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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      @slow
      @require_torch
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""    ,  """prajjwal1/bert-tiny""")
          _lowerCamelCase     :		List[str] 						=						BertTokenizer.from_pretrained("""bert-base-uncased""")

          _lowerCamelCase     :		Union[str, Any] 						=						bertabert.config.encoder.vocab_size
          _lowerCamelCase     :		List[Any] 						=						tokenizer.sep_token_id
          _lowerCamelCase     :		Any 						=						tokenizer.cls_token_id
          _lowerCamelCase     :		Optional[Any] 						=						128

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

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

          _lowerCamelCase     :		Tuple 						=						4

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

              _lowerCamelCase     :		Tuple 						=						outputs.input_ids
              _lowerCamelCase     :		Optional[int] 						=						outputs.input_ids.copy()
              _lowerCamelCase     :		List[str] 						=						[
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
              ]
              _lowerCamelCase     :		List[str] 						=						outputs.attention_mask

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

              return batch

          def _compute_metrics(_UpperCamelCase     :				str):
              _lowerCamelCase     :		List[str] 						=						pred.label_ids
              _lowerCamelCase     :		str 						=						pred.predictions

              # all unnecessary tokens are removed
              _lowerCamelCase     :		List[str] 						=						tokenizer.batch_decode(_UpperCamelCase    ,  skip_special_tokens=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase    ,  skip_special_tokens=_UpperCamelCase)

              _lowerCamelCase     :		Union[str, Any] 						=						sum([int(pred_str[i] == label_str[i]) for i in range(len(_UpperCamelCase))]) / len(_UpperCamelCase)

              return {"accuracy": accuracy}

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

          _lowerCamelCase     :		int 						=						self.get_auto_remove_tmp_dir()

          _lowerCamelCase     :		Dict 						=						SeqaSeqTrainingArguments(
              output_dir=_UpperCamelCase    ,  per_device_train_batch_size=_UpperCamelCase    ,  per_device_eval_batch_size=_UpperCamelCase    ,  predict_with_generate=_UpperCamelCase    ,  evaluation_strategy="""steps"""    ,  do_train=_UpperCamelCase    ,  do_eval=_UpperCamelCase    ,  warmup_steps=0    ,  eval_steps=2    ,  logging_steps=2    ,  )

          # instantiate trainer
          _lowerCamelCase     :		Dict 						=						SeqaSeqTrainer(
              model=_UpperCamelCase    ,  args=_UpperCamelCase    ,  compute_metrics=_compute_metrics    ,  train_dataset=_UpperCamelCase    ,  eval_dataset=_UpperCamelCase    ,  tokenizer=_UpperCamelCase    ,  )

          # start training
          trainer.train()





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




