#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 Intel Corporation
#
# 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 copy
import itertools
from collections import OrderedDict
from .strategy import strategy_registry, TuneStrategy


@strategy_registry
class ExhaustiveTuneStrategy(TuneStrategy):
    """The tuning strategy using exhaustive search in tuning space.

    Args:
        model (object):                        The FP32 model specified for low precision tuning.
        conf (Conf):                           The Conf class instance initialized from user yaml
                                               config file.
        q_dataloader (generator):              Data loader for calibration, mandatory for
                                               post-training quantization.
                                               It is iterable and should yield a tuple (input,
                                               label) for calibration dataset containing label,
                                               or yield (input, _) for label-free calibration
                                               dataset. The input could be a object, list, tuple or
                                               dict, depending on user implementation, as well as
                                               it can be taken as model input.
        q_func (function, optional):           Reserved for future use.
        eval_dataloader (generator, optional): Data loader for evaluation. It is iterable
                                               and should yield a tuple of (input, label).
                                               The input could be a object, list, tuple or dict,
                                               depending on user implementation, as well as it can
                                               be taken as model input. The label should be able
                                               to take as input of supported metrics. If this
                                               parameter is not None, user needs to specify
                                               pre-defined evaluation metrics through configuration
                                               file and should set "eval_func" parameter as None.
                                               Tuner will combine model, eval_dataloader and
                                               pre-defined metrics to run evaluation process.
        eval_func (function, optional):        The evaluation function provided by user.
                                               This function takes model as parameter, and
                                               evaluation dataset and metrics should be
                                               encapsulated in this function implementation and
                                               outputs a higher-is-better accuracy scalar value.

                                               The pseudo code should be something like:

                                               def eval_func(model):
                                                    input, label = dataloader()
                                                    output = model(input)
                                                    accuracy = metric(output, label)
                                                    return accuracy
        dicts (dict, optional):                The dict containing resume information.
                                               Defaults to None.

    """

    def __init__(self, model, conf, q_dataloader, q_func=None,
                 eval_dataloader=None, eval_func=None, dicts=None):
        super().__init__(
            model,
            conf,
            q_dataloader,
            q_func,
            eval_dataloader,
            eval_func,
            dicts)

    def next_tune_cfg(self):
        # generate tuning space according to user chosen tuning strategy

        op_cfgs = {}
        op_cfgs['op'] = OrderedDict()
        for iterations in self.calib_iter:
            op_cfgs['calib_iteration'] = int(iterations)
            op_lists = []
            op_cfg_lists = []
            for op, configs in self.opwise_quant_cfgs.items():
                if len(configs) == 0:
                    configs = copy.deepcopy(
                        self.opwise_tune_cfgs[op])
                op_lists.append(op)
                op_cfg_lists.append(configs)
            for cfgs in itertools.product(*op_cfg_lists):
                index = 0
                for cfg in cfgs:
                    op_cfgs['op'][op_lists[index]] = cfg
                    index += 1

                yield op_cfgs

        return
