
    rh8P                        d dl mZmZ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 dd	l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$Z$m%Z% ddl&m'Z' ddl(m)Z)  e%jT                  e+      Z, ed       G d dejZ                               Z. G d dejZ                        Z/d Z0d:dZ1 G d dejZ                        Z2dejf                  de4dejf                  fdZ5	 d;d ejZ                  d!ejf                  d"ejf                  d#ejf                  d$eejf                     d%e6d&e6d'e e"   fd(Z7 G d) d*ejZ                        Z8 G d+ d,e      Z9e# G d- d.e             Z:e# G d/ d0e:             Z;e# G d1 d2e:e             Z< G d3 d4ee:      Z= G d5 d6ee:      Z> G d7 d8ee:      Z?g d9Z@y)<    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)use_kernel_forward_from_hub)create_causal_mask)GenericForQuestionAnswering GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuplelogging)check_model_inputs   )LlamaConfigRMSNormc                   ,     e Zd Zd fd	Zd Zd Z xZS )LlamaRMSNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)z;
        LlamaRMSNorm is equivalent to T5LayerNorm
        N)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeeps	__class__s      {/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/llama/modeling_llama.pyr%   zLlamaRMSNorm.__init__5   s1     	ll5::k#:; #    c                 "   |j                   }|j                  t        j                        }|j	                  d      j                  dd      }|t        j                  || j                  z         z  }| j                  |j                  |      z  S )N   T)keepdim)	dtypetor'   float32powmeanrsqrtr*   r)   )r+   hidden_statesinput_dtypevariances       r/   forwardzLlamaRMSNorm.forward=   sy    #))%((7 $$Q',,R,>%Ht?T?T4T(UU{{]--k:::r0   c                 ^    t        | j                  j                         d| j                   S )Nz, eps=)tupler)   shaper*   r+   s    r/   
extra_reprzLlamaRMSNorm.extra_reprD   s*    ))*+6$2G2G1HIIr0   )gư>)__name__
__module____qualname__r%   r>   rC   __classcell__r.   s   @r/   r"   r"   3   s    $;Jr0   r"   c                   ^     e Zd Zddef fdZ ej                         ed               Z xZ	S )LlamaRotaryEmbeddingconfigc                    t         |           t        |d      rUt        |j                  t
              r;|j                  j                  d|j                  j                  d            | _        nd| _        |j                  | _	        |j                  | _
        || _        t        | j                     | _        | j                  | j                  |      \  }| _        | j                  d|d       | j                   | _        y )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)r$   r%   hasattr
isinstancerM   dictgetrN   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrK   r   rope_init_fnattention_scalingregister_bufferrQ   original_inv_freq)r+   rK   devicerQ   r.   s       r/   r%   zLlamaRotaryEmbedding.__init__I   s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r0   c                 b   | 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        j                  |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   r3   r   mpscpuF)device_typeenabledr2   dim)r5   )rQ   floatexpandrA   r6   r^   rT   rO   strr'   autocast	transposecatcosr[   sinr5   )
r+   xposition_idsinv_freq_expandedposition_ids_expandedrb   freqsembrl   rm   s
             r/   r>   zLlamaRotaryEmbedding.forwardZ   sV    !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)
rD   rE   rF   r   r%   r'   no_gradr   r>   rG   rH   s   @r/   rJ   rJ   H   s3    /{ /" U]]_<  <r0   rJ   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..Nr3   r2   rd   )rA   r'   rk   )rn   x1x2s      r/   rotate_halfry   j   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r0   c                     |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.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        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.
    )	unsqueezery   )qkrl   rm   ro   unsqueeze_dimq_embedk_embeds           r/   apply_rotary_pos_embr   q   sY    ( --
&C
--
&C3w;q>C/0G3w;q>C/0GGr0   c                   $     e Zd Z fdZd Z xZS )LlamaMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  |j                        | _        t        j                  | j                  | j                  |j                        | _	        t        j                  | j                  | j                  |j                        | _
        t        |j                     | _        y )Nbias)r$   r%   rK   r,   intermediate_sizer   Linearmlp_bias	gate_projup_proj	down_projr   
hidden_actact_fnr+   rK   r.   s     r/   r%   zLlamaMLP.__init__   s    !--!'!9!94#3#3T5K5KRXRaRabyy!1!143I3IPVP_P_`4#9#94;K;KRXRaRabV../r0   c                     | j                  | j                  | j                  |            | j                  |      z        }|S rt   )r   r   r   r   )r+   rn   r   s      r/   r>   zLlamaMLP.forward   s6    NN4;;t~~a/@#ADLLQRO#ST	r0   )rD   rE   rF   r%   r>   rG   rH   s   @r/   r   r      s    0r0   r   r;   n_repreturnc                     | 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)rA   rg   reshape)r;   r   batchnum_key_value_headsslenhead_dims         r/   	repeat_kvr      so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr0   modulequerykeyvalueattention_maskscalingdropoutkwargsc                 T   t        || j                        }t        || j                        }	t        j                  ||j	                  dd            |z  }
|#|d d d d d d d |j
                  d   f   }|
|z   }
t        j                  j                  |
dt        j                        j                  |j                        }
t        j                  j                  |
|| j                        }
t        j                  |
|	      }|j	                  dd      j                         }||
fS )Nr2   r   r3   )re   r5   )ptrainingr   )r   num_key_value_groupsr'   matmulrj   rA   r   
functionalsoftmaxr7   r6   r5   r   r   
contiguous)r   r   r   r   r   r   r   r   
key_statesvalue_statesattn_weightscausal_maskattn_outputs                r/   eager_attention_forwardr      s    3 ; ;<JUF$?$?@L<<z';';Aq'ABWLL!$Q1.D
0@0@0D.D%DE#k1==((2U]](SVVW\WbWbcL==((6??([L,,|\:K''1-88:K$$r0   c                       e Zd ZdZdedef fdZ	 	 ddej                  de	ej                  ej                  f   de
ej                     de
e   d	e
ej                     d
ee   de	ej                  ej                  f   fdZ xZS )LlamaAttentionz=Multi-headed attention from 'Attention Is All You Need' paperrK   	layer_idxc                 d   t         |           || _        || _        t	        |d|j
                  |j                  z        | _        |j                  |j                  z  | _	        | j                  dz  | _
        |j                  | _        d| _        t        j                  |j
                  |j                  | j                  z  |j                        | _        t        j                  |j
                  |j                  | j                  z  |j                        | _        t        j                  |j
                  |j                  | j                  z  |j                        | _        t        j                  |j                  | j                  z  |j
                  |j                        | _        y )Nr   g      Tr   )r$   r%   rK   r   getattrr,   num_attention_headsr   r   r   r   attention_dropout	is_causalr   r   attention_biasq_projk_projv_projo_projr+   rK   r   r.   s      r/   r%   zLlamaAttention.__init__   sM   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
r0   r;   position_embeddingsr   past_key_valuecache_positionr   r   c                 4   |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
| j                  |      j                  |      j	                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  rt        | j                  j                     } || |	|
||f| j                  sdn| j                  | j                   d|\  }} |j"                  g |d j%                         }| j'                  |      }||fS )Nr3   r   r2   )rm   rl   r   eager        )r   r   )rA   r   r   viewrj   r   r   r   updater   r   rK   _attn_implementationr   r   r   r   r   r   r   )r+   r;   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rl   rm   cache_kwargsattention_interfacer   r   s                     r/   r>   zLlamaAttention.forward   s    $))#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&S#7jRUWZ#[ j%#&snUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r0   )NN)rD   rE   rF   __doc__r   intr%   r'   Tensorr@   r   r	   
LongTensorr   r   r>   rG   rH   s   @r/   r   r      s    G
{ 
s 
8 +/59))||)) #5<<#=>)) !.	))
 !)) !!1!12)) +,)) 
u||U\\)	*))r0   r   c                   (    e Zd Zdedef fdZ	 	 	 	 	 	 ddej                  deej                     deej                     dee
   dee   d	eej                     d
eeej                  ej                  f      dee   deej                     fdZ xZS )LlamaDecoderLayerrK   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        t        |j                  |j                        | _
        y )N)rK   r   r-   )r$   r%   r,   r   	self_attnr   mlpr"   rms_norm_epsinput_layernormpost_attention_layernormr   s      r/   r%   zLlamaDecoderLayer.__init__	  sl    !--'vKF#+F,>,>FDWDWX(4V5G5GVM`M`(a%r0   r;   r   ro   r   	use_cacher   r   r   r   c                     |}	| j                  |      } | j                  d|||||||d|\  }}
|	|z   }|}	| j                  |      }| j                  |      }|	|z   }|S )N)r;   r   ro   r   r   r   r    )r   r   r   r   )r+   r;   r   ro   r   r   r   r   r   residual_s              r/   r>   zLlamaDecoderLayer.forward  s     !,,];)4>> 	
')%)) 3	
 	
q !=0 !55mD/ =0r0   )NNNFNN)rD   rE   rF   r   r   r%   r'   r   r   r   r	   boolr@   r   r   r>   rG   rH   s   @r/   r   r     s    b{ bs b 2637*.$)59KO|| !. u//0	
 ! D> !!1!12 &eELL%,,,F&GH +, 
u||	r0   r   c                   J    e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedZy)LlamaPreTrainedModelrK   modelTr   past_key_values)r;   
attentionsN)rD   rE   rF   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr   r0   r/   r   r   5  sQ    &*#,-#4"5N!"&*$r0   r   c                       e Zd Zdef fdZee	 	 	 	 	 	 	 ddeej                     deej                     deej                     dee   deej                     deej                     d	ee   d
ee   defd              Z xZS )
LlamaModelrK   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   )rK   F)r$   r%   pad_token_idpadding_idx
vocab_sizer   	Embeddingr,   embed_tokens
ModuleListrangenum_hidden_layersr   layersr"   r   normrJ   
rotary_embgradient_checkpointing	post_initr   s      r/   r%   zLlamaModel.__init__J  s     !.. ++LL):):F<N<NPTP`P`ammCHIaIaCbcivy1c
 !!3!39L9LM	.f=&+# 	 ds   D	input_idsr   ro   r   inputs_embedsr   r   r   r   c           
      *   |d u |d uz  rt        d      || j                  |      }|r|
t               }|F||j                         nd}	t	        j
                  |	|	|j                  d   z   |j                        }||j                  d      }t        | j                  |||||      }
|}| j                  ||      }| j                  d | j                  j                   D ]  } ||f|
||||d|} | j                  |      }t        ||      S )Nz:You must specify exactly one of input_ids or inputs_embedsr   r   )r^   )rK   input_embedsr   r   r   ro   )r   ro   r   r   r   )last_hidden_stater   )
ValueErrorr   r
   get_seq_lengthr'   arangerA   r^   r{   r   rK   r   r   r   r   r   )r+   r   r   ro   r   r  r   r   r   past_seen_tokensr   r;   r   decoder_layers                 r/   r>   zLlamaModel.forwardZ  sT    -t";<YZZ *.*;*;I*FM0*nO!CRC^==?de+0<< "2]5H5H5K"KTaThTh,N )33A6L(;;&))+%
 &"oom\J![[)H4;;+H+HI 		M)*).-$7 M		 		-0&++
 	
r0   )NNNNNNN)rD   rE   rF   r   r%   r   r   r   r'   r   r   r	   FloatTensorr   r   r   r   r>   rG   rH   s   @r/   r   r   H  s    {    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r0   r   c                   p    e Zd ZdgZddiZddgdgfiZ fdZd Zd Ze	e
	 	 	 	 	 	 	 	 	 dd	eej                     d
eej                     deej                     dee   deej                      deej                     dee   deej                     deeej                  f   dee   defd              Z xZS )LlamaForCausalLMzlm_head.weightlm_headcolwise_repr;   logitsc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFr   )
r$   r%   r   r   r   r   r   r,   r  r   r   s     r/   r%   zLlamaForCausalLM.__init__  sU     '
 ++yy!3!3V5F5FUS 	r0   c                     || _         y rt   r   )r+   decoders     r/   set_decoderzLlamaForCausalLM.set_decoder  s	    
r0   c                     | j                   S rt   r  rB   s    r/   get_decoderzLlamaForCausalLM.get_decoder  s    zzr0   r   r   ro   r   r  labelsr   r   logits_to_keepr   r   c
                 z    | 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 )a  
        Example:

        ```python
        >>> from transformers import AutoTokenizer, LlamaForCausalLM

        >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-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."
        ```)r   r   ro   r   r  r   r   N)r  r  r   )lossr  r   r;   r   r   )r   r  rT   r   slicer  loss_functionrK   r   r   r   r;   r   )r+   r   r   ro   r   r  r  r   r   r  r   outputsr;   slice_indicesr  r  s                   r/   r>   zLlamaForCausalLM.forward  s    @ ,64:: 	,
)%+')	,
 	,
  118B>SV8W~ot4]kmA}a,?@A%4%%pVFt{{OeOepiopD%#33!//))
 	
r0   )	NNNNNNNNr   )rD   rE   rF   _tied_weights_keys_tp_plan_pp_planr%   r  r  r   r   r   r'   r   r   r	   r
  r   r   r   r   r   r   r>   rG   rH   s   @r/   r  r    s:   *+=)H_-z:;H  151537+/59-1$(59348
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 ))*8
 D>8
 !!1!128
 c5<</08
 +,8
 
 8
  8
r0   r  c                       e Zd Zy)LlamaForSequenceClassificationNrD   rE   rF   r   r0   r/   r#  r#        r0   r#  c                       e Zd ZdZy)LlamaForQuestionAnsweringtransformerN)rD   rE   rF   r   r   r0   r/   r'  r'    s    %r0   r'  c                       e Zd Zy)LlamaForTokenClassificationNr$  r   r0   r/   r*  r*    r%  r0   r*  )r  r   r   r#  r'  r*  )Nr   )r   )Atypingr   r   r   r'   r   activationsr   cache_utilsr	   r
   
generationr   integrationsr   masking_utilsr   modeling_layersr   r   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   utils.genericr   configuration_llamar   
get_loggerrD   loggerModuler"   rJ   ry   r   r   r   r   r   rf   r   r   r   r   r   r  r#  r'  r*  __all__r   r0   r/   <module>r=     s  ( - ,   ! . ) 7 /  L F & R R / , 
		H	% Y'J299 J (J(<299 <D(6ryy  	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4C)RYY C)L*2 *Z ?  $ K
% K
 K
\ N
+_ N
 N
b b%EG[ a& ;=Q & \"?AU [r0   