
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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  BioGptTokenizer
							_snake_case     		=  False



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													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(SCREAMING_SNAKE_CASE_     ,	range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
													__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(SCREAMING_SNAKE_CASE_			)			)
													with open(self.merges_file     ,	'''w'''			) as fp:
																			fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			'''lower newer'''
													__UpperCamelCase       =			'''lower newer'''
													return input_text, output_text



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			BioGptTokenizer(self.vocab_file     ,	self.merges_file			)

													__UpperCamelCase       =			'''lower'''
													__UpperCamelCase       =			['''low''', '''er</w>''']
													__UpperCamelCase       =			tokenizer.tokenize(SCREAMING_SNAKE_CASE_			)
													self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			tokens + ['''<unk>''']
													__UpperCamelCase       =			[14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)     ,	SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''			)

													__UpperCamelCase       =			tokenizer.encode('''sequence builders'''     ,	add_special_tokens=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			tokenizer.encode('''multi-sequence build'''     ,	add_special_tokens=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

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






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




