

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				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[Any]       				=  BioGptTokenizer
	_lowercase  :   str       				=  False







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						super().setUp()

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

						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						__magic_name__							=    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: Any  , __UpperCamelCase: str      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





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

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

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







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

						'''simple docstring'''





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

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

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

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







from __future__ import annotations




def 					_lowercase     (				a_				:					int				)				->	bool:




					'''simple docstring'''







					__magic_name__							=    str(a_				)
					return len(a_				) == 9 and set(a_				) == set('123456789'				)




def 					_lowercase     (				)				->	int | None:




					'''simple docstring'''







					for base_num in range(9_9_9_9					,4_9_9_9					,-1				):
										__magic_name__							=    1_0_0_0_0_2 * base_num
										if is_9_pandigital(a_				):
															return candidate

					for base_num in range(3_3_3					,9_9					,-1				):
										__magic_name__							=    1_0_0_2_0_0_3 * base_num
										if is_9_pandigital(a_				):
															return candidate

					return None


if __name__ == "__main__":
						print(f'''{solution() = }''')




