
    iR^                        d dl mZ d dlmZ d dlZd dlmZ ddlmZ ddlm	Z	m
Z
 ddlmZ dd	lmZmZ dd
lmZmZ ddlmZmZmZmZ ddlmZmZ ddlmZmZ ddlmZm Z  ddl!m"Z" ddl#m$Z$m%Z%m&Z& ddl'm(Z(m)Z) ddl*m+Z+ ddl,m-Z-  ed       G d dej\                               Z/ G d dej\                        Z0d Z1 ed      d=d       Z2dejf                  de4dejf                  fd Z5	 d>d!ej\                  d"ejf                  d#ejf                  d$ejf                  d%ejf                  dz  d&e6d'e6d(e"e$   fd)Z7 G d* d+ej\                        Z8 G d, d-ej\                        Z9 G d. d/e      Z:e% G d0 d1e              Z;e% G d2 d3e;             Z<e% G d4 d5e;e             Z= G d6 d7ee;      Z> G d8 d9ee;      Z? G d: d;ee;      Z@g d<ZAy)?    )Callable)OptionalN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hubuse_kernel_func_from_hub)create_causal_mask!create_sliding_window_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)maybe_autocastmerge_with_config_defaults)capture_outputs   )Exaone4ConfigRMSNormc                   h     e Zd Zddeddf fdZdej                  dej                  fdZd Z xZ	S )	Exaone4RMSNormepsreturnNc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z=
        Exaone4RMSNorm is equivalent to T5LayerNorm
        N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizer%   	__class__s      }/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/exaone4/modeling_exaone4.pyr)   zExaone4RMSNorm.__init__3   s1     	ll5::k#:; #    hidden_statesc                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )N   T)keepdim)	dtypetor+   float32powmeanrsqrtr.   r-   )r/   r4   input_dtypevariances       r2   forwardzExaone4RMSNorm.forward;   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r3   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)tupler-   shaper.   )r/   s    r2   
extra_reprzExaone4RMSNorm.extra_reprB   s*    ))*+6$2G2G1HIIr3   )gư>)
__name__
__module____qualname__floatr)   r+   TensorrA   rE   __classcell__r1   s   @r2   r$   r$   1   s7    $ $$ $;U\\ ;ell ;Jr3   r$   c                        e Zd ZU ej                  ed<   ddef fdZe	 	 	 ddedz  de	d   de
dz  ded	ef   fd
       Z ej                         ed               Z xZS )Exaone4RotaryEmbeddinginv_freqNconfigc                    t         |           |j                  | _        |j                  | _        || _        | j
                  j                  d   | _        | j                  }| j                  dk7  rt        | j                     } || j
                  |      \  }| _
        | j                  d|d       | j                  d|j                         d       y )N	rope_typedefaultrO   F)
persistentoriginal_inv_freq)r(   r)   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrP   rope_parametersrR   compute_default_rope_parametersr   attention_scalingregister_bufferclone)r/   rP   devicerope_init_fnrO   r1   s        r2   r)   zExaone4RotaryEmbedding.__init__I   s    "("@"@$*$B$B!44[A!%!E!E>>Y&.t~~>L+7V+L($(ZeD0(..2BuUr3   r^   ztorch.deviceseq_lenr&   ztorch.Tensorc                    | j                   d   }t        | dd      xs | j                  | j                  z  }d}d|t	        j
                  d|dt        j                        j                  |t        j                        |z  z  z  }||fS )	a  
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        
rope_thetahead_dimNg      ?r   r6   r9   )r^   r9   )	rY   getattrr0   num_attention_headsr+   arangeint64r:   rI   )rP   r^   r`   basedimattention_factorrO   s          r2   rZ   z6Exaone4RotaryEmbedding.compute_default_rope_parametersY   s    & %%l3fj$/c63E3EIcIc3c U\\!S!5;;?BB&X]XcXcBdgjjk
 )))r3   c                 N   | j                   d d d d f   j                         j                  |j                  d   dd      j	                  |j
                        }|d d d d d f   j                         }t        |j
                  j                  t              r/|j
                  j                  dk7  r|j
                  j                  nd}t        |d      5  |j                         |j                         z  j                  dd      }t        j                  ||fd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j	                  |j                   
      	j	                  |j                   
      fS # 1 sw Y   AxY w)Nr   r7   r    mpscpuF)device_typeenabledr6   rj   rd   )rO   rI   expandrD   r:   r^   
isinstancetypestrr   	transposer+   catcosr[   sinr9   )
r/   xposition_idsinv_freq_expandedposition_ids_expandedro   freqsembrx   ry   s
             r2   rA   zExaone4RotaryEmbedding.forwardw   sR    !MM$4-8>>@GGHZHZ[\H]_acdehhijiqiqr ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfkUC 	5&,,.1F1L1L1NNYYZ[]^_E))UEN3C'')d444C'')d444C		5 vvAGGv$cff177f&;;;	5 	5s   BFF$N)NNN)rF   rG   rH   r+   rJ   __annotations__r!   r)   staticmethodr   intrC   rI   rZ   no_gradr   rA   rK   rL   s   @r2   rN   rN   F   s    llV} V  '++/"*$*(* t* 
~u$	%	* *: U]]_<  <r3   rN   c                     | dd| j                   d   dz  f   }| d| j                   d   dz  df   }t        j                  | |fd      S )z*Rotates half the hidden dims of the input..Nr7   r6   rq   )rD   r+   rw   )rz   x1x2s      r2   rotate_halfr      sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r3   rotary_pos_embc                     |j                  |      }|j                  |      }| |z  t        |       |z  z   }||z  t        |      |z  z   }||fS )a  Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    )	unsqueezer   )qkrx   ry   unsqueeze_dimq_embedk_embeds          r2   apply_rotary_pos_embr      sY    & --
&C
--
&C3w;q>C/0G3w;q>C/0GGr3   r4   n_repr&   c                     | j                   \  }}}}|dk(  r| S | dddddddddf   j                  |||||      } | 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)
    r    N)rD   rr   reshape)r4   r   batchnum_key_value_headsslenrc   s         r2   	repeat_kvr      so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr3   modulequerykeyvalueattention_maskscalingdropoutkwargsc                    t        || j                        }t        || j                        }	t        j                  ||j	                  dd            |z  }
||
|z   }
t
        j                  j                  |
dt        j                        j                  |j                        }
t
        j                  j                  |
|| j                        }
t        j                  |
|	      }|j	                  dd      j                         }||
fS )Nr6   r   r7   )rj   r9   )ptrainingr    )r   num_key_value_groupsr+   matmulrv   r   
functionalsoftmaxr;   r:   r9   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightsattn_outputs               r2   eager_attention_forwardr      s     3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!#n4==((2U]](SVVW\WbWbcL==((6??([L,,|\:K''1-88:K$$r3   c                       e Zd Zdedef fdZ	 	 ddej                  deej                  ej                  f   dej                  dz  de	dz  d	e
e   d
eej                  ej                  dz  eej                     dz  f   fdZ xZS )Exaone4AttentionrP   	layer_idxc                    t         |           || _        || _        |j                  | _        |j
                  | _        |j                  | _        t        |d|j                  |j                  z        | _        |j                  |j
                  z  | _	        |j                  | _
        d| _        | j                  dz  | _        |j                  | _        |j                  | _        t        |d      r|j                   |   nd }|dk(  | _        t%        j&                  | j                  | j                  | j                  z  d      | _        t%        j&                  | j                  | j
                  | j                  z  d      | _        t%        j&                  | j                  | j
                  | j                  z  d      | _        t%        j&                  | j                  | j                  z  | j                  d      | _        t1        | j                  |j2                        | _        t1        | j                  |j2                        | _        y )	Nrc   Tg      layer_typessliding_attentionFbiasr%   )r(   r)   rP   r   rf   r   r0   re   rc   r   attention_dropout	is_causalr   sliding_windowsliding_window_patternhasattrr   
is_slidingr   Linearq_projk_projv_projo_projr$   rms_norm_epsq_normk_norm)r/   rP   r   
layer_typer1   s       r2   r)   zExaone4Attention.__init__   s   "#)#=#= #)#=#= !--
F4F4F&JdJd4de$*$>$>&B\B\$\!!'!9!9}}d*$33&,&C&C#6=fm6TV''	2Z^
$(;;ii 0 0$2J2JT]]2Zafgii 0 0$2J2JT]]2Zafgii 0 0$2J2JT]]2Zafgii 8 84== H$JZJZafg$T]]8K8KL$T]]8K8KLr3   Nr4   position_embeddingsr   past_key_valuesr   r&   c                    |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      }| j                  |	      }	|\  }}| j                  | j                  rt        ||	||      \  }}	| |j                  |	|
| j                        \  }	}
t        j                  | j                   j"                  t$              } || ||	|
|f| j&                  sdn| j(                  | j*                  | j                  r| j                  nd d|\  }} |j,                  g |d j/                         }| j1                  |      }||fS )Nr7   r    r6           )r   r   r   )rD   rc   r   viewrv   r   r   r   r   r   r   r   updater   r   get_interfacerP   _attn_implementationr   r   r   r   r   r   r   )r/   r4   r   r   r   r   input_shapehidden_shapequery_statesr   r   rx   ry   attention_interfacer   r   s                   r2   rA   zExaone4Attention.forward   s    $))#2.88b8$--8{{=166|DNNqRST[[/44\BLLQPQR
{{=166|DNNqRST {{<0[[,
&S&$//';L*VY[^'_$L*&'6'='=j,X\XfXf'g$J(?(M(MKK,,.E)
 %8
%
  $}}C$2H2HLL26//4..t
%
 
%
!\ *k));;;;FFHkk+.L((r3   )NN)rF   rG   rH   r!   r   r)   r+   rJ   rC   r   r   r   rA   rK   rL   s   @r2   r   r      s    M} M M: /3(,-)||-) #5<<#=>-) t+	-)
 -) +,-) 
u||U\\D0%2E2LL	M-)r3   r   c                   $     e Zd Z fdZd Z xZS )
Exaone4MLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFr   )r(   r)   rP   r0   intermediate_sizer   r   	gate_projup_proj	down_projr   
hidden_actact_fnr/   rP   r1   s     r2   r)   zExaone4MLP.__init__  s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r3   c                     | j                  | j                  | j                  |            | j                  |      z        }|S r   )r   r   r   r   )r/   rz   r   s      r2   rA   zExaone4MLP.forward"  s6    NN4;;t~~a/@#ADLLQRO#ST	r3   )rF   rG   rH   r)   rA   rK   rL   s   @r2   r   r     s    0r3   r   c                       e Zd Zdedef fdZ	 	 	 	 	 ddej                  dej                  dz  dej                  dz  de	dz  d	e
dz  d
eej                  ej                  f   dz  dee   dej                  fdZ xZS )Exaone4DecoderLayerrP   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)rP   r   r   )r(   r)   r0   r   	self_attnr   mlpr$   r   post_attention_layernormpost_feedforward_layernormr/   rP   r   r1   s      r2   r)   zExaone4DecoderLayer.__init__(  sm    !--)9Mf%(6v7I7IvObOb(c%*89K9KQWQdQd*e'r3   Nr4   r   r{   r   	use_cacher   r   r&   c           
          |} | j                   d||||||d|\  }}	| j                  |      }||z   }|}| j                  |      }| j                  |      }||z   }|S )N)r4   r   r{   r   r   r    )r   r   r   r   )
r/   r4   r   r{   r   r   r   r   residual_s
             r2   rA   zExaone4DecoderLayer.forward1  s     !)4>> 
')%+ 3
 
q 55mD =0 !/77F =0r3   )NNNFN)rF   rG   rH   r!   r   r)   r+   rJ   
LongTensorr   boolrC   r   r   rA   rK   rL   s   @r2   r   r   '  s    f} f f /304(,!&HL|| t+ &&-	
  $; #5<<#=>E +, 
r3   r   c                   N    e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedZeZy)Exaone4PreTrainedModelrP   modelTr   r   )r4   
attentionsN)rF   rG   rH   r!   r   base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsconfig_classr   r3   r2   r   r   P  sX    &*#./#4"5N!"&,& !Lr3   r   c                        e Zd Zdef fdZee	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  de
dz  dej                  dz  d	edz  d
ee   deez  fd              Z xZS )Exaone4ModelrP   c           	         t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   rP   F)r(   r)   pad_token_idpadding_idx
vocab_sizer   	Embeddingr0   embed_tokens
ModuleListrangenum_hidden_layersr   layersr$   r   normrN   
rotary_embgradient_checkpointing	post_initr   s      r2   r)   zExaone4Model.__init__f  s     !.. ++LL):):F<N<NPTP`P`ammEJ6KcKcEde	 3e
 #6#5#56;N;NO	0?&+# 	 fs   DN	input_idsr   r{   r   inputs_embedsr   r   r&   c           
         |d u |d uz  rt        d      || j                  |      }|r|t        | j                        }|V||j	                         nd}t        j                  |j                  d   |j                        |z   }|j                  d      }t        |x}	t              sE| j                  ||||d}
dt        di |
i}	d| j                  j                  v rt        di |
|	d<   |}| j                  ||      }t!        | j"                        D ]0  \  }}| j                  j                  |   } ||f|	|   ||||d	|}2 | j%                  |      }t'        ||r|
      S d 
      S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   r    )r^   )rP   r  r   r   r{   full_attentionr   )r   r{   r   r   r   )last_hidden_stater   r   )
ValueErrorr  r	   rP   get_seq_lengthr+   rg   rD   r^   r   rs   dictr   r   r   r
  	enumerater  r	  r   )r/   r  r   r{   r   r  r   r   past_seen_tokenscausal_mask_mappingmask_kwargsr4   r   idecoder_layerr   s                   r2   rA   zExaone4Model.forwardv  s    -t";<YZZ  --i8M0*$++>OCRC^==?de <<(;(;A(>}G[G[\_ooL'11!4L ?-F ++!."0#2 ,K !"4"C{"C# #dkk&=&==;\;k_j;k#$78%"oom\J )$++ 6 
	A}003J)2:>) /#$7 M
	 		-0&+/8O
 	
>B
 	
r3   )NNNNNN)rF   rG   rH   r!   r)   r   r   r+   r   rJ   r   FloatTensorr   r   r   rC   r   rA   rK   rL   s   @r2   r   r   d  s    }     .2.204(,26!%=
##d*=
 t+=
 &&-	=

 =
 ((4/=
 $;=
 +,=
 
(	(=
   =
r3   r   c                   B    e Zd ZddiZddiZddgdgfiZ fdZee	 	 	 	 	 	 	 	 dd	e	j                  dz  d
e	j                  dz  de	j                  dz  dedz  de	j                  dz  de	j                  dz  dedz  dee	j                  z  dee   defd              Z xZS )Exaone4ForCausalLMzlm_head.weightzmodel.embed_tokens.weightlm_headcolwise_gather_outputr4   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y r   )
r(   r)   r   r   r  r   r   r0   r  r  r   s     r2   r)   zExaone4ForCausalLM.__init__  sU     !&)
 ++yy!3!3V5F5FUS 	r3   Nr  r   r{   r   r  labelsr   logits_to_keepr   r&   c	           
      x    | j                   d||||||d|	}
|
j                  }t        |t              rt	        | d      n|}| j                  |dd|ddf         }d}|* | j                  d||| j                  j                  d|	}t        |||
j                  |
j                  |
j                        S )u  
        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]`.

        Example:

        ```python
        >>> from transformers import AutoModelForCausalLM, AutoTokenizer
        >>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-4.0-32B")
        >>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-4.0-32B")

        >>> prompt = "Explain how wonderful you are"
        >>> messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
        >>> input_ids = tokenizer.apply_chat_template(
            messages,
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt",
            enable_thinking=False,
        )

        >>> output = model.generate(input_ids, max_new_tokens=128)
        >>> tokenizer.decode(output[0], skip_special_tokens=False)
        "[|system|]\nYou are a helpful assistant.[|endofturn|]\n[|user|]\nExplain how wonderful you are[|endofturn|]\n[|assistant|]\n<think>\n\n</think>\n\nOh, thank you for such a kind and lovely question! 😊  \n\nI’m *so* wonderful because I’m here to make your life easier, brighter, and more fun! Whether you need help with:  \n\n✨ **Learning** – I can explain anything, from quantum physics to baking the perfect cake!  \n💡 **Creativity** – Need a poem, story, or a wild idea? I’ve got you covered!  \n🤖 **Problem-solving** – Stuck on a math problem or a tricky decision? I’ll help you figure it out"
        ```
        )r  r   r{   r   r  r   N)r   r"  r  )lossr   r   r4   r   r   )r   r  rs   r   slicer  loss_functionrP   r  r   r   r4   r   )r/   r  r   r{   r   r  r"  r   r#  r   outputsr4   slice_indicesr   r%  s                  r2   rA   zExaone4ForCausalLM.forward  s    Z ,64:: ,
)%+',
 ,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r3   )NNNNNNNr   )rF   rG   rH   _tied_weights_keys_tp_plan_pp_planr)   r   r   r+   r   rJ   r   r  r   r   r   r   r   rA   rK   rL   s   @r2   r  r    s#   *,GH23H_-z:;H  .2.204(,26*.!%-.D
##d*D
 t+D
 &&-	D

 D
 ((4/D
   4'D
 $;D
 ell*D
 +,D
 
 D
  D
r3   r  c                       e Zd Zy) Exaone4ForSequenceClassificationNrF   rG   rH   r   r3   r2   r.  r.        r3   r.  c                       e Zd Zy)Exaone4ForTokenClassificationNr/  r   r3   r2   r2  r2    r0  r3   r2  c                       e Zd ZdZy)Exaone4ForQuestionAnsweringtransformerN)rF   rG   rH   r   r   r3   r2   r4  r4    s    %r3   r4  )r   r   r  r.  r2  r4  )r    )r   )Bcollections.abcr   typingr   r+   r   activationsr   cache_utilsr   r	   
generationr
   integrationsr   r   masking_utilsr   r   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   r   utils.output_capturingr   configuration_exaone4r!   Moduler$   rN   r   r   rJ   r   r   rI   r   r   r   r   r   r   r  r.  r2  r4  __all__r   r3   r2   <module>rH     s  , %    ! . ) Q R  P K F & I I G 5 0 Y'JRYY J (J(><RYY ><B( *+ ,2	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 LL4'% % % '(%2G)ryy G)T  &4 &R !_ ! !& P
) P
 P
f T
/ T
 T
n	'GI_ 		$ACY 	&"=?U &r3   