'''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


SCREAMING_SNAKE_CASE_:					List[str]  =logging.getLogger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  """sequence-classification"""



    def __init__(self   :   Union[str, Any]       ,      __a   :   List[str]			):
         if type(__a			) == dict:
              UpperCAmelCase_	   =       Namespace(**__a			)
         UpperCAmelCase_	   =       glue_output_modes[hparams.task]
         UpperCAmelCase_	   =       glue_tasks_num_labels[hparams.task]

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



    def  _lowercase						(self   :   Optional[Any]       ,      **__a   :   Optional[int]			):
         return self.model(**__a			)



    def  _lowercase						(self   :   str       ,      __a   :   str       ,      __a   :   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(**__a			)
         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  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.hparams
         UpperCAmelCase_	   =       processors[args.task]()
         UpperCAmelCase_	   =       processor.get_labels()

         for mode in ["train", "dev"]:
              UpperCAmelCase_	   =       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_	   =       (
                       processor.get_dev_examples(args.data_dir			)
                       if mode == "dev"
                       else processor.get_train_examples(args.data_dir			)
                   )
                   UpperCAmelCase_	   =       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  _lowercase						(self   :   Any       ,      __a   :   str       ,      __a   :   int       ,      __a   :   bool = False			):
         UpperCAmelCase_	   =       "dev" if mode == "test" else mode

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



    def  _lowercase						(self   :   Dict       ,      __a   :   Dict       ,      __a   :   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(**__a			)
         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  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         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(__a       ,      axis=1			)
         elif self.hparams.glue_output_mode == "regression":
              UpperCAmelCase_	   =       np.squeeze(__a			)

         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       ,      __a       ,      __a			)}

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



    def  _lowercase						(self   :   str       ,      __a   :   list			):
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       self._eval_end(__a			)
         UpperCAmelCase_	   =       ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def  _lowercase						(self   :   List[str]       ,      __a   :   Optional[int]			):
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       self._eval_end(__a			)
         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  _lowercase						(__a   :   Any       ,      __a   :   List[Any]			):
         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      lowerCAmelCase_    ( )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser()
     add_generic_args(snake_case_ ,   os.getcwd()      )
     UpperCAmelCase_	   =       GLUETransformer.add_model_specific_args(snake_case_ ,   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(snake_case_      )
     UpperCAmelCase_	   =       generic_train(snake_case_ ,   snake_case_      )

     # 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=snake_case_      )      )
          UpperCAmelCase_	   =       model.load_from_checkpoint(checkpoints[-1]      )
          return trainer.test(snake_case_      )


if __name__ == "__main__":
    main()





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




