# Copyright (c) 2020 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

import six
import abc
import numpy as np
import paddle.fluid as fluid

import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)

__all__ = ['Metric', 'Accuracy']


@six.add_metaclass(abc.ABCMeta)
class Metric(object):
    """
    Base class for metric, encapsulates metric logic and APIs

    Usage:
    m = SomeMetric()
    for prediction, label in ...:
        m.update(prediction, label)
    m.accumulate()
    """

    @abc.abstractmethod
    def reset(self):
        """
        Reset states and result
        """
        raise NotImplementedError("function 'reset' not implemented in {}.".
                                  format(self.__class__.__name__))

    @abc.abstractmethod
    def update(self, *args):
        """
        Update states for metric

        Inputs of :code:`update` is the outputs of :code:`Metric.add_metric_op`,
        if :code:`add_metric_op` is not defined, the inputs of :code:`update`
        will be flatten arguments of **output** of mode and **label** from data:
        :code:`update(output1, output2, ..., label1, label2,...)`

        see :code:`Metric.add_metric_op`
        """
        raise NotImplementedError("function 'update' not implemented in {}.".
                                  format(self.__class__.__name__))

    @abc.abstractmethod
    def accumulate(self):
        """
        Accumulates statistics, computes and returns the metric value
        """
        raise NotImplementedError(
            "function 'accumulate' not implemented in {}.".format(
                self.__class__.__name__))

    @abc.abstractmethod
    def name(self):
        """
        Returns metric name
        """
        raise NotImplementedError("function 'name' not implemented in {}.".
                                  format(self.__class__.__name__))

    def add_metric_op(self, *args):
        """
        This API is advanced usage to accelerate metric calculating, calulations
        from outputs of model to the states which should be updated by Metric can
        be defined here, where Paddle OPs is also supported. Outputs of this API
        will be the inputs of "Metric.update".

        If :code:`add_metric_op` is defined, it will be called with **outputs**
        of model and **labels** from data as arguments, all outputs and labels
        will be concatenated and flatten and each filed as a separate argument
        as follows:
        :code:`add_metric_op(output1, output2, ..., label1, label2,...)`

        If :code:`add_metric_op` is not defined, default behaviour is to pass
        input to output, so output format will be:
        :code:`return output1, output2, ..., label1, label2,...`

        see :code:`Metric.update`
        """
        return args


class Accuracy(Metric):
    """
    Encapsulates accuracy metric logic
    """

    def __init__(self, topk=(1, ), name=None, *args, **kwargs):
        super(Accuracy, self).__init__(*args, **kwargs)
        self.topk = topk
        self.maxk = max(topk)
        self._init_name(name)
        self.reset()

    def add_metric_op(self, pred, label, *args):
        pred = fluid.layers.argsort(pred, descending=True)[1][:, :self.maxk]
        correct = pred == label
        return fluid.layers.cast(correct, dtype='float32')

    def update(self, correct, *args):
        accs = []
        for i, k in enumerate(self.topk):
            num_corrects = correct[:, :k].sum()
            num_samples = len(correct)
            accs.append(float(num_corrects) / num_samples)
            self.total[i] += num_corrects
            self.count[i] += num_samples
        return accs

    def reset(self):
        self.total = [0.] * len(self.topk)
        self.count = [0] * len(self.topk)

    def accumulate(self):
        res = []
        for t, c in zip(self.total, self.count):
            res.append(float(t) / c)
        return res

    def _init_name(self, name):
        name = name or 'acc'
        if self.maxk != 1:
            self._name = ['{}_top{}'.format(name, k) for k in self.topk]
        else:
            self._name = [name]

    def name(self):
        return self._name
