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 numpy as np #line:10
class Trainer ():#line:11
    def __init__ (O00O0OO0OOOO0O0O0 ,OO00OOOOOO00O0OO0 ,OO0OO00O0OO0OO0OO ,OOO0OO0O000O00O00 ,O0O00O00O0OOO000O ,O0O00OOO00OO000OO ):#line:12
        O00O0OO0OOOO0O0O0 .args =OO00OOOOOO00O0OO0 #line:13
        O00O0OO0OOOO0O0O0 .scale =OO00OOOOOO00O0OO0 .scale #line:14
        O00O0OO0OOOO0O0O0 .ckp =O0O00OOO00OO000OO #line:16
        O00O0OO0OOOO0O0O0 .loader_train =OO0OO00O0OO0OO0OO .loader_train #line:17
        O00O0OO0OOOO0O0O0 .loader_test =OO0OO00O0OO0OO0OO .loader_test #line:18
        O00O0OO0OOOO0O0O0 .model =OOO0OO0O000O00O00 #line:22
        O00O0OO0OOOO0O0O0 .loss =O0O00O00O0OOO000O #line:25
        O00O0OO0OOOO0O0O0 .optimizer =utility .make_optimizer (OO00OOOOOO00O0OO0 ,O00O0OO0OOOO0O0O0 .model )#line:26
        O00O0OO0OOOO0O0O0 .scheduler =utility .make_scheduler (OO00OOOOOO00O0OO0 ,O00O0OO0OOOO0O0O0 .optimizer )#line:27
        if O00O0OO0OOOO0O0O0 .args .load !='.':#line:29
            O00O0OO0OOOO0O0O0 .optimizer .load_state_dict (torch .load (os .path .join (O0O00OOO00OO000OO .dir ,'optimizer.pt')))#line:32
            for _O00O000000OOO0O00 in range (len (O0O00OOO00OO000OO .log )):O00O0OO0OOOO0O0O0 .scheduler .step ()#line:33
        O00O0OO0OOOO0O0O0 .error_last =1e8 #line:35
    def train (OOOO0OOOO0OOOO0O0 ):#line:37
        OOOO0OOOO0OOOO0O0 .scheduler .step ()#line:38
        OOOO0OOOO0OOOO0O0 .loss .step ()#line:39
        O000O0OO00O0OOOO0 =OOOO0OOOO0OOOO0O0 .scheduler .last_epoch +1 #line:40
        O0OO00OOOO0O000O0 =OOOO0OOOO0OOOO0O0 .scheduler .get_lr ()[0 ]#line:41
        OOOO0OOOO0OOOO0O0 .ckp .write_log ('[Epoch {}]\tLearning rate: {:.2e}'.format (O000O0OO00O0OOOO0 ,Decimal (O0OO00OOOO0O000O0 )))#line:45
        OOOO0OOOO0OOOO0O0 .loss .start_log ()#line:46
        OOOO0OOOO0OOOO0O0 .model .train ()#line:47
        OOOO00000O0O0OO0O ,O0OOOO00O0OOO000O =utility .timer (),utility .timer ()#line:49
        for O000O000O00OOOO0O ,(O0OO00OOOO0O000O0 ,O00OO0OO0O00OO000 ,_OOOO00O0O0OO00O0O ,O00O0000000OOOOOO )in enumerate (OOOO0OOOO0OOOO0O0 .loader_train ):#line:50
            O0OO00OOOO0O000O0 ,O00OO0OO0O00OO000 =OOOO0OOOO0OOOO0O0 .prepare (O0OO00OOOO0O000O0 ,O00OO0OO0O00OO000 )#line:51
            OOOO00000O0O0OO0O .hold ()#line:52
            O0OOOO00O0OOO000O .tic ()#line:53
            OOOO0OOOO0OOOO0O0 .optimizer .zero_grad ()#line:55
            OOOOOO0OOO0O0OO00 =OOOO0OOOO0OOOO0O0 .model (O0OO00OOOO0O000O0 ,O00O0000000OOOOOO )#line:56
            OO00OOOO0000000OO =OOOO0OOOO0OOOO0O0 .loss (OOOOOO0OOO0O0OO00 ,O00OO0OO0O00OO000 )#line:57
            if OO00OOOO0000000OO .item ()<OOOO0OOOO0OOOO0O0 .args .skip_threshold *OOOO0OOOO0OOOO0O0 .error_last :#line:58
                OO00OOOO0000000OO .backward ()#line:59
                OOOO0OOOO0OOOO0O0 .optimizer .step ()#line:60
            else :#line:61
                print ('Skip this batch {}! (Loss: {})'.format (O000O000O00OOOO0O +1 ,OO00OOOO0000000OO .item ()))#line:64
            O0OOOO00O0OOO000O .hold ()#line:66
            if (O000O000O00OOOO0O +1 )%OOOO0OOOO0OOOO0O0 .args .print_every ==0 :#line:68
                OOOO0OOOO0OOOO0O0 .ckp .write_log ('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format ((O000O000O00OOOO0O +1 )*OOOO0OOOO0OOOO0O0 .args .batch_size ,len (OOOO0OOOO0OOOO0O0 .loader_train .dataset ),OOOO0OOOO0OOOO0O0 .loss .display_loss (O000O000O00OOOO0O ),O0OOOO00O0OOO000O .release (),OOOO00000O0O0OO0O .release ()))#line:74
            OOOO00000O0O0OO0O .tic ()#line:76
        OOOO0OOOO0OOOO0O0 .loss .end_log (len (OOOO0OOOO0OOOO0O0 .loader_train ))#line:78
        OOOO0OOOO0OOOO0O0 .error_last =OOOO0OOOO0OOOO0O0 .loss .log [-1 ,-1 ]#line:79
    def test (OOO00O0O00O0O00OO ):#line:81
        O0000O00OO0OO0O0O =OOO00O0O00O0O00OO .scheduler .last_epoch +1 #line:83
        OOO00O0O00O0O00OO .ckp .write_log ('\nEvaluation:')#line:84
        OOO00O0O00O0O00OO .ckp .add_log (torch .zeros (1 ,len (OOO00O0O00O0O00OO .scale )))#line:85
        OOO00O0O00O0O00OO .model .eval ()#line:86
        OOO0OO00000O0O000 =utility .timer ()#line:88
        O000O0O0000O0000O =0 #line:89
        O0OO0O0OOO0O00O0O =4 #line:90
        print ("here-----")#line:91
        with torch .no_grad ():#line:92
            for OOO00O000O0OO0OO0 ,OO0O0OOO000O000O0 in enumerate (OOO00O0O00O0O00OO .scale ):#line:93
                print ("self.scale----")#line:94
                OOOO00OOO0O0O0000 =0 #line:95
                OOOOOOO0O0O00O0OO =0 #line:96
                OOO00O0O00O0O00OO .loader_test .dataset .set_scale (OOO00O000O0OO0OO0 )#line:98
                OO0O00000O0O0O00O =tqdm (OOO00O0O00O0O00OO .loader_test ,ncols =80 )#line:99
                print ("ok----")#line:100
                for OOOOOO0000O0OO0O0 ,(O00O00OO00OOOOO0O ,OO0O0O00O0000OOO0 ,O0OOOOO00OOO0O000 ,_O00OO0000O0000OO0 )in enumerate (OO0O00000O0O0O00O ):#line:102
                    print ("tqdm_test---")#line:104
                    O0OOOOO00OOO0O000 =O0OOOOO00OOO0O000 [0 ]#line:105
                    O00O00OO00OOOOO0O =OOO00O0O00O0O00OO .prepare (O00O00OO00OOOOO0O )[0 ]#line:110
                    print ("LR.shape=",O00O00OO00OOOOO0O .shape )#line:112
                    for OO0O0OOOOOO00O0OO in range (O00O00OO00OOOOO0O .shape [-1 ]//3 ):#line:114
                        if OO0O0OOOOOO00O0OO ==0 :#line:116
                            O0O00O0O000O0OOOO =OOO00O0O00O0O00OO .model (O00O00OO00OOOOO0O [...,:3 ],OOO00O000O0OO0OO0 ).data .cpu ().numpy ()#line:117
                        elif OO0O0OOOOOO00O0OO ==O00O00OO00OOOOO0O .shape [-1 ]//3 -1 :#line:119
                            O0O00O0O000O0OOOO =np .concatenate ((O0O00O0O000O0OOOO ,OOO00O0O00O0O00OO .model (O00O00OO00OOOOO0O [...,OO0O0OOOOOO00O0OO *3 :],OOO00O000O0OO0OO0 ).data .cpu ().numpy ()),4 )#line:120
                        else :#line:122
                            O0O00O0O000O0OOOO =np .concatenate ((O0O00O0O000O0OOOO ,OOO00O0O00O0O00OO .model (O00O00OO00OOOOO0O [...,OO0O0OOOOOO00O0OO *3 :(OO0O0OOOOOO00O0OO +1 )*3 ],OOO00O000O0OO0OO0 ).data .cpu ().numpy ()),4 )#line:123
                    OOO000O00OO00O00O =[O0O00O0O000O0OOOO ]#line:136
                    print (O0O00O0O000O0OOOO .shape ,O00O00OO00OOOOO0O .shape )#line:137
                    if OOO00O0O00O0O00OO .args .save_results :#line:151
                        OOO00O0O00O0O00OO .ckp .save_results (O0OOOOO00OOO0O000 ,OOO000O00OO00O00O ,OO0O0OOO000O000O0 )#line:153
                OOO00O0O00O0O00OO .ckp .log [-1 ,OOO00O000O0OO0OO0 ]=OOOO00OOO0O0O0000 /(len (OOO00O0O00O0O00OO .loader_test )-O000O0O0000O0000O )#line:155
                OOOOOOO0O0O00O0OO =OOOOOOO0O0O00O0OO /(len (OOO00O0O00O0O00OO .loader_test )-O000O0O0000O0000O )#line:156
                O000O0OOOO00OO0O0 =OOO00O0O00O0O00OO .ckp .log .max (0 )#line:158
                OOO00O0O00O0O00OO .ckp .write_log ('[{} x{}]\tPSNR: {:.3f} ORIG: {:.3f} (Best: {:.3f} @epoch {})'.format (OOO00O0O00O0O00OO .args .data_test ,OO0O0OOO000O000O0 ,OOO00O0O00O0O00OO .ckp .log [-1 ,OOO00O000O0OO0OO0 ],OOOOOOO0O0O00O0OO ,O000O0OOOO00OO0O0 [0 ][OOO00O000O0OO0OO0 ],O000O0OOOO00OO0O0 [1 ][OOO00O000O0OO0OO0 ]+1 ))#line:168
        OOO00O0O00O0O00OO .ckp .write_log ('Total time: {:.2f}s\n'.format (OOO0OO00000O0O000 .toc ()),refresh =True )#line:172
        if not OOO00O0O00O0O00OO .args .test_only :#line:173
            OOO00O0O00O0O00OO .ckp .save (OOO00O0O00O0O00OO ,O0000O00OO0OO0O0O ,is_best =(O000O0OOOO00OO0O0 [1 ][0 ]+1 ==O0000O00OO0OO0O0O ))#line:174
    def prepare (OOO00OOO0O0OOOO00 ,*OO00000O0O0OO0OOO ):#line:176
        OOOOO000OOOOO000O =torch .device ('cpu'if OOO00OOO0O0OOOO00 .args .cpu else 'cuda')#line:177
        def _O0OO000OO0OOO0O00 (O0OO0O0O0OO000OO0 ):#line:178
            if OOO00OOO0O0OOOO00 .args .precision =='half':O0OO0O0O0OO000OO0 =O0OO0O0O0OO000OO0 .half ()#line:179
            return O0OO0O0O0OO000OO0 .to (OOOOO000OOOOO000O )#line:180
        return [_O0OO000OO0OOO0O00 (O000O00OO0000OO00 )for O000O00OO0000OO00 in OO00000O0O0OO0OOO ]#line:182
    def terminate (OO00O0O0OOOOO00OO ):#line:184
        if OO00O0O0OOOOO00OO .args .test_only :#line:185
            OO00O0O0OOOOO00OO .test ()#line:186
            return True #line:187
        else :#line:188
            OOO0OO000O00000OO =OO00O0O0OOOOO00OO .scheduler .last_epoch +1 #line:189
            return OOO0OO000O00000OO >=OO00O0O0OOOOO00OO .args .epochs #line:190
