§
    ‚ŠtjqQ  ã                   óD  — 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 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$Z$ ddl%m&Z&m'Z'm(Z( ddl)m*Z* ddl+m,Z,  G d„ dej-        ¦  «        Z.d„ Z/ ed¦  «        d8d„¦   «         Z0dej1        de2dej1        fd„Z3	 d9dej-        dej1        d ej1        d!ej1        d"ej1        dz  d#e4d$e4d%e!e#         fd&„Z5 ee0¦  «         G d'„ d(ej-        ¦  «        ¦   «         Z6 G d)„ d*ej-        ¦  «        Z7 G d+„ d,e¦  «        Z8e$ G d-„ d.e¦  «        ¦   «         Z9e$ G d/„ d0e9¦  «        ¦   «         Z:e$ G d1„ d2e9e¦  «        ¦   «         Z; G d3„ d4ee9¦  «        Z< G d5„ d6ee9¦  «        Z=g d7¢Z>dS ):é    )ÚCallable)ÚOptionalNé   )ÚACT2FN)ÚCacheÚDynamicCache)ÚGenerationMixin)Úuse_kernel_func_from_hubÚuse_kernelized_func)Úcreate_causal_mask)Ú GenericForSequenceClassificationÚGenericForTokenClassificationÚGradientCheckpointingLayer)ÚBaseModelOutputWithPastÚCausalLMOutputWithPast)ÚROPE_INIT_FUNCTIONSÚdynamic_rope_update)ÚALL_ATTENTION_FUNCTIONSÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstring)Úcan_return_tupleÚmaybe_autocastÚmerge_with_config_defaults)Úcapture_outputsé   )Ú	PhiConfigc                   óÔ   ‡ — e Zd ZU ej        ed<   ddefˆ 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ˆ xZS )ÚPhiRotaryEmbeddingÚinv_freqNÚconfigc                 ó²  •— t          ¦   «                              ¦   «          |j        | _        |j        | _        || _        | j        j        d         | _        | j        }| j        dk    rt          | j                 } || j        |¦  «        \  }| _
        |                      d|d¬¦  «         |                      d|                     ¦   «         d¬¦  «         d S )NÚ	rope_typeÚdefaultr!   F)Ú
persistentÚoriginal_inv_freq)ÚsuperÚ__init__Úmax_position_embeddingsÚmax_seq_len_cachedÚoriginal_max_seq_lenr"   Úrope_parametersr$   Úcompute_default_rope_parametersr   Úattention_scalingÚregister_bufferÚclone)Úselfr"   ÚdeviceÚrope_init_fnr!   Ú	__class__s        €úb/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/phi/modeling_phi.pyr)   zPhiRotaryEmbedding.__init__$   sÊ   ø€ Ý‰Œ×ÒÑÔÐØ"(Ô"@ˆÔØ$*Ô$BˆÔ!àˆŒàœÔ4°[ÔAˆŒØ!%Ô!EˆØŒ>˜YÒ&Ð&Ý.¨t¬~Ô>ˆLØ+7¨<¸¼ÀVÑ+LÔ+LÑ(ˆ�$Ô(à×Ò˜Z¨¸eÐÑDÔDÐDØ×ÒÐ0°(·.².Ñ2BÔ2BÈuÐÑUÔUÐUÐUÐUó    r3   ztorch.deviceÚseq_lenÚreturnztorch.Tensorc                 óV  — | j         d         }| j                              dd¦  «        }t          | dd¦  «        p| j        | j        z  }t          ||z  ¦  «        }d}d|t          j        d|dt          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).
        Ú
rope_thetaÚpartial_rotary_factorg      ð?Úhead_dimNr   é   ©Údtype)r3   r@   )r-   ÚgetÚgetattrÚhidden_sizeÚnum_attention_headsÚintÚtorchÚarangeÚint64ÚtoÚfloat)	r"   r3   r8   Úbaser<   r=   ÚdimÚattention_factorr!   s	            r6   r.   z2PhiRotaryEmbedding.compute_default_rope_parameters4   sº   € ð& Ô% lÔ3ˆØ &Ô 6× :Ò :Ð;RÐTWÑ XÔ XÐÝ˜6 :¨tÑ4Ô4Ðh¸Ô8JÈfÔNhÑ8hˆÝ�(Ð2Ñ2Ñ3Ô3ˆàÐð Ø•U”\ ! S¨!µ5´;Ð?Ñ?Ô?×BÒBÈ&ÕX]ÔXcÐBÑdÔdÐgjÑjÑkñ
ˆð Ð)Ð)Ð)r7   c                 óN  — | j         d d d …d f                              ¦   «                              |j        d         dd¦  «                             |j        ¦  «        }|d d …d d d …f                              ¦   «         }t          |j        j        t          ¦  «        r|j        j        dk    r|j        j        nd}t          |d¬¦  «        5  |                     ¦   «         |                     ¦   «         z   
                    dd¦  «        }t          j        ||fd¬	¦  «        }|                     ¦   «         | j        z  }|                     ¦   «         | j        z  }	d d d ¦  «         n# 1 swxY w Y   |                     |j        ¬
¦  «        |	                     |j        ¬
¦  «        fS )Nr   éÿÿÿÿr   ÚmpsÚcpuF)Údevice_typeÚenabledr>   ©rL   r?   )r!   rJ   ÚexpandÚshaperI   r3   Ú
isinstanceÚtypeÚstrr   Ú	transposerF   ÚcatÚcosr/   Úsinr@   )
r2   ÚxÚposition_idsÚinv_freq_expandedÚposition_ids_expandedrR   ÚfreqsÚembr\   r]   s
             r6   ÚforwardzPhiRotaryEmbedding.forwardT   s·  € ð !œM¨$°°°°4¨-Ô8×>Ò>Ñ@Ô@×GÒGÈÔHZÐ[\ÔH]Ð_aÐcdÑeÔe×hÒhÐijÔiqÑrÔrÐØ ,¨Q¨Q¨Q°°a°a°a¨ZÔ 8× >Ò >Ñ @Ô @Ðå'1°!´(´-ÅÑ'EÔ'EÐkÈ!Ì(Ì-Ð[`ÒJ`ÐJ`�a”h”m�mÐfkˆÝ¨¸UÐCÑCÔCð 	5ð 	5Ø&×,Ò,Ñ.Ô.Ð1F×1LÒ1LÑ1NÔ1NÑN×YÒYÐZ[Ð]^Ñ_Ô_ˆEÝ”)˜U E˜N°Ð3Ñ3Ô3ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCØ—'’'‘)”)˜dÔ4Ñ4ˆCð		5ð 	5ð 	5ñ 	5ô 	5ð 	5ð 	5ð 	5ð 	5ð 	5ð 	5øøøð 	5ð 	5ð 	5ð 	5ð �vŠv˜AœGˆvÑ$Ô$ c§f¢f°1´7 fÑ&;Ô&;Ð;Ð;s   ÃBE&Å&E*Å-E*©N)NNN)Ú__name__Ú
__module__Ú__qualname__rF   ÚTensorÚ__annotations__r   r)   Ústaticmethodr   rE   ÚtuplerJ   r.   Úno_gradr   rd   Ú__classcell__©r5   s   @r6   r    r    !   sù   ø€ € € € € € ØŒlÐÐÑðVð V˜yð Vð Vð Vð Vð Vð Vð  à#'Ø+/Ø"ð*ð *Ø˜DÑ ð*à˜Ô(ð*ð �t‘ð*ð 
ˆ~˜uÐ$Ô	%ð	*ð *ð *ñ „\ð*ð> €U„]�_„_Øð<ð <ñ Ôñ „_ð<ð <ð <ð <ð <r7   r    c                 óœ   — | 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..NrO   r>   rT   )rV   rF   r[   )r^   Úx1Úx2s      r6   Úrotate_halfrs   d   s]   € à	
ˆ3Ð"�!”'˜"”+ Ñ"Ð"Ð"Ô	#€BØ	
ˆ3�”˜”˜qÑ Ð"Ð"Ð"Ô	#€BÝŒ9�r�c˜2�Y BÐ'Ñ'Ô'Ð'r7   Úrotary_pos_embc                 ó¾   — |                      |¦  «        }|                      |¦  «        }| |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.
    )Ú	unsqueezers   )ÚqÚkr\   r]   Úunsqueeze_dimÚq_embedÚk_embeds          r6   Úapply_rotary_pos_embr|   k   sc   € ð& �-Š-˜Ñ
&Ô
&€CØ
�-Š-˜Ñ
&Ô
&€CØ�3‰w�; q™>œ>¨CÑ/Ñ0€GØ�3‰w�; q™>œ>¨CÑ/Ñ0€GØ�GÐÐr7   Úhidden_statesÚn_repr9   c                 ó¸   — | j         \  }}}}|dk    r| S | dd…dd…ddd…dd…f                              |||||¦  «        } |                      |||z  ||¦  «        S )zÔ
    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)rV   rU   Úreshape)r}   r~   ÚbatchÚnum_key_value_headsÚslenr=   s         r6   Ú	repeat_kvr„   …   s„   € ð
 2?Ô1DÑ.€EÐ  hØ�‚z€zØÐØ! ! ! ! Q Q Q¨¨a¨a¨a°°°Ð"2Ô3×:Ò:¸5ÐBUÐW\Ð^bÐdlÑmÔm€MØ× Ò  Ð(;¸eÑ(CÀTÈ8ÑTÔTÐTr7   ç        ÚmoduleÚqueryÚkeyÚvalueÚattention_maskÚscalingÚdropoutÚkwargsc                 ó  — t          || j        ¦  «        }t          || j        ¦  «        }	t          j        ||                     dd¦  «        ¦  «        |z  }
|�|
|z   }
t
          j                             |
dt          j        ¬¦  «         	                    |j
        ¦  «        }
t
          j                             |
|| j        ¬¦  «        }
t          j        |
|	¦  «        }|                     dd¦  «                             ¦   «         }||
fS )Nr>   r   rO   )rL   r@   )ÚpÚtrainingr   )r„   Únum_key_value_groupsrF   ÚmatmulrZ   ÚnnÚ
functionalÚsoftmaxÚfloat32rI   r@   rŒ   r�   Ú
contiguous)r†   r‡   rˆ   r‰   rŠ   r‹   rŒ   r�   Ú
key_statesÚvalue_statesÚattn_weightsÚattn_outputs               r6   Úeager_attention_forwardrœ   ‘   sé   € õ ˜3 Ô ;Ñ<Ô<€JÝ˜U FÔ$?Ñ@Ô@€Lå”<  z×';Ò';¸A¸qÑ'AÔ'AÑBÔBÀWÑL€LØÐ!Ø# nÑ4ˆå”=×(Ò(¨¸2ÅUÄ]Ð(ÑSÔS×VÒVÐW\ÔWbÑcÔc€LÝ”=×(Ò(¨¸È6Ì?Ð(Ñ[Ô[€LÝ”,˜|¨\Ñ:Ô:€KØ×'Ò'¨¨1Ñ-Ô-×8Ò8Ñ:Ô:€Kà˜Ð$Ð$r7   c                   óº   ‡ — e Zd ZdZdedefˆ 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j        ej        dz  f         f
d„Zˆ xZS )ÚPhiAttentionz=Multi-headed attention from 'Attention Is All You Need' paperr"   Ú	layer_idxc                 ó¶  •— t          ¦   «                              ¦   «          || _        || _        t	          |d|j        |j        z  ¦  «        | _        |j        |j        z  | _	        | j        dz  | _
        |j        | _        d| _        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¬¦  «        | _        t'          | j        |j        d         z  ¦  «        | _        |j        | _        | j        r^t          j        |j        |j        z  |j        d¬¦  «        | _        t          j        |j        |j        z  |j        d¬¦  «        | _        d S d S )Nr=   g      à¿T©Úbiasr<   )ÚepsÚelementwise_affine)r(   r)   r"   rŸ   rB   rC   rD   r=   r‚   r‘   r‹   Úattention_dropoutÚ	is_causalr“   ÚLinearÚq_projÚk_projÚv_projÚdenserE   r-   Úrotary_ndimsÚqk_layernormÚ	LayerNormÚlayer_norm_epsÚq_layernormÚk_layernorm©r2   r"   rŸ   r5   s      €r6   r)   zPhiAttention.__init__®   s¼  ø€ Ý‰Œ×ÒÑÔÐØˆŒØ"ˆŒÝ ¨
°FÔ4FÈ&ÔJdÑ4dÑeÔeˆŒØ$*Ô$>À&ÔB\Ñ$\ˆÔ!Ø”} dÑ*ˆŒØ!'Ô!9ˆÔØˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐeiÐjÑjÔjˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐeiÐjÑjÔjˆŒÝ”i Ô 2°FÔ4NÐQUÔQ^Ñ4^ÐeiÐjÑjÔjˆŒÝ”Y˜vÔ9¸D¼MÑIÈ6ÔK]ÐdhÐiÑiÔiˆŒ
Ý ¤°Ô0FÐG^Ô0_Ñ _Ñ`Ô`ˆÔØ"Ô/ˆÔØÔð 	Ý!œ|ØÔ" fÔ&@Ñ@ÀfÔF[Ðptð ñ  ô  ˆDÔõ  "œ|ØÔ" fÔ&@Ñ@ÀfÔF[Ðptð ñ  ô  ˆDÔÐÐð		ð 	r7   Nr}   Úposition_embeddingsrŠ   Úpast_key_valuesr9   c                 ól  — |j         d d…         }g |¢d‘| j        ‘R }|                      |¦  «                             |¦  «                             dd¦  «        }|                      |¦  «                             |¦  «                             dd¦  «        }	|                      |¦  «                             |¦  «                             dd¦  «        }
| j        r*|                      |¦  «        }|  	                    |	¦  «        }	|\  }}|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¬¦  «        }	|�|                     |	|
| j        ¦  «        \  }	}
t!          j        | j        j        t(          ¦  «        } || ||	|
|f| j        sdn| j        | j        dœ|¤Ž\  }} |j        g |¢d‘R Ž                      ¦   «         }|                      |¦  «        }||fS )NrO   r   r>   .rT   r…   )rŒ   r‹   )rV   r=   r¨   ÚviewrZ   r©   rª   r­   r°   r±   r¬   r|   rF   r[   ÚupdaterŸ   r   Úget_interfacer"   Ú_attn_implementationrœ   r�   r¥   r‹   r€   r—   r«   )r2   r}   r³   rŠ   r´   r�   Úinput_shapeÚhidden_shapeÚquery_statesr˜   r™   r\   r]   Ú	query_rotÚ
query_passÚkey_rotÚkey_passÚattention_interfacer›   rš   s                       r6   rd   zPhiAttention.forwardÅ   s�  € ð $Ô)¨#¨2¨#Ô.ˆØ8˜Ð8 bÐ8¨$¬-Ð8Ð8ˆà—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆØ—[’[ Ñ/Ô/×4Ò4°\ÑBÔB×LÒLÈQÐPQÑRÔRˆ
Ø—{’{ =Ñ1Ô1×6Ò6°|ÑDÔD×NÒNÈqÐRSÑTÔTˆàÔð 	6Ø×+Ò+¨LÑ9Ô9ˆLØ×)Ò)¨*Ñ5Ô5ˆJà&‰ˆˆSð ˜Ð1 Ô 1Ð1Ð1Ô2Ø˜˜dÔ/Ð1Ð1Ð1Ô2ð ˆ	ð
 �sÐ/˜dÔ/Ð/Ð/Ô0Ø�s˜DÔ-Ð/Ð/Ð/Ô0ð ˆõ
 2°)¸WÀcÈ3ÑOÔOÑˆ	�7õ ”y )¨ZÐ!8¸bÐAÑAÔAˆÝ”Y ¨Ð2¸Ð;Ñ;Ô;ˆ
àÐ&Ø'6×'=Ò'=¸jÈ,ÐX\ÔXfÑ'gÔ'gÑ$ˆJ˜å(?Ô(MØŒKÔ,Õ.Eñ)
ô )
Ðð %8Ð$7ØØØØØð	%
ð  $œ}ÐH�C�C°$Ô2HØ”Lð	%
ð 	%
ð ð	%
ð 	%
Ñ!ˆ�\ð *�kÔ)Ð;¨;Ð;¸Ð;Ð;Ð;×FÒFÑHÔHˆØ—j’j Ñ-Ô-ˆØ˜LÐ(Ð(r7   re   )rf   rg   rh   Ú__doc__r   rE   r)   rF   ri   rl   r   rd   rn   ro   s   @r6   rž   rž   ª   sÍ   ø€ € € € € àGÐGð˜yð °Sð ð ð ð ð ð ð8 )-ð8)ð 8)à”|ð8)ð # 5¤<°´Ð#=Ô>ð8)ð œ tÑ+ð	8)ð
  ™ð8)ð 
ˆuŒ|˜Uœ\¨DÑ0Ð0Ô	1ð8)ð 8)ð 8)ð 8)ð 8)ð 8)ð 8)ð 8)r7   rž   c                   óB   ‡ — e Zd Zˆ fd„Zdej        dej        fd„Zˆ xZS )ÚPhiMLPc                 ó  •— t          ¦   «                              ¦   «          || _        t          |j                 | _        t          j        |j        |j	        ¦  «        | _
        t          j        |j	        |j        ¦  «        | _        d S re   )r(   r)   r"   r   Ú
hidden_actÚactivation_fnr“   r§   rC   Úintermediate_sizeÚfc1Úfc2©r2   r"   r5   s     €r6   r)   zPhiMLP.__init__  sf   ø€ Ý‰Œ×ÒÑÔÐØˆŒÝ# FÔ$5Ô6ˆÔÝ”9˜VÔ/°Ô1IÑJÔJˆŒÝ”9˜VÔ5°vÔ7IÑJÔJˆŒˆˆr7   r}   r9   c                 ó„   — |                       |¦  «        }|                      |¦  «        }|                      |¦  «        }|S re   )rÉ   rÇ   rÊ   )r2   r}   s     r6   rd   zPhiMLP.forward  s=   € ØŸš Ñ/Ô/ˆØ×*Ò*¨=Ñ9Ô9ˆØŸš Ñ/Ô/ˆØÐr7   )rf   rg   rh   r)   rF   ri   rd   rn   ro   s   @r6   rÄ   rÄ      sc   ø€ € € € € ðKð Kð Kð Kð Kð U¤\ð °e´lð ð ð ð ð ð ð ð r7   rÄ   c                   óÒ   ‡ — e Zd Zdedefˆ 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ˆ xZS )ÚPhiDecoderLayerr"   rŸ   c                 ó"  •— t          ¦   «                              ¦   «          t          ||¬¦  «        | _        t	          |¦  «        | _        t          j        |j        |j	        ¬¦  «        | _
        t          j        |j        ¦  «        | _        d S )N)rŸ   ©r£   )r(   r)   rž   Ú	self_attnrÄ   Úmlpr“   r®   rC   r¯   Úinput_layernormÚDropoutÚresid_pdropÚresid_dropoutr²   s      €r6   r)   zPhiDecoderLayer.__init__  sr   ø€ Ý‰Œ×ÒÑÔÐÝ% f¸	ÐBÑBÔBˆŒÝ˜&‘>”>ˆŒÝ!œ|¨FÔ,>ÀFÔDYÐZÑZÔZˆÔÝœZ¨Ô(:Ñ;Ô;ˆÔÐÐr7   NFr}   rŠ   r_   r´   Ú	use_cacher³   r�   r9   c           
      óì   — |}|                       |¦  «        } | j        d||||||dœ|¤Ž\  }	}
|                      |	¦  «        }	|                      |                      |¦  «        ¦  «        }|	|z   |z   }|S )N)r}   rŠ   r_   r´   r×   r³   © )rÓ   rÑ   rÖ   rÒ   )r2   r}   rŠ   r_   r´   r×   r³   r�   ÚresidualÚattn_outputsÚ_Úfeed_forward_hidden_statess               r6   rd   zPhiDecoderLayer.forward  s¤   € ð !ˆà×,Ò,¨]Ñ;Ô;ˆà(˜$œ.ð 
Ø'Ø)Ø%Ø+ØØ 3ð
ð 
ð ð
ð 
‰ˆ�að ×)Ò)¨,Ñ7Ô7ˆà%)×%7Ò%7¸¿ºÀÑ8OÔ8OÑ%PÔ%PÐ"Ø$Ð'AÑAÀHÑLˆàÐr7   )NNNFN)rf   rg   rh   r   rE   r)   rF   ri   Ú
LongTensorr   Úboolrl   r   r   rd   rn   ro   s   @r6   rÎ   rÎ     s÷   ø€ € € € € ð<˜yð <°Sð <ð <ð <ð <ð <ð <ð /3Ø04Ø(,Ø!&ØHLðð à”|ðð œ tÑ+ðð Ô&¨Ñ-ð	ð
  ™ðð ˜$‘;ðð # 5¤<°´Ð#=Ô>ÀÑEðð Ð+Ô,ðð 
Œðð ð ð ð ð ð ð r7   rÎ   c                   óL   — 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œZdS )ÚPhiPreTrainedModelr"   ÚmodelTrÎ   r´   )r}   Ú
attentionsN)rf   rg   rh   r   rj   Úbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_skip_keys_device_placementÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_can_compile_fullgraphÚ_supports_attention_backendrÎ   rž   Ú_can_record_outputsrÙ   r7   r6   rá   rá   6  sl   € € € € € € àÐÐÑØÐØ&*Ð#Ø*Ð+ÐØ#4Ð"5ÐØÐØ€NØÐà!ÐØ"&Ðà(Ø"ðð ÐÐÐr7   rá   c                   óâ   ‡ — e Zd Zdefˆ fd„Ze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fd„¦   «         ¦   «         ¦   «         Zˆ xZS )ÚPhiModelr"   c                 ó$  •‡— t          ¦   «                              ‰¦  «         ‰j        | _        ‰j        | _        t          j        ‰j        ‰j        | j        ¦  «        | _        t          j	        ˆfd„t          ‰j        ¦  «        D ¦   «         ¦  «        | _        t          ‰¬¦  «        | _        d| _        t          j        ‰j        ¦  «        | _        t          j        ‰j        ‰j        ¬¦  «        | _        |                      ¦   «          d S )Nc                 ó0   •— g | ]}t          ‰|¦  «        ‘ŒS rÙ   )rÎ   )Ú.0rŸ   r"   s     €r6   ú
<listcomp>z%PhiModel.__init__.<locals>.<listcomp>R  s#   ø€ ÐaÐaÐa°I�_˜V YÑ/Ô/ÐaÐaÐar7   ©r"   FrÐ   )r(   r)   Úpad_token_idÚpadding_idxÚ
vocab_sizer“   Ú	EmbeddingrC   Úembed_tokensÚ
ModuleListÚrangeÚnum_hidden_layersÚlayersr    Ú
rotary_embÚgradient_checkpointingrÔ   Ú
embd_pdropÚembed_dropoutr®   r¯   Úfinal_layernormÚ	post_initrË   s    `€r6   r)   zPhiModel.__init__K  sì   øø€ Ý‰Œ×Ò˜Ñ Ô Ð Ø!Ô.ˆÔØ Ô+ˆŒåœL¨Ô):¸FÔ<NÐPTÔP`ÑaÔaˆÔÝ”mØaÐaÐaÐaÅÀvÔG_ÑA`ÔA`ÐaÑaÔañ
ô 
ˆŒõ -°FÐ;Ñ;Ô;ˆŒØ&+ˆÔ#ÝœZ¨Ô(9Ñ:Ô:ˆÔÝ!œ|¨FÔ,>ÀFÔDYÐZÑZÔZˆÔð 	�ŠÑÔÐÐÐr7   NÚ	input_idsrŠ   r_   r´   Úinputs_embedsr×   r�   r9   c           
      ór  — |d u |d uz  rt          d¦  «        ‚|€|                      |¦  «        }|r|€t          | j        ¬¦  «        }|€V|�|                     ¦   «         nd}t          j        |j        d         |j        ¬¦  «        |z   }| 	                    d¦  «        }t          | j        ||||¬¦  «        }	|                      |¦  «        }|}
|                      |
|¬¦  «        }| j        d | j        j        …         D ]} ||
f|	||||dœ|¤Ž}
Œ|                      |
¦  «        }
t!          |
|¬	¦  «        S )
Nz:You must specify exactly one of input_ids or inputs_embedsrô   r   r   )r3   )r"   r  rŠ   r´   r_   )r_   )rŠ   r_   r´   r×   r³   )Úlast_hidden_stater´   )Ú
ValueErrorrù   r   r"   Úget_seq_lengthrF   rG   rV   r3   rv   r   r  rþ   rý   rü   r  r   )r2   r  rŠ   r_   r´   r  r×   r�   Úpast_seen_tokensÚcausal_maskr}   r³   Údecoder_layers                r6   rd   zPhiModel.forward\  sŸ  € ð ˜Ð -°tÐ";Ñ<ð 	[ÝÐYÑZÔZÐZàÐ Ø ×-Ò-¨iÑ8Ô8ˆMàð 	?˜Ð0Ý*°$´+Ð>Ñ>Ô>ˆOàÐØCRÐC^˜×=Ò=Ñ?Ô?Ð?ÐdeÐÝ œ<¨Ô(;¸AÔ(>À}ÔG[Ð\Ñ\Ô\Ð_oÑoˆLØ'×1Ò1°!Ñ4Ô4ˆLå(Ø”;Ø'Ø)Ø+Ø%ð
ñ 
ô 
ˆð ×*Ò*¨=Ñ9Ô9ˆØ%ˆØ"Ÿošo¨mÈ,˜oÑWÔWÐà!œ[Ð)H¨4¬;Ô+HÐ)HÔIð 		ð 		ˆMØ)˜MØðà*Ø)Ø /Ø#Ø$7ðð ð ðð ˆMˆMð ×,Ò,¨]Ñ;Ô;ˆå&Ø+Ø+ð
ñ 
ô 
ð 	
r7   )NNNNNN)rf   rg   rh   r   r)   r   r   r   rF   rÞ   ri   r   ÚFloatTensorrß   r   r   r   rd   rn   ro   s   @r6   rï   rï   I  s  ø€ € € € € ð˜yð ð ð ð ð ð ð"  ØØð .2Ø.2Ø04Ø(,Ø26Ø!%ð4
ð 4
àÔ# dÑ*ð4
ð œ tÑ+ð4
ð Ô&¨Ñ-ð	4
ð
  ™ð4
ð Ô(¨4Ñ/ð4
ð ˜$‘;ð4
ð Ð+Ô,ð4
ð 
!ð4
ð 4
ð 4
ñ „^ñ „_ñ  Ôð4
ð 4
ð 4
ð 4
ð 4
r7   rï   c                   ó  ‡ — e Zd ZddiZddiZddgdgfiZˆ 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ˆ xZS )ÚPhiForCausalLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputr}   Úlogitsc                 óþ   •— t          ¦   «                              |¦  «         t          |¦  «        | _        |j        | _        t          j        |j        |j        d¬¦  «        | _        |  	                    ¦   «          d S )NTr¡   )
r(   r)   rï   râ   r÷   r“   r§   rC   r  r  rË   s     €r6   r)   zPhiForCausalLM.__init__œ  sj   ø€ Ý‰Œ×Ò˜Ñ Ô Ð Ý˜fÑ%Ô%ˆŒ
Ø Ô+ˆŒÝ”y Ô!3°VÔ5FÈTÐRÑRÔRˆŒð 	�ŠÑÔÐÐÐr7   Nr   r  rŠ   r_   r´   r  Úlabelsr×   Úlogits_to_keepr�   r9   c	           
      óP  —  | j         d||||||dœ|	¤Ž}
|
j        }t          |t          ¦  «        rt	          | d¦  «        n|}|                      |dd…|dd…f         ¦  «        }d}|� | j        d||| j        j        dœ|	¤Ž}t          |||
j
        |
j        |
j        ¬¦  «        S )aÃ  
        Example:

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

        >>> model = PhiForCausalLM.from_pretrained("meta-phi/Phi-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi/Phi-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  rŠ   r_   r´   r  r×   N)r  r  r÷   )Úlossr  r´   r}   rã   rÙ   )râ   r  rW   rE   Úslicer  Úloss_functionr"   r÷   r   r´   r}   rã   )r2   r  rŠ   r_   r´   r  r  r×   r  r�   Úoutputsr}   Úslice_indicesr  r  s                  r6   rd   zPhiForCausalLM.forward¥  sô   € ð> ,6¨4¬:ð ,
ØØ)Ø%Ø+Ø'Øð,
ð ,
ð ð,
ð ,
ˆð  Ô1ˆå8BÀ>ÕSVÑ8WÔ8WÐk�˜~˜o¨tÑ4Ô4Ð4Ð]kˆØ—’˜m¨A¨A¨A¨}¸a¸a¸aÐ,?Ô@ÑAÔAˆàˆØÐØ%�4Ô%Ðp¨V¸FÈtÌ{ÔOeÐpÐpÐioÐpÐpˆDå%ØØØ#Ô3Ø!Ô/ØÔ)ð
ñ 
ô 
ð 	
r7   )NNNNNNNr   )rf   rg   rh   Ú_tied_weights_keysÚ_tp_planÚ_pp_planr)   r   r   rF   rÞ   ri   r   r  rß   rE   r   r   r   rd   rn   ro   s   @r6   r  r  –  sK  ø€ € € € € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€Hðð ð ð ð ð Øð .2Ø.2Ø04Ø(,Ø26Ø*.Ø!%Ø-.ð6
ð 6
àÔ# dÑ*ð6
ð œ tÑ+ð6
ð Ô&¨Ñ-ð	6
ð
  ™ð6
ð Ô(¨4Ñ/ð6
ð Ô  4Ñ'ð6
ð ˜$‘;ð6
ð ˜eœlÑ*ð6
ð Ð+Ô,ð6
ð 
 ð6
ð 6
ð 6
ñ „^ñ Ôð6
ð 6
ð 6
ð 6
ð 6
r7   r  c                   ó   — e Zd ZdS )ÚPhiForSequenceClassificationN©rf   rg   rh   rÙ   r7   r6   r   r   à  ó   € € € € € Ø€Dr7   r   c                   ó   — e Zd ZdS )ÚPhiForTokenClassificationNr!  rÙ   r7   r6   r$  r$  ä  r"  r7   r$  )rá   rï   r  r   r$  )r   )r…   )?Úcollections.abcr   Útypingr   rF   Útorch.nnr“   Úactivationsr   Úcache_utilsr   r   Ú
generationr	   Úintegrationsr
   r   Úmasking_utilsr   Úmodeling_layersr   r   r   Úmodeling_outputsr   r   Úmodeling_rope_utilsr   r   Úmodeling_utilsr   r   Úprocessing_utilsr   Úutilsr   r   Úutils.genericr   r   r   Úutils.output_capturingr   Úconfiguration_phir   ÚModuler    rs   r|   ri   rE   r„   rJ   rœ   rž   rÄ   rÎ   rá   rï   r  r   r$  Ú__all__rÙ   r7   r6   ú<module>r8     sˆ  ðð %Ð $Ð $Ð $Ð $Ð $Ø Ð Ð Ð Ð Ð à €€€Ø Ð Ð Ð Ð Ð à !Ð !Ð !Ð !Ð !Ð !Ø .Ð .Ð .Ð .Ð .Ð .Ð .Ð .Ø )Ð )Ð )Ð )Ð )Ð )Ø IÐ IÐ IÐ IÐ IÐ IÐ IÐ IØ /Ð /Ð /Ð /Ð /Ð /ðð ð ð ð ð ð ð ð ð ð
 PÐ OÐ OÐ OÐ OÐ OÐ OÐ OØ KÐ KÐ KÐ KÐ KÐ KÐ KÐ KØ FÐ FÐ FÐ FÐ FÐ FÐ FÐ FØ &Ð &Ð &Ð &Ð &Ð &Ø 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ð 7Ø YÐ YÐ YÐ YÐ YÐ YÐ YÐ YÐ YÐ YØ 5Ð 5Ð 5Ð 5Ð 5Ð 5Ø (Ð (Ð (Ð (Ð (Ð (ð@<ð @<ð @<ð @<ð @<˜œñ @<ô @<ð @<ðF(ð (ð (ð ÐÐ*Ñ+Ô+ðð ð ñ ,Ô+ðð2	U˜Uœ\ð 	U°#ð 	U¸%¼,ð 	Uð 	Uð 	Uð 	Uð& ð%ð %ØŒIð%àŒ<ð%ð 
Œð%ð Œ<ð	%ð
 ”L 4Ñ'ð%ð ð%ð ð%ð Ð'Ô(ð%ð %ð %ð %ð2 ÐÐ)Ñ*Ô*ðR)ð R)ð R)ð R)ð R)�2”9ñ R)ô R)ñ +Ô*ðR)ðjð ð ð ð ˆRŒYñ ô ð ð$ð $ð $ð $ð $Ð0ñ $ô $ð $ðN ðð ð ð ð ˜ñ ô ñ „ðð$ ðI
ð I
ð I
ð I
ð I
Ð!ñ I
ô I
ñ „ðI
ðX ðF
ð F
ð F
ð F
ð F
Ð'¨ñ F
ô F
ñ „ðF
ðR	ð 	ð 	ð 	ð 	Ð#CÐEWñ 	ô 	ð 	ð	ð 	ð 	ð 	ð 	Ð =Ð?Qñ 	ô 	ð 	ðð ð €€€r7   