T^(hISLddlZddlmZddlmZmZmZmZmZddl Z ddl m Z ddl m cm ZddlZ ddlmZddlmZddlmZmZddlmZdd lmZdd lmZdd lmZmZm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&ddl'm(Z(ddl)m*Z*m+Z+m,Z,m-Z-m.Z.ddl/m0Z0m1Z1e,r ddl2m3Z3ddl4m5Z5e-jle7Z8dZ9dZ:Gdde jvZ<Gdde jvZ=Gdde jjvZ>Gdde jvZ?Gdde jvZ@Gd d!e jvZAd"e jd#e jd$e jd%ee je jffd&ZCd'e jd(eDd%e jfd)ZE d]d*e jvd+e jd,e jd-e jd.ee jd/eFd0eFfd1ZGGd2d3e jvZHGd4d5e jvZId6ZJe*d7eJGd8d9e&ZKd:ZLe*d7eJGd;deKeZNeGd?d@e!ZOGdAdBe jjvZPGdCdDe jvZQdEZRGdFdGe jvZSdHZTdIe jd+e jfdJZUd+e jd,e jdIe jd%ee je jffdKZVGdLdMe jvZWGdNdOe jvZXGdPdQe jvZYGdRdSe jvZZGdTdUe jvZ[GdVdWe jvZ\GdXdYeKZ]GdZd[eKeZ^gd\Z_y)^N) dataclass)CallableListOptionalTupleUnion)Llama4VisionConfig)ACT2FN)CacheHybridChunkedCache)GenerationMixin)AttentionMaskConverter)FlashAttentionKwargs)BaseModelOutputBaseModelOutputWithPastCausalLMOutputWithPast ModelOutput)ROPE_INIT_FUNCTIONS)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)add_start_docstrings%add_start_docstrings_to_model_forwardis_torch_flex_attn_availableloggingreplace_return_docstrings) Llama4ConfigLlama4TextConfig) BlockMask)make_flex_block_causal_maskzmeta-ai/Llama-4-17Brc\eZdZdeffd Zdej dej fdZxZS)Llama4TextExpertsconfigct||j|_|j|_|j |_|j|_tjtj|j|j d|j z|_ tjtj|j|j |j f|_ t|j|_yN)super__init__num_local_experts num_expertsintermediate_size hidden_size expert_dimnn Parametertorchempty gate_up_proj down_projr hidden_actact_fnselfr% __class__s h/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/llama4/modeling_llama4.pyr*zLlama4TextExperts.__init__>s !33!'!9!9!--00LLT5E5EtGWGWYZ]a]l]lYl)mnekk43C3CT__VZVfVf2g&hiV../ hidden_statesreturnc\|j|jd|j}tj||j }|j dd\}}tj||j|z|j}|jd|j}|S)a2 This should really not be run on a single machine, as we are reaching compute bound: - the inputs are expected to be "sorted" per expert already. - the weights are viewed with another dim, to match num_expert, 1, shape * num_tokens, shape Args: hidden_states (torch.Tensor): (batch_size * token_num, hidden_size) selected_experts (torch.Tensor): (batch_size * token_num, top_k) routing_weights (torch.Tensor): (batch_size * token_num, top_k) Returns: torch.Tensor r(dim) viewr,r.r2bmmr4chunkr7r5)r9r=gate_upgateup next_statess r;forwardzLlama4TextExperts.forwardHs&**4+;+;RAQAQR ))M4+<+<====+biidkk$&7!7$..I !&&r4+;+;< r<) __name__ __module__ __qualname__rr*r2TensorrJ __classcell__r:s@r;r$r$=s*0|0U\\ellr<r$c&eZdZdfd ZdZxZS) Llama4TextMLPcft|| |j}||_t j |j |d|_t j |j |d|_t j ||j d|_ t|j|_ yNFbias) r)r*r-r%r0Linearr. gate_projup_projr5r r6 activation_fn)r9r%r-r:s r;r*zLlama4TextMLP.__init___s   $ & 8 8  6#5#57HuUyy!3!35FUS #4f6H6HuU#F$5$56r<c|j|j||j|z}|j|SN)rZrXrYr5)r9xr5s r;rJzLlama4TextMLP.forwardks7&&t~~a'89DLLOK ~~i((r<r\rKrLrMr*rJrOrPs@r;rRrR^s  7)r<rRc8eZdZddeffd ZdZdZdZxZS)Llama4TextL2Normepsc0t|||_yr\)r)r*ra)r9rar:s r;r*zLlama4TextL2Norm.__init__qs r<c|tj|jdjdd|jzzSNr(r@T)keepdimr2rsqrtpowmeanrar9r]s r;_normzLlama4TextL2Norm._normu45;;quuQx}}R}>IJJJr<c^|j|jj|Sr\)rkfloattype_asrjs r;rJzLlama4TextL2Norm.forwardxs"zz!'')$,,Q//r<c d|jS)Nzeps=rar9s r; extra_reprzLlama4TextL2Norm.extra_repr{sdhhZ  r<)gư>) rKrLrMrnr*rkrJrsrOrPs@r;r`r`psEK0!r<r`c2eZdZdfd ZdZdZdZxZS)Llama4TextRMSNormct|||_tjt j ||_y)z< Llama4RMSNorm is equivalent to T5LayerNorm N)r)r*rar0r1r2onesweight)r9r.rar:s r;r*zLlama4TextRMSNorm.__init__s0 ll5::k#:; r<c|tj|jdjdd|jzzSrdrfrjs r;rkzLlama4TextRMSNorm._normrlr<c||j|jj|}||jzSr\)rkrnrorx)r9r]outputs r;rJzLlama4TextRMSNorm.forwards0AGGI&..q1 ##r<c^t|jjd|jS)Nz, eps=)tuplerxshaperarrs r;rszLlama4TextRMSNorm.extra_reprs' ))*+6$((<)rKrLrMr*rkrJrsrOrPs@r;rurus<K$=r<ruc$eZdZfdZdZxZS) Llama4TextMoec*t||j|_|j|_|j |_t||_ tj|j|j d|_ t||_yrT)r)r*num_experts_per_toktop_kr. hidden_dimr+r,r$expertsr0rWrouterrR shared_expertr8s r;r*zLlama4TextMoe.__init__sp //  ,,!33(0 ii 2 2F4L4LSXY *62r<c,|j\}}}|jd|j}|j|j dd}||z}t j |j dd|jd\}}t j|j ddtdjd||j dd} t j||jjddj| jdd}t j| jj!|j"} |j%ddjd|}t j&|d|j!|j} | | j%ddz} |j)| } |j+|} | j-d|| jd|| | fS) Nr@rrrAz-infdevice)inputrBindex)rBrsrc)r~rCrr transposer2topkr full_likernscatter_arangerexpandsizesigmoidtodtypereshapegatherrr scatter_add_) r9r=batchseq_lenr router_logitstokens_per_expertrouter_top_valuerouter_indices router_scores routed_in routed_outouts r;rJzLlama4TextMoe.forwards%2%8%8"w %**2t?  M2<%>v%FD "  T-C-CPU V&*&?&?D # 4g .r<cd|jvr|j||j|jddddfj j |j ddd}|dddddfj }|jj}t|tr|dk7r|nd}tj|d 5|j|j|zjdd }tjtj||}ddd|j z}|S#1swYxYw) Ndynamicrrr@rmpscpuF) device_typeenabledr()rrrrrnrr~type isinstancestrr2autocastrrpolar ones_liker)r9r]rinv_freq_expandedposition_ids_expandedrfreqs freqs_ciss r;rJz!Llama4TextRotaryEmbedding.forwards!  &  * *< * I MM$4-8>>@GG HZHZ[\H]_acde ,QaZ 8 > > @hhmm %/ S%AkUZFZk`e ^^ U C C&))!((36KKVVWXZ[\E EOOE$:EBI C  6 66   C Cs AD==Er\) rKrLrMr r*rr2no_gradrJrOrPs@r;rrs2 // /@&U]]_r<rxqxkrr>c &tj|jjg|jdddd}tj|jjg|jdddd}tj ||dddddddfzj d}tj ||dddddddfzj d}|j||j|fS)Nr@r(r )r2view_as_complexrnrr~ view_as_realflattenro)rrrxq_xk_xq_outxk_outs r;apply_rotary_embrs    2 2 2 IBHHSbM I2 Iq I JC    2 2 2 IBHHSbM I2 Iq I JC   i1dA &> > ? G G JF   i1dA &> > ? G G JF >>" v~~b1 11r<r=n_repc|j\}}}}|dk(r|S|dddddddddfj|||||}|j|||z||S)z This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) rN)r~rr)r=rrnum_key_value_headsslenhead_dims r; repeat_kvrso 2?1D1D.E h z!!Qa"23::5BUW\^bdlmM  (;e(CT8 TTr<modulequerykeyvalueattention_maskscalingdropoutc t||j}t||j} tj||j ddt j |jz } |#|ddddddd|jdf} | | z} tjj| jdj|j} tjj| ||j } tj| | } | j ddj#} | | fS)Nr(r r@rAptrainingr)rnum_key_value_groupsr2matmulrmathsqrtrr~r0 functionalsoftmaxrnrrrr contiguous) rrrrrrrkwargs key_states value_states attn_weights causal_mask attn_outputs r;eager_attention_forwardrs3 ; ;$>&B\B\$\!#)#=#= }}d*  ++!--'-'E'E$!'!9!9Y]a/145 ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii    : :T]] JQWQfQf  ii  & & 68J8JQWQfQf   ;; " "t}}+F,?,?@DL(5 "r<r=position_embeddingsrpast_key_valuecache_positionrr>c |jdd}g|d|j}|j|j|} |j |jg|d|j} |j |j|j dd} |jr)t| | |j| j\} } t|dr"|j| } |j| } |jr|jstjtj |j#dz|j$z dz|j&zdz} | jd|dddfj)g|dd} | | zj| j*} | j dd} | j dd} |%d|i} |j-| | |j.| \} } t0}|j2j4dk7r^|j2j4dk(r(|j7d d rt8j;d nt<|j2j4}||| | | |f|j>sd n |j@|jBd |\}}|jDg|djG}|jI|}||fS)Nr@rr(r ?reagersdpaoutput_attentionsF`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.)rr)%r~rrrCrrrrrrrhasattrr rr2logfloorrnrrrrupdaterrr%_attn_implementationgetlogger warning_oncerrrrrrr )r9r=rrrrr input_shape hidden_shape query_statesrr attn_scales cache_kwargsattention_interfacerrs r;rJzLlama4TextAttention.forwardIs$))#2. 88b8$--8 {{=166|D 4T[[/44UkU2Ut}}U {{=166|DNNqRST =='7j*=*@*@ATAT*U( $L* 4 #<< 5Lj1J  ' '  %++~';';'='CtGWGW&WX[^^_bfbqbqqtww &**A{21+EFMMNbP[Nb]^Nb`aNbcK(;6::<;M;MNL#--a3 ))!Q/  %,n=L'5'<'d##L '>dkk>^>^&_#$7      % $}}C$2H2HLL %  % ! \*k));;;;FFH kk+. L((r<NN)rKrLrM__doc__r r*r2rNrrr LongTensorrrrJrOrPs@r;rr(sGA/AF+/59 ?)||?)#5<<#=>?)!. ?) ! ?) !!1!12 ?)-.?) u||Xell3XeELL>Q5RR S?)r<rceZdZfdZ ddej deej deej deejdeeej dee dee d ee d eejd eeej ej fd e e d eejeeejejfffdZ xZS)Llama4TextDecoderLayerct||j|_t|||_t |dzdzdk7|_||jv|_|jrt||_ nt||j|_ t|j|j|_t|j|j|_||_y)Nrrr)r-rq)r)r*r.r self_attnruse_chunked_attention moe_layers is_moe_layerr feed_forwardrRintermediate_size_mlprur input_layernormpost_attention_layernormrr s r;r*zLlama4TextDecoderLayer.__init__s !--,VY?%()a-1)<)A%B"%):)::    -f 5D  -fHdHd eD 01C1CI\I\](9&:L:LRXReRe(f%"r<r=rchunk_causal_maskrrroutput_router_logits use_cacherrrr>c b|} |j|}|jr||}|jd|| ||||| d| \} }| | z}|} |j|}|j |}|j r|\}}nd}| |j | jz}|f}|r||fz }|r||fz }|S)N)r=rrrrr6r)r2r-r,r3r0r/rCr~)r9r=rr4rrrr5r6rrrresidualattention_statesself_attn_weightsroutputss r;rJzLlama4TextDecoderLayer.forwards!,,];   % %*;*G.N/=dnn / ' 3))/) /  / ++!#33 !55mD ))-8   +8 (M= M =#5#5hnn#EE  "  )+ +G   ' 'Gr<) NNNNFFFNN)rKrLrMr*r2rNrr(rboolrr FloatTensorrJrOrPs@r;r*r*s.#&2648378<,1/4$)59KO5||5!.5$ELL1 5 u//0 5 !u||!45 5$D>5'tn5D>5!!1!125&eELL%,,,F&GH5-.5 u  (51B1BEDUDU1U+V"WW X5r<r*aK This model inherits from [`PreTrainedModel`]. 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Llama4Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. zTThe bare Llama4 Model outputting raw hidden-states without any specific head on top.c<eZdZeZdZdgZdZdZdZ dZ dZ dZ dZ dZy)Llama4PreTrainedModelTpast_key_valuesFct|jdr|jjn|jjj}t |t j rY|jjjd||j%|jjjyyt |t jrf|jjjd||j2|jj|jjyyy)Ninitializer_ranger)ristd)rr%rC text_configrr0rWrxdatanormal_rVzero_ Embedding padding_idx)r9rrDs r; _init_weightsz#Llama4PreTrainedModel._init_weightsst{{$78 KK ) )((:: fbii ( MM   & &CS & 9{{&   &&('  - MM   & &CS & 9!!- ""6#5#56<<>..r<N)rKrLrMr config_classsupports_gradient_checkpointing_skip_keys_device_placement_supports_flash_attn_2_supports_sdpa_supports_flex_attn_supports_cache_class_supports_quantized_cache_supports_static_cache_supports_attention_backendrKr8r<r;r@r@sF  L&*##4"5"N  $!"& ?r<r@a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). 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. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. ceZdZdgZdZeZdeffd ZdZdZ e e  d"de jdee jd ee jd eed ee j"d eed eedeedeedee jdeedeeeffdZe j2j5d d#de jde jde jd ed ef dZdededede j:de jf dZede jdedede j@de j:de jd efd!Z!xZ"S)$Llama4TextModelr*modelr%c  t|||j|_|j|_t j |j|j|j|_t jt|jDcgc]}t||c}|_ t|j|j|_t#||_d|_|j)ycc}w)Nrq)r%F)r)r* pad_token_idrJ vocab_sizer0rIr. embed_tokens ModuleListrangenum_hidden_layersr*layersrur normr rotary_embgradient_checkpointing post_initr s r;r*zLlama4TextModel.__init__Ys  !.. ++LL):):FQ>QR 36B&+#  isDc|jSr\r\rrs r;get_input_embeddingsz$Llama4TextModel.get_input_embeddingsis   r<c||_yr\rfr9rs r;set_input_embeddingsz$Llama4TextModel.set_input_embeddingsls !r< input_idsrrrA inputs_embedsr6routput_hidden_states return_dictrflash_attn_kwargsr>c  ||n|jj}||n|jj}||n|jj}| | n|jj} |du|duz r t d|j r%|jr|rtjdd}|>|j|j|jjj}|r3|1t|j|jd|jd}| F||j!nd} t#j$| | |jdz|j} || j'd}|j)||| |||\} }|}|j+||}|rdnd}|rdnd}|j,d|jj.D]k}|r||fz }|j r2|jr&|j1|j2|| ||||d|| | }n||f| |||||| |d | }|d}|sc||dfz }m|j5|}|r||fz }t7||r|nd|| }| r|S|j9S) N:You must specify exactly one of input_ids or inputs_embedszX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.Frrr)r6r8)rr4rrrr6rr)last_hidden_staterAr= attentions)r%rrmr6use_return_dict ValueErrorrcrrrr\rrxrr r~get_seq_lengthr2r unsqueeze_update_causal_maskrbr`r__gradient_checkpointing_func__call__rarto_tuple)r9rkrrrArlr6rrmrnrropast_seen_tokensrr4r=freq_cisall_hidden_statesall_self_attns decoder_layer layer_outputsr{s r;rJzLlama4TextModel.forwardos2C1N-TXT_T_TqTq$8$D $++JjJj "+!6IDKKQ>QRS>TVcViVijkVlmO  !CRC^==?de "\\ "2]5H5H5K"KTaThThN  )33A6L)-)A)A M>?L]ir*B* & && ??=,?#7BD0d![[)H4;;+H+HI# 6M#!m%55!**t}} $ A A!**!% #%" ! !.! !#.&7!-#2&7'#1(0 !( ! *!,M =#3"55G# 6J -0  -!1 1 (+/8Od+%   %v;&//*;;r<F) recursive input_tensorc 2|jjdk(r||dk(jr||fSy|jjdvry|jd}|j |j }|jj } |d} ||jxs|} n||jdn|} | | k\} | | k| |z| kDz} |r5tj| | |zdz tj| | |z| n| }|jjdk(r{t|tjrM| t| | z dzdf}t||jj ||| }t||| | df }||fSt|tr||fS|j|j }}|j!||t| | ||||jd }| |jj kDr?t| | z dzd}||z}|j#|jj ||| }|dd||f}|jd| k}|r2t$j&j)|d| |jdz f}|s|dd| dddf}n |dd|ddf}|j+|jdddd}||ddddddfz}|jjd k(rHtj,|j.}tj|dk(|dj |}|jjdk(r`|^|j j0dvrF|j2dk(r7|s5tj,|j.}t5j6||}|jjdk(rG|E|j9}|j9}t5j:||| |j<rd}||fS)Nflash_attention_2rr&)rflex_attentionrrrr@r)offsets) query_length key_lengthr)sequence_length target_lengthrrr batch_size)startendrrr)cudaxpur)rlpast_key_values_length is_training)r%ranyr~rrattention_chunk_sizeget_max_cache_shaper2whererrNrr"r!r5_prepare_4d_causal_attention_mask_with_cache_positioncreate_chunked_attention_maskr0rpadrfinfominrndimr_unmask_unattendedr=_ignore_causal_mask_sdpar)r9rrrrArchunked_attention_maskr6rrfirst_cache_positionfull_cache_lengthcond1cond2rrrrr start_idxend_idxlocal_attention_maskrequires_padding min_dtypes r;rxz#Llama4TextModel._update_causal_masks ;; + +/B B)~/D.I.I.K%~55 ;; + +3V V&,,Q/'**4;;7#{{??-a0  & / C C E X RSTVZ\`bcSc>d%d "{{//7:!KK.22 ).5Kq5PR[]`)a)d)dej)k& KK , , 6*%%**o=##q(%  E*..I0CCKQZ[K ;; + +v 5:P:\%;%@%@%B "%**,K%>>*'; MM  # 222r<rrrrc(tj|||}tj|jd|z|jd|zz }|jd|jdz }|dk(|dkz}|j |S)u Generate the following: 'What' : 0 ■ ⬚ ⬚ ⬚ ⬚ ⬚ | '▁is' : 1 ■ ■ ⬚ ⬚ ⬚ ⬚ | '▁ch' : 2 ■ ■ ■ ⬚ ⬚ ⬚ | 'unked' : 3 ⬚ ⬚ ⬚ ■ ⬚ ⬚ | '▁attention': 4 ⬚ ⬚ ⬚ ■ ■ ⬚ | '?' : 5 ⬚ ⬚ ⬚ ■ ■ ■ | If the chunk size is 3. This can just be appplied over the already created attention mask rrr)r2rabsrwr) r9rrrr arange_vector block_pos token_posmasks r;rz-Llama4TextModel.create_chunked_attention_mask[s  UC? II  # #A &*> >AXAXYZA[_sAs s "++A.1H1H1KK Q9>2wwvr<rrrrc ||jdk(r|}|Stj|j} tj||f| ||}|dk7rtj |d}|tj |||j|jddkDz}|ddddddfj|ddd}||j}|jd} |ddddddd| f|ddddddfj|z} | dk(} |ddddddd| fj| | |ddddddd| f<|S) a Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. Nr fill_valuerrrdiagonalrr@r) rBr2rrfulltriurrrrcloner~ masked_fill rrrrrrrrrr mask_length padding_masks r;rzELlama4TextModel._prepare_4d_causal_attention_mask_with_cache_positionssxB  %.*<*<*>!*C(K&# E*..I** -0Ye\bK!##jjqA 5<< fEHYHYZ`HaHiHijlnoHpp pK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDghnDoo +q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r<) NNNNNNNNNN)FNT)#rKrLrM_no_split_modulesbase_model_prefixr rLr*rgrjrLLAMA4_INPUTS_DOCSTRINGr2r(rrNr r>r=rrrrrrJcompilerdisablerxrrr staticmethodrrrOrPs@r;rWrWPsU 22#L/ !"++BC'+1537+/59$(,0/3&*59k<##k<!.k<u//0 k< "% k<   1 12 k<D>k<$D>k<'tnk<d^k<!!1!12k<$$89k< u-- .k<Dkc  8||n|jj}| | n|jj} | | n|jj} |jd|||||||| | | d | }|d}t | t r t| dn| }|j|dd|ddf}d}|*|jd|||jjd| }| s|f|ddz}||f|zS|St|||j|j|jS)a> Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (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]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from transformers import AutoTokenizer, Llama4ForCausalLM >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> 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] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```N) rkrrrArlr6rrmrnrr)logitsrr[r)lossrrAr=rsr8)r%rrmrtrXrrslicer loss_functionr[rrAr=rs)r9rkrrrArlrr6rrmrnrrrr<r= slice_indicesrrr{s r;rJzLlama4ForCausalLM.forwards`d2C1N-TXT_T_TqTq$8$D $++JjJj &1%SV8W~ot4]k mA}a,?@A  %4%%pVFt{{OeOepiopDY,F'+'7D7V# CV C%#33!//))   r<) NNNNNNNNNNNr)!rKrLrMr_tied_weights_keys_tp_planr rLr*rgrjrrrrrrrr_CONFIG_FOR_DOCr2r(rrNrr rr>r=rrrJrOrPs@r;rrs(*+=)H#L/'(&++BC+AP_`'+1537KO59-1$(,0/3&*5934W ##W !.W u//0 W "%tE4E4E/F(F"GH W   1 12 W ))*W D>W $D>W 'tnW d^W !!1!12W c5<</0W  u,, -W aDW r<rceZdZUdZdZeejed<dZ ejed<dZ ee ejed<dZ ee ejed<dZee ejed<dZeejed<y) Llama4CausalLMOutputWithPasta Base class for Llava causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. NrrrAr=rsimage_hidden_states)rKrLrMr'rrr2r>__annotations__rrArr=rrsrr8r<r;rr)s<)-D(5$$ %, $FE  $9=OXd5#4#456=8cPt||j}|j|Sr\)rr r)r9rs r;rJz#Llama4VisionPixelShuffleMLP.forwards#'9Q9QRxx((r<rKrLrMr*r2rNrJrOrPs@r;r r s#,)u||) )r<r ac This model inherits from [`PreTrainedModel`]. 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`LlavaConfig`] or [`LlavaVisionConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. freqs_cic|j}t|jDcgc]\}}|dk(s||dz k(r|nd}}}|j|Scc}}w)Nr)r enumerater~rC)rrridr~s r;reshape_for_broadcastrsW ::D=Fu{{=S TTQ!q&AMQq 0 TE T 8==%   UsAcBtj|jjg|jdddd}tj|jjg|jdddd}t ||}|j |j}tj||zjd}tj||zjd}|j||j|fS)Nr@r()rrr ) r2rrnrr~rrrrrro)rrrquery_key_ query_outkey_outs r;vision_apply_rotary_embrs  " "#85;;=#8#8#R%++cr:J#RB#RPQ#R SF  !4!4!4!Lciin!Lb!L!!L MD$hfEH{{6==)H""6H#45==a@I  199!UYZ r<r=rrrrr>c R|jdd}g|d|j}|j|j|}|j |j|} |j |j|} t || |\}} |jdd}| jdd} | jdd} t} |jjdvr^|jjdk(r(|jddrtjd nt|jj} | ||| | df|jsd n |j ddd |\} } | j"g|dj%} |j'| } | | fS) Nr@)rrr()rrrrFrr)rrr)r~rrrCrrrrrr%rrrrrrrrrr )r9r=rrrrr r!r"rrr%rrs r;rJzLlama4VisionAttention.forwards$))#2. 88b8$--8 {{=166|D [[/44\B {{=166|D #:<^f#g j#--a3 ))!Q/ #--a3 (? ;; + +3N N{{//69fjjI\^c>d##L '>dkk>^>^&_#$7      % $}}C$2H2H %  % ! \*k));;;;FFH kk+. L((r<r&)rKrLrMr r*r2rNrr rrrrJrOrPs@r;rrs [1 [$26*. .)||.),,.)!. .) ! .) -. .) u||Xell3XeELL>Q5RR S.)r<rcVeZdZfdZdej dej fdZxZS)Llama4VisionMLPc&t|||_tj|_tj |j|jd|_ tj |j|jd|_ y)NTrU) r)r*r%r0rrZrWr.r-rrr8s r;r*zLlama4VisionMLP.__init__se  WWY99V//1I1IPTU99V55v7I7IPTUr<r=r>cl|j|}|j|}|j|}|Sr\)rrZrrs r;rJzLlama4VisionMLP.forwards4/ **=9 / r<rrPs@r;r%r%s$VU\\ellr<r%c eZdZdeffd Z ddej dej deej defdZ xZ S) Llama4VisionEncoderLayerr%ct||j|_t||_t ||_tj|j|_ tj|j|_ yr\) r)r*r.rr,r%rr0 LayerNormr2r3r8s r;r*z!Llama4VisionEncoderLayer.__init__sb !--.v6"6*!||F,>,>?(* V5G5G(H%r< hidden_staterrrc|}|j|}|j|||\}}||z}|}|j|}|j|}||z}|f}|r||fz }|S)N)rr)r2r,r3r)r9r,rrrr9rr<s r;rJz Llama4VisionEncoderLayer.forwards ++L9 %)^^ )&4& " l  ,.  44\B xx - ,. /    &Gr<r&) rKrLrMr r*r2rNrr=rJrOrPs@r;r)r)sVI1I26"& ll,,!.   r<r)ceZdZdZdeffd Z d dejdejdeejdee dee d ee d e e e ffd Z xZS) Llama4VisionEncoderz Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Llama4VisionEncoderLayer`]. Args: config: Llama4VisionConfig r%ct|||_tjt |j Dcgc] }t|c}|_d|_ ||_ycc}w)NF) r)r*r%r0r]r^r_r)r`rc)r9r%_r:s r;r*zLlama4VisionEncoder.__init__;sW  mmuU[UmUmOn$o!%=f%E$op &+# %psA*r=rrrrmrnr>c||n|jj}||n|jj}||n|jj}|rdnd}|rdnd}|jD]^} |r||fz}|j r,|j r |j| j||||} n | ||||} |r || dfz}| d}`|r||fz}|std|||fDSt|||S)ad Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): 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. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) 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. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Nr8)r,rrrrrc3&K|] }|| ywr\r8.0vs r; z.Llama4VisionEncoder.forward..seqWXWderrr=rs) r%rrmrtr`rcrryrzr}r) r9r=rrrrmrnencoder_statesall_attentions encoder_layerrs r;rJzLlama4VisionEncoder.forwardBs6>2C1N-TXT_T_TqTq$8$D $++JjJj &1%>Ne]NN$See e+>Vd  r<NNNN)rKrLrMr'r r*r2rNrr=rrrrJrOrPs@r;r/r/2s126,0/3&*G ||G ,,G !. G $D> G 'tn G d^G  uo% &G r<r/cVeZdZfdZdej dej fdZxZS)Llama4UnfoldConvolutionc<t||j}t|tr||f}t j j||j|_t j|j|dz|dz|jd|_ y)N) kernel_sizestriderrFrU) r)r*rrrr2r0UnfoldunfoldrW num_channelsr.linear)r9r%rAr:s r;r*z Llama4UnfoldConvolution.__init__s '' k3 '& 4Khhoo+fFWFWoX ii   +a. 0;q> A     r<r=r>cp|j|}|jddd}|j|}|S)Nrr(r)rDrrFrs r;rJzLlama4UnfoldConvolution.forwards8 M2 %--aA6  M2 r<rrPs@r;r?r?s#  U\\ellr<r?c$eZdZfdZdZxZS)Llama4VisionRotaryEmbeddingct ||j|jz}t j |dztj j|dzd}t j||ddgd}d|d<||z}||z}|j|jzdz}d|jt j d|dd|dzj|z zz }|dzd |ddddfzjdd }|dzd |ddddfzjdd } t j|| gd jjd dddf} | j|jd dddkd} t j t j"t j$| t j&| gd } | |_y) Nr()rrrrAr)r@r@r).Nr@.)r)r* image_sizerr2rint32rcatr.r rope_thetarnrepeat_interleaverrrstackcossinr) r9r%idximg_idx frequencies_x frequencies_yfreq_dim rope_freqfreqs_xfreqs_yrr}r:s r;r*z$Llama4VisionRotaryEmbedding.__init__s 6#4#44,,sAvU[[9AA#q&!L))Wgbqk2:# 3 %%)C)CCqH6,,a11MN_QY]^Q^1`1f1f1hks1stu !A%y1IdD!m4LL__`agi_j!A%y1IdD!m4LL__`agi_j 7G,"5;;=HHJ3PSRSPS8T!!'//"a";a"?C((eii6F RWHX5Y_a)bc  r<cL|jj|jSr\)rrrrs r;rJz#Llama4VisionRotaryEmbedding.forwards}} 4 455r<r^rPs@r;rIrIs !"6r<rIceZdZdZdgZeZdeffd ZdZ dde jde e jde e d e e d e e d e eee jd fff d ZxZS)Llama4VisionModel vision_modelr)r%crt|||j|_|j|_|j|_|j |_|j|jzdzdz|_|jdz|_t||_ tj|jtj|jz|_tj|jtj|j |jz|_t!||_tj$|j|_tj$|j|_t+||_t/||_|j3y)Nr(rr)r)r*rKrr.rErscaler?patch_embeddingr0r1r2randnclass_embeddingpositional_embedding_vlmrIrotary_embeddingr+ layernorm_prelayernorm_postr/rXr vision_adapterrdr8s r;r*zLlama4VisionModel.__init__sA   ++ ++!--"// OOt>1DqH''- 6v>!||DJJTEUEU9V,VW(* TZZ%++dN^N^`d`p`pBq5q(r% ;F C \\$*:*:; ll4+;+;<)0 9&A r<c|jS)zg This function is used to fetch the first embedding layer to activate grads on inputs. )rarrs r;rgz&Llama4VisionModel.get_input_embeddingss###r< pixel_valuesrrrmrnr>.c ||n|jj}||n|jj}||n|jj}|j\}}}} d} d} |j |} | j\} }}| j || z| z||} |jj| jdd| jd}tj| |gd} |dz }| j || z| ||} |jj| j| j}| |z} |j| } | j!|d|} |j#|}|j%| d|||}|j&} |j)| } | ddddddf} |j+| } |r |j,nd}|r|d}nd}|st/d | ||fDSt1| || S) a Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, MllamaVisionModel >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision" >>> model = MllamaVisionModel.from_pretrained(checkpoint) >>> processor = AutoProcessor.from_pretrained(checkpoint) >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> output = model(**inputs) >>> print(output.last_hidden_state.shape) torch.Size([1, 1, 4, 1025, 7680]) ``` Nrrr@rA)rr)rrmrrr(c3&K|] }|| ywr\r8r4s r;r7z,Llama4VisionModel.forward..3s_qQRQ^_r8r9)r%rrmrtr~rarrcrr2rMrdrrrrfrCrerXrrrgrhr=r}r)r9rjrrrmrnbatch_size_times_num_tilesrErrnum_concurrent_media num_chunksr,r1rrrcpositional_embeddingrr{r=rss r;rJzLlama4VisionModel.forwardsS>2C1N-TXT_T_TqTq$8$D $++JjJj &1% _ 'tn _ d^ _  ellC&7 88 9_ r<r]c&eZdZiZdZeZdZdeffd ZdZ dZ dZ dZ d Z d Zd ej d eeeefd efdZeee d&dej2d ej deej6deej2deeej deej d eeeeefd eedeej2deedeedeedeedeej2deeej6fdej6deeeff"dZ d'dZe dej6d ed!ed"ejBd#ejDdej6d$efd%Z#xZ$S)(Llama4ForConditionalGenerationTr%cht||t|j|_t ||_t|j|_ |jj|_ |jj|jjnd|_ |jy)Nr@)r)r*r]rr^rmulti_modal_projectorrrErr[r%rZrdr8s r;r*z'Llama4ForConditionalGeneration.__init__Bs  -f.B.BC%>v%F"/0B0BC ,,778< 8P8P8\DKK44bd r<c6|jjSr\)rrgrrs r;rgz3Llama4ForConditionalGeneration.get_input_embeddingsMs""7799r<c:|jj|yr\)rrjris r;rjz3Llama4ForConditionalGeneration.set_input_embeddingsPs 007r<c6|jjSr\)rrrrs r;rz4Llama4ForConditionalGeneration.get_output_embeddingsSs""88::r<c:|jj|yr\)rrrs r;rz4Llama4ForConditionalGeneration.set_output_embeddingsVs 11.Ar<c:|jj|yr\)rrrs r;rz*Llama4ForConditionalGeneration.set_decoderYs ''0r<c6|jjSr\)rrrrs r;rz*Llama4ForConditionalGeneration.get_decoder\s""..00r<rjvision_feature_layervision_feature_select_strategyc |dvrtd|j|jDcic] \}}| || }}}|j|fddi|}|j}|Scc}}w)a Obtains image last hidden states from the vision tower and apply al projection. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`) The tensors corresponding to the input images. vision_feature_layer (`Union[int, List[int]]`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` Returns: image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`). )rrz$Unexpected select feature strategy: rmF)rur}itemsr^rr) r9rjr|r}rkr6 image_outputsr,s r;get_image_featuresz1Llama4ForConditionalGeneration.get_image_features_s. *1D DCDDgDgChij j#)<<>C41aQ]!Q$CC))),]U]V\] $66 Ds A&A&rrkrrrArlrr6rrmrnrr image_sizesr>c | | n|jj} | | n|jj} | | n|jj} ||n|jjj }||n|jjj }|du|duz r td| | td||j|}|b|j||||}|j}|jd|jd}|j|}||jjk(jd}|j!|j"}|jd|jd}|dj%d}|j'}||jdk7r td|d |jd|jdj)d|jd}|j+||}|j|}|j,d||||| | | | ||d |}|d}d}| <||dd|jd d z dfj!|j"}|d ddddf|j!|j"dk7j/}| d d df|j!| j"dk7j/} n1|d ddddfj/}| d d dfj/} t1j2}!|!|jd|jd| jdj!|j"}| s|f|d dz}"||f|"zS|"St5|||j6|j8|j:| Sd S)aW labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (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]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, LlavaForConditionalGeneration >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> prompt = "USER: \nWhat's the content of the image? ASSISTANT:" >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, text=prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs, max_new_tokens=15) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed" ```NrqzdYou cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one)rjr|r}rr@).rrzMismatch: final_mask wants z0 embeddings, but multi_modal_projector returned ) rrrArlr6rrmrnrrr.)rrrAr=rsrr8)r%rrmrtrr|r}rurgrr~rCrruimage_token_indexrwrrrsumrmasked_scatterrrr0CrossEntropyLossrrAr=rs)#r9rkrjrrrArlr|r}rr6rrmrnrrr lm_kwargsroriginal_inputs_embeds_shape vision_flatprojected_vision_flatspecial_image_mask final_mask final_mask_1dnum_tokens_to_fill expanded_maskr<rrshift_attention_mask shift_logits shift_labelsloss_fctr{s# r;rJz&Llama4ForConditionalGeneration.forward}sav2C1N-TXT_T_TqTq$8$D $++JjJj &1%+> ((--b.2E2Eb2IJK$($>$>{$K !"+t{{/L/L"L!W!WXZ![ +..}/C/CDJ)..r=3E3Eb3IJM&v.66r:M!.!2!2!4 !%:%?%?%BB 12D1EF::O:T:TUV:W9XZ *33B7>>r=CUCUVXCYZM)88H]^M)../KLM%$%%  )%+'/!5#))      )(6a6<<?Q;N9O9Q6Q'R'U'UV\VcVc'd$%c3B3k23G3J3J6==3Y]^3^_jjl %c12g/C/F/Fv}}/UYZ/Z[ffh %c3B3k2==? %c12g99; **,H!!"l&7&7&;O>OPR>S>V>VWcWjWj>kDY,F'+'7D7V# CV C+#33!//))2>2J   QU   r<c f|jj|f|||||d|} |ddk(r|| d<| S)N)rArlrrrrrj)rprepare_inputs_for_generation) r9rkrArlrjrrrr model_inputss r;rz!*C(K*' E*..I** -0Ye\bK!##jjqA 5<< fEH^H^_acdHee eK%dD!Q&67>>z1bRTUK))//1 ,2226 *1aL[L+@ANSTVZ\`bcScDdDgDg&&E   ,q0 5@Aq,;,AV5W5c5c )6 Aq!\k\12r<)NNNNNNNNNNNNNNrN)NNNNNN)%rKrLrMrrrrLrQr*rgrjrrrrr2r>rrrrrrrrr(rrNr=rrJrrrrrrOrPs@r;rrrr<sHL | :8;B11''$CcN3), <+GVef'+*.1537=A59@D8<-1$(,0/3&*5934$(#_ ##_ ''_ !. _ u//0 _ "$u'8'8"9: _   1 12_ 'uS$s)^'<=_ )1 _ ))*_ D>_ $D>_ 'tn_ d^_ !!1!12_ c5<</0!_ "\\#_ & u22 3'_ g_ H<7 777{{ 7  7  777r<rr)r@rWr]rrr)r)`r dataclassesrtypingrrrrrr2torch.nnr0torch.nn.functionalrrtorch.utils.checkpoint/transformers.models.llama4.configuration_llama4r activationsr cache_utilsr r generationrmodeling_attn_mask_utilsrmodeling_flash_attention_utilsrmodeling_outputsrrrrmodeling_rope_utilsrmodeling_utilsrrprocessing_utilsrutilsrrrrrconfiguration_llama4rr !torch.nn.attention.flex_attentionr!integrations.flex_attentionr" get_loggerrKr_CHECKPOINT_FOR_DOCrModuler$rRr`rurrrNrrrrnrrr*LLAMA4_START_DOCSTRINGr@rrWrrrrrr LLAVA_START_DOCSTRINGrrrr%r)r/r?rIr]rr__all__r8r<r;rs  !99 N!4)>B 7F&A !;J   H %+  B)BII)$ !uxx != =(+"BII+"\3 3l 2  2  2|| 2 5<< %& 2 UU\\ U# U%,, U&% II% <<% % << % U\\* %  %%2`)"))`)FFRYYFR"Z?O? ?8HVZU+U Up z -z z $<;$< $