
import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  List[str]    					=       BertweetTokenizer
			__UpperCamelCase					:  int    					=       False

			def 					lowerCAmelCase__		(				self       :				Dict					):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase_:							int		=  ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
							UpperCamelCase_:							Any		=  dict(zip(snake_case_  ,		range(len(snake_case_					)					)					)					)
							UpperCamelCase_:							Optional[Any]		=  ["""#version: 0.2""", """a m</w>"""]
							UpperCamelCase_:							Optional[int]		=  {"""unk_token""": """<unk>"""}

							UpperCamelCase_:							str		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
							UpperCamelCase_:							int		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""merges_file"""]					)
							with open(self.vocab_file  ,		"""w"""  ,		encoding="""utf-8"""					) as fp:
											for token in vocab_tokens:
															fp.write(f'''{token} {vocab_tokens[token]}\n'''					)
							with open(self.merges_file  ,		"""w"""  ,		encoding="""utf-8"""					) as fp:
											fp.write("""\n""".join(snake_case_					)					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		**snake_case_       :				Dict					):
							kwargs.update(self.special_tokens_map					)
							return BertweetTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							Tuple		=  """I am VinAI Research"""
							UpperCamelCase_:							List[str]		=  """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
							return input_text, output_text




			def 					lowerCAmelCase__		(				self       :				str					):
							UpperCamelCase_:							Any		=  BertweetTokenizer(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map					)
							UpperCamelCase_:							int		=  """I am VinAI Research"""
							UpperCamelCase_:							str		=  """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
							UpperCamelCase_:							Optional[Any]		=  tokenizer.tokenize(snake_case_					)
							self.assertListEqual(snake_case_  ,		snake_case_					)

							UpperCamelCase_:							Optional[Any]		=  tokens + [tokenizer.unk_token]

							UpperCamelCase_:							List[Any]		=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_					)  ,		snake_case_					)








def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""  )
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""  )
				UpperCamelCase_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


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






