



"""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 				_UpperCAmelCase				(					_lowercase						,unittest.TestCase ):





     '''simple docstring'''



     a__   							=BioGptTokenizer
     a__   							=False





     def 					__lowerCAmelCase      (   self						)				->   Optional[int]:
         super().setUp()

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

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





     def 					__lowerCAmelCase      (   self			,      A						)				->   int:
         _UpperCAmelCase				:    int											=       '''lower newer'''
         _UpperCAmelCase				:    Tuple											=       '''lower newer'''
         return input_text, output_text





     def 					__lowerCAmelCase      (   self						)				->   Any:
         _UpperCAmelCase				:    Union[str, Any]											=       BioGptTokenizer(self.vocab_file			,      self.merges_file						)

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

         _UpperCAmelCase				:    Union[str, Any]											=       tokens + ['''<unk>''']
         _UpperCAmelCase				:    List[str]											=       [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_						)			,      A_						)





     @slow
     def 					__lowerCAmelCase      (   self						)				->   Optional[Any]:
         _UpperCAmelCase				:    List[str]											=       BioGptTokenizer.from_pretrained('''microsoft/biogpt'''						)

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

         _UpperCAmelCase				:    Union[str, Any]											=       tokenizer.build_inputs_with_special_tokens(A_						)
         _UpperCAmelCase				:    int											=       tokenizer.build_inputs_with_special_tokens(A_			,      A_						)

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




"""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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

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

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




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

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