





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class     SCREAMING_SNAKE_CASE__ :
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	1
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	None



 def 				UpperCamelCase			(							self ):
        return self.__class__(**{k: copy.deepcopy(__lowerCamelCase ) for k, v in self.__dict__.items()} )












import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__:      List[str]									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''sequence-classification'''



 def __init__(							self,__lowerCamelCase ):
        if type(__lowerCamelCase ) == dict:
               A__											=						Namespace(**__lowerCamelCase )
        A__											=						glue_output_modes[hparams.task]
        A__											=						glue_tasks_num_labels[hparams.task]

        super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return self.model(**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__											=						outputs[0]

        A__											=						self.trainer.lr_schedulers[0]['''scheduler''']
        A__											=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}



 def 				UpperCamelCase			(							self ):
        A__											=						self.hparams
        A__											=						processors[args.task]()
        A__											=						processor.get_labels()

        for mode in ["train", "dev"]:
               A__											=						self._feature_file(__lowerCamelCase )
               if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s''',__lowerCamelCase )
               else:
                      logger.info('''Creating features from dataset file at %s''',args.data_dir )
                      A__											=						(
                          processor.get_dev_examples(args.data_dir )
                          if mode == '''dev'''
                          else processor.get_train_examples(args.data_dir )
                      )
                      A__											=						convert_examples_to_features(
                          __lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
                      logger.info('''Saving features into cached file %s''',__lowerCamelCase )
                      torch.save(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
        A__											=						'''dev''' if mode == '''test''' else mode

        A__											=						self._feature_file(__lowerCamelCase )
        logger.info('''Loading features from cached file %s''',__lowerCamelCase )
        A__											=						torch.load(__lowerCamelCase )
        A__											=						torch.tensor([f.input_ids for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.attention_mask for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
        if self.hparams.glue_output_mode == "classification":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.long )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.float )

        return DataLoader(
            TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__					,	A__											=						outputs[:2]
        A__											=						logits.detach().cpu().numpy()
        A__											=						inputs['''labels'''].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
        A__											=						np.concatenate([x['''pred'''] for x in outputs],axis=0 )

        if self.hparams.glue_output_mode == "classification":
               A__											=						np.argmax(__lowerCamelCase,axis=1 )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						np.squeeze(__lowerCamelCase )

        A__											=						np.concatenate([x['''target'''] for x in outputs],axis=0 )
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]

        A__											=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}

        A__											=						dict(results.items() )
        A__											=						results
        return ret, preds_list, out_label_list



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 @staticmethod
 def 				UpperCamelCase			(							__lowerCamelCase,__lowerCamelCase ):
        BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
        parser.add_argument(
            '''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            ),)

        parser.add_argument(
            '''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
        parser.add_argument(
            '''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)

        parser.add_argument(
            '''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )

        return parser


def 	UpperCamelCase__(       )->Any:
       A__											=						argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						GLUETransformer.add_model_specific_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              A__											=						os.path.join(
                  '''./results''' ,     f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}" ,     )
              os.makedirs(args.output_dir  )

       A__											=						GLUETransformer(UpperCamelCase__  )
       A__											=						generic_train(UpperCamelCase__ ,     UpperCamelCase__  )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              A__											=						sorted(glob.glob(os.path.join(args.output_dir ,     '''checkpoint-epoch=*.ckpt'''  ) ,     recursive=UpperCamelCase__  )  )
              A__											=						model.load_from_checkpoint(checkpoints[-1]  )
              return trainer.test(UpperCamelCase__  )


if __name__ == "__main__":
       main()





