


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__  (    lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Union[str, Any]					       =						BioGptTokenizer
      snake_case__    :					str					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Dict:
             super().setUp()

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

             a_ :		Optional[int]							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']					)
             a_ :		Union[str, Any]							=  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 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Union[str, Any]:
             a_ :		int							=  'lower newer'
             a_ :		Any							=  'lower newer'
             return input_text, output_text
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Dict							=  BioGptTokenizer(self.vocab_file					,							self.merges_file					)

             a_ :		int							=  'lower'
             a_ :		Optional[Any]							=  ['low', 'er</w>']
             a_ :		Union[str, Any]							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
             self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             a_ :		Union[str, Any]							=  tokens + ['<unk>']
             a_ :		Optional[int]							=  [1_4, 1_5, 2_0]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__					)					,							SCREAMING_SNAKE_CASE__					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             a_ :		Tuple							=  BioGptTokenizer.from_pretrained('microsoft/biogpt'					)

             a_ :		Tuple							=  tokenizer.encode('sequence builders'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  tokenizer.encode('multi-sequence build'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

             a_ :		Any							=  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[int]							=  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 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		bool:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       if len(__A    ) == 1:
              return True
       a_ :		Tuple							=  series[1] - series[0]
       for index in range(len(__A    ) - 1    ):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True







def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		float:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       a_ :		str							=  0
       for val in series:
              answer += val
       return answer / len(__A    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


