"""
 Copyright 2020 Tianshu AI Platform. 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 numpy as np
import torch

from kamal.core.metrics.stream_metrics import Metric
from typing import Callable

__all__=['MeanSquaredError', 'RootMeanSquaredError', 'MeanAbsoluteError', 
         'ScaleInveriantMeanSquaredError', 'RelativeDifference', 
         'AbsoluteRelativeDifference', 'SquaredRelativeDifference', 'Threshold' ]

class MeanSquaredError(Metric):
    def __init__(self, log_scale=False, attach_to=None):
        super(MeanSquaredError, self).__init__( attach_to=attach_to )
        self.reset()
        self.log_scale=log_scale

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        if self.log_scale:
            diff = torch.sum((torch.log(outputs+1e-8) - torch.log(targets+1e-8))**2)
        else:
            diff = torch.sum((outputs - targets)**2)
        self._accum_sq_diff += diff
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_sq_diff / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_sq_diff = 0.
        self._cnt = 0.


class RootMeanSquaredError(MeanSquaredError):
    def get_results(self):
        return torch.sqrt( (self._accum_sq_diff / self._cnt) ).detach().cpu()


class MeanAbsoluteError(Metric):
    def __init__(self, attach_to=None ):
        super(MeanAbsoluteError, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff = torch.sum((outputs - targets).abs())
        self._accum_abs_diff += diff
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_abs_diff / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_abs_diff = 0.
        self._cnt = 0.


class ScaleInveriantMeanSquaredError(Metric):
    def __init__(self, attach_to=None ):
        super(ScaleInveriantMeanSquaredError, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff_log = torch.log( outputs+1e-8 ) - torch.log( targets+1e-8 )
        self._accum_log_diff = diff_log.sum()
        self._accum_sq_log_diff = (diff_log**2).sum()
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return ( self._accum_sq_log_diff / self._cnt - 0.5 * (self._accum_log_diff**2 / self._cnt**2) ).detach().cpu()
    
    def reset(self):
        self._accum_log_diff = 0.
        self._accum_sq_log_diff = 0.
        self._cnt = 0.


class RelativeDifference(Metric):
    def __init__(self, attach_to=None ):
        super(RelativeDifference, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff = (outputs - targets).abs()
        self._accum_abs_rel += (diff/targets).sum()
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_abs_rel / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_abs_rel = 0.
        self._cnt = 0.


class AbsoluteRelativeDifference(Metric):
    def __init__(self, attach_to=None ):
        super(AbsoluteRelativeDifference, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff = (outputs - targets).abs()
        self._accum_abs_rel += (diff/targets).sum()
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_abs_rel / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_abs_rel = 0.
        self._cnt = 0.


class AbsoluteRelativeDifference(Metric):
    def __init__(self, attach_to=None ):
        super(AbsoluteRelativeDifference, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff = (outputs - targets).abs()
        self._accum_abs_rel += (diff/targets).sum()
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_abs_rel / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_abs_rel = 0.
        self._cnt = 0.


class SquaredRelativeDifference(Metric):
    def __init__(self, attach_to=None ):
        super(SquaredRelativeDifference, self).__init__( attach_to=attach_to )
        self.reset()

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        diff = (outputs - targets)**2
        self._accum_sq_rel += (diff/targets).sum()
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return (self._accum_sq_rel / self._cnt).detach().cpu()
    
    def reset(self):
        self._accum_sq_rel = 0.
        self._cnt = 0.


class Threshold(Metric):
    def __init__(self, thresholds=[1.25, 1.25**2, 1.25**3], attach_to=None ):
        super(Threshold, self).__init__( attach_to=attach_to )
        self.thresholds = thresholds
        self.reset()
        

    @torch.no_grad()
    def update(self, outputs, targets):
        outputs, targets = self._attach(outputs, targets)
        sigma = torch.max(outputs / targets, targets / outputs)
        for thres in self.thresholds:
            self._accum_thres[thres]+=torch.sum( sigma<thres )
        self._cnt += torch.numel(outputs)

    def get_results(self):
        return { thres: (self._accum_thres[thres] / self._cnt).detach().cpu() for thres in self.thresholds }
    
    def reset(self):
        self._cnt = 0.
        self._accum_thres = {thres: 0. for thres in self.thresholds}