






'''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 a_    (    _snake_case							):







   @slow
   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      int     							= EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'					,   'prajjwal1/bert-tiny')
      __snake_case				:      Tuple     							= BertTokenizer.from_pretrained('bert-base-uncased')

      __snake_case				:      Dict     							= bertabert.config.encoder.vocab_size
      __snake_case				:      int     							= tokenizer.sep_token_id
      __snake_case				:      List[str]     							= tokenizer.cls_token_id
      __snake_case				:      List[str]     							= 1_2_8

      __snake_case				:      str     							= datasets.load_dataset('cnn_dailymail'					,   '3.0.0'					,   split='train[:1%]')
      __snake_case				:      Optional[Any]     							= datasets.load_dataset('cnn_dailymail'					,   '3.0.0'					,   split='validation[:1%]')

      __snake_case				:      List[Any]     							= train_dataset.select(range(3_2))
      __snake_case				:      List[Any]     							= val_dataset.select(range(1_6))

      __snake_case				:      Union[str, Any]     							= 4

      def _map_to_encoder_decoder_inputs(__a):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         __snake_case				:      Optional[int]     							= tokenizer(batch['article']					,   padding='max_length'					,   truncation=__a					,   max_length=5_1_2)
         __snake_case				:      Dict     							= tokenizer(batch['highlights']					,   padding='max_length'					,   truncation=__a					,   max_length=1_2_8)
         __snake_case				:      List[str]     							= inputs.input_ids
         __snake_case				:      Optional[int]     							= inputs.attention_mask

         __snake_case				:      List[str]     							= outputs.input_ids
         __snake_case				:      Optional[int]     							= outputs.input_ids.copy()
         __snake_case				:      Dict     							= [
             [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
         ]
         __snake_case				:      Union[str, Any]     							= outputs.attention_mask

         assert all(len(__a) == 5_1_2 for x in inputs.input_ids)
         assert all(len(__a) == 1_2_8 for x in outputs.input_ids)

         return batch

      def _compute_metrics(__a):
         __snake_case				:      int     							= pred.label_ids
         __snake_case				:      Optional[Any]     							= pred.predictions

         # all unnecessary tokens are removed
         __snake_case				:      List[Any]     							= tokenizer.batch_decode(__a					,   skip_special_tokens=__a)
         __snake_case				:      Tuple     							= tokenizer.batch_decode(__a					,   skip_special_tokens=__a)

         __snake_case				:      List[str]     							= sum([int(pred_str[i] == label_str[i]) for i in range(len(__a))]) / len(__a)

         return {"accuracy": accuracy}

      # map train dataset
      __snake_case				:      Dict     							= train_dataset.map(
          _map_to_encoder_decoder_inputs					,   batched=__a					,   batch_size=__a					,   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
      __snake_case				:      Optional[Any]     							= val_dataset.map(
          _map_to_encoder_decoder_inputs					,   batched=__a					,   batch_size=__a					,   remove_columns=['article', 'highlights']					,   )
      val_dataset.set_format(
          type='torch'					,   columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']					,   )

      __snake_case				:      Any     							= self.get_auto_remove_tmp_dir()

      __snake_case				:      Optional[Any]     							= SeqaSeqTrainingArguments(
          output_dir=__a					,   per_device_train_batch_size=__a					,   per_device_eval_batch_size=__a					,   predict_with_generate=__a					,   evaluation_strategy='steps'					,   do_train=__a					,   do_eval=__a					,   warmup_steps=0					,   eval_steps=2					,   logging_steps=2					,   )

      # instantiate trainer
      __snake_case				:      int     							= SeqaSeqTrainer(
          model=__a					,   args=__a					,   compute_metrics=_compute_metrics					,   train_dataset=__a					,   eval_dataset=__a					,   tokenizer=__a					,   )

      # start training
      trainer.train()







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       list  )    -> list:







   """simple docstring"""
   __snake_case				:      Tuple     							= False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __snake_case				:      Optional[Any]     							= True
      for i in range(0  ,		len(A  ) - 1  ,		2  ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __snake_case							,__snake_case				:      int     							= input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __snake_case				:      List[Any]     							= False

      for i in range(1  ,		len(A  ) - 1  ,		2  ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __snake_case							,__snake_case				:      Tuple     							= input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __snake_case				:      Any     							= False
   return input_list


if __name__ == "__main__":
      print('''Enter list to be sorted''')
      __A    	=   [int(x) for x in input().split()]
      # inputing elements of the list in one line
      __A    	=   odd_even_sort(input_list)
      print('''The sorted list is''')
      print(sorted_list)