in multiple devices.
      * TODO: transform the program for optimized inference or distributed
              training.

    Example:


    Args:
        program: Program instance that contains the model logic.

    """

    def __init__(self, program):
        self._program = program
        self._scope = None
        self._place = None
        self._executor = None
        self._compiled = False
        self._is_data_parallel = False

    def _with_data_parallel(self,
                            loss_name=None,
                            build_strategy=None,
                            exec_strategy=None,
                            share_vars_from=None):
        assert not self._is_data_parallel, "Already compiled with parallel."
        self._is_data_parallel = True
        self._build_strategy = build_strategy
        self._exec_strategy = exec_strategy
        self._loss_name = loss_name
        self._share_vars_from = share_vars_from
        return self

    def _with_distributed(self):
        raise NotImplementedError()

    def _with_inference_optimize(self):
        raise NotImplementedError()

    def _compile_data_parallel(self):
        if self._share_vars_from:
            if self._scope:
                sys.stderr.write("share_vars_from is set, scope is ignored.\n")
            if not self._share_vars_from._is_data_parallel:
                raise ValueError("share_vars_from is not data parallel. Cannot "
                                 "share vars from it.")
            if self._share_vars_from._executor is None:
                raise ValueError(
                    "share_vars_from is not compiled and run, so there is no "
                    "var to share.")
            self._local_scopes = self._share_vars_from._executor.local_scopes()
        else:
            self._local_scopes = []

        self._places = []
        if self._exec_strategy is None:
            self._exec_strategy = ExecutionStrategy()
        if self._build_strategy is None:
            self._build_strategy = BuildStrategy()

        self._exec_strategy.use_cuda = isinstance(self._place, core.CUDAPlace)
        if self._exec_strategy.use_cuda:
            gpus_env = os.getenv("FLAGS_selected_gpus")
            if gpus_env:
                gpus = [int(s) for s in gpus_env.split(",")]
            else:
                gpus = [
                    i for i in six.moves.range(core.get_cuda_device_count())
                ]
            self._places = [core.CUDAPlace(i) for i in gpus]
        else:
            cpu_num = int(
                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
            self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
        assert self._places, "no place for execution"

        if self._exec_strategy.num_threads == 0:
            if self._exec_strategy.use_cuda:
                # Experiments on se-resnext shows that too many threads hurt
                # performance. Worth tunning for other models in the future.
                self._exec_strategy.num_threads = len(self._places) * 4
            else:
                cpu_num = int(
                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
                self._exec_strategy.num_threads = cpu_num * 2

        trainers_endpoints = self._program._trainers_endpoints
        if self._build_strategy.num_trainers > 1 and trainers_endpoints:
            assert self._build_strategy.num_trainers == len(
                trainers_endpoints), "num_trainers == len(end_points)"
            self._build_strategy.trainers_endpoints = trainers_endpoints

        self._persistable_vars = set([
            cpt.to_text(v.name)
            for v in [
                var for var in self._program.list_vars()
                if var.persistable and var.type != core.VarDesc.VarType.RAW
            ]
        ])

        places = list(map(_place_obj, self._places))
        return core.ParallelExecutor(
            places, self._persistable_vars, self._program.desc,
            cpt.to_text(self._loss_name)
            if self._loss_name else six.u(''), self._scope, self._local_scopes,
            self._exec_strategy, self._build_strategy)

    def _compile(self, scope, place):
        if self._compiled:
            if scope and self._scope != scope:
                raise ValueError("Cannot compile with different scope")
            if place and self._place != place:
                raise ValueError("Cannot compile with different place")
            return self

        self._scope = scope
        self._place = place
        if self._is_data_parallel:
            self._executor = self._compile_data_parallel()
        else:
            p = _place_obj(self._place)
            self._executor = core.Executor(p)
        return self