# Copyright (c) 2016 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 os
import optimizer
import layer
import activation
import parameters
import trainer
import event
import data_type
import topology
import networks
import evaluator
from . import dataset
from . import reader
from . import plot
import attr
import op
import pooling
import inference
import networks
import minibatch
import plot
import image
import paddle.trainer.config_parser as cp

__all__ = [
    'default_startup_program',
    'default_main_program',
    'optimizer',
    'layer',
    'activation',
    'parameters',
    'init',
    'trainer',
    'event',
    'data_type',
    'attr',
    'pooling',
    'dataset',
    'reader',
    'topology',
    'networks',
    'infer',
    'plot',
    'evaluator',
    'image',
    'master',
]

cp.begin_parse()


def set_env_vars(trainer_count):
    '''Auto set CPU environment if have not set before.
       For MKL:
         export KMP_AFFINITY, OMP_DYNAMIC according to the Hyper Threading status.
         export OMP_NUM_THREADS, MKL_NUM_THREADS according to trainer_count.
       For OpenBLAS:
         export OPENBLAS_NUM_THREADS, OPENBLAS_MAIN_FREE according to trainer_count. 
    '''
    import platform, paddle
    if not platform.system() in ['Linux', 'Darwin']:
        return

    def set_env(key, value):
        '''If the key has not been set in the environment, set it with value.'''
        assert isinstance(key, str)
        assert isinstance(value, str)
        envset = os.environ.get(key)
        if envset is None:
            os.environ[key] = value

    def num_physical_cores():
        '''Get the number of physical cores'''
        if platform.system() == "Linux":
            num_sockets = int(
                os.popen("grep 'physical id' /proc/cpuinfo | sort -u | wc -l")
                .read())
            num_cores_per_socket = int(
                os.popen("grep 'core id' /proc/cpuinfo | sort -u | wc -l")
                .read())
            return num_sockets * num_cores_per_socket
        else:
            cmds = {"Darwin": "sysctl -n hw.physicalcpu"}
            return int(os.popen(cmds.get(platform.system(), "expr 1")).read())

    def num_logical_processors():
        '''Get the number of logical processors'''
        cmds = {
            "Linux": "grep \"processor\" /proc/cpuinfo|sort -u|wc -l",
            "Darwin": "sysctl -n hw.logicalcpu"
        }
        return int(os.popen(cmds.get(platform.system(), "expr 1")).read())

    num_cores = num_physical_cores()
    num_processors = num_logical_processors()
    if paddle.version.mkl() == 'ON':
        if num_processors > num_cores:  # Hyper Threading is enabled
            set_env("OMP_DYNAMIC", "true")
            set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
        else:
            set_env("OMP_DYNAMIC", "false")
            set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
    threads = num_processors / trainer_count
    threads = '1' if threads < 1 else str(threads)
    if paddle.version.mkl() == 'ON':
        set_env("OMP_NUM_THREADS", threads)
        set_env("MKL_NUM_THREADS", threads)
    else:
        set_env("OPENBLAS_NUM_THREADS", threads)
        if threads > 1:
            set_env("OPENBLAS_MAIN_FREE", '1')


def init(**kwargs):
    import py_paddle.swig_paddle as api
    args = []
    args_dict = {}
    # NOTE: append arguments if they are in ENV
    for ek, ev in os.environ.iteritems():
        if ek.startswith("PADDLE_INIT_"):
            args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)

    args_dict.update(kwargs)
    # NOTE: overwrite arguments from ENV if it is in kwargs
    for key in args_dict.keys():
        args.append('--%s=%s' % (key, str(args_dict[key])))

    set_env_vars(kwargs.get('trainer_count', 1))

    if 'use_gpu' in kwargs:
        cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
    if 'use_mkldnn' in kwargs:
        cp.g_command_config_args['use_mkldnn'] = kwargs['use_mkldnn']
    if 'use_mkl_packed' in kwargs:
        cp.g_command_config_args['use_mkl_packed'] = kwargs['use_mkl_packed']
    assert 'parallel_nn' not in kwargs, ("currently 'parallel_nn' is not "
                                         "supported in v2 APIs.")

    api.initPaddle(*args)


infer = inference.infer
batch = minibatch.batch