'''simple docstring'''





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						List[str]          =  BioGptTokenizer
    a__       :						int          =  False



    def 					_lowercase						(self    :    Optional[Any] ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCAmelCase_           =   [
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "w</w>",
             "r</w>",
             "t</w>",
             "lo",
             "low",
             "er</w>",
             "low</w>",
             "lowest</w>",
             "newer</w>",
             "wider</w>",
             "<unk>",
         ]
         UpperCAmelCase_           =   dict(zip(__a   ,			range(len(__a ) ) ) )
         UpperCAmelCase_           =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["vocab_file"] )
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["merges_file"] )
         with open(self.vocab_file   ,			"w" ) as fp:
              fp.write(json.dumps(__a ) )
         with open(self.merges_file   ,			"w" ) as fp:
              fp.write("\n".join(__a ) )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   "lower newer"
         UpperCAmelCase_           =   "lower newer"
         return input_text, output_text



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   BioGptTokenizer(self.vocab_file   ,			self.merges_file )

         UpperCAmelCase_           =   "lower"
         UpperCAmelCase_           =   ["low", "er</w>"]
         UpperCAmelCase_           =   tokenizer.tokenize(__a )
         self.assertListEqual(__a   ,			__a )

         UpperCAmelCase_           =   tokens + ["<unk>"]
         UpperCAmelCase_           =   [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__a )   ,			__a )






    @slow
    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   BioGptTokenizer.from_pretrained("microsoft/biogpt" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         self.assertTrue(encoded_sentence == [2] + text )
         self.assertTrue(encoded_pair == [2] + text + [2] + text_a )





'''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()




