"""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


lowerCAmelCase__						:						int		  =			logging.getLogger(__name__)
class        snake_case (				SCREAMING_SNAKE_CASE_       ):




					"""simple docstring"""

					snake_case__       					=				"sequence-classification"







					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			str    ):
									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     :			str					,**lowerCamelCase__     :			Optional[Any]    ):
									return self.model(**UpperCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Dict    ):
									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]    ):
									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     :			Dict					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			List[str] = False    ):
									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     :			Union[str, Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[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__    =							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     :			Union[str, Any]					,lowerCamelCase__     :			int    ):
									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     :			List[str]					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							self._eval_end(UpperCamelCase__    )
									UpperCAmelCase__    =							ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]    ):
									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		(       lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Optional[int]    ):
									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_		(      ):
				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"""






import random
class        snake_case :




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[ord(lowerCamelCase__    ) for i in text]
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for i in plain:
													UpperCAmelCase__    =							random.randint(1					,300    )
													UpperCAmelCase__    =							(i + k) * k
													cipher.append(lowerCamelCase__    )
													key.append(lowerCamelCase__    )
									return cipher, key







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(lowerCamelCase__    )    ):
													UpperCAmelCase__    =							int((cipher[i] - (key[i]) ** 2) / key[i]    )
													plain.append(chr(lowerCamelCase__    )    )
									return "".join(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCAmelCase__  ,			lowerCAmelCase__						:						Dict		  =			Onepad().encrypt('Hello')
						print(c, k)
						print(Onepad().decrypt(c, k))



