U &ºcê)ã@sXdZddlZddlmZddlmZmZmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZddlmZddlmZddlmZdd lmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'ddl(m)Z)m*Z*ddl+m,Z,m-Z-ddl.m/Z/ddl0m1Z1m2Z2ddl3m4Z4ddl5m6Z6m7Z7ddl8m9Z9ddl:m;Z;mZ>ddl?m@Z@mAZAddlBmCZCddlDmEZEddlFmGZGe HeI¡ZJeee>dffe e)e*ffeedffe e%dffeeEdffee4dffeedffee;etj|f|ž|ŽS| dd¡}t ¡D]J\}\}}t||ƒrR|r†|r†|j|f|ž|ŽS|j|f|ž|ŽSqRtd  |j d  dd „t  ¡Dƒ¡¡ƒ‚dS) a 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/') ÚconfigNzbert-base-japaneseÚuse_fastFz^Unrecognized configuration class {} to build an AutoTokenizer. Model type should be one of {}.z, css|] }|jVqdS)N)Ú__name__)Ú.0Úcr5r5r6Ú Ðsz0AutoTokenizer.from_pretrained..) ÚpopÚ isinstancerrÚfrom_pretrainedrÚTOKENIZER_MAPPINGÚitemsÚ ValueErrorÚformatÚ __class__ÚjoinÚkeys) ÚclsZpretrained_model_name_or_pathÚinputsÚkwargsr8r9Z config_classZtokenizer_class_pyZtokenizer_class_fastr5r5r6r@s"?    þÿzAutoTokenizer.from_pretrainedN)r:Ú __module__Ú __qualname__Ú__doc__r7Ú classmethodr@r5r5r5r6r2[sr2)MrMÚloggingÚ collectionsrZconfiguration_autorrrrrr r r r r rrrrrrrrZconfiguration_marianrZconfiguration_utilsrZtokenization_albertrZtokenization_bartrZtokenization_bertrrZtokenization_bert_japaneserZtokenization_camembertrZtokenization_ctrlrZtokenization_distilbertrr Ztokenization_electrar!r"Ztokenization_flaubertr#Ztokenization_gpt2r$r%Ztokenization_marianr&Ztokenization_openair'r(Ztokenization_reformerr)Ztokenization_robertar*r+Ztokenization_t5r,Ztokenization_transfo_xlr-r.Ztokenization_xlmr/Ztokenization_xlm_robertar0Ztokenization_xlnetr1Ú getLoggerr:ÚloggerrAr2r5r5r5r6Ús^ P                                 îÿ