'''simple docstring'''


from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__	:     List[Any]      =    logging.get_logger(__name__)

snake_case__	:     int      =    {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[int]		       =						'''led'''
  lowerCamelCase_						:List[str]		       =						{
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''hidden_size''': '''d_model''',
      '''attention_probs_dropout_prob''': '''attention_dropout''',
      '''initializer_range''': '''init_std''',
  }


  def __init__(					self ,						snake_case_=5_0_2_6_5 ,						snake_case_=1_6_3_8_4 ,						snake_case_=1_0_2_4 ,						snake_case_=1_2 ,						snake_case_=4_0_9_6 ,						snake_case_=1_6 ,						snake_case_=1_2 ,						snake_case_=4_0_9_6 ,						snake_case_=1_6 ,						snake_case_=0.0 ,						snake_case_=0.0 ,						snake_case_=True ,						snake_case_=True ,						snake_case_="gelu" ,						snake_case_=1_0_2_4 ,						snake_case_=0.1 ,						snake_case_=0.0 ,						snake_case_=0.0 ,						snake_case_=0.02 ,						snake_case_=2 ,						snake_case_=0.0 ,						snake_case_=1 ,						snake_case_=0 ,						snake_case_=2 ,						snake_case_ = 5_1_2 ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = vocab_size
       UpperCAmelCase_				:  Union[str, Any]  = max_encoder_position_embeddings
       UpperCAmelCase_				:  int  = max_decoder_position_embeddings
       UpperCAmelCase_				:  int  = d_model
       UpperCAmelCase_				:  Optional[Any]  = encoder_ffn_dim
       UpperCAmelCase_				:  Any  = encoder_layers
       UpperCAmelCase_				:  Dict  = encoder_attention_heads
       UpperCAmelCase_				:  List[Any]  = decoder_ffn_dim
       UpperCAmelCase_				:  Optional[Any]  = decoder_layers
       UpperCAmelCase_				:  Union[str, Any]  = decoder_attention_heads
       UpperCAmelCase_				:  int  = dropout
       UpperCAmelCase_				:  str  = attention_dropout
       UpperCAmelCase_				:  List[str]  = activation_dropout
       UpperCAmelCase_				:  Any  = activation_function
       UpperCAmelCase_				:  Any  = init_std
       UpperCAmelCase_				:  str  = encoder_layerdrop
       UpperCAmelCase_				:  List[str]  = decoder_layerdrop
       UpperCAmelCase_				:  Dict  = classifier_dropout
       UpperCAmelCase_				:  Optional[Any]  = use_cache
       UpperCAmelCase_				:  Union[str, Any]  = encoder_layers
       UpperCAmelCase_				:  Dict  = attention_window

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










def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

