





"""simple docstring"""






import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 				__lowercase			(			_A   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  XLMTokenizer
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						str			       =  [
									    "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>",
									]
									__a :						List[Any]			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						Tuple			       =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Optional[Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Dict			       =  "lower newer"
									__a :						Optional[int]			       =  "lower newer"
									return input_text, output_text







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  XLMTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  "lower"
									__a :						List[Any]			       =  ["low", "er</w>"]
									__a :						Tuple			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

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







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''     )

									__a :						Union[str, Any]			       =  tokenizer.encode('''sequence builders'''	,			add_special_tokens=_UpperCAmelCase     )
									__a :						Optional[Any]			       =  tokenizer.encode('''multi-sequence build'''	,			add_special_tokens=_UpperCAmelCase     )

									__a :						str			       =  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
									__a :						Dict			       =  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	,			_UpperCAmelCase     )

									assert encoded_sentence == [0] + text + [1]
									assert encoded_pair == [0] + text + [1] + text_a + [1]






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float   ,       )		->		float:

				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()