'''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__  :      Dict        =					logging.getLogger(__name__)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	"""sequence-classification"""






  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       List[str]  )				->Dict:

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

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






  def  lowerCAmelCase__ (      self			:       Any  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        return self.model(**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       str  )				->Optional[Any]:
        UpperCAmelCase_            =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

        UpperCAmelCase_            =  self(**UpperCAmelCase__  )
        UpperCAmelCase_            =  outputs[0]

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






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Union[str, Any]:
        UpperCAmelCase_            =  self.hparams
        UpperCAmelCase_            =  processors[args.task]()
        UpperCAmelCase_            =  processor.get_labels()

        for mode in ["train", "dev"]:
              UpperCAmelCase_            =  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  )
                    UpperCAmelCase_            =  (
                        processor.get_dev_examples(args.data_dir  )
                        if mode == '''dev'''
                        else processor.get_train_examples(args.data_dir  )
                    )
                    UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Union[str, Any] = False  )				->Union[str, Any]:
        UpperCAmelCase_            =  '''dev''' if mode == '''test''' else mode

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

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






  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

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

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






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[str]  )				->Tuple:
        UpperCAmelCase_            =  torch.stack([x['''val_loss'''] for x in outputs]  ).mean().detach().cpu().item()
        UpperCAmelCase_            =  np.concatenate([x['''pred'''] for x in outputs]  ,							axis=0  )

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

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

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

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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Optional[Any]  )				->Optional[Any]:
        UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  self._eval_end(UpperCAmelCase__  )
        UpperCAmelCase_            =  ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[int]  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  self._eval_end(UpperCAmelCase__  )
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple  )				->Union[str, Any]:
        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  __lowerCamelCase       (      ):


      '''simple docstring'''


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

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

      UpperCAmelCase_            =  GLUETransformer(_UpperCamelCase					)
      UpperCAmelCase_            =  generic_train(_UpperCamelCase							,    _UpperCamelCase					)

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


if __name__ == "__main__":
      main()






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





