code: 
  
  
  """simple docstring"""
  
  
  
  
  import os
  import unittest
  
  from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  class  __snake_case      (lowerCamelCase ,		unittest.TestCase     ):
  							__a				   =					BertweetTokenizer
  							__a				   =					False
  
  
  
  
  							def      __a      (    self:						Any      ):
  										super().setUp()
  
  										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  										__lowerCamelCase        =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
  										__lowerCamelCase        =			dict(zip(A_    ,       range(len(A_      )      )      )      )
  										__lowerCamelCase        =			["""#version: 0.2""", """a m</w>"""]
  										__lowerCamelCase        =			{"""unk_token""": """<unk>"""}
  
  										__lowerCamelCase        =			os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]      )
  										__lowerCamelCase        =			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(A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict    ,       **A_:						Any      ):
  										kwargs.update(self.special_tokens_map      )
  										return BertweetTokenizer.from_pretrained(self.tmpdirname    ,       **A_      )
  
  
  
  
  							def      __a      (    self:						Any    ,       A_:						Any      ):
  										__lowerCamelCase        =			"""I am VinAI Research"""
  										__lowerCamelCase        =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
  										return input_text, output_text
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			BertweetTokenizer(self.vocab_file    ,       self.merges_file    ,       **self.special_tokens_map      )
  										__lowerCamelCase        =			"""I am VinAI Research"""
  										__lowerCamelCase        =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
  										__lowerCamelCase        =			tokenizer.tokenize(A_      )
  										self.assertListEqual(A_    ,       A_      )
  
  										__lowerCamelCase        =			tokens + [tokenizer.unk_token]
  
  										__lowerCamelCase        =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
  										self.assertListEqual(tokenizer.convert_tokens_to_ids(A_      )    ,       A_      )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  