

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_     :    Optional[int]     		=   logging.getLogger(__name__)




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    ="sequence-classification"

    def __init__(      self			:   List[Any]				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     if type(snake_case__     ) == dict:
      SCREAMING_SNAKE_CASE		    =      Namespace(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      glue_output_modes[hparams.task]
     SCREAMING_SNAKE_CASE		    =      glue_tasks_num_labels[hparams.task]

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

    def UpperCamelCase      (      self			:   Dict				,     **snake_case__			:   Any     ):

     """simple docstring"""






     return self.model(**snake_case__     )

    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Any				,     snake_case__			:   List[str]     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE		    =      self(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      outputs[0]

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

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.hparams
     SCREAMING_SNAKE_CASE		    =      processors[args.task]()
     SCREAMING_SNAKE_CASE		    =      processor.get_labels()

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

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   str				,     snake_case__			:   int				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'dev' if mode == 'test' else mode

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

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

    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   Dict				,     snake_case__			:   Optional[int]     ):

     """simple docstring"""






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

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

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

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

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
     SCREAMING_SNAKE_CASE		    =      np.concatenate([x['pred'] for x in outputs]				,     axis=0     )

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

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

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

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

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   list     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self._eval_end(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      ret['log']
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self._eval_end(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      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      (      snake_case__			:   List[str]				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     BaseTransformer.add_model_specific_args(snake_case__				,     snake_case__     )
     parser.add_argument(
         '--max_seq_length'				,     default=1_2_8				,     type=snake_case__				,     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=snake_case__				,     required=snake_case__				,     help='The GLUE task to run'				,     )
     parser.add_argument(
         '--gpus'				,     default=0				,     type=snake_case__				,     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 							__lowerCAmelCase     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      argparse.ArgumentParser()
 add_generic_args(_UpperCamelCase		,				os.getcwd()						)
 SCREAMING_SNAKE_CASE		    =      GLUETransformer.add_model_specific_args(_UpperCamelCase		,				os.getcwd()						)
 SCREAMING_SNAKE_CASE		    =      parser.parse_args()

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

 SCREAMING_SNAKE_CASE		    =      GLUETransformer(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      generic_train(_UpperCamelCase		,				_UpperCamelCase						)

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


if __name__ == "__main__":
      main()




import baseaa


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				bytes:
 '''simple docstring'''


 return baseaa.aaaencode(string.encode('utf-8'						)						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bytes						)		->				str:
 '''simple docstring'''


 return baseaa.aaadecode(_UpperCamelCase						).decode('utf-8'						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

