import os #line:1
import math #line:2
from decimal import Decimal #line:3
from .import utility #line:5
import scipy .misc #line:6
import torch #line:7
from torch .autograd import Variable #line:8
from tqdm import tqdm #line:9
import pickle #line:10
import numpy as np #line:11
class Trainer ():#line:12
    def __init__ (O0000O0O0OOOO000O ,OO0OO0OOOOO00O000 ,O000OOO0000O0OO0O ,OO0000OOOO0O0O0OO ,O0OOOO00O0OOO0O00 ,O0OO00OO0O00O0O00 ):#line:13
        O0000O0O0OOOO000O .args =OO0OO0OOOOO00O000 #line:14
        O0000O0O0OOOO000O .scale =OO0OO0OOOOO00O000 .scale #line:15
        O0000O0O0OOOO000O .ckp =O0OO00OO0O00O0O00 #line:17
        O0000O0O0OOOO000O .loader_train =O000OOO0000O0OO0O .loader_train #line:18
        O0000O0O0OOOO000O .loader_test =O000OOO0000O0OO0O .loader_test #line:19
        print ('***',len (O0000O0O0OOOO000O .loader_test ))#line:20
        O0000O0O0OOOO000O .model =OO0000OOOO0O0O0OO #line:22
        O0000O0O0OOOO000O .loss =O0OOOO00O0OOO0O00 #line:23
        O0000O0O0OOOO000O .optimizer =utility .make_optimizer (OO0OO0OOOOO00O000 ,O0000O0O0OOOO000O .model )#line:24
        O0000O0O0OOOO000O .scheduler =utility .make_scheduler (OO0OO0OOOOO00O000 ,O0000O0O0OOOO000O .optimizer )#line:25
        if O0000O0O0OOOO000O .args .load !='.':#line:27
            O0000O0O0OOOO000O .optimizer .load_state_dict (torch .load (os .path .join (O0OO00OO0O00O0O00 .dir ,'optimizer.pt')))#line:30
            for _OO00OO0OOO00OO0O0 in range (len (O0OO00OO0O00O0O00 .log )):O0000O0O0OOOO000O .scheduler .step ()#line:31
        O0000O0O0OOOO000O .error_last =1e8 #line:33
    def train (OO0OOOOO0000000OO ):#line:35
        OO0OOOOO0000000OO .scheduler .step ()#line:36
        OO0OOOOO0000000OO .loss .step ()#line:37
        OO0OOO0O00OO000O0 =OO0OOOOO0000000OO .scheduler .last_epoch +1 #line:38
        OOOOO0OO000OO0O0O =OO0OOOOO0000000OO .scheduler .get_lr ()[0 ]#line:39
        OO0OOOOO0000000OO .ckp .write_log ('[Epoch {}]\tLearning rate: {:.2e}'.format (OO0OOO0O00OO000O0 ,Decimal (OOOOO0OO000OO0O0O )))#line:43
        OO0OOOOO0000000OO .loss .start_log ()#line:44
        OO0OOOOO0000000OO .model .train ()#line:45
        O00OO0OOOO0OOOO00 ,O00O000OO0OOO0O0O =utility .timer (),utility .timer ()#line:47
        for O0OOO0OO0O000OOO0 ,(OOOOO0OO000OO0O0O ,O000OO0OOO000O000 ,OOO0O000O00O00O00 ,_OO0O0O000OO0OOOO0 ,O0O000O000000O000 )in enumerate (OO0OOOOO0000000OO .loader_train ):#line:48
            OOOOO0OO000OO0O0O ,O000OO0OOO000O000 ,OOO0O000O00O00O00 =OO0OOOOO0000000OO .prepare (OOOOO0OO000OO0O0O ,O000OO0OOO000O000 ,OOO0O000O00O00O00 )#line:49
            O00OO0OOOO0OOOO00 .hold ()#line:50
            O00O000OO0OOO0O0O .tic ()#line:51
            OO0OOOOO0000000OO .optimizer .zero_grad ()#line:53
            O00000OO0OO0OOOOO =OO0OOOOO0000000OO .model (OOOOO0OO000OO0O0O ,O0O000O000000O000 ,OOO0O000O00O00O00 )#line:54
            O0000000O0O0O0OOO =OO0OOOOO0000000OO .loss (O00000OO0OO0OOOOO ,O000OO0OOO000O000 )#line:55
            if O0000000O0O0O0OOO .item ()<OO0OOOOO0000000OO .args .skip_threshold *OO0OOOOO0000000OO .error_last :#line:56
                O0000000O0O0O0OOO .backward ()#line:57
                OO0OOOOO0000000OO .optimizer .step ()#line:58
            else :#line:59
                print ('Skip this batch {}! (Loss: {})'.format (O0OOO0OO0O000OOO0 +1 ,O0000000O0O0O0OOO .item ()))#line:62
            O00O000OO0OOO0O0O .hold ()#line:64
            if (O0OOO0OO0O000OOO0 +1 )%OO0OOOOO0000000OO .args .print_every ==0 :#line:66
                OO0OOOOO0000000OO .ckp .write_log ('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format ((O0OOO0OO0O000OOO0 +1 )*OO0OOOOO0000000OO .args .batch_size ,len (OO0OOOOO0000000OO .loader_train .dataset ),OO0OOOOO0000000OO .loss .display_loss (O0OOO0OO0O000OOO0 ),O00O000OO0OOO0O0O .release (),O00OO0OOOO0OOOO00 .release ()))#line:72
            O00OO0OOOO0OOOO00 .tic ()#line:74
        OO0OOOOO0000000OO .loss .end_log (len (OO0OOOOO0000000OO .loader_train ))#line:76
        OO0OOOOO0000000OO .error_last =OO0OOOOO0000000OO .loss .log [-1 ,-1 ]#line:77
    def test (OO0OOOO0O00OOOOOO ):#line:79
        O00OOOOOO00OO0O0O =OO0OOOO0O00OOOOOO .scheduler .last_epoch +1 #line:80
        OO0OOOO0O00OOOOOO .ckp .write_log ('\nEvaluation:')#line:81
        OO0OOOO0O00OOOOOO .ckp .add_log (torch .zeros (1 ,len (OO0OOOO0O00OOOOOO .scale )))#line:82
        OO0OOOO0O00OOOOOO .model .eval ()#line:83
        O000OOOO0000O0O00 =utility .timer ()#line:85
        O00O00OO0O0000O00 =0 #line:86
        OOO0OO00O0000OO0O =1 #line:87
        print ("debug---trainer_test")#line:88
        with torch .no_grad ():#line:89
            for O00OO0O0O0000OOO0 ,OOOOOO0OO0000O0O0 in enumerate (OO0OOOO0O00OOOOOO .scale ):#line:90
                print (O00OO0O0O0000OOO0 ,OOOOOO0OO0000O0O0 )#line:91
                OOOOO00O0O0000O00 =0 #line:92
                OO0OOOO0O00OOOOOO .loader_test .dataset .set_scale (O00OO0O0O0000OOO0 )#line:94
                OO0OOOO0OO000O000 =tqdm (OO0OOOO0O00OOOOOO .loader_test ,ncols =80 )#line:95
                for OOO0O0O0O00OOO00O ,(OO00O0OOO00000O0O ,OOO0OOO0OOOO0OOOO ,O00OOOO0O000000O0 ,O00O0O0O0000OOO00 ,_O0O00000OOOO0O000 )in enumerate (OO0OOOO0OO000O000 ):#line:96
                    O00O0O0O0000OOO00 =O00O0O0O0000OOO00 [0 ]#line:98
                    OO00O0OOO00000O0O =OO0OOOO0O00OOOOOO .prepare (OO00O0OOO00000O0O )[0 ][0 ].permute (1 ,0 ,2 ,3 )#line:99
                    O00OOOO0O000000O0 =torch .cat ([OO0OOOO0O00OOOOOO .prepare (O00OOOO0O000000O0 )[0 ]]*OOO0OO00O0000OO0O )#line:101
                    print (OO00O0OOO00000O0O .shape [0 ])#line:103
                    for O0O0O00O0OO0O0OOO in range (OO00O0OOO00000O0O .shape [0 ]//OOO0OO00O0000OO0O ):#line:104
                        if O0O0O00O0OO0O0OOO ==0 :#line:107
                            OOO0OO0OOO0000O00 =OO0OOOO0O00OOOOOO .model (OO00O0OOO00000O0O [O0O0O00O0OO0O0OOO *OOO0OO00O0000OO0O :(O0O0O00O0OO0O0OOO +1 )*OOO0OO00O0000OO0O ],O00OO0O0O0000OOO0 ,O00OOOO0O000000O0 ).permute (1 ,0 ,2 ,3 ).data .cpu ().numpy ()#line:108
                            print ("debug---i==0")#line:109
                            print (OOO0OO0OOO0000O00 .shape )#line:110
                        else :#line:112
                            print ("debug---else")#line:113
                            OOO0OO0OOO0000O00 =np .concatenate ((OOO0OO0OOO0000O00 ,OO0OOOO0O00OOOOOO .model (OO00O0OOO00000O0O [O0O0O00O0OO0O0OOO *OOO0OO00O0000OO0O :(O0O0O00O0OO0O0OOO +1 )*OOO0OO00O0000OO0O ],O00OO0O0O0000OOO0 ,O00OOOO0O000000O0 ).permute (1 ,0 ,2 ,3 ).cpu ().numpy ()),1 )#line:119
                            print (OOO0OO0OOO0000O00 .shape )#line:120
                    if OO0OOOO0O00OOOOOO .args .model =='MSR_RDN':#line:123
                        OOO0OO0OOO0000O00 =OOO0OO0OOO0000O00 [0 ]#line:124
                    O0OO0OO0OOO00OO00 =[OOO0OO0OOO0000O00 ]#line:125
                    if OO0OOOO0O00OOOOOO .args .save_results :#line:135
                        OO0OOOO0O00OOOOOO .ckp .save_results (O00O0O0O0000OOO00 ,O0OO0OO0OOO00OO00 ,OOOOOO0OO0000O0O0 )#line:137
                print ("loader_test-----",OO0OOOO0O00OOOOOO .loader_test )#line:138
                print ("eval_acc----",OOOOO00O0O0000O00 )#line:139
                print ("len(self.loader_test)",len (OO0OOOO0O00OOOOOO .loader_test ))#line:140
                OO0OOOO0O00OOOOOO .ckp .log [-1 ,O00OO0O0O0000OOO0 ]=OOOOO00O0O0000O00 /len (OO0OOOO0O00OOOOOO .loader_test )#line:141
                O0OO00OO0O0OO0O00 =OO0OOOO0O00OOOOOO .ckp .log .max (0 )#line:143
                OO0OOOO0O00OOOOOO .ckp .write_log ('[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format (OO0OOOO0O00OOOOOO .args .data_test ,OOOOOO0OO0000O0O0 ,OO0OOOO0O00OOOOOO .ckp .log [-1 ,O00OO0O0O0000OOO0 ],O0OO00OO0O0OO0O00 [0 ][O00OO0O0O0000OOO0 ],O0OO00OO0O0OO0O00 [1 ][O00OO0O0O0000OOO0 ]+1 ))#line:153
        OO0OOOO0O00OOOOOO .ckp .write_log ('Total time: {:.2f}s\n'.format (O000OOOO0000O0O00 .toc ()),refresh =True )#line:157
        if not OO0OOOO0O00OOOOOO .args .test_only :#line:158
            OO0OOOO0O00OOOOOO .ckp .save (OO0OOOO0O00OOOOOO ,O00OOOOOO00OO0O0O ,is_best =(O0OO00OO0O0OO0O00 [1 ][0 ]+1 ==O00OOOOOO00OO0O0O ))#line:159
    def prepare (OOO0OOOOO0O0OO00O ,*OO00000O0000O00O0 ):#line:161
        OO00OO0OOOOO000OO =torch .device ('cpu'if OOO0OOOOO0O0OO00O .args .cpu else 'cuda')#line:162
        def _OO00000OOO00000OO (O0000O000OO000O00 ):#line:163
            if OOO0OOOOO0O0OO00O .args .precision =='half':O0000O000OO000O00 =O0000O000OO000O00 .half ()#line:164
            return O0000O000OO000O00 .to (OO00OO0OOOOO000OO )#line:165
        return [_OO00000OOO00000OO (O0OO0O0OOOO00O00O )for O0OO0O0OOOO00O00O in OO00000O0000O00O0 ]#line:167
    def terminate (O000O000OO00OOOO0 ):#line:169
        if O000O000OO00OOOO0 .args .test_only :#line:170
            O000O000OO00OOOO0 .test ()#line:171
            return True #line:172
        else :#line:173
            O0O0OO00OO0OOOO00 =O000O000OO00OOOO0 .scheduler .last_epoch +1 #line:174
            return O0O0OO00OO0OOOO00 >=O000O000OO00OOOO0 .args .epochs #line:175
