




"""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        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    int         =   BioGptTokenizer
				__lowerCAmelCase    :    Optional[Any]         =   False







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					super().setUp()

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

					A_  :				int	   =		os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['''vocab_file''']       )
					A_  :				Tuple	   =		os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['''merges_file''']       )
					with open(self.vocab_file      ,     '''w'''       ) as fp:
						fp.write(json.dumps(_lowerCamelCase       )       )
					with open(self.merges_file      ,     '''w'''       ) as fp:
						fp.write('''\n'''.join(_lowerCamelCase       )       )







				def 					a_  (							self   :					List[Any]      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				Dict	   =		'''lower newer'''
					A_  :				Optional[Any]	   =		'''lower newer'''
					return input_text, output_text







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		BioGptTokenizer(self.vocab_file      ,     self.merges_file       )

					A_  :				Tuple	   =		'''lower'''
					A_  :				int	   =		['''low''', '''er</w>''']
					A_  :				List[str]	   =		tokenizer.tokenize(_lowerCamelCase       )
					self.assertListEqual(_lowerCamelCase      ,     _lowerCamelCase       )

					A_  :				Tuple	   =		tokens + ['''<unk>''']
					A_  :				Dict	   =		[14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase       )      ,     _lowerCamelCase       )







				@slow
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

					A_  :				List[str]	   =		tokenizer.encode('''sequence builders'''      ,     add_special_tokens=_lowerCamelCase       )
					A_  :				Optional[int]	   =		tokenizer.encode('''multi-sequence build'''      ,     add_special_tokens=_lowerCamelCase       )

					A_  :				Any	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase       )
					A_  :				str	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      ,     _lowerCamelCase       )

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











"""simple docstring"""

from __future__ import annotations

from collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





