U 9%eI@sdZddlZddlmZddlmZmZmZmZm Z m Z ddl Z ddl mZddlmZmZddlmZerxdd lmZeeZd d iZd d id didZd diZdZd\ZZd\ZZ dZ!GdddeZ"dS)zTokenization classes for LLaMA.N)copyfile) TYPE_CHECKINGAnyDictListOptionalTuple)import_protobuf) AddedTokenPreTrainedTokenizer)logging) TextInput vocab_fileztokenizer.modelz#hf-internal-testing/llama-tokenizerzWhttps://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.modelz]https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json)rZtokenizer_fileiu▁)z[INST]z[/INST])z<> z <> aYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.c s:eZdZdZeZeZeZ ddgZ d2e e e efd fd d Zed dZd3ddZddZddZeddZddZd4dee dfdd ZddZdd Zd!d"Zd#d$Zd5e e ee d%d&d'Zd6d(d)Zd7ee e ee e!ee d*fd+d, Z"d8ee e ee ee d-d.d/Z#ed0d1Z$Z%S)9LlamaTokenizera Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model. Args: vocab_file (`str`): Path to the vocabulary file. legacy (`bool`, *optional*): Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple example: - `legacy=True`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True) >>> tokenizer.encode("Hello .") [8774, 32099, 3, 5, 1] ``` - `legacy=False`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False) >>> tokenizer.encode("Hello .") # the extra space `[3]` is no longer here [8774, 32099, 5, 1] ``` Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details. Z input_idsZattention_maskNTF)sp_model_kwargsc  s|dkr in||_t|tr*t|dddn|}t|trFt|dddn|}t|trbt|dddn|}t|tr~t|dddn|}| dkrtd|jdd} | |_||_||_ ||_ | |_ | | dd|_tjf|||||||j| | | | d | dS)NF)lstriprstripz2You are using the default legacy behaviour of the a`. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565T from_slow) bos_token eos_token unk_token pad_token add_bos_token add_eos_tokenrclean_up_tokenization_spacesuse_default_system_promptspaces_between_special_tokenslegacy)r isinstancestrr loggerZ warning_once __class__r!rrrrget_spm_processorpopsp_modelsuper__init__)selfrrrrrrrrrrr r!kwargsr%k/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/llama/tokenization_llama.pyr*hs>  zLlamaTokenizer.__init__cCst|jt|jSN)lenr(encoder#rr+r.r.r/unk_token_lengthszLlamaTokenizer.unk_token_lengthc Cstjf|j}|js|r(||j|St|jdZ}|}td|j j d}|j |}| }d|_|j||}||W5QRX|S)NrbzThe new behaviour of z (with `self.legacy = False`)F)spmSentencePieceProcessorrr!Loadropenreadr r%__name__Z ModelProtoZ FromStringZNormalizerSpecZadd_dummy_prefixnormalizer_specZ MergeFromZSerializeToStringLoadFromSerializedProto)r+rZ tokenizerfr(Z model_pb2modelr<r.r.r/r&s    z LlamaTokenizer.get_spm_processorcCs$|j}d|d<|j|d<|S)Nr(sp_model_proto)__dict__copyr(serialized_model_proto)r+stater.r.r/ __getstate__s zLlamaTokenizer.__getstate__cCs(||_tjf|j|_|j|jdSr0)rAr6r7rr(r=r@)r+dr.r.r/ __setstate__szLlamaTokenizer.__setstate__cCs |jS)zReturns vocab size)r(Zget_piece_sizer3r.r.r/ vocab_sizeszLlamaTokenizer.vocab_sizecs(fddtjD}|j|S)zReturns vocab as a dictcsi|]}||qSr.)Zconvert_ids_to_tokens).0ir3r.r/ sz,LlamaTokenizer.get_vocab..)rangerHupdateZadded_tokens_encoder)r+Zvocabr.r3r/ get_vocabs zLlamaTokenizer.get_vocabr)textreturnc st|jst|dkr"tj|f|Stjt|tdf|}t|dkrp|dtkrp|d|jkrp|dd}|S)z Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the first token is special. r N)r!r1r)tokenizeSPIECE_UNDERLINEreplaceall_special_tokens)r+rOZadd_special_tokensr,tokensr-r.r/rSs & zLlamaTokenizer.tokenizecKs^|jj|td}|js$|tdfs(|S|jj|j|td}t||jkrZ||jdS|S)u( Returns a tokenized string. We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`. `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`. )Zout_typerQN) r(r2r#r! startswithrTrr1r4)r+rOr,rWr.r.r/ _tokenizes zLlamaTokenizer._tokenizecCs |j|S)z0Converts a token (str) in an id using the vocab.)r(Z piece_to_id)r+tokenr.r.r/_convert_token_to_idsz#LlamaTokenizer._convert_token_to_idcCs|j|}|S)z=Converts an index (integer) in a token (str) using the vocab.)r(Z IdToPiece)r+indexrZr.r.r/_convert_id_to_tokens z#LlamaTokenizer._convert_id_to_tokencCs|dtr"|ddd|d<g}d}d}t|D]X\}}||jkr|sb|dkrb|jrb|d7}||j||7}d}g}q6||d}q6||j|7}|S)z:Converts a sequence of tokens (string) in a single string.rrRNFrQT)rXrT enumeraterVr!r(decodeappend)r+rWZcurrent_sub_tokensZ out_stringZprev_is_specialrJrZr.r.r/convert_tokens_to_strings   z'LlamaTokenizer.convert_tokens_to_string)filename_prefixrPc Cstj|s"td|ddStj||r6|dndtd}tj|jtj|krztj |jrzt |j|n8tj |jst |d}|j }||W5QRX|fS)a Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. Returns: `Tuple(str)`: Paths to the files saved. zVocabulary path (z) should be a directoryN-r^rwb)ospathisdirr$errorjoinVOCAB_FILES_NAMESabspathrisfilerr9r(rCwrite)r+Zsave_directoryrcZout_vocab_filefiZcontent_spiece_modelr.r.r/save_vocabulary s (  zLlamaTokenizer.save_vocabularycCsL|jr|jgng}|jr |jgng}|||}|dk rH||||}|Sr0)r bos_token_idr eos_token_idr+ token_ids_0 token_ids_1rqrroutputr.r.r/ build_inputs_with_special_tokens(s  z/LlamaTokenizer.build_inputs_with_special_tokens)rtrualready_has_special_tokensrPcs|rtj||ddS|jr"dgng}|jr2dgng}|dkrT|dgt||S|dgt|||dgt||S)a Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. T)rtrurxrRNr)r)get_special_tokens_maskrrr1)r+rtrurxrqrrr-r.r/ry3s,  z&LlamaTokenizer.get_special_tokens_mask)rtrurPcCs`|jr|jgng}|jr |jgng}dgt|||}|dk r\|dgt|||7}|S)a Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` if token_ids_1 is None, only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of ids. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). rNrR)rrqrrrr1rsr.r.r/$create_token_type_ids_from_sequencesXs z3LlamaTokenizer.create_token_type_ids_from_sequencescCs>d}|d|jrdnd}tdddd}|d |}|S) aZ LLaMA uses [INST] and [/INST] to indicate user messages, and <> and <> to indicate system messages. Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering rather than needing special tokens. The system message is partly 'embedded' in the first user message, which results in an unusual token ordering when it is present. This template should definitely be changed if you wish to fine-tune a model with more flexible role ordering! The output should look something like: [INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer [INST] Prompt [/INST] Answer [INST] Prompt [/INST] a1{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\n' + system_message + '\n<>\n\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\n' + content.strip() + '\n<>\n\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}ZUSE_DEFAULT_PROMPTtruefalse z\n'z\'ZDEFAULT_SYSTEM_MESSAGE)rUrDEFAULT_SYSTEM_PROMPT)r+templateZdefault_messager.r.r/default_chat_templateys  z$LlamaTokenizer.default_chat_template) rrrNNTFFTFN)F)F)N)N)NF)N)&r; __module__ __qualname____doc__rkZvocab_files_namesPRETRAINED_VOCAB_FILES_MAPZpretrained_vocab_files_map&PRETRAINED_POSITIONAL_EMBEDDINGS_SIZESZmax_model_input_sizesZmodel_input_namesrrr#rr*propertyr4r&rErGrHrNrrSrYr[r]rbrrprwintboolryrzr __classcell__r.r.r-r/rBsd 6     &  !r)#rrfshutilrtypingrrrrrrZ sentencepiecer6Zconvert_slow_tokenizerr Ztokenization_utilsr r utilsr Ztokenization_utils_baserZ get_loggerr;r$rkrrrTZB_INSTZE_INSTZB_SYSZE_SYSrrr.r.r.r/s4