


"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets




class 	lowercase__				(					A_				):





   @slow
   @require_torch
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					int   =		EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""      ,   """prajjwal1/bert-tiny""")
      _lowerCamelCase	:					Dict   =		BertTokenizer.from_pretrained("""bert-base-uncased""")

      _lowerCamelCase	:					Tuple   =		bertabert.config.encoder.vocab_size
      _lowerCamelCase	:					int   =		tokenizer.sep_token_id
      _lowerCamelCase	:					List[str]   =		tokenizer.cls_token_id
      _lowerCamelCase	:					Optional[int]   =		128

      _lowerCamelCase	:					int   =		datasets.load_dataset("""cnn_dailymail"""      ,   """3.0.0"""      ,   split="""train[:1%]""")
      _lowerCamelCase	:					Union[str, Any]   =		datasets.load_dataset("""cnn_dailymail"""      ,   """3.0.0"""      ,   split="""validation[:1%]""")

      _lowerCamelCase	:					List[Any]   =		train_dataset.select(range(32))
      _lowerCamelCase	:					Optional[int]   =		val_dataset.select(range(16))

      _lowerCamelCase	:					Any   =		4

      def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         _lowerCamelCase	:					List[Any]   =		tokenizer(batch["""article"""]      ,   padding="""max_length"""      ,   truncation=SCREAMING_SNAKE_CASE      ,   max_length=512)
         _lowerCamelCase	:					Any   =		tokenizer(batch["""highlights"""]      ,   padding="""max_length"""      ,   truncation=SCREAMING_SNAKE_CASE      ,   max_length=128)
         _lowerCamelCase	:					Optional[Any]   =		inputs.input_ids
         _lowerCamelCase	:					Optional[int]   =		inputs.attention_mask

         _lowerCamelCase	:					Union[str, Any]   =		outputs.input_ids
         _lowerCamelCase	:					str   =		outputs.input_ids.copy()
         _lowerCamelCase	:					List[Any]   =		[
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
         ]
         _lowerCamelCase	:					Dict   =		outputs.attention_mask

         assert all(len(SCREAMING_SNAKE_CASE) == 512 for x in inputs.input_ids)
         assert all(len(SCREAMING_SNAKE_CASE) == 128 for x in outputs.input_ids)

         return batch

      def _compute_metrics(SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Any   =		pred.label_ids
         _lowerCamelCase	:					Optional[int]   =		pred.predictions

         # all unnecessary tokens are removed
         _lowerCamelCase	:					Tuple   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Tuple   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Tuple   =		sum([int(pred_str[i] == label_str[i]) for i in range(len(SCREAMING_SNAKE_CASE))]) / len(SCREAMING_SNAKE_CASE)

         return {"accuracy": accuracy}

      # map train dataset
      _lowerCamelCase	:					str   =		train_dataset.map(
          _map_to_encoder_decoder_inputs      ,   batched=SCREAMING_SNAKE_CASE      ,   batch_size=SCREAMING_SNAKE_CASE      ,   remove_columns=["""article""", """highlights"""]      ,   )
      train_dataset.set_format(
          type="""torch"""      ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,   )

      # same for validation dataset
      _lowerCamelCase	:					Union[str, Any]   =		val_dataset.map(
          _map_to_encoder_decoder_inputs      ,   batched=SCREAMING_SNAKE_CASE      ,   batch_size=SCREAMING_SNAKE_CASE      ,   remove_columns=["""article""", """highlights"""]      ,   )
      val_dataset.set_format(
          type="""torch"""      ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,   )

      _lowerCamelCase	:					Optional[Any]   =		self.get_auto_remove_tmp_dir()

      _lowerCamelCase	:					Union[str, Any]   =		SeqaSeqTrainingArguments(
          output_dir=SCREAMING_SNAKE_CASE      ,   per_device_train_batch_size=SCREAMING_SNAKE_CASE      ,   per_device_eval_batch_size=SCREAMING_SNAKE_CASE      ,   predict_with_generate=SCREAMING_SNAKE_CASE      ,   evaluation_strategy="""steps"""      ,   do_train=SCREAMING_SNAKE_CASE      ,   do_eval=SCREAMING_SNAKE_CASE      ,   warmup_steps=0      ,   eval_steps=2      ,   logging_steps=2      ,   )

      # instantiate trainer
      _lowerCamelCase	:					Optional[Any]   =		SeqaSeqTrainer(
          model=SCREAMING_SNAKE_CASE      ,   args=SCREAMING_SNAKE_CASE      ,   compute_metrics=_compute_metrics      ,   train_dataset=SCREAMING_SNAKE_CASE      ,   eval_dataset=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   )

      # start training
      trainer.train()





import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


