
"""simple docstring"""




from ...configuration_utils import PretrainedConfig


A__   :					Dict							=   {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}


class 			__magic_name__       (       __A ):
       UpperCamelCase_   =		'''tapas'''
       def __init__(     self ,				A_=3_0522 ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.1 ,				A_=0.1 ,				A_=1024 ,				A_=[3, 256, 256, 2, 256, 256, 10] ,				A_=0.02 ,				A_=1E-12 ,				A_=0 ,				A_=10.0 ,				A_=0 ,				A_=1.0 ,				A_=None ,				A_=1.0 ,				A_=False ,				A_=None ,				A_=1.0 ,				A_=1.0 ,				A_=False ,				A_=False ,				A_="ratio" ,				A_=None ,				A_=None ,				A_=64 ,				A_=32 ,				A_=False ,				A_=True ,				A_=False ,				A_=False ,				A_=True ,				A_=False ,				A_=None ,				A_=None ,				**A_ ,				)     ->	List[str]:





         """simple docstring"""




         super().__init__(pad_token_id=A_ ,				**A_       )

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         _lowercase:			str       =     vocab_size
         _lowercase:			str       =     hidden_size
         _lowercase:			Optional[Any]       =     num_hidden_layers
         _lowercase:			Any       =     num_attention_heads
         _lowercase:			List[str]       =     hidden_act
         _lowercase:			Tuple       =     intermediate_size
         _lowercase:			Optional[int]       =     hidden_dropout_prob
         _lowercase:			Optional[int]       =     attention_probs_dropout_prob
         _lowercase:			Tuple       =     max_position_embeddings
         _lowercase:			int       =     type_vocab_sizes
         _lowercase:			Optional[Any]       =     initializer_range
         _lowercase:			Any       =     layer_norm_eps

         # Fine-tuning task hyperparameters
         _lowercase:			int       =     positive_label_weight
         _lowercase:			int       =     num_aggregation_labels
         _lowercase:			Optional[int]       =     aggregation_loss_weight
         _lowercase:			List[str]       =     use_answer_as_supervision
         _lowercase:			List[str]       =     answer_loss_importance
         _lowercase:			List[Any]       =     use_normalized_answer_loss
         _lowercase:			Dict       =     huber_loss_delta
         _lowercase:			List[str]       =     temperature
         _lowercase:			List[str]       =     aggregation_temperature
         _lowercase:			int       =     use_gumbel_for_cells
         _lowercase:			Tuple       =     use_gumbel_for_aggregation
         _lowercase:			int       =     average_approximation_function
         _lowercase:			List[str]       =     cell_selection_preference
         _lowercase:			Tuple       =     answer_loss_cutoff
         _lowercase:			str       =     max_num_rows
         _lowercase:			Optional[int]       =     max_num_columns
         _lowercase:			Tuple       =     average_logits_per_cell
         _lowercase:			Optional[int]       =     select_one_column
         _lowercase:			Tuple       =     allow_empty_column_selection
         _lowercase:			int       =     init_cell_selection_weights_to_zero
         _lowercase:			Optional[Any]       =     reset_position_index_per_cell
         _lowercase:			Any       =     disable_per_token_loss

         # Aggregation hyperparameters
         _lowercase:			Dict       =     aggregation_labels
         _lowercase:			Union[str, Any]       =     no_aggregation_label_index

         if isinstance(self.aggregation_labels ,				A_       ):
           _lowercase:			List[str]       =     {int(A_       ): v for k, v in aggregation_labels.items()}


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if num <= 0:
    raise ValueError('''Input must be a positive integer'''						)

  _lowercase:			Tuple       =     [True] * (num + 1)

  _lowercase:			List[str]       =     2
  while p * p <= num:
    if primes[p]:
      for i in range(p * p ,				num + 1 ,				_UpperCamelCase						):
        _lowercase:			List[str]       =     False
    p += 1

  return [prime for prime in range(2 ,				num + 1						) if primes[prime]]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  A__   :					List[Any]							=   int(input('Enter a positive integer: ').strip())
  print(prime_sieve_eratosthenes(user_num))
