'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     int  ={
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}




class        __A      ( UpperCamelCase__	):
    a__       :						List[Any]          =  """xlm"""
    a__       :						Tuple          =  {
        """hidden_size""": """emb_dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
        """n_words""": """vocab_size""",  # For backward compatibility
    }



    def __init__(self    :    Tuple   ,			__a    :    Tuple=30145   ,			__a    :    str=2048   ,			__a    :    Optional[int]=12   ,			__a    :    Tuple=16   ,			__a    :    int=0.1   ,			__a    :    Union[str, Any]=0.1   ,			__a    :    Any=True   ,			__a    :    Optional[Any]=False   ,			__a    :    List[str]=False   ,			__a    :    Tuple=False   ,			__a    :    int=1   ,			__a    :    int=True   ,			__a    :    Optional[Any]=512   ,			__a    :    str=2048**-0.5   ,			__a    :    Optional[int]=1E-12   ,			__a    :    List[Any]=0.02   ,			__a    :    Any=0   ,			__a    :    str=1   ,			__a    :    Optional[Any]=2   ,			__a    :    Union[str, Any]=3   ,			__a    :    str=5   ,			__a    :    Optional[Any]=True   ,			__a    :    str="first"   ,			__a    :    int=True   ,			__a    :    Dict=None   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=0.1   ,			__a    :    int=5   ,			__a    :    Any=5   ,			__a    :    Any=0   ,			__a    :    List[Any]=0   ,			__a    :    List[str]=2   ,			__a    :    Optional[int]=0   ,			**__a    :    List[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   emb_dim
         UpperCAmelCase_           =   n_layers
         UpperCAmelCase_           =   n_heads
         UpperCAmelCase_           =   dropout
         UpperCAmelCase_           =   attention_dropout
         UpperCAmelCase_           =   gelu_activation
         UpperCAmelCase_           =   sinusoidal_embeddings
         UpperCAmelCase_           =   causal
         UpperCAmelCase_           =   asm
         UpperCAmelCase_           =   n_langs
         UpperCAmelCase_           =   use_lang_emb
         UpperCAmelCase_           =   layer_norm_eps
         UpperCAmelCase_           =   bos_index
         UpperCAmelCase_           =   eos_index
         UpperCAmelCase_           =   pad_index
         UpperCAmelCase_           =   unk_index
         UpperCAmelCase_           =   mask_index
         UpperCAmelCase_           =   is_encoder
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   embed_init_std
         UpperCAmelCase_           =   init_std
         UpperCAmelCase_           =   summary_type
         UpperCAmelCase_           =   summary_use_proj
         UpperCAmelCase_           =   summary_activation
         UpperCAmelCase_           =   summary_proj_to_labels
         UpperCAmelCase_           =   summary_first_dropout
         UpperCAmelCase_           =   start_n_top
         UpperCAmelCase_           =   end_n_top
         UpperCAmelCase_           =   mask_token_id
         UpperCAmelCase_           =   lang_id

         if "n_words" in kwargs:
              UpperCAmelCase_           =   kwargs["n_words"]

         super().__init__(pad_token_id=__a   ,			bos_token_id=__a   ,			**__a )




class        __A      ( UpperCamelCase__	):



    @property
    def 					_lowercase						(self    :    int ):
         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()




