text: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Auto Tokenizer class. """
  
  
  import logging
  from collections import OrderedDict
  
  from .configuration_auto import (
      AlbertConfig,
      AutoConfig,
      BartConfig,
      BertConfig,
      CamembertConfig,
      CTRLConfig,
      DistilBertConfig,
      ElectraConfig,
      FlaubertConfig,
      GPT2Config,
      OpenAIGPTConfig,
      ReformerConfig,
      RobertaConfig,
      T5Config,
      TransfoXLConfig,
      XLMConfig,
      XLMRobertaConfig,
      XLNetConfig,
  )
  from .configuration_utils import PretrainedConfig
  from .tokenization_albert import AlbertTokenizer
  from .tokenization_bart import BartTokenizer
  from .tokenization_bert import BertTokenizer, BertTokenizerFast
  from .tokenization_bert_japanese import BertJapaneseTokenizer
  from .tokenization_camembert import CamembertTokenizer
  from .tokenization_ctrl import CTRLTokenizer
  from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast
  from .tokenization_electra import ElectraTokenizer, ElectraTokenizerFast
  from .tokenization_flaubert import FlaubertTokenizer
  from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
  from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
  from .tokenization_reformer import ReformerTokenizer
  from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
  from .tokenization_t5 import T5Tokenizer
  from .tokenization_transfo_xl import TransfoXLTokenizer, TransfoXLTokenizerFast
  from .tokenization_xlm import XLMTokenizer
  from .tokenization_xlm_roberta import XLMRobertaTokenizer
  from .tokenization_xlnet import XLNetTokenizer
  
  
  logger = logging.getLogger(__name__)
  
  
  TOKENIZER_MAPPING = OrderedDict(
      [
          (T5Config, (T5Tokenizer, None)),
          (DistilBertConfig, (DistilBertTokenizer, DistilBertTokenizerFast)),
          (AlbertConfig, (AlbertTokenizer, None)),
          (CamembertConfig, (CamembertTokenizer, None)),
          (XLMRobertaConfig, (XLMRobertaTokenizer, None)),
          (BartConfig, (BartTokenizer, None)),
          (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)),
          (ReformerConfig, (ReformerTokenizer, None)),
          (ElectraConfig, (ElectraTokenizer, ElectraTokenizerFast)),
          (BertConfig, (BertTokenizer, BertTokenizerFast)),
          (OpenAIGPTConfig, (OpenAIGPTTokenizer, OpenAIGPTTokenizerFast)),
          (GPT2Config, (GPT2Tokenizer, GPT2TokenizerFast)),
          (TransfoXLConfig, (TransfoXLTokenizer, TransfoXLTokenizerFast)),
          (XLNetConfig, (XLNetTokenizer, None)),
          (FlaubertConfig, (FlaubertTokenizer, None)),
          (XLMConfig, (XLMTokenizer, None)),
          (CTRLConfig, (CTRLTokenizer, None)),
      ]
  )
  
  
  class AutoTokenizer:
      r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class
          that will be instantiated as one of the tokenizer classes of the library
          when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
          class method.
  
          The `from_pretrained()` method take care of returning the correct tokenizer class instance
          based on the `model_type` property of the config object, or when it's missing,
          falling back to using pattern matching on the `pretrained_model_name_or_path` string.
  
          The tokenizer class to instantiate is selected as the first pattern matching
          in the `pretrained_model_name_or_path` string (in the following order):
              - contains `t5`: T5Tokenizer (T5 model)
              - contains `distilbert`: DistilBertTokenizer (DistilBert model)
              - contains `albert`: AlbertTokenizer (ALBERT model)
              - contains `camembert`: CamembertTokenizer (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
              - contains `roberta`: RobertaTokenizer (RoBERTa model)
              - contains `bert`: BertTokenizer (Bert model)
              - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
              - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
              - contains `xlnet`: XLNetTokenizer (XLNet model)
              - contains `xlm`: XLMTokenizer (XLM model)
              - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
              - contains `electra`: ElectraTokenizer (Google ELECTRA model)
  
          This class cannot be instantiated using `__init__()` (throw an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoTokenizer is designed to be instantiated "
              "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
          )
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
          r""" Instantiate one of the tokenizer classes of the library
          from a pre-trained model vocabulary.
  
          The tokenizer class to instantiate is selected as the first pattern matching
          in the `pretrained_model_name_or_path` string (in the following order):
              - contains `t5`: T5Tokenizer (T5 model)
              - contains `distilbert`: DistilBertTokenizer (DistilBert model)
              - contains `albert`: AlbertTokenizer (ALBERT model)
              - contains `camembert`: CamembertTokenizer (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
              - contains `roberta`: RobertaTokenizer (RoBERTa model)
              - contains `bert-base-japanese`: BertJapaneseTokenizer (Bert model)
              - contains `bert`: BertTokenizer (Bert model)
              - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
              - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
              - contains `xlnet`: XLNetTokenizer (XLNet model)
              - contains `xlm`: XLMTokenizer (XLM model)
              - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
              - contains `electra`: ElectraTokenizer (Google ELECTRA model)
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
                  - (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
  
              cache_dir: (`optional`) string:
                  Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
  
              force_download: (`optional`) boolean, default False:
                  Force to (re-)download the vocabulary files and override the cached versions if they exists.
  
              resume_download: (`optional`) boolean, default False:
                  Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
  
              proxies: (`optional`) dict, default None:
                  A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                  The proxies are used on each request.
  
              use_fast: (`optional`) boolean, default False:
                  Indicate if transformers should try to load the fast version of the tokenizer (True) or use the Python one (False).
  
              inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
  
              kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
  
          Examples::
  
              # Download vocabulary from S3 and cache.
              tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
  
              # Download vocabulary from S3 (user-uploaded) and cache.
              tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
  
              # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
              tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/')
  
          """
          config = kwargs.pop("config", None)
          if not isinstance(config, PretrainedConfig):
              config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
  
          if "bert-base-japanese" in pretrained_model_name_or_path:
              return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  
          use_fast = kwargs.pop("use_fast", False)
          for config_class, (tokenizer_class_py, tokenizer_class_fast) in TOKENIZER_MAPPING.items():
              if isinstance(config, config_class):
                  if tokenizer_class_fast and use_fast:
                      return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
                  else:
                      return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  
          raise ValueError(
              "Unrecognized configuration class {} to build an AutoTokenizer.\n"
              "Model type should be one of {}.".format(
                  config.__class__, ", ".join(c.__name__ for c in TOKENIZER_MAPPING.keys())
              )
          )
  