code: 
  
  
  """simple docstring"""
  
  
  
  
  import json
  import os
  import re
  import unicodedata
  from typing import Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...utils import logging
  
  
  __magic_name__	:   str          =							logging.get_logger(__name__)
  
  __magic_name__	:   Optional[Any]          =							{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  __magic_name__	:   str          =							{
      'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
      'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
  }
  
  __magic_name__	:   Optional[int]          =							{
      'openai-gpt': 5_1_2,
  }
  
  
  
  
  
  def 			a_   (  lowercase__   :Any    ):
  			__lowerCamelCase        =			text.strip()
  			if not text:
  						return []
  			__lowerCamelCase        =			text.split()
  			return tokens
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def __init__(    self:						List[str]    ,       A_:						List[str]=True    ,       A_:						List[Any]=None    ,       A_:						Any=True    ,       A_:						Any=None    ,       A_:						str=True    ,       ):
  										if never_split is None:
  													__lowerCamelCase        =			[]
  										__lowerCamelCase        =			do_lower_case
  										__lowerCamelCase        =			set(A_      )
  										__lowerCamelCase        =			tokenize_chinese_chars
  										__lowerCamelCase        =			strip_accents
  										__lowerCamelCase        =			do_split_on_punc
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						Union[str, Any]    ,       A_:						int=None      ):
  										__lowerCamelCase        =			self.never_split.union(set(A_      )      ) if never_split else self.never_split
  										__lowerCamelCase        =			self._clean_text(A_      )
  
  										# This was added on November 1st, 2018 for the multilingual and Chinese
  										# models. This is also applied to the English models now, but it doesn't
  										# matter since the English models were not trained on any Chinese data
  										# and generally don't have any Chinese data in them (there are Chinese
  										# characters in the vocabulary because Wikipedia does have some Chinese
  										# words in the English Wikipedia.).
  										if self.tokenize_chinese_chars:
  													__lowerCamelCase        =			self._tokenize_chinese_chars(A_      )
  										# prevents treating the same character with different unicode codepoints as different characters
  										__lowerCamelCase        =			unicodedata.normalize("""NFC"""    ,       A_      )
  										__lowerCamelCase        =			whitespace_tokenize(A_      )
  										__lowerCamelCase        =			[]
  										for token in orig_tokens:
  													if token not in never_split:
  																if self.do_lower_case:
  																			__lowerCamelCase        =			token.lower()
  																			if self.strip_accents is not False:
  																						__lowerCamelCase        =			self._run_strip_accents(A_      )
  																elif self.strip_accents:
  																			__lowerCamelCase        =			self._run_strip_accents(A_      )
  													split_tokens.extend(self._run_split_on_punc(A_    ,       A_      )      )
  
  										__lowerCamelCase        =			whitespace_tokenize(""" """.join(A_      )      )
  										return output_tokens
  
  
  
  
  							def      __a      (    self:						Optional[int]    ,       A_:						Union[str, Any]      ):
  										__lowerCamelCase        =			unicodedata.normalize("""NFD"""    ,       A_      )
  										__lowerCamelCase        =			[]
  										for char in text:
  													__lowerCamelCase        =			unicodedata.category(A_      )
  													if cat == "Mn":
  																continue
  													output.append(A_      )
  										return "".join(A_      )
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						int    ,       A_:						Union[str, Any]=None      ):
  										if not self.do_split_on_punc or (never_split is not None and text in never_split):
  													return [text]
  										__lowerCamelCase        =			list(A_      )
  										__lowerCamelCase        =			0
  										__lowerCamelCase        =			True
  										__lowerCamelCase        =			[]
  										while i < len(A_      ):
  													__lowerCamelCase        =			chars[i]
  													if _is_punctuation(A_      ):
  																output.append([char]      )
  																__lowerCamelCase        =			True
  													else:
  																if start_new_word:
  																			output.append([]      )
  																__lowerCamelCase        =			False
  																output[-1].append(A_      )
  													i += 1
  
  										return ["".join(A_      ) for x in output]
  
  
  
  
  							def      __a      (    self:						List[str]    ,       A_:						Optional[int]      ):
  										__lowerCamelCase        =			[]
  										for char in text:
  													__lowerCamelCase        =			ord(A_      )
  													if self._is_chinese_char(A_      ):
  																output.append(""" """      )
  																output.append(A_      )
  																output.append(""" """      )
  													else:
  																output.append(A_      )
  										return "".join(A_      )
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						Optional[int]      ):
  										# This defines a "chinese character" as anything in the CJK Unicode block:
  										#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  										#
  										# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  										# despite its name. The modern Korean Hangul alphabet is a different block,
  										# as is Japanese Hiragana and Katakana. Those alphabets are used to write
  										# space-separated words, so they are not treated specially and handled
  										# like the all of the other languages.
  										if (
  										    (cp >= 0X4E00 and cp <= 0X9FFF)
  										    or (cp >= 0X3400 and cp <= 0X4DBF)  #
  										    or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
  										    or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
  										    or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
  										    or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
  										    or (cp >= 0XF900 and cp <= 0XFAFF)
  										    or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
  										):  #
  													return True
  
  										return False
  
  
  
  
  							def      __a      (    self:						Optional[Any]    ,       A_:						Tuple      ):
  										__lowerCamelCase        =			[]
  										for char in text:
  													__lowerCamelCase        =			ord(A_      )
  													if cp == 0 or cp == 0XFFFD or _is_control(A_      ):
  																continue
  													if _is_whitespace(A_      ):
  																output.append(""" """      )
  													else:
  																output.append(A_      )
  										return "".join(A_      )
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			set()
  			__lowerCamelCase        =			word[0]
  			for char in word[1:]:
  						pairs.add((prev_char, char)    )
  						__lowerCamelCase        =			char
  			return pairs
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[Any]    ):
  			__lowerCamelCase        =			text.replace("""—""", """-"""    )
  			__lowerCamelCase        =			text.replace("""–""", """-"""    )
  			__lowerCamelCase        =			text.replace("""―""", """-"""    )
  			__lowerCamelCase        =			text.replace("""…""", """..."""    )
  			__lowerCamelCase        =			text.replace("""´""", """'"""    )
  			__lowerCamelCase        =			re.sub(r"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)""", r""" \1 """, lowercase__    )
  			__lowerCamelCase        =			re.sub(r"""\s*\n\s*""", """ \n """, lowercase__    )
  			__lowerCamelCase        =			re.sub(r"""[^\S\n]+""", """ """, lowercase__    )
  			return text.strip()
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  							__a				   =					VOCAB_FILES_NAMES
  							__a				   =					PRETRAINED_VOCAB_FILES_MAP
  							__a				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							__a				   =					['''input_ids''', '''attention_mask''']
  
  
  
  
  							def __init__(    self:						Union[str, Any]    ,       A_:						int    ,       A_:						Dict    ,       A_:						Dict="<unk>"    ,       **A_:						Union[str, Any]      ):
  										super().__init__(unk_token=A_    ,       **A_      )
  
  										try:
  													import ftfy
  													from spacy.lang.en import English
  
  													__lowerCamelCase        =			English()
  													__lowerCamelCase        =			_nlp.tokenizer
  													__lowerCamelCase        =			ftfy.fix_text
  										except ImportError:
  													logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""      )
  													__lowerCamelCase        =			BasicTokenizer(do_lower_case=A_      )
  													__lowerCamelCase        =			None
  
  										with open(A_    ,       encoding="""utf-8"""      ) as vocab_handle:
  													__lowerCamelCase        =			json.load(A_      )
  										__lowerCamelCase        =			{v: k for k, v in self.encoder.items()}
  										with open(A_    ,       encoding="""utf-8"""      ) as merges_handle:
  													__lowerCamelCase        =			merges_handle.read().split("""\n"""      )[1:-1]
  										__lowerCamelCase        =			[tuple(merge.split()      ) for merge in merges]
  										__lowerCamelCase        =			dict(zip(A_    ,       range(len(A_      )      )      )      )
  										__lowerCamelCase        =			{}
  
  
  
  
  							@property
  							def      __a      (    self:						List[str]      ):
  										return True
  
  
  
  
  							@property
  							def      __a      (    self:						Optional[Any]      ):
  										return len(self.encoder      )
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										return dict(self.encoder    ,       **self.added_tokens_encoder      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						Tuple      ):
  										__lowerCamelCase        =			tuple(token[:-1]      ) + (token[-1] + """</w>""",)
  										if token in self.cache:
  													return self.cache[token]
  										__lowerCamelCase        =			get_pairs(A_      )
  
  										if not pairs:
  													return token + "</w>"
  
  										while True:
  													__lowerCamelCase        =			min(A_    ,       key=lambda A_      : self.bpe_ranks.get(A_    ,       float("""inf"""      )      )      )
  													if bigram not in self.bpe_ranks:
  																break
  													__lowerCamelCase    ,__lowerCamelCase        =			bigram
  													__lowerCamelCase        =			[]
  													__lowerCamelCase        =			0
  													while i < len(A_      ):
  																try:
  																			__lowerCamelCase        =			word.index(A_    ,       A_      )
  																except ValueError:
  																			new_word.extend(word[i:]      )
  																			break
  																else:
  																			new_word.extend(word[i:j]      )
  																			__lowerCamelCase        =			j
  
  																if word[i] == first and i < len(A_      ) - 1 and word[i + 1] == second:
  																			new_word.append(first + second      )
  																			i += 2
  																else:
  																			new_word.append(word[i]      )
  																			i += 1
  													__lowerCamelCase        =			tuple(A_      )
  													__lowerCamelCase        =			new_word
  													if len(A_      ) == 1:
  																break
  													else:
  																__lowerCamelCase        =			get_pairs(A_      )
  										__lowerCamelCase        =			""" """.join(A_      )
  										if word == "\n  </w>":
  													__lowerCamelCase        =			"""\n</w>"""
  										__lowerCamelCase        =			word
  										return word
  
  
  
  
  							def      __a      (    self:						Any    ,       A_:						Any      ):
  										__lowerCamelCase        =			[]
  										if self.fix_text is None:
  													# Using BERT's BasicTokenizer
  													__lowerCamelCase        =			self.nlp.tokenize(A_      )
  													for token in text:
  																split_tokens.extend(list(self.bpe(A_      ).split(""" """      )      )      )
  										else:
  													# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
  													__lowerCamelCase        =			self.nlp(text_standardize(self.fix_text(A_      )      )      )
  													for token in text:
  																split_tokens.extend(list(self.bpe(token.text.lower()      ).split(""" """      )      )      )
  										return split_tokens
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						List[str]      ):
  										return self.encoder.get(A_    ,       self.encoder.get(self.unk_token      )      )
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						Union[str, Any]      ):
  										return self.decoder.get(A_    ,       self.unk_token      )
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						Any      ):
  										__lowerCamelCase        =			"""""".join(A_      ).replace("""</w>"""    ,       """ """      ).strip()
  										return out_string
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						str    ,       A_:						Optional[str] = None      ):
  										if not os.path.isdir(A_      ):
  													logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
  													return
  										__lowerCamelCase        =			os.path.join(
  										    A_    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
  										__lowerCamelCase        =			os.path.join(
  										    A_    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )
  
  										with open(A_    ,       """w"""    ,       encoding="""utf-8"""      ) as f:
  													f.write(json.dumps(self.encoder    ,       indent=2    ,       sort_keys=A_    ,       ensure_ascii=A_      ) + """\n"""      )
  
  										__lowerCamelCase        =			0
  										with open(A_    ,       """w"""    ,       encoding="""utf-8"""      ) as writer:
  													writer.write("""#version: 0.2\n"""      )
  													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,       key=lambda A_      : kv[1]      ):
  																if index != token_index:
  																			logger.warning(
  																			    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  																			    """ Please check that the tokenizer is not corrupted!"""      )
  																			__lowerCamelCase        =			token_index
  																writer.write(""" """.join(A_      ) + """\n"""      )
  																index += 1
  
  										return vocab_file, merge_file
  
  
  
  
  
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      )
  
  
  
  
  