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			:							int     =logging.getLogger(__name__)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'sequence-classification'



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				str)				->Tuple:


          """simple docstring"""


          if type(_UpperCamelCase) == dict:
              _lowerCamelCase     :		Any 						=						Namespace(**_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						glue_output_modes[hparams.task]
          _lowerCamelCase     :		Optional[int] 						=						glue_tasks_num_labels[hparams.task]

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  **_UpperCamelCase     :				Dict)				->List[str]:


          """simple docstring"""


          return self.model(**_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

          _lowerCamelCase     :		Optional[Any] 						=						self(**_UpperCamelCase)
          _lowerCamelCase     :		int 						=						outputs[0]

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->str:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.hparams
          _lowerCamelCase     :		Optional[int] 						=						processors[args.task]()
          _lowerCamelCase     :		Optional[Any] 						=						processor.get_labels()

          for mode in ["train", "dev"]:
              _lowerCamelCase     :		Union[str, Any] 						=						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)
                  _lowerCamelCase     :		Tuple 						=						(
                      processor.get_dev_examples(args.data_dir)
                      if mode == """dev"""
                      else processor.get_train_examples(args.data_dir)
                  )
                  _lowerCamelCase     :		Optional[int] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				bool = False)				->DataLoader:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						"""dev""" if mode == """test""" else mode

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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				List[str])				->Dict:


          """simple docstring"""


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

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

          _lowerCamelCase     :		Any 						=						self(**_UpperCamelCase)
          _lowerCamelCase						,							_lowerCamelCase     :		Optional[int] 						=						outputs[:2]
          _lowerCamelCase     :		str 						=						logits.detach().cpu().numpy()
          _lowerCamelCase     :		int 						=						inputs["""labels"""].detach().cpu().numpy()

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Tuple)				->tuple:


          """simple docstring"""


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

          if self.hparams.glue_output_mode == "classification":
              _lowerCamelCase     :		Any 						=						np.argmax(_UpperCamelCase    ,  axis=1)
          elif self.hparams.glue_output_mode == "regression":
              _lowerCamelCase     :		Tuple 						=						np.squeeze(_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						np.concatenate([x["""target"""] for x in outputs]    ,  axis=0)
          _lowerCamelCase     :		Any 						=						[[] for _ in range(out_label_ids.shape[0])]
          _lowerCamelCase     :		Any 						=						[[] for _ in range(out_label_ids.shape[0])]

          _lowerCamelCase     :		int 						=						{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task    ,  _UpperCamelCase    ,  _UpperCamelCase)}

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				list)				->dict:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		int 						=						self._eval_end(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						ret["""log"""]
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[int])				->dict:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		str 						=						self._eval_end(_UpperCamelCase)
          _lowerCamelCase     :		str 						=						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    (					_UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Tuple)				->Any:


          """simple docstring"""


          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 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						argparse.ArgumentParser()
    add_generic_args(__A   , os.getcwd()	)
    _lowerCamelCase     :		str 						=						GLUETransformer.add_model_specific_args(__A   , os.getcwd()	)
    _lowerCamelCase     :		Any 						=						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     :		List[str] 						=						GLUETransformer(__A	)
    _lowerCamelCase     :		str 						=						generic_train(__A   , __A	)

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


if __name__ == "__main__":
    main()





from __future__ import annotations




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						key



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Any 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Optional[Any] 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""encrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""decrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




