


'''simple docstring'''







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


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

_SCREAMING_SNAKE_CASE							: Union[str, Any]										=			{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class 		_snake_case     (					__lowerCamelCase		):






						'''simple docstring'''







						__snake_case				=   'biogpt'







						def __init__(	self:						Tuple   ,				__UpperCamelCase:						Union[str, Any]=4_2384   ,				__UpperCamelCase:						str=1024   ,				__UpperCamelCase:						Any=24   ,				__UpperCamelCase:						List[str]=16   ,				__UpperCamelCase:						List[str]=4096   ,				__UpperCamelCase:						int="gelu"   ,				__UpperCamelCase:						List[Any]=0.1   ,				__UpperCamelCase:						int=0.1   ,				__UpperCamelCase:						int=1024   ,				__UpperCamelCase:						Optional[Any]=0.0_2   ,				__UpperCamelCase:						Union[str, Any]=1E-12   ,				__UpperCamelCase:						Tuple=True   ,				__UpperCamelCase:						int=True   ,				__UpperCamelCase:						Optional[int]=0.0   ,				__UpperCamelCase:						Any=0.0   ,				__UpperCamelCase:						str=1   ,				__UpperCamelCase:						Any=0   ,				__UpperCamelCase:						int=2   ,				**__UpperCamelCase:						Tuple   ,				)				->						Optional[int]:
										__magic_name__					:       Dict    							=							vocab_size
										__magic_name__					:       List[str]    							=							max_position_embeddings
										__magic_name__					:       int    							=							hidden_size
										__magic_name__					:       str    							=							num_hidden_layers
										__magic_name__					:       Any    							=							num_attention_heads
										__magic_name__					:       str    							=							intermediate_size
										__magic_name__					:       Optional[int]    							=							hidden_act
										__magic_name__					:       str    							=							hidden_dropout_prob
										__magic_name__					:       Dict    							=							attention_probs_dropout_prob
										__magic_name__					:       Tuple    							=							initializer_range
										__magic_name__					:       Tuple    							=							layer_norm_eps
										__magic_name__					:       Union[str, Any]    							=							scale_embedding
										__magic_name__					:       Dict    							=							use_cache
										__magic_name__					:       str    							=							layerdrop
										__magic_name__					:       Optional[Any]    							=							activation_dropout
										super().__init__(pad_token_id=__UpperCamelCase   ,				bos_token_id=__UpperCamelCase   ,				eos_token_id=__UpperCamelCase   ,				**__UpperCamelCase  )

from __future__ import annotations


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						if resistor <= 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(a	)
						first_sum += 1 / float(a	)
						index += 1
			return 1 / first_sum


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative value!"""
									raise ValueError(a	)
						index += 1
			return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()