




def 		lowerCamelCase__       (						_A = 1000				):







	'''simple docstring'''





	snake_case_						       =	2**power
	snake_case_						       =	0
	while n:
		snake_case_       , snake_case_						       =	r + n % 10, n // 10
	return r


if __name__ == "__main__":
		print(solution(int(str(input()).strip())))



'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase					:			str       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "blenderbot-small"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=50_265   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			str=2_048   ,				__SCREAMING_SNAKE_CASE   :			Tuple=16   ,				__SCREAMING_SNAKE_CASE   :			Dict=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Any=1   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = d_model
      __SCREAMING_SNAKE_CASE      = encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = encoder_attention_heads
      __SCREAMING_SNAKE_CASE      = decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = decoder_layers
      __SCREAMING_SNAKE_CASE      = decoder_attention_heads
      __SCREAMING_SNAKE_CASE      = dropout
      __SCREAMING_SNAKE_CASE      = attention_dropout
      __SCREAMING_SNAKE_CASE      = activation_dropout
      __SCREAMING_SNAKE_CASE      = activation_function
      __SCREAMING_SNAKE_CASE      = init_std
      __SCREAMING_SNAKE_CASE      = encoder_layerdrop
      __SCREAMING_SNAKE_CASE      = decoder_layerdrop
      __SCREAMING_SNAKE_CASE      = use_cache
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE   ,				decoder_start_token_id=__SCREAMING_SNAKE_CASE   ,				forced_eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )

           if self.use_past:
                __SCREAMING_SNAKE_CASE      = {0: """batch"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,				direction="""inputs"""     )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                   ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
               ]     )

      return common_inputs
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super().outputs
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     ).outputs
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      return common_outputs
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      # Generate decoder inputs
      __SCREAMING_SNAKE_CASE      = seq_length if not self.use_past else 1
      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      __SCREAMING_SNAKE_CASE      = dict(**__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           __SCREAMING_SNAKE_CASE      = common_inputs["""decoder_input_ids"""].shape[1]
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           __SCREAMING_SNAKE_CASE      = decoder_seq_length + 3
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )

           __SCREAMING_SNAKE_CASE      = []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE      = min(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = max(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) - min_num_layers
           __SCREAMING_SNAKE_CASE      = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

           for _ in range(__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                    )     )
           # TODO: test this.
           __SCREAMING_SNAKE_CASE      = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     ))     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           __SCREAMING_SNAKE_CASE      = seqlen + 2
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = common_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )
           __SCREAMING_SNAKE_CASE      = [
               (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
           ]
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_batch   ,				num_token_to_add=0     )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __SCREAMING_SNAKE_CASE      = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=__SCREAMING_SNAKE_CASE     )

      # Generate dummy inputs according to compute batch and sequence
      __SCREAMING_SNAKE_CASE      = [""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      __SCREAMING_SNAKE_CASE      = dict(tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      elif self.task == "causal-lm":
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_causal_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      return common_inputs


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     )._flatten_past_key_values_(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






