




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      a__			(   _lowercase ):
     a						:     List[str]						   =      '''sequence-classification'''







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



          '''simple docstring'''



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

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







     def      lowerCAmelCase_							(      self    ,						**A					)    ->       List[str]:



          '''simple docstring'''



          return self.model(**A					)







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



          '''simple docstring'''



          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(**A					)
          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      lowerCAmelCase_							(      self					)    ->       Any:



          '''simple docstring'''



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

          for mode in ["train", "dev"]:
               a           = self._feature_file(A					)
               if os.path.exists(A					) and not args.overwrite_cache:
                    logger.info("Loading features from cached file %s"    ,						A					)
               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(
                        A    ,						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"    ,						A					)
                    torch.save(A    ,						A					)







     def      lowerCAmelCase_							(      self    ,						A    ,						A    ,						A = False					)    ->       DataLoader:



          '''simple docstring'''



          a           = "dev" if mode == "test" else mode

          a           = self._feature_file(A					)
          logger.info("Loading features from cached file %s"    ,						A					)
          a           = torch.load(A					)
          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(A    ,						A    ,						A    ,						A					)    ,						batch_size=A    ,						shuffle=A    ,						)







     def      lowerCAmelCase_							(      self    ,						A    ,						A					)    ->       Tuple:



          '''simple docstring'''



          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(**A					)
          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      lowerCAmelCase_							(      self    ,						A					)    ->       tuple:



          '''simple docstring'''



          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(A    ,						axis=1					)
          elif self.hparams.glue_output_mode == "regression":
               a           = np.squeeze(A					)

          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    ,						A    ,						A					)}

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







     def      lowerCAmelCase_							(      self    ,						A					)    ->       dict:



          '''simple docstring'''



          a    , a    , a           = self._eval_end(A					)
          a           = ret["log"]
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







     def      lowerCAmelCase_							(      self    ,						A					)    ->       dict:



          '''simple docstring'''



          a    , a    , a           = self._eval_end(A					)
          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      lowerCAmelCase_							(      A    ,						A					)    ->       List[str]:



          '''simple docstring'''



          BaseTransformer.add_model_specific_args(A    ,						A					)
          parser.add_argument(
              "--max_seq_length"    ,						default=128    ,						type=A    ,						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=A    ,						required=A    ,						help="The GLUE task to run"    ,						)
          parser.add_argument(
              "--gpus"    ,						default=0    ,						type=A    ,						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    SCREAMING_SNAKE_CASE				(  )     ->	List[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()






import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





