# 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 absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import argparse
import json
import yaml
import six
import logging

logging_only_message = "%(message)s"
logging_details = "%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s"


class JsonConfig(object):
    """
    A high-level api for handling json configure file.
    """

    def __init__(self, config_path):
        self._config_dict = self._parse(config_path)

    def _parse(self, config_path):
        try:
            with open(config_path) as json_file:
                config_dict = json.load(json_file)
        except:
            raise IOError("Error in parsing bert model config file '%s'" %
                          config_path)
        else:
            return config_dict

    def __getitem__(self, key):
        return self._config_dict[key]

    def print_config(self):
        for arg, value in sorted(six.iteritems(self._config_dict)):
            print('%s: %s' % (arg, value))
        print('------------------------------------------------')


class ArgumentGroup(object):
    def __init__(self, parser, title, des):
        self._group = parser.add_argument_group(title=title, description=des)

    def add_arg(self, name, type, default, help, **kwargs):
        type = str2bool if type == bool else type
        self._group.add_argument(
            "--" + name,
            default=default,
            type=type,
            help=help + ' Default: %(default)s.',
            **kwargs)


class ArgConfig(object):
    """
    A high-level api for handling argument configs.
    """

    def __init__(self):
        parser = argparse.ArgumentParser()

        train_g = ArgumentGroup(parser, "training", "training options.")
        train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
        train_g.add_arg("learning_rate", float, 5e-5,
                        "Learning rate used to train with warmup.")
        train_g.add_arg(
            "lr_scheduler",
            str,
            "linear_warmup_decay",
            "scheduler of learning rate.",
            choices=['linear_warmup_decay', 'noam_decay'])
        train_g.add_arg("weight_decay", float, 0.01,
                        "Weight decay rate for L2 regularizer.")
        train_g.add_arg(
            "warmup_proportion", float, 0.1,
            "Proportion of training steps to perform linear learning rate warmup for."
        )
        train_g.add_arg("save_steps", int, 1000,
                        "The steps interval to save checkpoints.")
        train_g.add_arg("use_fp16", bool, False,
                        "Whether to use fp16 mixed precision training.")
        train_g.add_arg(
            "loss_scaling", float, 1.0,
            "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled."
        )
        train_g.add_arg("pred_dir", str, None,
                        "Path to save the prediction results")

        log_g = ArgumentGroup(parser, "logging", "logging related.")
        log_g.add_arg("skip_steps", int, 10,
                      "The steps interval to print loss.")
        log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")

        run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
        run_type_g.add_arg("use_cuda", bool, True,
                           "If set, use GPU for training.")
        run_type_g.add_arg(
            "use_fast_executor", bool, False,
            "If set, use fast parallel executor (in experiment).")
        run_type_g.add_arg(
            "num_iteration_per_drop_scope", int, 1,
            "Ihe iteration intervals to clean up temporary variables.")
        run_type_g.add_arg("do_train", bool, True,
                           "Whether to perform training.")
        run_type_g.add_arg("do_predict", bool, True,
                           "Whether to perform prediction.")

        custom_g = ArgumentGroup(parser, "customize", "customized options.")

        self.custom_g = custom_g

        self.parser = parser

    def add_arg(self, name, dtype, default, descrip):
        self.custom_g.add_arg(name, dtype, default, descrip)

    def build_conf(self):
        return self.parser.parse_args()


def str2bool(v):
    # because argparse does not support to parse "true, False" as python
    # boolean directly
    return v.lower() in ("true", "t", "1")


def print_arguments(args, log=None):
    if not log:
        print('-----------  Configuration Arguments -----------')
        for arg, value in sorted(six.iteritems(vars(args))):
            print('%s: %s' % (arg, value))
        print('------------------------------------------------')
    else:
        log.info('-----------  Configuration Arguments -----------')
        for arg, value in sorted(six.iteritems(vars(args))):
            log.info('%s: %s' % (arg, value))
        log.info('------------------------------------------------')


class PDConfig(object):
    """
    A high-level API for managing configuration files in PaddlePaddle.
    Can jointly work with command-line-arugment, json files and yaml files.
    """

    def __init__(self, json_file="", yaml_file="", fuse_args=True):
        """
            Init funciton for PDConfig.
            json_file: the path to the json configure file.
            yaml_file: the path to the yaml configure file.
            fuse_args: if fuse the json/yaml configs with argparse.
        """
        assert isinstance(json_file, str)
        assert isinstance(yaml_file, str)

        if json_file != "" and yaml_file != "":
            raise Warning(
                "json_file and yaml_file can not co-exist for now. please only use one configure file type."
            )
            return

        self.args = None
        self.arg_config = {}
        self.json_config = {}
        self.yaml_config = {}

        parser = argparse.ArgumentParser()

        self.default_g = ArgumentGroup(parser, "default", "default options.")
        self.yaml_g = ArgumentGroup(parser, "yaml", "options from yaml.")
        self.json_g = ArgumentGroup(parser, "json", "options from json.")
        self.com_g = ArgumentGroup(parser, "custom", "customized options.")

        self.default_g.add_arg("do_train", bool, False,
                               "Whether to perform training.")
        self.default_g.add_arg("do_predict", bool, False,
                               "Whether to perform predicting.")
        self.default_g.add_arg("do_eval", bool, False,
                               "Whether to perform evaluating.")
        self.default_g.add_arg(
            "do_save_inference_model", bool, False,
            "Whether to perform model saving for inference.")

        # NOTE: args for profiler
        self.default_g.add_arg(
            "is_profiler", int, 0,
            "the switch of profiler tools. (used for benchmark)")
        self.default_g.add_arg(
            "profiler_path", str, './',
            "the profiler output file path. (used for benchmark)")
        self.default_g.add_arg("max_iter", int, 0,
                               "the max train batch num.(used for benchmark)")

        self.parser = parser

        if json_file != "":
            self.load_json(json_file, fuse_args=fuse_args)

        if yaml_file:
            self.load_yaml(yaml_file, fuse_args=fuse_args)

    def load_json(self, file_path, fuse_args=True):

        if not os.path.exists(file_path):
            raise Warning("the json file %s does not exist." % file_path)
            return

        with open(file_path, "r") as fin:
            self.json_config = json.loads(fin.read())
            fin.close()

        if fuse_args:
            for name in self.json_config:
                if isinstance(self.json_config[name], list):
                    self.json_g.add_arg(
                        name,
                        type(self.json_config[name][0]),
                        self.json_config[name],
                        "This is from %s" % file_path,
                        nargs=len(self.json_config[name]))
                    continue
                if not isinstance(self.json_config[name], int) \
                    and not isinstance(self.json_config[name], float) \
                    and not isinstance(self.json_config[name], str) \
                    and not isinstance(self.json_config[name], bool):

                    continue

                self.json_g.add_arg(name,
                                    type(self.json_config[name]),
                                    self.json_config[name],
                                    "This is from %s" % file_path)

    def load_yaml(self, file_path, fuse_args=True):

        if not os.path.exists(file_path):
            raise Warning("the yaml file %s does not exist." % file_path)
            return

        with open(file_path, "r") as fin:
            self.yaml_config = yaml.load(fin, Loader=yaml.SafeLoader)
            fin.close()

        if fuse_args:
            for name in self.yaml_config:
                if isinstance(self.yaml_config[name], list):
                    self.yaml_g.add_arg(
                        name,
                        type(self.yaml_config[name][0]),
                        self.yaml_config[name],
                        "This is from %s" % file_path,
                        nargs=len(self.yaml_config[name]))
                    continue

                if not isinstance(self.yaml_config[name], int) \
                    and not isinstance(self.yaml_config[name], float) \
                    and not isinstance(self.yaml_config[name], str) \
                    and not isinstance(self.yaml_config[name], bool):

                    continue

                self.yaml_g.add_arg(name,
                                    type(self.yaml_config[name]),
                                    self.yaml_config[name],
                                    "This is from %s" % file_path)

    def build(self):
        self.args = self.parser.parse_args()
        self.arg_config = vars(self.args)

    def __add__(self, new_arg):
        assert isinstance(new_arg, list) or isinstance(new_arg, tuple)
        assert len(new_arg) >= 3
        assert self.args is None

        name = new_arg[0]
        dtype = new_arg[1]
        dvalue = new_arg[2]
        desc = new_arg[3] if len(
            new_arg) == 4 else "Description is not provided."

        self.com_g.add_arg(name, dtype, dvalue, desc)

        return self

    def __getattr__(self, name):
        if name in self.arg_config:
            return self.arg_config[name]

        if name in self.json_config:
            return self.json_config[name]

        if name in self.yaml_config:
            return self.yaml_config[name]

        raise Warning("The argument %s is not defined." % name)

    def Print(self):

        print("-" * 70)
        for name in self.arg_config:
            print("%s:\t\t\t\t%s" % (str(name), str(self.arg_config[name])))

        for name in self.json_config:
            if name not in self.arg_config:
                print("%s:\t\t\t\t%s" %
                      (str(name), str(self.json_config[name])))

        for name in self.yaml_config:
            if name not in self.arg_config:
                print("%s:\t\t\t\t%s" %
                      (str(name), str(self.yaml_config[name])))

        print("-" * 70)


if __name__ == "__main__":
    """
    pd_config = PDConfig(json_file = "./test/bert_config.json")
    pd_config.build()

    print(pd_config.do_train)
    print(pd_config.hidden_size)

    pd_config = PDConfig(yaml_file = "./test/bert_config.yaml")
    pd_config.build()

    print(pd_config.do_train)
    print(pd_config.hidden_size)
    """

    pd_config = PDConfig(yaml_file="./test/bert_config.yaml")
    pd_config += ("my_age", int, 18, "I am forever 18.")
    pd_config.build()

    print(pd_config.do_train)
    print(pd_config.hidden_size)
    print(pd_config.my_age)