rh`OddlmZmZmZddlZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZdd lmZdd lmZmZdd lmZmZd d lmZmZmZmZmZd dlm Z erddl!m"Z"ddiZ#dZ$ejJe&Z'Gdde Z(Gdde eZ)GddejTZ+GddeZ,GddeZ-GddeZ.Gdd eZ/Gd!d"eZ0gd#Z1y)$) TYPE_CHECKINGAnyOptionalN)nn)Cache DynamicCache)PretrainedConfig)create_causal_mask)BaseModelOutputWithPast)Unpack) AddedTokenPreTrainedTokenizer)TransformersKwargslogging)LlamaForCausalLMLlamaForSequenceClassificationLlamaForTokenClassificationLlamaMLP LlamaModel)LlamaTokenizer) TextInput vocab_fileztokenizer.modelu▁ceZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z d fd ZxZ S) GemmaConfiga This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma-7B. e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GemmaModel`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The legacy activation function. It is overwritten by the `hidden_activation`. hidden_activation (`str` or `function`, *optional*): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import GemmaModel, GemmaConfig >>> # Initializing a Gemma gemma-7b style configuration >>> configuration = GemmaConfig() >>> # Initializing a model from the gemma-7b style configuration >>> model = GemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```gemmapast_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormc ||_| |_||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ ||_ ||_||_t!|Dd||||d|y)N) pad_token_id bos_token_id eos_token_idtie_word_embeddings) vocab_sizemax_position_embeddings hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headshead_dimnum_key_value_heads hidden_acthidden_activationinitializer_range rms_norm_eps use_cache rope_thetaattention_biasattention_dropoutsuper__init__)selfr.r0r1r2r3r5r4r6r7r/r8r9r:r)r+r*r,r;r<r=kwargs __class__s z/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/gemma/modular_gemma.pyr?zGemmaConfig.__init__s0%'>$&!2!2#6   #6 $!2!2("$,!2  %%% 3    )ii i`rFgelu_pytorch_tanhNi g{Gz?ư>TrrTg@Fg) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planr? __classcell__rBs@rCrr2sA FJ#4"5%.%.%.%."+ )"+&(9:#%568IJ!"_$56& $ +/ / rDrc eZdZdZ ddeeeeffdZdZ dZ ddd e efd Z d Z dd e ed eded efdZdZy)GemmaTokenizera Construct a Gemma 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. unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): The end of sequence token. pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`): A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. sp_model_kwargs (`dict[str, Any]`, `Optional`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. add_bos_token (`bool`, *optional*, defaults to `True`): Whether or not to add an `bos_token` at the start of sequences. add_eos_token (`bool`, *optional*, defaults to `False`): Whether or not to add an `eos_token` at the end of sequences. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. use_default_system_prompt (`bool`, *optional*, defaults to `False`): Whether or not the default system prompt for Gemma should be used. spaces_between_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to add spaces between special tokens. Nsp_model_kwargsc  |in||_t|trt|ddn|}t|trt|ddn|}t|trt|ddn|}t|trt|ddn|}||_||_||_| |_tjdi|j|_ |jj|tj|f|||||||| | | d | y)NFT) normalizedspecial) bos_token eos_token unk_token pad_token add_bos_token add_eos_tokenrWclean_up_tokenization_spacesuse_default_system_promptspaces_between_special_tokensr-)rW isinstancestrrrr_r`rbspmSentencePieceProcessorsp_modelLoadrr?) r@rr]r[r\r^rWr_r`rarbrcrAs rCr?zGemmaTokenizer.__init__s &5%> rD) zzzzNTFFFF)FF)rKrLrMrNrdictrerr?rorrlistrvr{intboolrrr-rDrCrVrVs,b48%*"'&+) "$sCx.1) V55B[BtCyB8%*.3 898"8(, 8 8: rDrVc<eZdZddedeffd ZdZdZdZxZ S) GemmaRMSNormdimepsct|||_tjt j ||_y)N)r>r?rr Parametertorchzerosweight)r@rrrBs rCr?zGemmaRMSNorm.__init__Vs. ll5;;s#34 rDc|tj|jdjdd|jzzS)NrT)keepdim)rrsqrtpowmeanr)r@xs rC_normzGemmaRMSNorm._norm[s45;;quuQx}}R}>IJJJrDc|j|j}|d|jjzz}|j|S)Ng?)rfloatrtype_as)r@routputs rCforwardzGemmaRMSNorm.forward^sCAGGI&3!2!2!445~~a  rDc^t|jjd|jS)Nz, eps=)tuplershaperrns rC extra_reprzGemmaRMSNorm.extra_repres' ))*+6$((<r?rLinearr0r1 gate_projup_proj down_proj)r@configrBs rCr?zGemmaMLP.__init__jsq 4#3#3T5K5KRWXyy!1!143I3IPUV 4#9#94;K;KRWXrD)rKrLrMr?rSrTs@rCrrisYYrDrceZdZ d deej deej deej deedeejdee deej d e e d e fd Z y) GemmaModelNr!r$ position_idsrr"r:cache_positionrArtc |du|duz r td||j|}|r | t}|F||jnd} t j | | |j dz|j}||jd}t|j|||||} |} |j| |} t j|jjdz| j} | | z} |jd|jj D]}|| f| ||||| d|} |j#| } t%| |r| Sd S) Nz:You must specify exactly one of input_ids or inputs_embedsrrJ)device)r input_embedsr$rrrg?)dtype)r$rpast_key_valuer:rposition_embeddings)last_hidden_stater) ValueErrorr%r get_seq_lengthrarangerr unsqueezer r rotary_embtensorr0rr&r2r'r )r@r!r$rrr"r:rrApast_seen_tokens causal_maskr#r normalizer decoder_layers rCrzGemmaModel.forwardrs  -t"; <YZ Z   --i8M 0*nO  !CRC^==?de "\\ "2]5H5H5K"KTaThThN  )33A6L(;;&))+%  & #oom\J \\$++"9"93">mFYFYZ % 2 ![[)H4;;+H+HI M) *).#-$7  M  -0 &+/8O  >B  rD)NNNNNNN)rKrLrMrr LongTensorTensorr FloatTensorrr rr rr-rDrCrrqs151537+/59$(59A E,,-A !.A u//0 A "% A   1 12 A D>A !!1!12A +,A  !A rDrceZdZfdZxZS)GemmaForCausalLMc 8tjdi|S)a| Example: ```python >>> from transformers import AutoTokenizer, GemmaForCausalLM >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b") >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") >>> prompt = "What is your favorite condiment?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "What is your favorite condiment?" ```r-)r>r) super_kwargsrBs rCrzGemmaForCausalLM.forwards$uw...rD)rKrLrMrrSrTs@rCrrs //rDrc eZdZy)GemmaForSequenceClassificationNrKrLrMr-rDrCrrrDrc eZdZy)GemmaForTokenClassificationNrr-rDrCrrrrDr)rrVrrrrGemmaPreTrainedModel)2typingrrr sentencepiecerfrr cache_utilsrr configuration_utilsr masking_utilsr modeling_outputsr processing_utilsr tokenization_utilsrrutilsrrllama.modeling_llamarrrrrllama.tokenization_llamartokenization_utils_baserVOCAB_FILES_NAMESr get_loggerrKloggerrrVModulerrrrrr__all__r-rDrCrs 0/ .3/7&A064!#45   H %D "D NY^%8Yx=299=(YxYB B J/'/, %C  "=  rD