





from typing import Mapping

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


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Any							=   't5'
      A_						:    Optional[Any]							=   ['past_key_values']
      A_						:    Union[str, Any]							=   {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


      def __init__(self	:				Optional[int]    ,      a__	:				List[str]=3_2128    ,      a__	:				List[Any]=512    ,      a__	:				Any=64    ,      a__	:				Any=2048    ,      a__	:				Tuple=6    ,      a__	:				Optional[Any]=None    ,      a__	:				Optional[int]=8    ,      a__	:				Optional[int]=32    ,      a__	:				List[str]=128    ,      a__	:				Dict=0.1    ,      a__	:				str=1E-6    ,      a__	:				List[Any]=1.0    ,      a__	:				str="relu"    ,      a__	:				Optional[int]=True    ,      a__	:				Any=True    ,      a__	:				Any=0    ,      a__	:				Any=1    ,      **a__	:				Optional[int]    ,      ):





         """simple docstring"""



         __snake_case								=       vocab_size
         __snake_case								=       d_model
         __snake_case								=       d_kv
         __snake_case								=       d_ff
         __snake_case								=       num_layers
         __snake_case								=       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __snake_case								=       num_heads
         __snake_case								=       relative_attention_num_buckets
         __snake_case								=       relative_attention_max_distance
         __snake_case								=       dropout_rate
         __snake_case								=       layer_norm_epsilon
         __snake_case								=       initializer_factor
         __snake_case								=       feed_forward_proj
         __snake_case								=       use_cache

         __snake_case								=       self.feed_forward_proj.split('''-'''				)
         __snake_case								=       act_info[-1]
         __snake_case								=       act_info[0] == '''gated'''

         if len(a__				) > 1 and act_info[0] != "gated" or len(a__				) > 2:
            raise ValueError(
                f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                '''\'gated-gelu\' or \'relu\''''				)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
            __snake_case								=       '''gelu_new'''

         super().__init__(
             pad_token_id=a__    ,      eos_token_id=a__    ,      is_encoder_decoder=a__    ,      **a__    ,      )






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      @property
      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       {
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
            __snake_case								=       '''past_encoder_sequence + sequence'''
            __snake_case								=       {0: '''batch'''}
            __snake_case								=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            __snake_case								=       {0: '''batch''', 1: '''decoder_sequence'''}
            __snake_case								=       {0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
            self.fill_with_past_key_values_(a__    ,      direction='''inputs'''				)

         return common_inputs


      @property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         return 13








def lowerCamelCase__   (							snake_case_     :  int = 1000	)				->    int:
   __snake_case								=       2**power
   __snake_case								=       str(snake_case_	)
   __snake_case								=       list(snake_case_	)
   __snake_case								=       0

   for i in list_num:
      sum_of_num += int(snake_case_	)

   return sum_of_num


if __name__ == "__main__":
    snake_case_           =      int(input('Enter the power of 2: ').strip())
    print('2 ^ ', power, ' = ', 2**power)
    snake_case_           =      solution(power)
    print('Sum of the digits is: ', result)

