
    rh?                        d Z ddlmZmZmZ ddl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 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(  e%       rddl)m*Z* ddl+m,Z,  e&jZ                  e.      Z/ G d dej`                        Z1d Z2d2dZ3 G d dej`                        Z4	 d3dej`                  dejj                  dejj                  dejj                  deejj                     d e6d!e6fd"Z7 G d# d$ej`                        Z8 G d% d&e      Z9e# G d' d(e             Z:e# G d) d*e:             Z; G d+ d,e:e      Z< G d- d.ee:      Z= G d/ d0ee:      Z>g d1Z?y)4zPyTorch Persimmon model.    )CallableOptionalUnionN)nn   )ACT2FN)CacheDynamicCache)GenerationMixin)AttentionMaskConverter)FlashAttentionKwargs) GenericForSequenceClassificationGenericForTokenClassificationGradientCheckpointingLayer)BaseModelOutputWithPastCausalLMOutputWithPast)ROPE_INIT_FUNCTIONSdynamic_rope_update)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)auto_docstringcan_return_tupleis_torch_flex_attn_availablelogging   )PersimmonConfig)	BlockMask)make_flex_block_causal_maskc                   ^     e Zd Zddef fdZ ej                         ed               Z xZ	S )PersimmonRotaryEmbeddingconfigc                    t         |           t        |d      rUt        |j                  t
              r;|j                  j                  d|j                  j                  d            | _        nd| _        |j                  | _	        |j                  | _
        || _        t        | j                     | _        | j                  | j                  |      \  }| _        | j                  d|d       | j                   | _        y )Nrope_scaling	rope_typetypedefaultinv_freqF)
persistent)super__init__hasattr
isinstancer$   dictgetr%   max_position_embeddingsmax_seq_len_cachedoriginal_max_seq_lenr"   r   rope_init_fnattention_scalingregister_bufferr(   original_inv_freq)selfr"   devicer(   	__class__s       /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/persimmon/modeling_persimmon.pyr+   z!PersimmonRotaryEmbedding.__init__<   s    6>*z&:M:Mt/T#0044[&BUBUBYBYZ`BabDN&DN"("@"@$*$B$B!/?+/+<+<T[[&+Q($(ZeD!%    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   r   mpscpuF)device_typeenabled   dim)dtype)r(   floatexpandshapetor8   r-   r&   strtorchautocast	transposecatcosr4   sinrE   )
r7   xposition_idsinv_freq_expandedposition_ids_expandedr@   freqsembrO   rP   s
             r:   forwardz PersimmonRotaryEmbedding.forwardM   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)
__name__
__module____qualname__r   r+   rK   no_gradr   rW   __classcell__r9   s   @r:   r!   r!   ;   s3    / /" U]]_<  <r;   r!   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..Nr=   rB   rC   )rH   rK   rN   )rQ   x1x2s      r:   rotate_halfrb   ^   sZ    	
3"!''"+"""	#B	
3q ""	#B99rc2YB''r;   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.
    )	unsqueezerb   )qkrO   rP   rR   unsqueeze_dimq_embedk_embeds           r:   apply_rotary_pos_embrj   f   sY    ( --
&C
--
&C3w;q>C/0G3w;q>C/0GGr;   c                   $     e Zd Z fdZd Z xZS )PersimmonMLPc                    t         |           t        j                  |j                  |j
                        | _        t        j                  |j
                  |j                        | _        t        |j                     | _
        y rX   )r*   r+   r   Linearhidden_sizeintermediate_sizedense_h_to_4hdense_4h_to_hr   
hidden_actactr7   r"   r9   s     r:   r+   zPersimmonMLP.__init__   s^    YYv'9'96;S;STYYv'?'?ASAST&++,r;   c                 l    | j                  |      }| j                  |      }| j                  |      }|S rX   )rq   rt   rr   )r7   hidden_statess     r:   rW   zPersimmonMLP.forward   s6    **=9/**=9r;   )rY   rZ   r[   r+   rW   r]   r^   s   @r:   rl   rl      s    -r;   rl   modulequerykeyvalueattention_maskscalingdropoutc                    t        j                  ||j                  dd            |z  }|#|d d d d d d d |j                  d   f   }	||	z   }t        j
                  j                  |dt         j                        j                  |j                        }t        j
                  j                  ||| j                        }t        j                  ||      }
|
j                  dd      j                         }
|
|fS )NrB   r   r=   )rD   rE   )ptrainingr   )rK   matmulrM   rH   r   
functionalsoftmaxfloat32rI   rE   r~   r   
contiguous)rx   ry   rz   r{   r|   r}   r~   kwargsattn_weightscausal_maskattn_outputs              r:   eager_attention_forwardr      s     <<s}}Q':;gEL!$Q1o		"o%=>#k1==((2U]](SVVW\WbWbcL==((6??([L,,|U3K''1-88:K$$r;   c                       e Zd ZdZddedee   f fdZdej                  de
ej                  ej                  ej                  f   fdZ	 	 	 	 	 	 	 ddej                  d	eej                     d
eej                     dee   dededeej                     dee
ej                  ej                  f      dee   de
ej                  eej                     ee
ej                        f   fdZ xZS )PersimmonAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr"   	layer_idxc                    t         |           || _        || _        |-t        j                  d| j                  j                   d       |j                  | _        |j                  | _
        | j                  | j                  z  | _        |j                  | _        t        | j                  |j                  z        | _        d| _        | j                  | j                  z  | j                  k7  r&t#        d| j                   d| j                   d      t%        j&                  | j                  d| j                  z  d      | _        t%        j&                  | j                  | j                  z  | j                  d      | _        |j,                  | _        | j                  d	z  | _        | j,                  r|t%        j0                  |j                  | j                  z  |j2                  d
      | _        t%        j0                  |j                  | j                  z  |j2                  d
      | _        t%        j8                  |j:                        | _        t=        | j                        | _        y )NzInstantiating z without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.Tz?hidden_size must be divisible by num_heads (got `hidden_size`: z and `num_heads`: z).r   biasg      )epselementwise_affiner"   ) r*   r+   r"   r   loggerwarning_oncer9   rY   ro   num_attention_heads	num_headshead_dim
rope_thetaintpartial_rotary_factorrotary_ndims	is_causal
ValueErrorr   rn   query_key_valuedenseqk_layernormr}   	LayerNormlayer_norm_epsq_layernormk_layernormDropoutattention_dropoutr!   
rotary_embr7   r"   r   r9   s      r:   r+   zPersimmonAttention.__init__   s   " !8!8 9 :, , "--33((DNN: ++0L0L LMMMDNN*t/?/??QRVRbRbQc$T^^$4B8   "yy)9)91t?O?O;OVZ[YYt~~=t?O?OVZ[
"//}}d*!||""dnn4&:O:Odh D  "||""dnn4&:O:Odh D "$F,D,D!E2$++Fr;   	fused_qkvreturnc                     |j                   \  }}}|j                  ||| j                  d| j                        }|ddddf   |ddddf   |ddddf   fS )a  
        Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
        storage as `fused_qkv`

        Args:
            fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim]

        Returns:
            query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
            value: [batch_size, seq_length, num_heads, head_dim]
        r   .r   Nr   rB   )rH   viewr   r   )r7   r   
batch_size
seq_lengththree_times_hidden_sizes        r:   _split_headszPersimmonAttention._split_heads   sb     ;D//7
J 7NN:z4>>1dmm\	a#YsAqy%99S!QY;OOOr;   rw   r|   rR   past_key_valueoutput_attentions	use_cachecache_positionposition_embeddingsr   c	                    |j                         \  }
}}| j                  |      }| j                  |      \  }}}| j                  r"| j	                  |      }| j                  |      }|j                  dd      }|j                  dd      }|j                  dd      }|\  }}|dd | j                  f   |d| j                  d f   }}|dd | j                  f   |d| j                  d f   }}t        ||||      \  }}t        j                  ||fd      }t        j                  ||fd      }|2||| j                  |d}|j                  ||| j                  |      \  }}t        }| j                  j                  dk7  rt         | j                  j                     } || ||||f| j"                  sdn| j                  j$                  | j&                  d	|	\  }}|j)                  |
|d      }| j+                  |      }|sd }||fS )
Nr   rB   .r=   rC   )rP   rO   partial_rotation_sizer   eager        )r~   r}   )sizer   r   r   r   r   rM   r   rj   rK   rN   updater   r   r"   _attn_implementationr   r   r   r}   reshaper   )r7   rw   r|   rR   r   r   r   r   r   r   bszq_len_r   query_states
key_statesvalue_statesrO   rP   	query_rot
query_passkey_rotkey_passcache_kwargsattention_interfacer   r   s                              r:   rW   zPersimmonAttention.forward   sU    &**,UA ((7	 483D3DY3O0z<++L9L))*5J $--a3#--a3))!Q/
&S 1 1 1112d//112 	
 s/d////0sD--//0 
 2)Wc3O	7 yy)Z!8bAYY2;
% )-):):"0	L (6'<'<ZW[WeWegs't$J(?;;++w6"9$++:Z:Z"[$7	%
  $}}C$++2O2OLL	%
 	%
!\ "))#ub9jj- LL((r;   rX   NNNFFNN)rY   rZ   r[   __doc__r   r   r   r+   rK   Tensortupler   
LongTensorr	   boolr   r   rW   r]   r^   s   @r:   r   r      sJ   G$G $G8C= $GLPell PuU\\5<<Y^YeYe=e7f P& 2637*."'59KON)||N) !.N) u//0	N)
 !N)  N) N) !!1!12N) &eELL%,,,F&GHN) -.N) 
u||Xell3XeELL>Q5RR	SN)r;   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
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 )PersimmonDecoderLayerr"   r   c                    t         |           |j                  | _        t        ||      | _        t        |      | _        t        j                  |j                  |j                        | _
        t        j                  |j                  |j                        | _        t        j                  |j                        | _        y )N)r"   r   r   )r*   r+   ro   r   	self_attnrl   mlpr   r   r   input_layernormpost_attention_layernormr   hidden_dropoutr~   r   s      r:   r+   zPersimmonDecoderLayer.__init__2  s    !--+6YO'!||F,>,>FDYDYZ(*V5G5GVMbMb(c%zz&"7"78r;   rw   r|   rR   r   r   r   r   r   r   r   c	                     |}
| j                  |      } | j                  d||||||||d|	\  }}|
|z   }|}
| j                  |      }| j                  |      }| j	                  |      }||
z   }|f}|r||f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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            position_ids (`torch.LongTensor` of shape `({0})`, *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_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.
        )rw   r|   rR   r   r   r   r   r    )r   r   r   r   r~   )r7   rw   r|   rR   r   r   r   r   r   r   residualself_attn_weightsoutputss                r:   rW   zPersimmonDecoderLayer.forward;  s    H !,,]; ,:4>> 
,
')%)/) 3
,
 
,
(( !=0 !55mD/]3%0 ")++Gr;   r   )rY   rZ   r[   r   r   r+   rK   r   r   r   r   r   r   r   FloatTensorrW   r]   r^   s   @r:   r   r   1  s    9 93 9 26378<,1$)59KOC||C !.C u//0	C
 !u||!45C $D>C D>C !!1!12C &eELL%,,,F&GHC -.C 
u  (51B1BEDUDU1U+V"WW	XCr;   r   c                   @    e Zd ZU eed<   dZdZdgZdZdZ	dZ
dZdZd Zy)PersimmonPreTrainedModelr"   modelTr   past_key_valuesc                    | j                   j                  }t        |t        j                        rY|j
                  j                  j                  d|       |j                  %|j                  j                  j                          y y t        |t        j                        rf|j
                  j                  j                  d|       |j                  2|j
                  j                  |j                     j                          y y t        |t        j                        rJ|j
                  j                  j                  d       |j                  j                  j                          y y )Nr   )meanstdg      ?)r"   initializer_ranger-   r   rn   weightdatanormal_r   zero_	Embeddingpadding_idxr   fill_)r7   rx   r   s      r:   _init_weightsz&PersimmonPreTrainedModel._init_weights  s    kk++fbii(MM&&CS&9{{&  &&( '-MM&&CS&9!!-""6#5#56<<> .-MM$$S)KK""$ .r;   N)rY   rZ   r[   r   __annotations__base_model_prefixsupports_gradient_checkpointing_no_split_modules_skip_keys_device_placement_can_compile_fullgraph_supports_sdpa_supports_flash_attn_supports_attention_backendr   r   r;   r:   r   r     s?    &*#01"3!N"&%r;   r   c                       e Zd 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   d
ee   dee   dee	j                     dee   defd              Z	 ddee	j                  df   de	j                  de	j                  ded
ef
dZede	j                  dedede	j.                  de	j                  defd       Z xZS )PersimmonModelz
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`]

    Args:
        config: PersimmonConfig
    r"   c           	          t         |   |       |j                  | _        |j                  | _        t        j                  |j                  |j                  | j                        | _        t        j                  t        |j                        D cg c]  }t        ||       c}      | _        t        j                  |j                  |j                        | _        t#        |      | _        d| _        | j)                          y c c}w )Nr   r   F)r*   r+   pad_token_idr   
vocab_sizer   r   ro   embed_tokens
ModuleListrangenum_hidden_layersr   layersr   r   final_layernormr!   r   gradient_checkpointing	post_initr   s      r:   r+   zPersimmonModel.__init__  s     !.. ++LL):):F<N<NPTP`P`ammGLVMeMeGfg)"695g
  "||F,>,>FDYDYZ2&A&+# hs   D	input_idsr|   rR   r   inputs_embedsr   r   output_hidden_statesr   r   r   c
                    ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }|d u |d uz  rt	        d      | j
                  r%| j                  r|rt        j                  d       d}t        |t        d       t        f      st	        d      |r|
t               }|| j                  |      }|	F||j                         nd}t        j                   |||j"                  d   z   |j$                        }	||	j'                  d      }| j)                  |||	||      }|}| j+                  ||      }|rdnd }|rdnd }| j,                  D ],  }|r||fz  } ||f||||||	|d	|
}|d   }|s$||d   fz  }. | j/                  |      }|r||fz  }t1        ||||
      S )Nz:You must specify exactly one of input_ids or inputs_embedszZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...FzBThe `past_key_values` should be either a `Cache` object or `None`.r   r   r8   r   )r|   rR   r   r   r   r   r   )last_hidden_stater   rw   
attentions)r"   r   r
  r   r   r  r   r   r   r-   r&   r	   r
   r   get_seq_lengthrK   arangerH   r8   rd   _update_causal_maskr   r  r  r   )r7   r  r|   rR   r   r	  r   r   r
  r   r   past_seen_tokensr   rw   r   all_hidden_statesall_self_attnsdecoder_layerlayer_outputss                      r:   rW   zPersimmonModel.forward  sA    2C1N-TXT_T_TqTq$8$D $++JjJj 	 "+!6IDKK<Q<Q	-t";<YZZ&&4==##p "	 /DJ+>?abb0*nO  --i8M!CRC^==?de"\\ "2]5H5H5K"KTaThThN )33A6L..M>?L]
 & #oom\J #7BD0d![[ 	6M#!m%55!)
*)."3#-$7
 
M *!,M =#3"55'	6* ,,];  -!11&+++%	
 	
r;   r   input_tensorc           	         | j                   j                  dk(  r||dk(  j                         r|S y | j                   j                  dk(  r't        |t        j
                        rt        |      }|S ||j                         nd}||j                  nd}| j                   j                  dk(  r(|s&|s$t        j                  |||| j                        ry |j                  }|j                  d   }	|r|j                         }
n1t        |t        j
                        r|j                  d	   n||	z   dz   }
| j                  ||	|
|||j                  d   
      }| j                   j                  dk(  rQ|O|j                   j"                  dv r7|s5t	        j$                  |      j&                  }t        j(                  ||      }|S )Nflash_attention_2r   flex_attentionr   Fsdpa)r	  past_key_values_lengthis_trainingr   r=   )sequence_lengthtarget_lengthrE   r   r   )cudaxpunpu)r"   r   anyr-   rK   r   r   r  is_compileabler   _ignore_causal_mask_sdpar   rE   rH   get_max_cache_shape5_prepare_4d_causal_attention_mask_with_cache_positionr8   r&   finfomin_unmask_unattended)r7   r|   r  r   r   r   r  using_compilable_cacherE   r  r  r   	min_dtypes                r:   r  z"PersimmonModel._update_causal_mask  s    ;;++/BB)~/D.I.I.K%%;;++/??.%,,7!<^!L!!
 @O?Z?99;`aCRC^!?!?di ;;++v5>T]n%>>*'7 MM	 ""&,,Q/!+??AM nell; $$R(%7!;  PP+')#))!, Q 
 KK,,6*%%**.DD%
 E*..I0CCKQZ[Kr;   r  r  rE   r   c                    | | j                         dk(  r| }|S t        j                  |      j                  }t        j                  ||f|||j
                        }|dk7  rt        j                  |d      }|t        j                  ||j
                        |j                  dd      kD  z  }|ddddddf   j                  |ddd      }| |j                         }| j                  d   }	|ddddddd|	f   | ddddddf   j                  |j
                        z   }
|
dk(  }
|ddddddd|	f   j                  |
|      |ddddddd|	f<   |S )	aM  
        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.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
        N   )
fill_valuerE   r8   r   )diagonalr  r=   r   )rD   rK   r(  r)  fullr8   triur  r   rG   clonerH   rI   masked_fill)r|   r  r  rE   r   r   r   r   r,  mask_lengthpadding_masks              r:   r'  zDPersimmonModel._prepare_4d_causal_attention_mask_with_cache_position\  s   > %.*<*<*>!*C(K* ' E*..I** -0Ye\j\q\qK !##jjqA5<<n>S>STWeWmWmnprsWtttK%dD!Q&67>>z1bRTUK))//1,2226*1aL[L+@ANSTVZ\`bcScDdDgDg&&E    ,q05@Aq,;,AV5W5c5c )6Aq!\k\12 r;   )	NNNNNNNNN)F)rY   rZ   r[   r   r   r+   r   r   r   rK   r   r   r	   r   r   r   r   r   rW   r   r  staticmethodr   rE   r'  r]   r^   s   @r:   r   r     s    "  151537+/59$(,0/359\
E,,-\
 !.\
 u//0	\

 "%\
   1 12\
 D>\
 $D>\
 'tn\
 !!1!12\
 -.\
 
!\
  \
J #(BellK78B llB 	B
 B  BH 444 4 {{	4
 4 4 4r;   r   c                   f    e Zd Zdg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   de	e   de	e
j                     deee
j                  f   defd              Z xZS )PersimmonForCausalLMzlm_head.weightc                     t         |   |       t        |      | _        |j                  | _        t        j                  |j                  |j                  d      | _        | j                          y )NFr   )
r*   r+   r   r   r   r   rn   ro   lm_headr  ru   s     r:   r+   zPersimmonForCausalLM.__init__  sU     #F+
 ++yy!3!3V5F5FUS 	r;   c                     || _         y rX   r   )r7   decoders     r:   set_decoderz PersimmonForCausalLM.set_decoder  s	    
r;   c                     | j                   S rX   r=  )r7   s    r:   get_decoderz PersimmonForCausalLM.get_decoder  s    zzr;   r  r|   rR   r   r	  labelsr   r   r
  r   logits_to_keepr   c                    ||n| j                   j                  }|	|	n| j                   j                  }	 | j                  d||||||||	|
d	|}|j                  }t        |t              rt        | d      n|}| j                  |dd|ddf         }d}|* | j                  ||fd| j                   j                  i|}t        |||j                  |j                  |j                        S )uk  
        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, PersimmonForCausalLM

        >>> model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-base")
        >>> tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-base")

        >>> prompt = "human: Hey, what should I eat for dinner?"
        >>> 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]
        'human: Hey, what should I eat for dinner?\n\ncat: 🐱\n\nhuman: 😐\n\n'
        ```N)	r  r|   rR   r   r	  r   r   r
  r   r   )losslogitsr   rw   r  r   )r"   r   r
  r   r  r-   r   slicer;  loss_functionr   r   r   rw   r  )r7   r  r|   rR   r   r	  rB  r   r   r
  r   rC  r   r   rw   slice_indicesrF  rE  s                     r:   rW   zPersimmonForCausalLM.forward  s*   P 2C1N-TXT_T_TqTq$8$D $++JjJj 	
 ,64:: ,
)%+'/!5),
 ,
  118B>SV8W~ot4]kmA}a,?@A%4%%  ;;11 	D &#33!//))
 	
r;   )NNNNNNNNNNr   )rY   rZ   r[   _tied_weights_keysr+   r?  rA  r   r   r   rK   r   r   r	   r   r   r   r   r   rW   r]   r^   s   @r:   r9  r9    s<   *+  151537+/59-1$(,0/35934M
E,,-M
 !.M
 u//0	M

 "%M
   1 12M
 ))*M
 D>M
 $D>M
 'tnM
 !!1!12M
 c5<</0M
 
 M
  M
r;   r9  c                       e Zd Zy)"PersimmonForSequenceClassificationNrY   rZ   r[   r   r;   r:   rL  rL        r;   rL  c                       e Zd Zy)PersimmonForTokenClassificationNrM  r   r;   r:   rP  rP    rN  r;   rP  )r9  r   r   rL  rP  )Nr   )r   )@r   typingr   r   r   rK   torch.utils.checkpointr   activationsr   cache_utilsr	   r
   
generationr   modeling_attn_mask_utilsr   modeling_flash_attention_utilsr   modeling_layersr   r   r   modeling_outputsr   r   modeling_rope_utilsr   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   configuration_persimmonr   !torch.nn.attention.flex_attentionr   integrations.flex_attentionr   
get_loggerrY   r   Moduler!   rb   rj   rl   r   rF   r   r   r   r   r   r9  rL  rP  __all__r   r;   r:   <module>rd     s  (  , ,    ! . ) > B 
 L F & \ \ 4  !;J 
		H	%<ryy <F(8299 * %II%<<% 
% <<	%
 U\\*% % %.G) G)TM6 M` % % %6 t- t tnd
3_ d
N j)IKc i d&CE] cr;   