code: import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  lowerCamelCase__           =				get_logger(__name__)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   List[Any]:
  		return TestCommand(
  		    args.dataset						,						args.name						,						args.cache_dir						,						args.data_dir						,						args.all_configs						,						args.save_info or args.save_infos						,						args.ignore_verifications						,						args.force_redownload						,						args.clear_cache						,						)
  
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   False  # to tell pytest it's not a test class
  
  
  
  
  					@staticmethod
  					def 							UpperCamelCase_						( _lowerCAmelCase :					ArgumentParser						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     parser.add_parser("test"      ,help="Test dataset implementation."						)
  							test_parser.add_argument("--name"      ,type=_lowerCAmelCase      ,default=_lowerCAmelCase      ,help="Dataset processing name"						)
  							test_parser.add_argument(
  							    "--cache_dir"      ,type=_lowerCAmelCase      ,default=_lowerCAmelCase      ,help="Cache directory where the datasets are stored."      ,)
  							test_parser.add_argument(
  							    "--data_dir"      ,type=_lowerCAmelCase      ,default=_lowerCAmelCase      ,help="Can be used to specify a manual directory to get the files from."      ,)
  							test_parser.add_argument("--all_configs"      ,action="store_true"      ,help="Test all dataset configurations"						)
  							test_parser.add_argument(
  							    "--save_info"      ,action="store_true"      ,help="Save the dataset infos in the dataset card (README.md)"						)
  							test_parser.add_argument(
  							    "--ignore_verifications"      ,action="store_true"      ,help="Run the test without checksums and splits checks."      ,)
  							test_parser.add_argument("--force_redownload"      ,action="store_true"      ,help="Force dataset redownload"						)
  							test_parser.add_argument(
  							    "--clear_cache"      ,action="store_true"      ,help="Remove downloaded files and cached datasets after each config test"      ,)
  							# aliases
  							test_parser.add_argument("--save_infos"      ,action="store_true"      ,help="alias to save_info"						)
  							test_parser.add_argument("dataset"      ,type=_lowerCAmelCase      ,help="Name of the dataset to download"						)
  							test_parser.set_defaults(func=_test_command_factory						)
  
  
  
  
  					def __init__( self :					Dict      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,_lowerCAmelCase :					bool      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     dataset
  							__snake_case		       =     name
  							__snake_case		       =     cache_dir
  							__snake_case		       =     data_dir
  							__snake_case		       =     all_configs
  							__snake_case		       =     save_infos
  							__snake_case		       =     ignore_verifications
  							__snake_case		       =     force_redownload
  							__snake_case		       =     clear_cache
  							if clear_cache and not cache_dir:
  									print(
  									    "When --clear_cache is used, specifying a cache directory is mandatory.\n"
  									    "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
  									    "Please provide a --cache_dir that will be used to test the dataset script."						)
  									exit(1						)
  							if save_infos:
  									__snake_case		       =     True
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							fl_logger().setLevel(_lowerCAmelCase						)
  							if self._name is not None and self._all_configs:
  									print("Both parameters `config` and `all_configs` can't be used at once."						)
  									exit(1						)
  							__snake_case       ,       __snake_case		       =     self._dataset, self._name
  							__snake_case		       =     dataset_module_factory(_lowerCAmelCase						)
  							__snake_case		       =     import_main_class(module.module_path						)
  							__snake_case		       =     len(builder_cls.BUILDER_CONFIGS						) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
  							def get_builders() -> Generator[DatasetBuilder, None, None]:
  									if self._all_configs and builder_cls.BUILDER_CONFIGS:
  											for i, config in enumerate(builder_cls.BUILDER_CONFIGS						):
  													if "config_name" in module.builder_kwargs:
  															yield builder_cls(
  															    cache_dir=self._cache_dir      ,data_dir=self._data_dir      ,**module.builder_kwargs      ,)
  													else:
  															yield builder_cls(
  															    config_name=config.name      ,cache_dir=self._cache_dir      ,data_dir=self._data_dir      ,**module.builder_kwargs      ,)
  									else:
  											if "config_name" in module.builder_kwargs:
  													yield builder_cls(cache_dir=self._cache_dir      ,data_dir=self._data_dir      ,**module.builder_kwargs						)
  											else:
  													yield builder_cls(
  													    config_name=_lowerCAmelCase      ,cache_dir=self._cache_dir      ,data_dir=self._data_dir      ,**module.builder_kwargs      ,)
  
  							for j, builder in enumerate(get_builders()						):
  									print(F"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""						)
  									__snake_case		       =     os.path.exists(
  									    os.path.join(builder.get_imported_module_dir()      ,datasets.config.DATASETDICT_INFOS_FILENAME						)						)  # record checksums only if we need to update a (deprecated) dataset_infos.json
  									builder.download_and_prepare(
  									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  									    if not self._force_redownload
  									    else DownloadMode.FORCE_REDOWNLOAD      ,verification_mode=VerificationMode.NO_CHECKS
  									    if self._ignore_verifications
  									    else VerificationMode.ALL_CHECKS      ,try_from_hf_gcs=_lowerCAmelCase      ,)
  									builder.as_dataset()
  									if self._save_infos:
  											builder._save_infos()
  
  									# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
  									# The dataset_infos are saved in the YAML part of the README.md
  
  									# Let's move it to the original directory of the dataset script, to allow the user to
  									# upload them on S3 at the same time afterwards.
  									if self._save_infos:
  											__snake_case		       =     os.path.join(builder_cls.get_imported_module_dir()      ,"README.md"						)
  											__snake_case		       =     Path(_lowerCAmelCase						).name + ".py"
  											__snake_case		       =     os.path.join(_lowerCAmelCase      ,_lowerCAmelCase						)
  											if os.path.isfile(_lowerCAmelCase						):
  													__snake_case		       =     os.path.dirname(_lowerCAmelCase						)
  											elif os.path.isfile(_lowerCAmelCase						):
  													__snake_case		       =     path
  											elif os.path.isdir(_lowerCAmelCase						):  # for local directories containing only data files
  													__snake_case		       =     path
  											else:  # in case of a remote dataset
  													__snake_case		       =     None
  													print(F"""Dataset card saved at {dataset_readme_path}"""						)
  
  											# Move dataset_info back to the user
  											if dataset_dir is not None:
  													__snake_case		       =     os.path.join(_lowerCAmelCase      ,"README.md"						)
  													copyfile(_lowerCAmelCase      ,_lowerCAmelCase						)
  													print(F"""Dataset card saved at {user_dataset_readme_path}"""						)
  
              # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
  									if self._clear_cache:
  											if os.path.isdir(builder._cache_dir						):
  													logger.warning(F"""Clearing cache at {builder._cache_dir}"""						)
  													rmtree(builder._cache_dir						)
  											__snake_case		       =     os.path.join(self._cache_dir      ,datasets.config.DOWNLOADED_DATASETS_DIR						)
  											if os.path.isdir(_lowerCAmelCase						):
  													logger.warning(F"""Clearing cache at {download_dir}"""						)
  													rmtree(_lowerCAmelCase						)
  
  							print("Test successful."						)
  
  
  
  
  
  
style_context: 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
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  lowerCamelCase__           =				{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowerCamelCase__           =				{
      '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'},
  }
  
  lowerCamelCase__           =				{
      'openai-gpt': 5_12,
  }
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   Any:
  		__snake_case		       =     text.strip()
  		if not text:
  				return []
  		__snake_case		       =     text.split()
  		return tokens
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					str=True      ,_lowerCAmelCase :					Any=None      ,_lowerCAmelCase :					Any=True      ,_lowerCAmelCase :					Dict=None      ,_lowerCAmelCase :					List[Any]=True      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							if never_split is None:
  									__snake_case		       =     []
  							__snake_case		       =     do_lower_case
  							__snake_case		       =     set(_lowerCAmelCase						)
  							__snake_case		       =     tokenize_chinese_chars
  							__snake_case		       =     strip_accents
  							__snake_case		       =     do_split_on_punc
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Optional[int]=None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.never_split.union(set(_lowerCAmelCase						)						) if never_split else self.never_split
  							__snake_case		       =     self._clean_text(_lowerCAmelCase						)
  
  							# 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:
  									__snake_case		       =     self._tokenize_chinese_chars(_lowerCAmelCase						)
  							# prevents treating the same character with different unicode codepoints as different characters
  							__snake_case		       =     unicodedata.normalize("NFC"      ,_lowerCAmelCase						)
  							__snake_case		       =     whitespace_tokenize(_lowerCAmelCase						)
  							__snake_case		       =     []
  							for token in orig_tokens:
  									if token not in never_split:
  											if self.do_lower_case:
  													__snake_case		       =     token.lower()
  													if self.strip_accents is not False:
  															__snake_case		       =     self._run_strip_accents(_lowerCAmelCase						)
  											elif self.strip_accents:
  													__snake_case		       =     self._run_strip_accents(_lowerCAmelCase						)
  									split_tokens.extend(self._run_split_on_punc(_lowerCAmelCase      ,_lowerCAmelCase						)						)
  
  							__snake_case		       =     whitespace_tokenize(" ".join(_lowerCAmelCase						)						)
  							return output_tokens
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     unicodedata.normalize("NFD"      ,_lowerCAmelCase						)
  							__snake_case		       =     []
  							for char in text:
  									__snake_case		       =     unicodedata.category(_lowerCAmelCase						)
  									if cat == "Mn":
  											continue
  									output.append(_lowerCAmelCase						)
  							return "".join(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					List[Any]=None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not self.do_split_on_punc or (never_split is not None and text in never_split):
  									return [text]
  							__snake_case		       =     list(_lowerCAmelCase						)
  							__snake_case		       =     0
  							__snake_case		       =     True
  							__snake_case		       =     []
  							while i < len(_lowerCAmelCase						):
  									__snake_case		       =     chars[i]
  									if _is_punctuation(_lowerCAmelCase						):
  											output.append([char]						)
  											__snake_case		       =     True
  									else:
  											if start_new_word:
  													output.append([]						)
  											__snake_case		       =     False
  											output[-1].append(_lowerCAmelCase						)
  									i += 1
  
  							return ["".join(_lowerCAmelCase						) for x in output]
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							for char in text:
  									__snake_case		       =     ord(_lowerCAmelCase						)
  									if self._is_chinese_char(_lowerCAmelCase						):
  											output.append(" "						)
  											output.append(_lowerCAmelCase						)
  											output.append(" "						)
  									else:
  											output.append(_lowerCAmelCase						)
  							return "".join(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							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 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							for char in text:
  									__snake_case		       =     ord(_lowerCAmelCase						)
  									if cp == 0 or cp == 0Xfffd or _is_control(_lowerCAmelCase						):
  											continue
  									if _is_whitespace(_lowerCAmelCase						):
  											output.append(" "						)
  									else:
  											output.append(_lowerCAmelCase						)
  							return "".join(_lowerCAmelCase						)
  
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		__snake_case		       =     set()
  		__snake_case		       =     word[0]
  		for char in word[1:]:
  				pairs.add((prev_char, char)	)
  				__snake_case		       =     char
  		return pairs
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   int:
  		__snake_case		       =     text.replace("—"						,						"-"	)
  		__snake_case		       =     text.replace("–"						,						"-"	)
  		__snake_case		       =     text.replace("―"						,						"-"	)
  		__snake_case		       =     text.replace("…"						,						"..."	)
  		__snake_case		       =     text.replace("´"						,						"'"	)
  		__snake_case		       =     re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"						,						r" \1 "						,						__snake_case	)
  		__snake_case		       =     re.sub(r"\s*\n\s*"						,						" \n "						,						__snake_case	)
  		__snake_case		       =     re.sub(r"[^\S\n]+"						,						" "						,						__snake_case	)
  		return text.strip()
  
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   VOCAB_FILES_NAMES
  					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
  					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Tuple="<unk>"      ,**_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__(unk_token=_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  							try:
  									import ftfy
  									from spacy.lang.en import English
  
  									__snake_case		       =     English()
  									__snake_case		       =     _nlp.tokenizer
  									__snake_case		       =     ftfy.fix_text
  							except ImportError:
  									logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."						)
  									__snake_case		       =     BasicTokenizer(do_lower_case=_lowerCAmelCase						)
  									__snake_case		       =     None
  
  							with open(_lowerCAmelCase      ,encoding="utf-8"						) as vocab_handle:
  									__snake_case		       =     json.load(_lowerCAmelCase						)
  							__snake_case		       =     {v: k for k, v in self.encoder.items()}
  							with open(_lowerCAmelCase      ,encoding="utf-8"						) as merges_handle:
  									__snake_case		       =     merges_handle.read().split("\n"						)[1:-1]
  							__snake_case		       =     [tuple(merge.split()						) for merge in merges]
  							__snake_case		       =     dict(zip(_lowerCAmelCase      ,range(len(_lowerCAmelCase						)						)						)						)
  							__snake_case		       =     {}
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return True
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return len(self.encoder						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return dict(self.encoder      ,**self.added_tokens_encoder						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     tuple(token[:-1]						) + (token[-1] + "</w>",)
  							if token in self.cache:
  									return self.cache[token]
  							__snake_case		       =     get_pairs(_lowerCAmelCase						)
  
  							if not pairs:
  									return token + "</w>"
  
  							while True:
  									__snake_case		       =     min(_lowerCAmelCase      ,key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase      ,float("inf"						)						)						)
  									if bigram not in self.bpe_ranks:
  											break
  									__snake_case       ,       __snake_case		       =     bigram
  									__snake_case		       =     []
  									__snake_case		       =     0
  									while i < len(_lowerCAmelCase						):
  											try:
  													__snake_case		       =     word.index(_lowerCAmelCase      ,_lowerCAmelCase						)
  											except ValueError:
  													new_word.extend(word[i:]						)
  													break
  											else:
  													new_word.extend(word[i:j]						)
  													__snake_case		       =     j
  
  											if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
  													new_word.append(first + second						)
  													i += 2
  											else:
  													new_word.append(word[i]						)
  													i += 1
  									__snake_case		       =     tuple(_lowerCAmelCase						)
  									__snake_case		       =     new_word
  									if len(_lowerCAmelCase						) == 1:
  											break
  									else:
  											__snake_case		       =     get_pairs(_lowerCAmelCase						)
  							__snake_case		       =     " ".join(_lowerCAmelCase						)
  							if word == "\n  </w>":
  									__snake_case		       =     "\n</w>"
  							__snake_case		       =     word
  							return word
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							if self.fix_text is None:
  									# Using BERT's BasicTokenizer
  									__snake_case		       =     self.nlp.tokenize(_lowerCAmelCase						)
  									for token in text:
  											split_tokens.extend(list(self.bpe(_lowerCAmelCase						).split(" "						)						)						)
  							else:
  									# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
  									__snake_case		       =     self.nlp(text_standardize(self.fix_text(_lowerCAmelCase						)						)						)
  									for token in text:
  											split_tokens.extend(list(self.bpe(token.text.lower()						).split(" "						)						)						)
  							return split_tokens
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.encoder.get(_lowerCAmelCase      ,self.encoder.get(self.unk_token						)						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.decoder.get(_lowerCAmelCase      ,self.unk_token						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "".join(_lowerCAmelCase						).replace("</w>"      ," "						).strip()
  							return out_string
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not os.path.isdir(_lowerCAmelCase						):
  									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
  									return
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)
  
  							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as f:
  									f.write(json.dumps(self.encoder      ,indent=2      ,sort_keys=_lowerCAmelCase      ,ensure_ascii=_lowerCAmelCase						) + "\n"						)
  
  							__snake_case		       =     0
  							with open(_lowerCAmelCase      ,"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 _lowerCAmelCase						: 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!"						)
  													__snake_case		       =     token_index
  											writer.write(" ".join(_lowerCAmelCase						) + "\n"						)
  											index += 1
  
  							return vocab_file, merge_file
  
  
  
  
  
  