






'''simple docstring'''



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


SCREAMING_SNAKE_CASE						=	logging.getLogger(__name__)
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''sequence-classification'''






      def __init__( self			:		Any     ,      UpperCAmelCase			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       if type(UpperCAmelCase		) == dict:
        lowercase					:   Any				  =Namespace(**UpperCAmelCase		)
       lowercase					:   int				  =glue_output_modes[hparams.task]
       lowercase					:   Any				  =glue_tasks_num_labels[hparams.task]

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






      def 				A__      ( self			:		Dict     ,      **UpperCAmelCase			:		Any		)  ->  str:


       '''simple docstring'''

       return self.model(**UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

       lowercase					:   Union[str, Any]				  =self(**UpperCAmelCase		)
       lowercase					:   List[Any]				  =outputs[0]

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






      def 				A__      ( self			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.hparams
       lowercase					:   str				  =processors[args.task]()
       lowercase					:   Optional[int]				  =processor.get_labels()

       for mode in ["train", "dev"]:
        lowercase					:   List[str]				  =self._feature_file(UpperCAmelCase		)
        if os.path.exists(UpperCAmelCase		) and not args.overwrite_cache:
         logger.info('''Loading features from cached file %s'''     ,      UpperCAmelCase		)
        else:
         logger.info('''Creating features from dataset file at %s'''     ,      args.data_dir		)
         lowercase					:   Optional[int]				  =(
             processor.get_dev_examples(args.data_dir		)
             if mode == '''dev'''
             else processor.get_train_examples(args.data_dir		)
         )
         lowercase					:   Optional[int]				  =convert_examples_to_features(
             UpperCAmelCase     ,      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'''     ,      UpperCAmelCase		)
         torch.save(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		bool = False		)  ->  DataLoader:


       '''simple docstring'''

       lowercase					:   Tuple				  ='''dev''' if mode == '''test''' else mode

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

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






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Any				  ={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

       lowercase					:   Union[str, Any]				  =self(**UpperCAmelCase		)
       lowercase  ,		lowercase					:   List[Any]				  =outputs[:2]
       lowercase					:   Union[str, Any]				  =logits.detach().cpu().numpy()
       lowercase					:   int				  =inputs['''labels'''].detach().cpu().numpy()

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		Dict		)  ->  tuple:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =torch.stack([x['''val_loss'''] for x in outputs]		).mean().detach().cpu().item()
       lowercase					:   List[str]				  =np.concatenate([x['''pred'''] for x in outputs]     ,      axis=0		)

       if self.hparams.glue_output_mode == "classification":
        lowercase					:   Optional[Any]				  =np.argmax(UpperCAmelCase     ,      axis=1		)
       elif self.hparams.glue_output_mode == "regression":
        lowercase					:   int				  =np.squeeze(UpperCAmelCase		)

       lowercase					:   Any				  =np.concatenate([x['''target'''] for x in outputs]     ,      axis=0		)
       lowercase					:   int				  =[[] for _ in range(out_label_ids.shape[0]		)]
       lowercase					:   Tuple				  =[[] for _ in range(out_label_ids.shape[0]		)]

       lowercase					:   Any				  ={**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task     ,      UpperCAmelCase     ,      UpperCAmelCase		)}

       lowercase					:   Optional[Any]				  =dict(results.items()		)
       lowercase					:   str				  =results
       return ret, preds_list, out_label_list






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		list		)  ->  dict:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase					:   Dict				  =self._eval_end(UpperCAmelCase		)
       lowercase					:   Any				  =ret['''log''']
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Tuple		)  ->  dict:


       '''simple docstring'''

       lowercase  ,		lowercase  ,		lowercase					:   Union[str, Any]				  =self._eval_end(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =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 				A__      ( UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Any		)  ->  Union[str, Any]:


       '''simple docstring'''

       BaseTransformer.add_model_specific_args(UpperCAmelCase     ,      UpperCAmelCase		)
       parser.add_argument(
           '''--max_seq_length'''     ,      default=128     ,      type=UpperCAmelCase     ,      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=UpperCAmelCase     ,      required=UpperCAmelCase     ,      help='''The GLUE task to run'''     ,      )
       parser.add_argument(
           '''--gpus'''     ,      default=0     ,      type=UpperCAmelCase     ,      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 							lowercase_				(    )       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Tuple				  =argparse.ArgumentParser()
 add_generic_args(__A					,      os.getcwd()			)
 lowercase					:   Any				  =GLUETransformer.add_model_specific_args(__A					,      os.getcwd()			)
 lowercase					:   Optional[Any]				  =parser.parse_args()

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

 lowercase					:   str				  =GLUETransformer(__A			)
 lowercase					:   Any				  =generic_train(__A					,      __A			)

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


if __name__ == "__main__":
      main()











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



