





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


__lowerCamelCase :  Dict										=  logging.getLogger(__name__)



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Any							=     'sequence-classification'





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       if type(A_     ) == dict:
        UpperCamelCase			:      Any 			=     Namespace(**A_     )
       UpperCamelCase			:      Optional[int] 			=     glue_output_modes[hparams.task]
       UpperCamelCase			:      Any 			=     glue_tasks_num_labels[hparams.task]

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





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return self.model(**A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

       UpperCamelCase			:      List[Any] 			=     self(**A_     )
       UpperCamelCase			:      Dict 			=     outputs[0]

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.hparams
       UpperCamelCase			:      Tuple 			=     processors[args.task]()
       UpperCamelCase			:      List[str] 			=     processor.get_labels()

       for mode in ["train", "dev"]:
        UpperCamelCase			:      str 			=     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     )
         UpperCamelCase			:      Optional[Any] 			=     (
             processor.get_dev_examples(args.data_dir     )
             if mode == "dev"
             else processor.get_train_examples(args.data_dir     )
         )
         UpperCamelCase			:      Any 			=     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  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = False     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     "dev" if mode == "test" else mode

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

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





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

       UpperCamelCase			:      Tuple 			=     self(**A_     )
       UpperCamelCase			:      Dict 			=     outputs[:2]
       UpperCamelCase			:      Dict 			=     logits.detach().cpu().numpy()
       UpperCamelCase			:      int 			=     inputs["labels"].detach().cpu().numpy()

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





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     torch.stack([x["val_loss"] for x in outputs]     ).mean().detach().cpu().item()
       UpperCamelCase			:      Optional[int] 			=     np.concatenate([x["pred"] for x in outputs] ,							axis=0     )

       if self.hparams.glue_output_mode == "classification":
        UpperCamelCase			:      Any 			=     np.argmax(A_ ,							axis=1     )
       elif self.hparams.glue_output_mode == "regression":
        UpperCamelCase			:      List[str] 			=     np.squeeze(A_     )

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

       UpperCamelCase			:      List[str] 			=     {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task ,							A_ ,							A_     )}

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





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self._eval_end(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self._eval_end(A_     )
       UpperCamelCase			:      List[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  __UpperCamelCase(				A_ ,							A_     ):
       '''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 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Dict 			=     argparse.ArgumentParser()
 add_generic_args(_lowerCAmelCase							,     os.getcwd()  )
 UpperCamelCase			:      Optional[int] 			=     GLUETransformer.add_model_specific_args(_lowerCAmelCase							,     os.getcwd()  )
 UpperCamelCase			:      List[str] 			=     parser.parse_args()

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

 UpperCamelCase			:      Optional[int] 			=     GLUETransformer(_lowerCAmelCase  )
 UpperCamelCase			:      str 			=     generic_train(_lowerCAmelCase							,     _lowerCAmelCase  )

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


if __name__ == "__main__":
    main()








from ..utils import DummyObject, requires_backends



class       A__     ( metaclass=__snake_case    ):
      _UpperCAmelCase			:Tuple							=     ['note_seq']





      def __init__(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(self ,							["note_seq"]     )





      @classmethod
      def  __UpperCamelCase(				cls ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(cls ,							["note_seq"]     )





      @classmethod
      def  __UpperCamelCase(				cls ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(cls ,							["note_seq"]     )
