


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
      UpperCAmelCase             =						"data2vec-text"




      def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


          """simple docstring"""





          super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

          _SCREAMING_SNAKE_CASE			  =vocab_size
          _SCREAMING_SNAKE_CASE			  =hidden_size
          _SCREAMING_SNAKE_CASE			  =num_hidden_layers
          _SCREAMING_SNAKE_CASE			  =num_attention_heads
          _SCREAMING_SNAKE_CASE			  =hidden_act
          _SCREAMING_SNAKE_CASE			  =intermediate_size
          _SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
          _SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE			  =max_position_embeddings
          _SCREAMING_SNAKE_CASE			  =type_vocab_size
          _SCREAMING_SNAKE_CASE			  =initializer_range
          _SCREAMING_SNAKE_CASE			  =layer_norm_eps
          _SCREAMING_SNAKE_CASE			  =position_embedding_type
          _SCREAMING_SNAKE_CASE			  =use_cache
          _SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




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


          """simple docstring"""





          if self.task == "multiple-choice":
              _SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              _SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]  )



from collections.abc import Generator







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =0, 1
				while True:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =b, a + b
								yield b







def 						lowerCamelCase(   a__ = 1000):
				_SCREAMING_SNAKE_CASE			  =1
				_SCREAMING_SNAKE_CASE			  =fibonacci_generator()
				while len(str(next(a__))) < n:
								answer += 1
				return answer + 1


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