





'''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 					SCREAMING_SNAKE_CASE       (a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			BioGptTokenizer
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''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		:					Union[str, Any]						=			dict(zip(_UpperCAmelCase		,      range(len(_UpperCAmelCase))))
          __A		:					str						=			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

          __A		:					Optional[Any]						=			os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['vocab_file'])
          __A		:					Dict						=			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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			'lower newer'
          __A		:					int						=			'lower newer'
          return input_text, output_text


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			BioGptTokenizer(self.vocab_file		,      self.merges_file)

          __A		:					List[Any]						=			'lower'
          __A		:					Optional[int]						=			['low', 'er</w>']
          __A		:					Optional[Any]						=			tokenizer.tokenize(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase)

          __A		:					List[Any]						=			tokens + ['<unk>']
          __A		:					Optional[int]						=			[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase)		,      _UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			BioGptTokenizer.from_pretrained('microsoft/biogpt')

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

          __A		:					Any						=			tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase)
          __A		:					List[str]						=			tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		,      _UpperCAmelCase)

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






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					int     )	->	float:
     __A		:					Dict						=			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total






def 			_lowerCAmelCase     (    )	->	Union[str, Any]:
     print(sum_of_series(1					,				1					,				10     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()