
"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a									=				logging.getLogger(__name__)


@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,)




@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys() )} )
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
  SCREAMING_SNAKE_CASE__     :		int    	=						field(
      default=128							,metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Dict     	=       default_data_collator(__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       batch.pop("pairID",						__lowerCamelCase    )
   return batch







def       __a	(						):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   UpperCAmelCase_   :   str     	=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       parser.parse_args_into_dataclasses()

   if (
       os.path.exists(training_args.output_dir    )
       and os.listdir(training_args.output_dir    )
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
          " --overwrite_output_dir to overcome."    )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",						datefmt="%m/%d/%Y %H:%M:%S",						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,						)
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",						training_args.local_rank,						training_args.device,						training_args.n_gpu,						bool(training_args.local_rank != -1    ),						training_args.fpaa,						)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank    ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info("Training/evaluation parameters %s",						__lowerCamelCase    )

   # Set seed
   set_seed(training_args.seed    )

   try:
      UpperCAmelCase_   :   List[Any]     	=       hans_tasks_num_labels[data_args.task_name]
   except KeyError:
      raise ValueError("Task not found: %s" % (data_args.task_name)    )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   UpperCAmelCase_   :   Optional[Any]     	=       AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,						num_labels=__lowerCamelCase,						finetuning_task=data_args.task_name,						cache_dir=model_args.cache_dir,						)
   UpperCAmelCase_   :   Any     	=       AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,						cache_dir=model_args.cache_dir,						)
   UpperCAmelCase_   :   str     	=       AutoModelForSequenceClassification.from_pretrained(
       model_args.model_name_or_path,						from_tf=bool(".ckpt" in model_args.model_name_or_path    ),						config=__lowerCamelCase,						cache_dir=model_args.cache_dir,						)

   # Get datasets
   UpperCAmelCase_   :   Optional[Any]     	=       (
       HansDataset(
           data_dir=data_args.data_dir,						tokenizer=__lowerCamelCase,						task=data_args.task_name,						max_seq_length=data_args.max_seq_length,						overwrite_cache=data_args.overwrite_cache,						)
       if training_args.do_train
       else None
   )
   UpperCAmelCase_   :   Optional[int]     	=       (
       HansDataset(
           data_dir=data_args.data_dir,						tokenizer=__lowerCamelCase,						task=data_args.task_name,						max_seq_length=data_args.max_seq_length,						overwrite_cache=data_args.overwrite_cache,						evaluate=__lowerCamelCase,						)
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   UpperCAmelCase_   :   str     	=       Trainer(
       model=__lowerCamelCase,						args=__lowerCamelCase,						train_dataset=__lowerCamelCase,						eval_dataset=__lowerCamelCase,						data_collator=__lowerCamelCase,						)

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"    )

      UpperCAmelCase_   :   List[Any]     	=       trainer.predict(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       output.predictions
      UpperCAmelCase_   :   Union[str, Any]     	=       np.argmax(__lowerCamelCase,						axis=1    )

      UpperCAmelCase_   :   Tuple     	=       [ex.pairID for ex in eval_dataset]
      UpperCAmelCase_   :   str     	=       os.path.join(training_args.output_dir,						"hans_predictions.txt"    )
      UpperCAmelCase_   :   Union[str, Any]     	=       eval_dataset.get_labels()
      if trainer.is_world_master():
         with open(__lowerCamelCase,						"w"    ) as writer:
            writer.write("pairID,gold_label\n"    )
            for pid, pred in zip(__lowerCamelCase,						__lowerCamelCase    ):
               writer.write("ex" + str(__lowerCamelCase    ) + "," + label_list[int(__lowerCamelCase    )] + "\n"    )

      trainer._log(output.metrics    )







def       __a	(						__lowerCamelCase    ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()




"""simple docstring"""





def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("plaintext must be a string"    )
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("key must be a string"    )

   if not plaintext:
      raise ValueError("plaintext is empty"    )
   if not key:
      raise ValueError("key is empty"    )

   key += plaintext
   UpperCAmelCase_   :   Optional[int]     	=       plaintext.lower()
   UpperCAmelCase_   :   Any     	=       key.lower()
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   List[str]     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       ""
   while plaintext_iterator < len(__lowerCamelCase    ):
      if (
          ord(plaintext[plaintext_iterator]    ) < 97
          or ord(plaintext[plaintext_iterator]    ) > 122
      ):
         ciphertext += plaintext[plaintext_iterator]
         plaintext_iterator += 1
      elif ord(key[key_iterator]    ) < 97 or ord(key[key_iterator]    ) > 122:
         key_iterator += 1
      else:
         ciphertext += chr(
             (
                 (ord(plaintext[plaintext_iterator]    ) - 97 + ord(key[key_iterator]    ))
                 - 97
             )
             % 26
             + 97    )
         key_iterator += 1
         plaintext_iterator += 1
   return ciphertext







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("ciphertext must be a string"    )
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("key must be a string"    )

   if not ciphertext:
      raise ValueError("ciphertext is empty"    )
   if not key:
      raise ValueError("key is empty"    )

   UpperCAmelCase_   :   List[str]     	=       key.lower()
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   Dict     	=       ""
   while ciphertext_iterator < len(__lowerCamelCase    ):
      if (
          ord(ciphertext[ciphertext_iterator]    ) < 97
          or ord(ciphertext[ciphertext_iterator]    ) > 122
      ):
         plaintext += ciphertext[ciphertext_iterator]
      else:
         plaintext += chr(
             (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
             + 97    )
         key += chr(
             (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
             + 97    )
         key_iterator += 1
      ciphertext_iterator += 1
   return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _a									=				int(input('Type 1 to encrypt or 2 to decrypt:'))
    if operation == 1:
        _a									=				input('Typeplaintext to be encrypted:\n')
        _a									=				input('Type the key:\n')
        print(encrypt(plaintext, key))
    elif operation == 2:
        _a									=				input('Type the ciphertext to be decrypted:\n')
        _a									=				input('Type the key:\n')
        print(decrypt(ciphertext, key))
    decrypt('jsqqs avvwo', 'coffee')


