


from __future__ import annotations




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




						if len(__lowercase   ) < k or k < 0:
												raise ValueError("""Invalid Input"""   )
						a_		      =		sum(array[:k]   )
						for i in range(len(__lowercase   ) - k   ):
												a_		      =		current_sum - array[i] + array[i + k]
												a_		      =		max(__lowercase							,     __lowercase   )
						return max_sum


if __name__ == "__main__":
	from doctest import testmod
	from random import randint

	testmod()
	A_     :       Dict   							=[randint(-1000, 1000) for i in range(100)]
	A_     :       Union[str, Any]   							=randint(0, 110)
	print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')





'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



