



"""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


lowercase__		:				Optional[Any]          =	logging.getLogger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """sequence-classification"""



      def __init__(      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        if type(SCREAMING_SNAKE_CASE_       ) == dict:
          lowerCAmelCase_   :				List[Any]								= Namespace(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= glue_output_modes[hparams.task]
        lowerCAmelCase_   :				List[Any]								= glue_tasks_num_labels[hparams.task]

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Dict       ):
        return self.model(**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				str								= {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

        lowerCAmelCase_   :				Optional[int]								= self(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= outputs[0]

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[str]								= self.hparams
        lowerCAmelCase_   :				Optional[Any]								= processors[args.task]()
        lowerCAmelCase_   :				Tuple								= processor.get_labels()

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      bool = False       ):
        lowerCAmelCase_   :				List[Any]								= 'dev' if mode == 'test' else mode

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

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        lowerCAmelCase_   :				Tuple								= {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

        lowerCAmelCase_   :				int								= self(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_						,lowerCAmelCase_   :				str								= outputs[:2]
        lowerCAmelCase_   :				str								= logits.detach().cpu().numpy()
        lowerCAmelCase_   :				Optional[Any]								= inputs['labels'].detach().cpu().numpy()

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Optional[int]								= torch.stack([x['val_loss'] for x in outputs]       ).mean().detach().cpu().item()
        lowerCAmelCase_   :				Dict								= np.concatenate([x['pred'] for x in outputs]    ,			axis=0       )

        if self.hparams.glue_output_mode == "classification":
          lowerCAmelCase_   :				Union[str, Any]								= np.argmax(SCREAMING_SNAKE_CASE_    ,			axis=1       )
        elif self.hparams.glue_output_mode == "regression":
          lowerCAmelCase_   :				Dict								= np.squeeze(SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[str]								= np.concatenate([x['target'] for x in outputs]    ,			axis=0       )
        lowerCAmelCase_   :				Any								= [[] for _ in range(out_label_ids.shape[0]       )]
        lowerCAmelCase_   :				Union[str, Any]								= [[] for _ in range(out_label_ids.shape[0]       )]

        lowerCAmelCase_   :				Dict								= {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )}

        lowerCAmelCase_   :				List[Any]								= dict(results.items()       )
        lowerCAmelCase_   :				Dict								= results
        return ret, preds_list, out_label_list



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      list       ):
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= self._eval_end(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Tuple								= ret['log']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= self._eval_end(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				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      SCREAMING_SNAKE_CASE__ (      SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
        parser.add_argument(
            '--max_seq_length'    ,			default=1_2_8    ,			type=SCREAMING_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=SCREAMING_SNAKE_CASE_    ,			required=SCREAMING_SNAKE_CASE_    ,			help='The GLUE task to run'    ,			)
        parser.add_argument(
            '--gpus'    ,			default=0    ,			type=SCREAMING_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 				UpperCamelCase_    (    )						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= argparse.ArgumentParser()
  add_generic_args(lowerCAmelCase__    ,   os.getcwd()						)
  lowerCAmelCase_   :				List[str]								= GLUETransformer.add_model_specific_args(lowerCAmelCase__    ,   os.getcwd()						)
  lowerCAmelCase_   :				List[str]								= parser.parse_args()

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

  lowerCAmelCase_   :				str								= GLUETransformer(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= generic_train(lowerCAmelCase__    ,   lowerCAmelCase__						)

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


if __name__ == "__main__":
     main()










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





