'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[int]          =  """albert"""



    def __init__(self    :    Optional[int]   ,			__a    :    Optional[int]=30000   ,			__a    :    Optional[Any]=128   ,			__a    :    Any=4096   ,			__a    :    Tuple=12   ,			__a    :    int=1   ,			__a    :    str=64   ,			__a    :    Optional[int]=16384   ,			__a    :    Optional[Any]=1   ,			__a    :    Dict="gelu_new"   ,			__a    :    Dict=0   ,			__a    :    Dict=0   ,			__a    :    int=512   ,			__a    :    Optional[Any]=2   ,			__a    :    Tuple=0.02   ,			__a    :    Any=1E-12   ,			__a    :    List[Any]=0.1   ,			__a    :    Optional[Any]="absolute"   ,			__a    :    Union[str, Any]=0   ,			__a    :    Union[str, Any]=2   ,			__a    :    str=3   ,			**__a    :    Union[str, Any]   ,			):
         super().__init__(pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			**__a )

         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   embedding_size
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_hidden_groups
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   inner_group_num
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   type_vocab_size
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   layer_norm_eps
         UpperCAmelCase_           =   classifier_dropout_prob
         UpperCAmelCase_           =   position_embedding_type




class        __A      ( UpperCamelCase__	):



    @property
    def 					_lowercase						(self    :    Tuple ):
         if self.task == "multiple-choice":
              UpperCAmelCase_           =   {0: "batch", 1: "choice", 2: "sequence"}
         else:
              UpperCAmelCase_           =   {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
                 ("token_type_ids", dynamic_axis),
             ] )





'''simple docstring'''





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE_:     Optional[int]  =3_00  # TEMPERATURE (unit = K)





def 	lowerCAmelCase_				(					snake_case_       : float					,    snake_case_       : float					,    snake_case_       : float					,    )    ->      float:


     '''simple docstring'''




     if donor_conc <= 0:
          raise ValueError("Donor concentration should be positive"			)
     elif acceptor_conc <= 0:
          raise ValueError("Acceptor concentration should be positive"			)
     elif intrinsic_conc <= 0:
          raise ValueError("Intrinsic concentration should be positive"			)
     elif donor_conc <= intrinsic_conc:
          raise ValueError(
              "Donor concentration should be greater than intrinsic concentration"			)
     elif acceptor_conc <= intrinsic_conc:
          raise ValueError(
              "Acceptor concentration should be greater than intrinsic concentration"			)
     else:
          return (
              Boltzmann
              * T
              * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
              / physical_constants["electron volt"][0]
          )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




