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Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Keras [Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular Keras model and refer to the TF/Keras documentation for all matters related to general usage and behavior. Parameters: config ([`EsmConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a Args: input_ids (`tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. 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Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation ) rrrrrrrrrrrrr)r)r9rrrrrrrrrrrrrrr&r&r'rTPs +zTFEsmModel.callcC|j||SrUrr"r9rmrr&r&r'r"zTFEsmModel.predict_contactscCsd|jrdSd|_t|dddur0t|jj|jdWddS1s)wYdSdS)NTr)rdrer!rfrr6rDrHr&r&r'rDs"zTFEsmModel.build)Trr#)rrrrrrrrrrrrrrrrrrrrrrrrrrrRrrU)rVrWrXr8rr ESM_INPUTS_DOCSTRINGformatr_CHECKPOINT_FOR_DOCr _CONFIG_FOR_DOCrTr"rDrZr&r&r:r'r%Fs2 5r%z1ESM Model with a `language modeling` head on top.cseZdZdgZdgZfddZddZddZd d Ze e e d e eeed d             d'd(d!d"Zd#d$Zd)d%d&ZZS)*TFEsmForMaskedLMrr cst||jrtdt|ddd|_t|dd|_|j rRt t j |ddd|jjjd Wdn1sAwY|jjjjd |j_dSdS) NzjIf you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.Frr&lm_headr5rrsrr)r7r8rloggerwarningr r TFEsmLMHeadr4tie_word_embeddingsr!rfospathjoinZ _name_scoperrsrDweightsdecoderr9rr:r&r'r8s zTFEsmForMaskedLM.__init__cCrrUr4r=rr&r&r'get_output_embeddingsrz&TFEsmForMaskedLM.get_output_embeddingscCs ||j_dSrUr?)r9Znew_embeddingsr&r&r'set_output_embeddingss z&TFEsmForMaskedLM.set_output_embeddingscCs|jSrU)r4rr&r&r' get_lm_headszTFEsmForMaskedLM.get_lm_headr'z)r)r*r maskNFrrrrrrrrlabelsrrrrrrrR)Union[TFMaskedLMOutput, Tuple[tf.Tensor]]c  Cs| dur| n|jj} |j|||||||| | | | d } | d}||}d}|dur0|j||d}| sF|f| dd}|durD|f|S|St||| j| jdS)a! labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, *optional*, defaults to `{}`): Used to hide legacy arguments that have been deprecated. N) rrrrrrrrrrr)rDlogitsrlossrFrrn)ruse_return_dictrr4hf_compute_lossr rrn)r9rrrrrrrrDrrrrrr Zprediction_scoresZmasked_lm_lossrr&r&r'rTs8 zTFEsmForMaskedLM.callcCr+rUr,r-r&r&r'r"r.z!TFEsmForMaskedLM.predict_contactscC|jrdSd|_t|dddur-t|jj|jdWdn1s(wYt|dddurUt|jj|jdWddS1sNwYdSdS)NTrr4)rdrer!rfrr6rDr4rHr&r&r'rD"zTFEsmForMaskedLM.build) NNNNNNNNNNNF)rrrrrrrrrrrrrrrDrrrrrrrrrrRrErU)rVrWrXr$"_keys_to_ignore_on_load_unexpectedr8r@rArBrr r/r0rr1r r2rTr"rDrZr&r&r:r'r3s<  8r3cs<eZdZdZd fdd Zd ddZddZd d ZZS) r7z&ESM Head for masked language modeling.Ncsttj|dtjj|jt|jdd|_tjj |j dd|_ |j r&d|_ ntjj|jt|jddd|_ ||_dS) Nr5rrrvrwr=F)rr6r_)r7r8rrarbr}rr~rrrrvr8r=r|rrr:r&r'r8 s zTFEsmLMHead.__init__cCs<|jrdSd|_|jd|jjfddd|_t|dddur?t|jj |j dd|jj gWdn1s:wYt|dddurit|j j |j dd|jj gWdn1sdwYt|dddur|jj st|jj |j dd|jj gWddS1swYdSdSdS)NTrcZzeros)r*r?r@rrvr=)rdrErr|rcrer!rfrr6rDr}rvr8r=rHr&r&r'rDs""zTFEsmLMHead.buildcCs d|jiS)Nrc)rcrr&r&r'get_bias,s zTFEsmLMHead.get_biascCsX||}tj|}||}|jjr"tj||jdd|j }|S|||j }|S)NTr) rr!rrrvrr8rr=rc)r9featuresr$r&r&r'rT/s   zTFEsmLMHead.callrU) rVrWrXrYr8rDrNrTrZr&r&r:r'r7s  r7z ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. csleZdZdgZfddZeeede e e e d          ddddZ dddZZS)TFEsmForSequenceClassificationrcs<t||j|_||_t|ddd|_t|dd|_dSNFrr& classifierr5)r7r8 num_labelsrr rTFEsmClassificationHeadrRr>r:r&r'r8Fs z'TFEsmForSequenceClassification.__init__r'r(NFrrrrrrrDrrrrrrrR3Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]c  Cs| dur| n|jj} |j|||||||| | d } | d} || } |dur'dn||| }| sC| f| dd}|durA|f|S|St|| | j| jdS)a labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). NrrrrrrrrrrrG)rrIrrRrJr rrnr9rrrrrrDrrrrrr rFrHrr&r&r'rTNs0 z#TFEsmForSequenceClassification.callcCrKNTrrR)rdrer!rfrr6rDrRrHr&r&r'rDrLz$TFEsmForSequenceClassification.build NNNNNNNNNF)rrrrrrrrrrrDrrrrrrrrrrRrUrU)rVrWrXr$r8rr r/r0rr1r r2rTrDrZr&r&r:r'rP<s,  0rPz ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. csreZdZdgZdgZfddZeee de e e e d          ddddZdddZZS)TFEsmForTokenClassificationr rcsRt||j|_t|ddd|_tj|j|_ tjj |jdd|_ ||_ dSrQ) r7r8rSr rrrarrrrbrRrr>r:r&r'r8s  z$TFEsmForTokenClassification.__init__r'r(NFrrrrrrrDrrrrrrrR0Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]c  Cs| dur| n|jj} |j|||||||| | d } | d} |j| | d} || } |dur.dn||| }| sJ| f| dd}|durH|f|S|St|| | j| jdS)z labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. NrVrrrrG) rrIrrrRrJrrrnrWr&r&r'rTs2  z TFEsmForTokenClassification.callcCs|jrdSd|_t|dddur-t|jj|jdWdn1s(wYt|dddurZt|jj|jdd|jj gWddS1sSwYdSdSrX) rdrer!rfrr6rDrRrr}rHr&r&r'rDs"z!TFEsmForTokenClassification.buildrY)rrrrrrrrrrrDrrrrrrrrrrRr[rU)rVrWrXrMr$r8rr r/r0rr1rr2rTrDrZr&r&r:r'rZs.  1rZcs6eZdZdZd fdd Zd ddZd dd ZZS) rTz-Head for sentence-level classification tasks.Ncsdtj|dtjj|jt|jddd|_tj |j |_ tjj|j t|jddd|_ ||_dS)Nr5rr)rr`r6Zlinearout_proj)r7r8rrarbr}rr~rrrrrSr\rrr:r&r'r8s z TFEsmClassificationHead.__init__FcCsJ|dddddf}|j||d}||}|j||d}||}|S)Nrr)rrr\)r9rOrr$r&r&r'rTs   zTFEsmClassificationHead.callcCs|jrdSd|_t|dddur2t|jj|jdd|jjgWdn1s-wYt|dddur_t|j j|j dd|jjgWddS1sXwYdSdS)NTrr\) rdrer!rfrr6rDrr}r\rHr&r&r'rDs"zTFEsmClassificationHead.buildrUr)rVrWrXrYr8rTrDrZr&r&r:r'rTs  rTcCs0t||ktj}tj|dd||}||S)z Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: tf.Tensor x: Returns: tf.Tensor rr)r!rirZcumsum)rrrrCZincremental_indicesr&r&r'rs r)r3rPrZr%r)r)IrY __future__rr9typingrrrnumpynpZ tensorflowr!Z file_utilsrrr Zmodeling_tf_outputsr r r r rZmodeling_tf_utilsrrrrrrrrrZtf_utilsrrutilsrZconfiguration_esmrZ get_loggerrVr5r1r2r(r-r.r1raZLayerr2r[rrrrrrrrrrrZESM_START_DOCSTRINGr/r r%r3r7rPrZrTr__all__r&r&r&r'sz ,    ,(p(5i^ *_Nm6NQ (