
    rh^                        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 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# ddl$m%Z%  G d dejL                        Z' G d dejL                        Z( G d dejL                        Z)dejT                  de+dejT                  fdZ,	 d3dejL                  dejT                  dejT                  d ejT                  d!eejT                     d"e-d#e-d$ee   fd%Z.d& Z/d4d'Z0 G d( d)ejL                        Z1 G d* d+e      Z2e  G d, d-e             Z3e  G d. d/e3             Z4e  G d0 d1e3e             Z5g d2Z6y)5    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)create_causal_mask)FlashAttentionKwargs)GradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)check_model_inputs   )CohereConfigc                   &     e Zd Zd fd	Zd Z xZS )CohereLayerNormc                     t         |           t        j                  t	        j
                  |            | _        || _        y)zcThe hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dimN)super__init__r   	Parametertorchonesweightvariance_epsilon)selfhidden_sizeepsbias	__class__s       }/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/cohere/modeling_cohere.pyr    zCohereLayerNorm.__init__3   s/    ll5::k#:; #    c                    |j                   }|j                  t        j                        }|j	                  dd      }||z
  j                  d      j	                  dd      }||z
  t        j                  || j                  z         z  }| j                  j                  t        j                        |z  }|j                  |      S )NT)keepdim   )	dtypetor"   float32meanpowrsqrtr%   r$   )r&   hidden_statesinput_dtyper4   variances        r+   forwardzCohereLayerNorm.forward9   s    #))%((7!!"d!3!D(--a055b$5G&-XH]H]=]1^^u}}5E,,r,   )Ngh㈵>F__name__
__module____qualname__r    r:   __classcell__r*   s   @r+   r   r   2   s    $-r,   r   c                   ^     e Zd Zddef fdZ ej                         ed               Z xZ	S )CohereRotaryEmbedding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
isinstancerE   dictgetrF   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenrC   r   rope_init_fnattention_scalingregister_bufferrI   original_inv_freq)r&   rC   devicerI   r*   s       r+   r    zCohereRotaryEmbedding.__init__D   s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%r,   c                 .   | j                   d d d d f   j                         j                  |j                  d   dd      }|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                  |dd	      }|j                         | j                  z  }|j                         | j                  z  }	d d d        j                  |j                   
      	j                  |j                   
      fS # 1 sw Y   AxY w)Nr   r.   r   mpscpuF)device_typeenabledr0   dimr1   )rI   floatexpandshaperL   rV   rG   strr"   autocast	transposerepeat_interleavecosrS   sinr2   r1   )
r&   xposition_idsinv_freq_expandedposition_ids_expandedrZ   freqsembrf   rg   s
             r+   r:   zCohereRotaryEmbedding.forwardU   sD    !MM$4-8>>@GGHZHZ[\H]_acde ,QaZ 8 > > @'1!((--'E!((--[`J`ahhmmfk^^UC 	5&,,.1F1L1L1NNYYZ[]^_E))%;C'')d444C'')d444C		5 vvAGGv$cff177f&;;;	5 	5s   BFFN)
r<   r=   r>   r   r    r"   no_gradr   r:   r?   r@   s   @r+   rB   rB   C   s3    /| /" U]]_<  <r,   rB   c                   $     e Zd Z fdZd Z xZS )	CohereMLPc                    t         |           || _        |j                  | _        |j                  | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _        t        j                  | j                  | j                  d      | _	        t        |j                     | _        y NFr)   )r   r    rC   r'   intermediate_sizer   Linear	gate_projup_proj	down_projr   
hidden_actact_fnr&   rC   r*   s     r+   r    zCohereMLP.__init__f   s    !--!'!9!94#3#3T5K5KRWXyy!1!143I3IPUV4#9#94;K;KRWXV../r,   c                     | j                  | j                  | j                  |            | j                  |      z        }|S rn   )ry   r{   rw   rx   )r&   rh   ry   s      r+   r:   zCohereMLP.forwardp   s6    NN4;;t~~a/@#ADLLQRO#ST	r,   r;   r@   s   @r+   rq   rq   e   s    0r,   rq   r7   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   r`   reshape)r7   r~   batchnum_key_value_headsslenhead_dims         r+   	repeat_kvr   u   so    
 2?1D1D.Ehz!!Qa"23::5BUW\^bdlmM  (;e(CT8TTr,   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 )Nr0   r   r.   )r]   r1   )ptrainingr   )r   num_key_value_groupsr"   matmulrd   ra   r   
functionalsoftmaxr3   r2   r1   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$$r,   c                     | dd d df   }| ddd df   }t        j                  | |gd      j                  d      }|S )N.r0   r   r.   r\   r   )r"   stackflatten)rh   x1x2rot_xs       r+   rotate_halfr      sL    	
3!8B	
319BKK"b	r*2226ELr,   c                 6   | j                   }| j                         } |j                         }|j                  |      }|j                  |      }| |z  t        |       |z  z   }||z  t        |      |z  z   }|j	                  |      |j	                  |      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.
    r^   )r1   r_   	unsqueezer   r2   )	qkrf   rg   ri   unsqueeze_dimr1   q_embedk_embeds	            r+   apply_rotary_pos_embr      s    ( GGE		A		A
--
&C
--
&C3w;q>C/0G3w;q>C/0G::E:"GJJUJ$;;;r,   c                   >    e Zd ZdZddede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ej                     ee
ej                        f   fdZ xZS )CohereAttentionz=Multi-headed attention from 'Attention Is All You Need' paperrC   	layer_idxc                 h   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                        | _        |j(                  | _        | j(                  ret+        |j                  | j                  f|j,                        | _        t+        |j                  | j                  f|j,                        | _        y y )Nr   g      Trt   r'   r(   )r   r    rC   r   getattrr'   num_attention_headsr   r   r   r   attention_dropout	is_causalr   rv   attention_biasq_projk_projv_projo_projuse_qk_normr   layer_norm_epsq_normk_normr&   rC   r   r*   s      r+   r    zCohereAttention.__init__   s   "
F4F4F&JdJd4de$*$>$>&B\B\$\!}}d*!'!9!9ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii : :T]] JQWQfQf
 ii&&68J8JQWQfQf
 "--)#77GVMbMbDK *#77GVMbMbDK r,   r7   position_embeddingsr   past_key_valuecache_positionr   r   c                    |j                   d d }g |d| j                  }| j                  |      j                  |      }	| j	                  |      j                  |      }
| j                  |      j                  |      }| j                  r"| j                  |	      }	| j                  |
      }
|	j                  dd      }	|
j                  dd      }
|j                  dd      }|\  }}t        |	|
||      \  }	}
|'|||d}|j                  |
|| j                  |      \  }
}t        }| j                  j                  dk7  rt         | j                  j                     } || |	|
||f| j"                  sdn| j$                  | j&                  d|\  }} |j(                  g |d j+                         }| j-                  |      }||fS )Nr.   r   r0   )rg   rf   r   eager        )r   r   )ra   r   r   viewr   r   r   r   r   rd   r   updater   r   rC   _attn_implementationr   r   r   r   r   r   r   )r&   r7   r   r   r   r   r   input_shapehidden_shapequery_statesr   r   rf   rg   cache_kwargsattention_interfacer   r   s                     r+   r:   zCohereAttention.forward   s    $))#2.88b8$--8{{=166|D[[/44\B
{{=166|D;;|4LZ0J#--a3))!Q/
#--a3&S#7jRUWZ#[ j%#&snUL'5'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$2H2HLL	%
 	%
!\ *k));;;;FFHkk+.L((r,   rn   )NN)r<   r=   r>   __doc__r   r   intr    r"   Tensortupler	   
LongTensorr   r   r:   r?   r@   s   @r+   r   r      s    G|  J +/591)||1) #5<<#=>1) !.	1)
 !1) !!1!121) -.1) 
u||Xell3XeELL>Q5RR	S1)r,   r   c                   d    e Zd Zdedef fdZ	 	 	 	 	 	 ddej                  deej                     deej                     dee
   dee   d	eej                     d
eeej                  ej                  f      dee   deej                  eeej                  ej                  f      f   fdZ xZS )CohereDecoderLayerrC   r   c                     t         |           |j                  | _        t        ||      | _        t        |      | _        t        |j                  |j                        | _	        y )N)rC   r   r   )
r   r    r'   r   	self_attnrq   mlpr   r   input_layernormr   s      r+   r    zCohereDecoderLayer.__init__  sR    !--()LV$.F<N<NU[UjUjkr,   r7   r   ri   r   	use_cacher   r   r   r   c                     |}	| j                  |      } | j                  d|||||||d|\  }
}| j                  |      }|	|
z   |z   }|S )a  
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            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`).
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence
            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
        )r7   r   ri   r   r   r   r    )r   r   r   )r&   r7   r   ri   r   r   r   r   r   residualhidden_states_attention_hidden_states_mlps                r+   r:   zCohereDecoderLayer.forward   s{    < !,,];%3T^^ 	&
')%)) 3	&
 	&
" !HH]3 #::=NNr,   )NNNFNN)r<   r=   r>   r   r   r    r"   r   r   r   r	   boolr   r   r   FloatTensorr:   r?   r@   s   @r+   r   r     s    l| l l 2637*.$)59KO.||. !.. u//0	.
 !. D>. !!1!12. &eELL%,,,F&GH. -.. 
u  (51B1BEDUDU1U+V"WW	X.r,   r   c                   J    e Zd ZU eed<   dZdZdgZdgZdZ	dZ
dZdZdZeedZy)CoherePreTrainedModelrC   modelTr   past_key_values)r7   
attentionsN)r<   r=   r>   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_supports_flash_attn_supports_sdpa_supports_flex_attn_can_compile_fullgraph_supports_attention_backendr   r   _can_record_outputsr   r,   r+   r   r   Q  sQ    &*#-.#4"5N!"&+%r,   r   c                       e Zd Zdef fdZee	 	 	 	 	 	 	 ddeej                     deej                     deej                     dee   deej                     deej                     d	ee   d
ee   defd              Z xZS )CohereModelrC   c           	         t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   )rC   F)r   r    pad_token_idpadding_idx
vocab_sizer   	Embeddingr'   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   normrB   
rotary_embgradient_checkpointing	post_initr   s      r+   r    zCohereModel.__init__f  s     !.. ++LL):):F<N<NPTP`P`ammDI&JbJbDcdy	2d
 $1C1C&J_J_`	/v>&+# 	 es   D	input_idsr   ri   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   )rV   )rC   input_embedsr   r   r   ri   )r   ri   r   r   r   )last_hidden_stater   )
ValueErrorr   r
   get_seq_lengthr"   arangera   rV   r   r   rC   r   r   r   r   r   )r&   r  r   ri   r   r  r   r   r   past_seen_tokensr   r7   r   decoder_layers                 r+   r:   zCohereModel.forwardv  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)*).-$7 M		 		-0&++
 	
r,   )NNNNNNN)r<   r=   r>   r   r    r   r   r   r"   r   r   r	   r   r   r   r   r   r:   r?   r@   s   @r+   r   r   d  s    |    151537+/5959$(8
E,,-8
 !.8
 u//0	8

 "%8
   1 128
 !!1!128
 D>8
 +,8
 
!8
  8
r,   r   c                       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eeej$                     f      deej$                     deej                     dee   dee   dee   deej                     deeej                  f   dee   defd              Z xZS )CohereForCausalLMzlm_head.weightlm_headcolwise_repr7   logitsc                 ,   t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        |j                  | _	        |j                  | _
        | j                          y rs   )r   r    r   r   r   r   rv   r'   r  logit_scaletie_word_embeddingsr  r|   s     r+   r    zCohereForCausalLM.__init__  sq      (
 ++yy!3!3V5F5FUS!--#)#=#=  	r,   c                     || _         y rn   r   )r&   decoders     r+   set_decoderzCohereForCausalLM.set_decoder  s	    
r,   c                     | j                   S rn   r  )r&   s    r+   get_decoderzCohereForCausalLM.get_decoder  s    zzr,   r  r   ri   r   r  labelsr   output_attentionsoutput_hidden_statesr   logits_to_keepr   r   c                    ||n| j                   j                  }|	|	n| j                   j                  }	 | j                  d||||||||	|
d	|}|j                  }t        |t              rt        | d      n|}| j                  |dd|ddf         }|| j                  z  }d}|* | j                  d||| j                   j                  d|}t        |||j                  |j                  |j                        S )az  
        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 AutoTokenizer, CohereForCausalLM

        >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
        >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")

        >> 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)	r  r   ri   r   r  r   r  r  r   )r  r  r   )lossr  r   r7   r   r   )rC   r  r  r   r  rL   r   slicer  r  loss_functionr   r   r   r7   r   )r&   r  r   ri   r   r  r  r   r  r  r   r  r   outputsr7   slice_indicesr  r  s                     r+   r:   zCohereForCausalLM.forward  s+   N 2C1N-TXT_T_TqTq$8$D $++JjJj 	
 ,64:: ,
)%+'/!5),
 ,
  118B>SV8W~ot4]kmA}a,?@A$***%4%%pVFt{{OeOepiopD%#33!//))
 	
r,   )NNNNNNNNNNr   )r<   r=   r>   _tied_weights_keys_tp_plan_pp_planr    r  r  r   r   r   r"   r   r   r   r	   listr   r   r   r   r   r   r:   r?   r@   s   @r+   r  r    s   *+=)H_-z:;H	  151537KO59-1$(,0/35934H
E,,-H
 !.H
 u//0	H

 "%tE4E4E/F(F"GHH
   1 12H
 ))*H
 D>H
 $D>H
 'tnH
 !!1!12H
 c5<</0H
 +,H
 
 H
  H
r,   r  )r  r   r   )r   )Nr   )7typingr   r   r   r"   r   activationsr   cache_utilsr	   r
   
generationr   masking_utilsr   modeling_flash_attention_utilsr   modeling_layersr   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   utils.genericr   configuration_coherer   Moduler   rB   rq   r   r   r   r_   r   r   r   r   r   r   r   r  __all__r   r,   r+   <module>r8     s  < - ,   ! . ) / B 9 O K F & I I / .-bii -"<BII <D		  	UU\\ 	U# 	U%,, 	U& %II%<<% 
% <<	%
 U\\*% % % '(%4<<T)bii T)n63 6r O  $ K
' K
 K
\ `
- `
 `
F Hr,   