
    i              	       H   d Z ddlZddlZddlmZ ddl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 dd
lmZ ddlmZmZmZmZmZmZ ddlmZ ddlmZ ddl m!Z!m"Z"m#Z# ddl$m%Z% ddl&m'Z'  e"jP                  e)      Z*e e!d       G d de                    Z+dPdej                  de,de-dej                  fdZ. G d dej^                        Z0 G d dej^                        Z1 G d d ej^                        Z2 G d! d"ej^                        Z3 G d# d$e3      Z4 G d% d&ej^                        Z5e3e4d'Z6 G d( d)ej^                        Z7 G d* d+ej^                        Z8 G d, d-ej^                        Z9 G d. d/e      Z: G d0 d1ej^                        Z; G d2 d3ej^                        Z<e! G d4 d5e             Z=e! G d6 d7e=             Z> G d8 d9ej^                        Z? e!d:       G d; d<e=             Z@ e!d=       G d> d?e=             ZA G d@ dAej^                        ZB G dB dCej^                        ZC G dD dEej^                        ZD G dF dGej^                        ZE G dH dIej^                        ZFe! G dJ dKe=             ZG e!dL       G dM dNee=             ZHg dOZIy)QzPyTorch BEiT model.    N)	dataclass)Tensornn)CrossEntropyLoss   )initialization)ACT2FN)BackboneMixinfilter_output_hidden_states)GradientCheckpointingLayer)BackboneOutputBaseModelOutputBaseModelOutputWithPoolingImageClassifierOutputMaskedLMOutputSemanticSegmenterOutput)PreTrainedModel)#compile_compatible_method_lru_cache)auto_docstringlogging	torch_int)can_return_tuple   )
BeitConfigz-
    Class for outputs of [`BeitModel`].
    )custom_introc                       e Zd ZdZy)BeitModelOutputWithPoolingaF  
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
        Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
        *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
        will be returned.
    N)__name__
__module____qualname____doc__     w/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/beit/modeling_beit.pyr   r   .   s    r#   r   input	drop_probtrainingreturnc                    |dk(  s|s| S d|z
  }| j                   d   fd| j                  dz
  z  z   }|t        j                  || j                  | j
                        z   }|j                          | j                  |      |z  }|S )zc
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

            r   r   )r   )dtypedevice)shapendimtorchrandr+   r,   floor_div)r%   r&   r'   	keep_probr-   random_tensoroutputs          r$   	drop_pathr6   =   s    
 CxII[[^

Q 77E

5ELL YYMYYy!M1FMr#   c                   x     e Zd ZdZd	dedz  ddf fdZdej                  dej                  fdZde	fdZ
 xZS )
BeitDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr&   r(   c                 0    t         |           || _        y N)super__init__r&   )selfr&   	__class__s     r$   r<   zBeitDropPath.__init__O   s    "r#   hidden_statesc                 D    t        || j                  | j                        S r:   )r6   r&   r'   r=   r?   s     r$   forwardzBeitDropPath.forwardS   s    FFr#   c                      d| j                    S )Nzp=)r&   r=   s    r$   
extra_reprzBeitDropPath.extra_reprV   s    DNN#$$r#   r:   )r   r   r    r!   floatr<   r/   r   rB   strrE   __classcell__r>   s   @r$   r8   r8   L   sG    b#%$, #$ #GU\\ Gell G%C %r#   r8   c                        e Zd ZdZdeddf fdZdej                  dededej                  fd	Z		 dd
ej                  dej                  dz  dej                  fdZ xZS )BeitEmbeddingszc
    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.

    configr(   Nc                 2   t         |           t        j                  t	        j
                  dd|j                              | _        |j                  r:t        j                  t	        j
                  dd|j                              | _	        nd | _	        t        |      | _        |j                  | _        t        |j                  t        j                   j"                        r|j                  n|j                  |j                  f| _        | j                  j$                  }|j&                  r=t        j                  t	        j
                  d|dz   |j                              | _        nd | _        t        j*                  |j,                        | _        y )Nr   )r;   r<   r   	Parameterr/   zeroshidden_size	cls_tokenuse_mask_token
mask_tokenBeitPatchEmbeddingspatch_embeddings
patch_size
isinstance
image_sizecollectionsabcIterablenum_patches use_absolute_position_embeddingsposition_embeddingsDropouthidden_dropout_probdropout)r=   rL   r\   r>   s      r$   r<   zBeitEmbeddings.__init__b   s$   ekk!Q8J8J&KL   ll5;;q!V=O=O+PQDO"DO 3F ; ++ &++[__-E-EF ##V%6%67 	
 ++7722')||EKK;QR?TZTfTf4g'hD$'+D$zz&"<"<=r#   
embeddingsheightwidthc                    |j                   d   dz
  }| j                  j                   d   dz
  }t        j                  j	                         s||k(  r||k(  r| j                  S | j                  ddddf   }| j                  ddddf   }|j                   d   }|| j
                  z  }	|| j
                  z  }
t        |dz        }|j                  d|||      }|j                  dddd      }t        j                  j                  ||	|
fdd	
      }|j                  dddd      j                  dd|      }t        j                  ||fd      S )a   
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        r   Ng      ?r   r      bicubicFsizemodealign_cornersdim)r-   r^   r/   jit
is_tracingrV   r   reshapepermuter   
functionalinterpolateviewcat)r=   rb   rc   rd   r\   num_positionsclass_pos_embedpatch_pos_embedrn   
new_height	new_widthsqrt_num_positionss               r$   interpolate_pos_encodingz'BeitEmbeddings.interpolate_pos_encodingy   s`    !&&q)A-0066q9A= yy##%+*F6UZ?+++221bqb59221ab59r"t.
T__,	&}c'9:)11!5GI[]`a)11!Q1=--33i(	 4 
 *11!Q1=BB1b#Nyy/?;CCr#   pixel_valuesbool_masked_posc                    |j                   \  }}}}| j                  |      \  }\  }}|j                         \  }	}
}|K| j                  j	                  |	|
d      }|j                  d      j                  |      }|d|z
  z  ||z  z   }| j                  j	                  |	dd      }t        j                  ||fd      }| j                  || j                  |||      z   }| j                  |      }|||ffS Nrf   r   rm   )r-   rU   rj   rS   expand	unsqueezetype_asrQ   r/   rv   r^   r}   ra   )r=   r~   r   _rc   rd   rb   patch_heightpatch_width
batch_sizeseq_lenmask_tokensw
cls_tokenss                 r$   rB   zBeitEmbeddings.forward   s   
 +001fe262G2G2U/
/\;!+!2
GQ&//00WbIK))"-55kBA#q1u-a?J^^**:r2>
YY
J7Q?
##/#d&C&CJPVX]&^^J\\*-
L+666r#   r:   )r   r   r    r!   r   r<   r/   r   intr}   
BoolTensorrB   rH   rI   s   @r$   rK   rK   \   s    
>z >d >.&D5<< &D &DUX &D]b]i]i &DV 487ll7 ))D07 
	7r#   rK   c                   Z     e Zd ZdZ fdZdej                  dej                  fdZ xZS )rT   z
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    c                    t         |           |j                  |j                  }}|j                  |j
                  }}t        |t        j                  j                        r|n||f}t        |t        j                  j                        r|n||f}|d   |d   z  |d   |d   z  z  }|d   |d   z  |d   |d   z  f}|| _        || _        || _        || _
        || _        t        j                  ||||      | _        y )Nr   r   kernel_sizestride)r;   r<   rX   rV   num_channelsrP   rW   rY   rZ   r[   r\   patch_shaper   Conv2d
projection)	r=   rL   rX   rV   r   rP   r\   r   r>   s	           r$   r<   zBeitPatchEmbeddings.__init__   s   !'!2!2F4E4EJ
$*$7$79K9Kk#-j+//:R:R#SZZdfpYq
#-j+//:R:R#SZZdfpYq
!!}
15*Q-:VW=:XY!!}
15z!}
ST7UV$$(&&))L+:^hir#   r~   r(   c                 ^   |j                   \  }}}}|| j                  k7  rt        d      | j                  |j	                  | j                  j
                  j                              }|j                   d   |j                   d   }}|j                  d      j                  dd      }|||ffS )NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.rg   r   r   )	r-   r   
ValueErrorr   toweightr+   flatten	transpose)	r=   r~   r   r   rc   rd   rb   r   r   s	            r$   rB   zBeitPatchEmbeddings.forward   s    2>2D2D/
L&%4,,,w  __\__T__5K5K5Q5Q%RS
$.$4$4Q$79I9I!9Lk''*44Q:
L+666r#   )	r   r   r    r!   r<   r/   r   rB   rH   rI   s   @r$   rT   rT      s)    j"7ELL 7U\\ 7r#   rT   c                        e Zd Zddededz  ddf fdZ	 	 	 	 ddej                  dedej                  dz  d	ed
ee	   dz  deej                     eej                  ej                  f   z  fdZ
 xZS )BeitSelfAttentionNrL   window_sizer(   c                 <   t         |           || _        |j                  |j                  z  dk7  r2t        |d      s&t        d|j                   d|j                   d      |j                  | _        t        |j                  |j                  z        | _        | j                  | j                  z  | _	        t        j                  |j                  | j                        | _        t        j                  |j                  | j                  d      | _        t        j                  |j                  | j                        | _        t        j                  |j                         | _        t%        |      | _        | j&                  rt)        ||      | _        y y )	Nr   embedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .F)biasr   )r;   r<   rL   rP   num_attention_headshasattrr   r   attention_head_sizeall_head_sizer   Linearquerykeyvaluer_   attention_probs_dropout_probra   boolhas_relative_position_biasBeitRelativePositionBiasrelative_position_biasr=   rL   r   r>   s      r$   r<   zBeitSelfAttention.__init__   sP    : ::a?PVXhHi"6#5#5"6 7334A7 
 $*#=#= #&v'9'9F<V<V'V#W !558P8PPYYv1143E3EF
99V//1C1C%PYYv1143E3EF
zz&"E"EF*.{*;'***B6Wb*cD' +r#   r?   output_attentionsr   r}   
resolutionc                     |j                   d d }g |d| j                  }| j                  |      j                  |      j	                  dd      }| j                  |      j                  |      j	                  dd      }	| j                  |      j                  |      j	                  dd      }
t        j                  ||	j	                  dd            }|t        j                  | j                        z  }| j                  r[|\  }}|| j                  j                  z  || j                  j                  z  f}|| j                  |||j                   d         z   }|||z   }t        j                   j#                  |d      }| j%                  |      }t        j                  ||
      }|j'                  dddd      j)                         }|j+                         d d | j,                  fz   } |j                  | }|r||f}|S |f}|S )	Nrf   r   rg   dim_sizerm   r   r   )r-   r   r   ru   r   r   r   r/   matmulmathsqrtr   rL   rV   r   r   rs   softmaxra   rr   
contiguousrj   r   )r=   r?   r   r   r}   r   input_shapehidden_shapequery_layer	key_layervalue_layerattention_scoresrc   rd   r   attention_probscontext_layernew_context_layer_shapeoutputss                      r$   rB   zBeitSelfAttention.forward   s    $))#2.CCbC$*B*BCjj/44\BLLQPQRHH]+00>HHAN	jj/44\BLLQPQR !<<Y5H5HR5PQ+dii8P8P.QQ **&MFE!T[[%;%;;UdkkF\F\=\]K/$2M2M5@S@STU@V 3N 3  
 "-/2HH --//0@b/I ,,7_kB%--aAq9DDF"/"4"4"6s";t?Q?Q>S"S***,CD6G=/2 O\M]r#   r:   FNFNr   r   r    r   tupler<   r/   r   r   r   rB   rH   rI   s   @r$   r   r      s    dz d dPT d4 #(6:).(,.||.  . !&t 3	.
 #'. #J%. 
u||	uU\\5<<%?@	@.r#   r   c                       e Zd Z	 	 	 	 d	dej                  dedej                  dz  dedee   dz  deej                     eej                  ej                  f   z  fdZy)
BeitSdpaSelfAttentionNr?   r   r   r}   r   r(   c           	      :   |r,t         j                  | j                  j                   d       |j                  d d }g |d| j
                  }| j                  |      j                  |      j                  dd      }| j                  |      j                  |      j                  dd      }	| j                  |      j                  |      j                  dd      }
d }| j                  rX|\  }}|| j                  j                  z  || j                  j                  z  f}| j                  |||j                  d         }|
||}n||z  }dt        j                   | j
                        z  }t"        j$                  j&                  j)                  ||	|
|| j*                  r| j                  j,                  ndd|      }|j/                  d	ddd
      j1                         }|j3                         d d | j4                  fz   } |j                  | }|d fS )Nz does not support `output_attentions=True`. The returned attention weights will be `None`. If you want to get attention weights, please set `attn_implementation='eager'` when loading the model.rf   r   rg   r   r*   F)	attn_mask	dropout_p	is_causalscaler   r   r   )loggerwarning_oncer>   r   r-   r   r   ru   r   r   r   r   rL   rV   r   r   r   r/   r   rs   scaled_dot_product_attentionr'   r   rr   r   rj   r   )r=   r?   r   r   r}   r   r   r   r   r   r   	attn_biasrc   rd   r   scalingr   r   s                     r$   rB   zBeitSdpaSelfAttention.forward+  s    >>**+ ,D D $))#2.CCbC$*B*BCjj/44\BLLQPQRHH]+00>HHAN	jj/44\BLLQPQR	**&MFE!T[[%;%;;UdkkF\F\=\]K335@S@STU@V 4 I
 "- 2	33	dii 8 899++HHBF--dkk>>UX I 
 &--aAq9DDF"/"4"4"6s";t?Q?Q>S"S***,CDd""r#   r   )	r   r   r    r/   r   r   r   r   rB   r"   r#   r$   r   r   *  s     #(6:).(,/#||/#  /# !&t 3	/#
 #'/# #J%/# 
u||	uU\\5<<%?@	@/#r#   r   c                   ~     e Zd ZdZdeddf fdZd	dej                  dej                  dej                  fdZ xZ	S )
BeitSelfOutputz
    The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    rL   r(   Nc                     t         |           t        j                  |j                  |j                        | _        t        j                  |j                        | _        y r:   )	r;   r<   r   r   rP   denser_   r`   ra   r=   rL   r>   s     r$   r<   zBeitSelfOutput.__init__c  sB    YYv1163E3EF
zz&"<"<=r#   r?   input_tensorc                 J    | j                  |      }| j                  |      }|S r:   r   ra   )r=   r?   r   gammas       r$   rB   zBeitSelfOutput.forwardh  $    

=1]3r#   r:   )
r   r   r    r!   r   r<   r/   r   rB   rH   rI   s   @r$   r   r   ]  sD    
>z >d >
U\\  ^c^j^j r#   r   )eagersdpac                        e Zd Zddededz  ddf fdZ	 	 	 	 ddej                  dedej                  dz  d	ed
ee	   dz  deej                     eej                  ej                  f   z  fdZ
 xZS )BeitAttentionNrL   r   r(   c                     t         |           t        |j                     ||      | _        t        |      | _        y )Nr   )r;   r<   BEIT_SELF_ATTENTION_CLASSES_attn_implementation	attentionr   r5   r   s      r$   r<   zBeitAttention.__init__v  s5    4V5P5PQRXfqr$V,r#   r?   r   r   r}   r   c                 l    | j                  |||||      }| j                  |d   |      }|f|dd  z   }|S )Nr   r   )r   r5   )	r=   r?   r   r   r}   r   self_outputsattention_outputr   s	            r$   rB   zBeitAttention.forward{  sQ     ~~,.DF^`j
  ;;|AF#%QR(88r#   r:   r   r   rI   s   @r$   r   r   u  s    -z - -PT - #(6:).(,||   !&t 3	
 #' #J% 
u||	uU\\5<<%?@	@r#   r   c                   `     e Zd Zdeddf fdZdej                  dej                  fdZ xZS )BeitIntermediaterL   r(   Nc                    t         |           t        j                  |j                  |j
                        | _        t        |j                  t              rt        |j                     | _        y |j                  | _        y r:   )r;   r<   r   r   rP   intermediate_sizer   rW   
hidden_actrG   r	   intermediate_act_fnr   s     r$   r<   zBeitIntermediate.__init__  s]    YYv1163K3KL
f''-'-f.?.?'@D$'-'8'8D$r#   r?   c                 J    | j                  |      }| j                  |      }|S r:   )r   r   rA   s     r$   rB   zBeitIntermediate.forward  s&    

=100?r#   	r   r   r    r   r<   r/   r   rB   rH   rI   s   @r$   r   r     s1    9z 9d 9U\\ ell r#   r   c                   `     e Zd Zdeddf fdZdej                  dej                  fdZ xZS )
BeitOutputrL   r(   Nc                     t         |           t        j                  |j                  |j
                        | _        t        j                  |j                        | _	        y r:   )
r;   r<   r   r   r   rP   r   r_   r`   ra   r   s     r$   r<   zBeitOutput.__init__  sB    YYv779K9KL
zz&"<"<=r#   r?   c                 J    | j                  |      }| j                  |      }|S r:   r   rA   s     r$   rB   zBeitOutput.forward  r   r#   r   rI   s   @r$   r   r     s1    >z >d >
U\\ ell r#   r   c                        e Zd ZdZddededz  deddf fdZ	 	 	 	 ddej                  d	e
d
ej                  dz  de
deeef   dz  deej                     eej                  ej                  f   z  fdZ xZS )	BeitLayerz?This corresponds to the Block class in the timm implementation.NrL   r   drop_path_rater(   c                    t         |           |j                  | _        d| _        t	        ||      | _        t        |      | _        t        |      | _	        t        j                  |j                  |j                        | _        |dkD  rt        |      nt        j                          | _        t        j                  |j                  |j                        | _        |j&                  }|dkD  ryt        j(                  |t+        j,                  |j                        z  d      | _        t        j(                  |t+        j,                  |j                        z  d      | _        y d\  | _        | _        y )	Nr   r   epsr*   r   T)requires_grad)NN)r;   r<   chunk_size_feed_forwardseq_len_dimr   r   r   intermediater   r5   r   	LayerNormrP   layer_norm_epslayernorm_beforer8   Identityr6   layernorm_afterlayer_scale_init_valuerN   r/   oneslambda_1lambda_2)r=   rL   r   r   init_valuesr>   s        r$   r<   zBeitLayer.__init__  s   '-'E'E$&v;G,V4 ( "V-?-?VEZEZ [9G#9Mn5SUS^S^S`!||F,>,>FDYDYZ33?LLuzz&BTBT7U)UeijDMLLuzz&BTBT7U)UeijDM+5(DM4=r#   r?   r   r   r}   r   c                    | j                  | j                  |      ||||      }|d   }|dd  }| j                  | j                  |z  }| j                  |      |z   }| j	                  |      }	| j                  |	      }	| j                  |	      }	| j                  | j                  |	z  }	| j                  |	      |z   }	|	f|z   }|S )Nr   r   r}   r   r   r   )r   r  r
  r6   r  r  r5   r  )
r=   r?   r   r   r}   r   self_attention_outputsr   r   layer_outputs
             r$   rB   zBeitLayer.forward  s     "&!!-0/#9%=! "0 "
 2!4(, ==$#}}/?? '78=H ++M:((6{{<0==$==<7L ~~l3mC/G+r#   )Nr*   r   )r   r   r    r!   r   r   rF   r<   r/   r   r   r   rB   rH   rI   s   @r$   r   r     s    I6z 6 6]b 6mq 6* #(6:).-1'||'  ' !&t 3	'
 #'' #s(Od*' 
u||	uU\\5<<%?@	@'r#   r   c                        e Zd Zdededdf fdZ ed      deeef   dej                  fd       Z
dd	edej                  fd
Z xZS )r   rL   r   r(   Nc                     t         |           || _        d|d   z  dz
  d|d   z  dz
  z  dz   | _        t	        j
                  t        j                  | j                  |j                              | _	        y )Nrg   r   r   r   )
r;   r<   r   num_relative_distancer   rN   r/   rO   r   relative_position_bias_tabler   s      r$   r<   z!BeitRelativePositionBias.__init__  sr    &&'+a.&81&<[QR^ASVWAW%X[\%\",.LLKK22F4N4NO-
)r#   
   )maxsizec                    d|d   z  dz
  d|d   z  dz
  z  dz   }|d   |d   z  }t        j                  t        j                  |d         t        j                  |d         d      }t        j                  |      }t        j                  |d      }|dddddf   |dddddf   z
  }|j                  ddd      j                         }|dddddfxx   |d   dz
  z  cc<   |dddddfxx   |d   dz
  z  cc<   |dddddfxx   d|d   z  dz
  z  cc<   t        j                  |dz   fdz  |j                        }|j                  d	      |ddddf<   |dz
  |dddf<   |dz
  |dddf<   |dz
  |d
<   |S )z
        This method creates the relative position index, modified to support arbitrary window sizes,
        as introduced in [MiDaS v3.1](https://huggingface.co/papers/2307.14460).
        rg   r   r   r   ij)indexingN)rj   r+   rf   )r   r   )
r/   meshgridarangestackr   rr   r   rO   r+   sum)	r=   r   r  window_areagridcoordscoords_flattenrelative_coordsrelative_position_indexs	            r$    generate_relative_position_indexz9BeitRelativePositionBias.generate_relative_position_index  s    "#[^!3a!7AA<NQR<R SVW W "!n{1~5~~ell;q>:ELLUV<XcghT"vq1(At4~aqj7QQ)11!Q:EEG1a KNQ$66 1a KNQ$66 1a AA$6$:: "'++K!O3E3IQ`QfQf"g*9*=*=b*AAB')>)B12&)>)BA&(=(A%&&r#   r}   c                    d| j                   d   z  dz
  }d| j                   d   z  dz
  }d|d   z  dz
  }d|d   z  dz
  }| j                  }| j                  }	||z  dz   }
|d|	dz
   }|j                  d||d      j	                  dddd      }t
        j                  j                  |t        |      t        |      fd      }|j	                  dddd      j                  |
dz
  d      }t        j                  |||	dz
  d g      }| j                  |      }||j                  d         }|j                  |d   |d   z  dz   |d   |d   z  dz   d      }|j	                  ddd      j                         }|rCt
        j                  j                  |j                  d      ||fdd	
      j                  d      }|j                  d      S )zu
        Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
        rg   r   r   r   Nrf   bilinear)rj   rk   Fri   )r   r  r  rq   rr   r   rs   rt   r   r/   rv   r$  ru   r   r   squeeze)r=   r   r}   r   
old_height	old_widthrz   r{    old_relative_position_bias_tableold_num_relative_distancenew_num_relative_distanceold_sub_tablenew_sub_table new_relative_position_bias_tabler#  r   s                   r$   rB   z BeitRelativePositionBias.forward  s-    ))!,,q0
((++a/	Q'!+
A&*	+/+L+L($($>$>!$.$:Q$>!89X;TWX;XY%--aJKSSTUWXZ[]^_11:!6	)8L MT^ 2 
 &--aAq9AAB[^_B_acd+099<=VYZ=Z=\]^,
( #'"G"G"T!ABYB^B^_aBb!c "8!<!<N[^+a/Q+a.1PST1TVX"
 "8!?!?1a!H!S!S!U#%']]%>%>&003)#	 &? &
 gaj # &//22r#   )FN)r   r   r    r   r   r<   r   r   r/   r   r$  r   rB   rH   rI   s   @r$   r   r     sm    
z 
 
$ 
 )4'E#s(O 'PUP\P\ ' 5'0-3T -3]b]i]i -3r#   r   c                        e Zd Zddededz  ddf fdZ	 	 	 	 	 ddej                  deded	ed
ee	e	f   dz  dedee
z  fdZ xZS )BeitEncoderNrL   r   r(   c                    t         |           || _        |j                  | _        | j                  rt        ||      | _        t        j                  d|j                  |j                  d      D cg c]  }|j                          }}t        j                  t        |j                        D cg c]!  }t        ||j                   r|nd ||         # c}      | _        d| _        y c c}w c c}w )Nr   r   cpu)r,   )r   r   F)r;   r<   rL   !use_shared_relative_position_biasr   r   r   r/   linspacer   num_hidden_layersitemr   
ModuleListranger   use_relative_position_biaslayergradient_checkpointing)r=   rL   r   xdprir>   s         r$   r<   zBeitEncoder.__init__=  s    *0*R*R'***B6Wb*cD' "'63H3H&JbJbkp!qrAqvvxrr]] v778  /5/P/PVZ#&q6	

 ',# ss   5C.4&C3r?   r   output_hidden_statesr}   r   return_dictc                    |rdnd }|rdnd }t        | j                        D ]  \  }	}
|r||fz   }| j                  rY|\  }}|| j                  j                  z  || j                  j                  z  f}| j                  |||j                  d         }nd } |
|||||      }|d   }|s||d   fz   } |r||fz   }|st        d |||fD              S t        |||      S )Nr"   r   )r}   r   r  r   c              3   &   K   | ]	  }||  y wr:   r"   ).0vs     r$   	<genexpr>z&BeitEncoder.forward.<locals>.<genexpr>|  s     mq_`_lms   )last_hidden_stater?   
attentions)		enumerater;  r   rL   rV   r   r-   r   r   )r=   r?   r   r@  r}   r   rA  all_hidden_statesall_self_attentionsr?  layer_modulerc   rd   r   r   layer_outputss                   r$   rB   zBeitEncoder.forwardR  s6    #7BD$5b4(4 	POA|#$58H$H!.. *%)?)??$++J`J`A`a)-)D)D:R]j]p]pqr]s *E *& *.&("3'=)A%M *!,M &9]1=M<O&O#1	P4   1]4D Dm]4EGZ$[mmm++*
 	
r#   r:   )FFFNT)r   r   r    r   r   r<   r/   r   r   r   r   rB   rH   rI   s   @r$   r1  r1  <  s    ,z , ,PT ,0 #(%*).-1 /
||/
  /
 #	/

 #'/
 #s(Od*/
 /
 
	 /
r#   r1  c                   r     e Zd ZU eed<   dZdZdZdZdgZ	dgZ
dZ ej                          fd       Z xZS )	BeitPreTrainedModelrL   beit)imager~   Tr   z.*relative_position_index.*c                    t         |   |       t        |t              rwt	        j
                  |j                         |j                  t	        j
                  |j                         |j                   t	        j
                  |j                         yyt        |t              r t	        j
                  |j                         yt        |t              rv|j                  it	        j                  |j                  | j                  j                         t	        j                  |j                   | j                  j                         yyy)zInitialize the weightsN)r;   _init_weightsrW   rK   initzeros_rQ   rS   r^   r   r  r   r
  	constant_rL   r  r  )r=   moduler>   s     r$   rS  z!BeitPreTrainedModel._init_weights  s     	f%fn-KK(()  ,F--.))5F667 6 89KK;;<	**v0R0RSv0R0RS + +r#   )r   r   r    r   __annotations__base_model_prefixinput_modalitiesmain_input_namesupports_gradient_checkpointing_no_split_modules"_keys_to_ignore_on_load_unexpected_supports_sdpar/   no_gradrS  rH   rI   s   @r$   rO  rO    sR    !$O&*#$*H)I&NU]]_T Tr#   rO  c                        e Zd Zddededdf fdZd Ze	 	 	 	 	 ddej                  dej                  dz  d	edz  d
edz  dededz  deez  fd       Z xZS )	BeitModelrL   add_pooling_layerr(   Nc                    t         |   |       || _        t        |      | _        t        || j                  j                  j                        | _        |j                  rt        j                         n*t        j                  |j                  |j                        | _        |rt!        |      nd| _        | j%                          y)zv
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        r   r   N)r;   r<   rL   rK   rb   r1  rU   r   encoderuse_mean_poolingr   r  r  rP   r  	layernorm
BeitPoolerpooler	post_init)r=   rL   rc  r>   s      r$   r<   zBeitModel.__init__  s    
 	 (0"6t7W7W7c7cd $44BKKM",,vGYGY_e_t_t:u 	 ->j(4 	r#   c                 .    | j                   j                  S r:   rb   rU   rD   s    r$   get_input_embeddingszBeitModel.get_input_embeddings      ///r#   r~   r   r   r@  r}   rA  c                    ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }| j	                  ||      \  }}	|j
                  dd }
| j                  ||||
||      }|d   }| j                  |      }| j                  | j                  |      nd}|s|||fn|f}||dd z   S t        |||j                  |j                        S )z
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        N)r   rg   )r   r@  r   rA  r}   r   r   )rG  pooler_outputr?   rH  )rL   r   r@  rA  rb   r-   re  rg  ri  r   r?   rH  )r=   r~   r   r   r@  r}   rA  kwargsembedding_outputr   r   encoder_outputssequence_outputpooled_outputhead_outputss                  r$   rB   zBeitModel.forward  s$    2C1N-TXT_T_TqTq$8$D $++JjJj 	 &1%<k$++BYBY"oolOo\!!''+
,,/!5!#%= ' 
 *!,..98<8OO4UY?L?XO];_n^pL/!""555)-')77&11	
 	
r#   )T)NNNFN)r   r   r    r   r   r<   rm  r   r/   r   r   r   r   rB   rH   rI   s   @r$   rb  rb    s    z d d &0  48)-,0).#',
ll,
 ))D0,
  $;	,

 #Tk,
 #',
 D[,
 
+	+,
 ,
r#   rb  c                   `     e Zd Zdeddf fdZdej                  dej                  fdZ xZS )rh  rL   r(   Nc                     t         |           |j                  r1t        j                  |j
                  |j                        | _        y d | _        y )Nr   )r;   r<   rf  r   r  rP   r  rg  r   s     r$   r<   zBeitPooler.__init__  sA    KQKbKbBLL++1F1FG 	hl 	r#   r?   c                     | j                   0|d d dd d d f   }| j                  |j                  d            }|S |d d df   }|S )Nr   r   )rg  mean)r=   r?   patch_tokensru  s       r$   rB   zBeitPooler.forward  sU    >>%(AB2L NN<+<+<Q+?@M
  *!Q$/Mr#   r   rI   s   @r$   rh  rh    s1    
z 
d 
	U\\ 	ell 	r#   rh  a  
    Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
    visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
    predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImageModeling`], so you
    will need to use [`BeitForMaskedImageModeling`] directly if you wish to do masked image modeling with BEiT.
    c                        e Zd Zdeddf fdZd Ze	 	 	 	 	 	 	 ddej                  dz  dej                  dz  dej                  dz  d	e
dz  d
e
dz  de
de
dz  deez  fd       Z xZS )BeitForMaskedImageModelingrL   r(   Nc                 H   t         |   |       |j                  | _        t        |d      | _        t        j                  |j                  |j                        | _	        t        j                  |j                  |j                        | _        | j                          y )NFrc  r   )r;   r<   
num_labelsrb  rP  r   r  rP   r  rg  r   
vocab_sizelm_headrj  r   s     r$   r<   z#BeitForMaskedImageModeling.__init__  su      ++f>	 f&8&8f>S>STyy!3!3V5F5FG 	r#   c                      y r:   r"   rD   s    r$   get_output_embeddingsz0BeitForMaskedImageModeling.get_output_embeddings  s    r#   r~   r   labelsr   r@  r}   rA  c                 h   ||n| j                   j                  }| j                  ||||||      }	|	d   }
| j                  |
      }
| j	                  |
ddddf         }d}|t               } |||   |      }|s|f|	dd z   }||f|z   S |S t        |||	j                  |	j                        S )a  
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, BeitForMaskedImageModeling
        >>> import torch
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
        >>> model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, logits = outputs.loss, outputs.logits
        >>> list(logits.shape)
        [1, 196, 8192]
        ```N)r   r   r@  r}   rA  r   r   losslogitsr?   rH  )	rL   rA  rP  rg  r  r   r   r?   rH  )r=   r~   r   r  r   r@  r}   rA  rq  r   rt  prediction_scoresmasked_lm_lossloss_fctr5   s                  r$   rB   z"BeitForMaskedImageModeling.forward  s    \ &1%<k$++BYBY))+/!5%=#  
 "!*..9 LLAB)?@')H%&7&H&QN')GABK7F3A3M^%.YSYY$!//))	
 	
r#   )NNNNNFN)r   r   r    r   r<   r  r   r/   r   r   r   r   r   rB   rH   rI   s   @r$   r}  r}    s    z d   -137&*)-,0).#'J
llT)J
 ))D0J
 t#	J

  $;J
 #TkJ
 #'J
 D[J
 
	J
 J
r#   r}  z
    Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
    hidden states of the patch tokens) e.g. for ImageNet.
    c                        e Zd Zdeddf fdZe	 	 	 	 	 	 ddej                  dz  dej                  dz  dedz  dedz  d	ed
edz  de	e
z  fd       Z xZS )BeitForImageClassificationrL   r(   Nc                 .   t         |   |       |j                  | _        t        |d      | _        |j                  dkD  r*t        j                  |j                  |j                        nt        j                         | _	        | j                          y )NTr  r   )r;   r<   r  rb  rP  r   r   rP   r  
classifierrj  r   s     r$   r<   z#BeitForImageClassification.__init__j  ss      ++f=	 OUN_N_bcNc"))F$6$68I8IJikititiv 	r#   r~   r  r   r@  r}   rA  c                 \   ||n| j                   j                  }| j                  |||||      }|r|j                  n|d   }	| j	                  |	      }
d}|| j                  ||
| j                         }|s|
f|dd z   }||f|z   S |S t        ||
|j                  |j                        S )a  
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        Nr   r@  r}   rA  r   rg   r  )	rL   rA  rP  rp  r  loss_functionr   r?   rH  )r=   r~   r  r   r@  r}   rA  rq  r   ru  r  r  r5   s                r$   rB   z"BeitForImageClassification.forwardv  s    " &1%<k$++BYBY))/!5%=#  
 2=--'!*/%%ffdkkBDY,F)-)9TGf$EvE$!//))	
 	
r#   NNNNFN)r   r   r    r   r<   r   r/   r   r   r   r   rB   rH   rI   s   @r$   r  r  c  s    
z 
d 
  -1&*)-,0).#'*
llT)*
 t#*
  $;	*

 #Tk*
 #'*
 D[*
 
&	&*
 *
r#   r  c                        e Zd ZdZ	 	 	 ddededeeeef   z  deeeef   z  ez  dedeeeef   z  dd	f fd
Zde	j                  de	j                  fdZ xZS )BeitConvModuleaD  
    A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
    layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    in_channelsout_channelsr   paddingr   dilationr(   Nc                     t         |           t        j                  ||||||      | _        t        j
                  |      | _        t        j                         | _        y )N)r  r  r   r  r   r  )	r;   r<   r   r   convBatchNorm2dbnReLU
activation)r=   r  r  r   r  r   r  r>   s          r$   r<   zBeitConvModule.__init__  sQ     	II#%#
	 ...'')r#   r%   c                 l    | j                  |      }| j                  |      }| j                  |      }|S r:   )r  r  r  )r=   r%   r5   s      r$   rB   zBeitConvModule.forward  s0    5!(r#   )r   Fr   )r   r   r    r!   r   r   rG   r   r<   r/   r   rB   rH   rI   s   @r$   r  r    s     01*+$$ $ 5c?*	$
 uS#X&,$ $ c3h'$ 
$*U\\ ell r#   r  c                   h     e Zd Zdedededdf fdZdej                  dej                  fdZ xZS )	BeitPyramidPoolingBlock
pool_scaler  channelsr(   Nc                     t         |           t        j                  |      t	        ||d      g| _        t        | j
                        D ]   \  }}| j                  t        |      |       " y )Nr   r   )	r;   r<   r   AdaptiveAvgPool2dr  layersrI  
add_modulerG   )r=   r  r  r  r?  r;  r>   s         r$   r<   z BeitPyramidPoolingBlock.__init__  sa      ,;a@
 "$++. 	+HAuOOCFE*	+r#   r%   c                 <    |}| j                   D ]
  } ||      } |S r:   )r  )r=   r%   hidden_stater;  s       r$   rB   zBeitPyramidPoolingBlock.forward  s*    [[ 	/E .L	/r#   )	r   r   r    r   r<   r/   r   rB   rH   rI   s   @r$   r  r    s?    +3 +S +C +D +U\\ ell r#   r  c            
            e Zd ZdZdeedf   dedededdf
 fd	Zd
ej                  de
ej                     fdZ xZS )BeitPyramidPoolingModulea  
    Pyramid Pooling Module (PPM) used in PSPNet.

    Args:
        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
            Module.
        in_channels (int): Input channels.
        channels (int): Channels after modules, before conv_seg.
        align_corners (bool): align_corners argument of F.interpolate.

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    pool_scales.r  r  rl   r(   Nc                    t         |           || _        || _        || _        || _        g | _        t        |      D ]I  \  }}t        |||      }| j                  j                  |       | j                  t        |      |       K y )N)r  r  r  )r;   r<   r  rl   r  r  blocksrI  r  appendr  rG   )	r=   r  r  r  rl   r?  r  blockr>   s	           r$   r<   z!BeitPyramidPoolingModule.__init__  s    &*& &{3 	+MAz+z{emnEKKu%OOCFE*	+r#   r=  c                     g }| j                   D ]Y  } ||      }t        j                  j                  ||j	                         dd  d| j
                        }|j                  |       [ |S )Nrg   r&  ri   )r  r   rs   rt   rj   rl   r  )r=   r=  ppm_outsppmppm_outupsampled_ppm_outs         r$   rB   z BeitPyramidPoolingModule.forward  sn    ;; 	/C!fG " 9 9affhqrl4K]K] !: ! OO-.	/ r#   )r   r   r    r!   r   r   r   r<   r/   r   listrB   rH   rI   s   @r$   r  r    s[    
+E#s(O 
+# 
+QT 
+ei 
+nr 
+ $u||*< r#   r  c                   j     e Zd ZdZdeddf fdZd Zdej                  dej                  fdZ	 xZ
S )	BeitUperHeadz
    Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
    [UPerNet](https://huggingface.co/papers/1807.10221).

    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    rL   r(   Nc                    t         |           |j                  | _        |j                  gdz  | _        |j                  | _        d| _        t        j                  | j
                  |j                  d      | _
        t        | j                  | j                  d   | j
                  | j                        | _        t        | j                  d   t        | j                        | j
                  z  z   | j
                  dd      | _        t        j                          | _        t        j                          | _        | j                  d d D ]s  }t        || j
                  d      }t        | j
                  | j
                  dd      }| j"                  j'                  |       | j$                  j'                  |       u t        t        | j                        | j
                  z  | j
                  dd      | _        y )	N   Fr   r  rf   )rl   r   r   r  )r;   r<   r  rP   r  r  rl   r   r   r  r  r  psp_modulesr  len
bottleneckr8  lateral_convs	fpn_convsr  fpn_bottleneck)r=   rL   r  l_convfpn_convr>   s        r$   r<   zBeitUperHead.__init__  s   !--"../!3**"))DMM63D3DRST 4R MM,,	
 )R 3t'7'7#84==#HHMM	
  ]]_++CR0 	,K#KANF%dmmT]]PQ[\]H%%f-NN!!(+		, -  !DMM1MM	
r#   c                     |d   }|g}|j                  | j                  |             t        j                  |d      }| j	                  |      }|S r   )extendr  r/   rv   r  )r=   inputsr=  psp_outsr5   s        r$   psp_forwardzBeitUperHead.psp_forward-  sL    2J3((+,99X1-*r#   encoder_hidden_statesc                 P   t        | j                        D cg c]  \  }} |||          }}}|j                  | j                  |             t	        |      }t        |dz
  dd      D ]V  }||dz
     j                  dd  }||dz
     t        j                  j                  ||   |d| j                        z   ||dz
  <   X t        |dz
        D cg c]  } | j                  |   ||          }}|j                  |d          t        |dz
  dd      D ]E  }t        j                  j                  ||   |d   j                  dd  d| j                        ||<   G t        j                  |d      }| j                  |      }| j                  |      }|S c c}}w c c}w )Nr   r   rf   rg   r&  ri   rm   )rI  r  r  r  r  r9  r-   r   rs   rt   rl   r  r/   rv   r  r  )	r=   r  r?  lateral_convlateralsused_backbone_levels
prev_shapefpn_outsr5   s	            r$   rB   zBeitUperHead.forward6  s   R[\`\n\nRopq,L!6q!9:pp(()>?@  #8}+a/B7 	A!!a%..qr2J&q1uo0I0I*:TM_M_ 1J 1 HQUO	 =BBVYZBZ<[\q%DNN1%hqk2\\%+a/B7 	A--33(1+"3"3AB"7jX\XjXj 4 HQK	 99X1-$$X.(3 q ]s   FF#)r   r   r    r!   r   r<   r  r/   r   rB   rH   rI   s   @r$   r  r    s<    $
z $
d $
LU\\ ell r#   r  c                        e Zd ZdZ	 ddedededeeeef   z  ddf
 fdZd	ej                  dej                  fd
Z
 xZS )BeitFCNHeada  
    Fully Convolution Networks for Semantic Segmentation. This head is implemented of
    [FCNNet](https://huggingface.co/papers/1411.4038>).

    Args:
        config (BeitConfig): Configuration.
        in_channels
        kernel_size (int): The kernel size for convs in the head. Default: 3.
        dilation (int): The dilation rate for convs in the head. Default: 1.


    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
    rL   in_indexr   r  r(   Nc           
      <   t         |           |j                  | _        |j                  | _        |j                  | _        |j                  | _	        || _
        |dz  |z  }g }|j                  t        | j                  | j
                  |||             t        | j                  dz
        D ]5  }|j                  t        | j
                  | j
                  |||             7 | j                  dk(  rt        j                         | _        nt        j"                  | | _        | j                  r8t        | j                  | j
                  z   | j
                  ||dz        | _        t        j&                  | j
                  |j(                  d      | _        y )Nrg   )r   r  r  r   r   r  r  )r;   r<   rP   r  auxiliary_channelsr  auxiliary_num_convs	num_convsauxiliary_concat_inputconcat_inputr  r  r  r9  r   r  convs
Sequentialconv_catr   r  r  )	r=   rL   r  r   r  conv_paddingr  r?  r>   s	           r$   r<   zBeitFCNHead.__init__c  sX    	!--1133"99 #q(H4  $--[R^iq	

 t~~)* 	ALLMM4==kS_jr	 >>QDJ.DJ*  4==0$--[bmqrbrDM ))DMM63D3DRSTr#   r  c                     || j                      }| j                  |      }| j                  r(| j                  t	        j
                  ||gd            }| j                  |      }|S )Nr   rm   )r  r  r  r  r/   rv   r  )r=   r  r?   r5   s       r$   rB   zBeitFCNHead.forward  sX    -dmm<M*]]599mV-D!#LMF(r#   )rg   r   r   )r   r   r    r!   r   r   r   r<   r/   r   rB   rH   rI   s   @r$   r  r  T  sp     no U  U,/ UBE UUX[`adfiai[jUj U	 UDU\\ ell r#   r  c                        e Zd Zdeddf fdZd Ze	 	 	 	 	 	 ddej                  dz  dej                  dz  de	dz  d	e	dz  d
e	de	dz  de
ez  fd       Z xZS )BeitForSemanticSegmentationrL   r(   Nc                 x   t         |   |       |j                  | _        t        |d      | _        t        | j                  j                        dk7  rt        d      t        j                  t        j                  |j                  |j                  dd      t        j                  |j                        t        j                         t        j                  |j                  |j                  dd            | _        t        j                  t        j                  |j                  |j                  dd            | _        t        j"                         | _        t        j&                  dd      | _        t+        |      | _        |j.                  rt1        |      nd | _        | j5                          y )NFr  r  zBeitForSemanticSegmentation requires config.out_indices to be a list of 4 integers, specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of a base-sized architecture.rg   r   )r;   r<   r  rb  rP  r  rL   out_indicesr   r   r  ConvTranspose2drP   r  GELUfpn1fpn2r  fpn3	MaxPool2dfpn4r  decode_headuse_auxiliary_headr  auxiliary_headrj  r   s     r$   r<   z$BeitForSemanticSegmentation.__init__  sO     ++f>	 t{{&&'1,- 
 MMv1163E3EST]^_NN6--.GGIv1163E3EST]^_	
	 MMv1163E3EST]^_
	 KKM	LLQq9	 (/5;5N5Nk&1TX 	r#   c                 n   t         j                  j                  ||j                  dd  dd      }|0t         j                  j                  ||j                  dd  dd      }t	        | j
                  j                        } |||      }|}|% ||      }	|| j
                  j                  |	z  z  }|S )Nr   r&  Fri   )ignore_index)r   rs   rt   r-   r   rL   semantic_loss_ignore_indexauxiliary_loss_weight)
r=   r  auxiliary_logitsr  upsampled_logitsupsampled_auxiliary_logitsr  	main_lossr  auxiliary_losss
             r$   compute_lossz(BeitForSemanticSegmentation.compute_loss  s    ==44bc*5 5 
 ')+)B)B v||BC'8zY^ *C *& $1W1WX-v6	'%&@&INDKK55FFDr#   r~   r  r   r@  r}   rA  c           	      R   ||n| j                   j                  }||n| j                   j                  }|$| j                   j                  dk(  rt	        d      | j                  ||d||      }|r|j                  n|d   }	t        |	      D 
cg c]#  \  }
}|
dz   | j                   j                  v s"|% }}
}|j                  d   }| j                   j                  | j                   j                  z  }|D cg c]3  }|ddddddf   j                  ddd      j                  |d||      5 }}| j                  | j                  | j                   | j"                  g}t%        t'        |            D ]  } ||   ||         ||<    | j)                  |      }d}| j*                  | j+                  |      }d}|| j-                  |||      }|s|r
|f|dd z   }n	|f|dd z   }||f|z   S |S t/        |||r|j                  nd|j0                  	      S c c}}
w c c}w )
a  
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, BeitForSemanticSegmentation
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
        >>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)
        >>> # logits are of shape (batch_size, num_labels, height, width)
        >>> logits = outputs.logits
        ```Nr   z/The number of labels should be greater than oneTr  r   rg   rf   r  )rL   rA  r@  r  r   rP  r?   rI  r  r-   rX   rV   rr   rq   r  r  r  r  r9  r  r  r  r  r   rH  )r=   r~   r  r   r@  r}   rA  rq  r   r  idxfeaturefeaturesr   patch_resolutionr=  opsr?  r  r  r  r5   s                         r$   rB   z#BeitForSemanticSegmentation.forward  s_   H &1%<k$++BYBY$8$D $++JjJj 	 $++"8"8A"=NOO))/!%%=#  
 :E 5 5'RS* 1::O0PwWTWZ[T[_c_j_j_v_vTvGww!''*
;;11T[[5K5KKnv
ijAaQhK1a(00RAQScd
 

 yy$))TYY		:s8}% 	.A #a&!-HQK	. !!(+*#228<$$V-=vFD# WQR[0 WQR[0)-)9TGf$EvE&3G'//T))	
 	
; x
s   #H6H=8H$r  )r   r   r    r   r<   r  r   r/   r   r   r   r   rB   rH   rI   s   @r$   r  r    s    z d @&  -1&*)-,0).#'Y
llT)Y
 t#Y
  $;	Y

 #TkY
 #'Y
 D[Y
 
(	(Y
 Y
r#   r  zM
    BEiT backbone, to be used with frameworks like DETR and MaskFormer.
    c                   x     e Zd Z fdZd Zeee	 	 	 d
dede	dz  de	dz  de	dz  de
f
d	                     Z xZS )BeitBackbonec                    t         |   |       t        |j                  dz         D cg c]  }|j                   c}| _        t        |      | _        t        || j                  j                  j                        | _        |j                  rt        | j                  j                        dk7  rt!        d      |j                  }t#        j$                  t#        j&                  ||dd      t#        j(                  ||j*                        t#        j,                         t#        j&                  ||dd            | _        t#        j$                  t#        j&                  ||dd            | _        t#        j2                         | _        t#        j6                  dd      | _        | j;                          y c c}w )Nr   r   r  zBeitBackbone requires config.out_indices to be a list of 4 integers, specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of a base-sized architecture.rg   r   r   )r;   r<   r9  r6  rP   num_featuresrK   rb   r1  rU   r   re  add_fpnr  rL   r  r   r   r  r  r  batch_norm_epsr  r  r  r  r  r  r  rj  )r=   rL   r   rP   r>   s       r$   r<   zBeitBackbone.__init__'  sN    9>v?W?WZ[?[9\]AV//](0"6t7W7W7c7cd>>4;;**+q0 1 
 !,,K"";STU{0E0EF	"";STU	DI b&8&8k_`ij&klDIDI1=DI 	1 ^s   F?c                 .    | j                   j                  S r:   rl  rD   s    r$   rm  z!BeitBackbone.get_input_embeddingsD  rn  r#   Nr~   r@  r   rA  r(   c                    ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }|j                  d   }| j                  |      \  }\  }}	|j                  dd }
| j                  |d||
|      }|r|j                  n|d   }d}t        | j                  |      D ]e  \  }}|| j                  v s| j                   j                  r5|ddddddf   }|j                  ddd      }|j                  |d||	      }||fz  }g | j                   j                  rY| j                  |d         | j!                  |d         | j#                  |d         | j%                  |d	         g}t'        |      }|s|r|f|dd z   }|S |f|dd z   }|S t)        ||r|j                  nd|j*                  
      S )a  
        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
        >>> model = AutoBackbone.from_pretrained(
        ...     "microsoft/beit-base-patch16-224", out_features=["stage1", "stage2", "stage3", "stage4"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 768, 14, 14]
        ```Nr   rg   T)r@  r   r   rA  r   r"   rf   r   )feature_mapsr?   rH  )rL   rA  r@  r   r-   rb   re  r?   zipstage_namesout_featuresreshape_hidden_statesrr   rq   r  r  r  r  r  r   r   rH  )r=   r~   r@  r   rA  rq  r   rr  r   r   r   r   r?   r  stager  r5   s                    r$   rB   zBeitBackbone.forwardG  s   J &1%<k$++BYBY$8$D $++JjJj 	 2C1N-TXT_T_TqTq!''*
8<8U55<!''+
,,!%/!#  
 2=--'!*#&t'7'7#G 	0E<)));;44#/12q#9L#/#7#71a#@L#/#7#7
BVa#bL/	0 ;;		,q/*		,q/*		,q/*		,q/*	L !.L#&712;6 M '712;6M%3G'//T))
 	
r#   )NNN)r   r   r    r<   rm  r   r   r   r   r   r   rB   rH   rI   s   @r$   r   r   !  s    :0   -1)-#'T
T
 #TkT
  $;	T

 D[T
 
T
  ! T
r#   r   )r  r}  r  rb  rO  r   )r*   F)Jr!   collections.abcrY   r   dataclassesr   r/   r   r   torch.nnr    r   rT  activationsr	   backbone_utilsr
   r   modeling_layersr   modeling_outputsr   r   r   r   r   r   modeling_utilsr   pytorch_utilsr   utilsr   r   r   utils.genericr   configuration_beitr   
get_loggerr   r   r   rF   r   r6   Moduler8   rK   rT   r   r   r   r   r   r   r   r   r   r1  rO  rb  rh  r}  r  r  r  r  r  r  r  r   __all__r"   r#   r$   <module>r     s      !   % & ! H 9  . @ 7 7 - * 
		H	% 
!;  U\\ e T V[VbVb %299 % \7RYY \7~#7")) #7LF		 FR0#- 0#fRYY & ! BII 0ryy  
 
<* <~P3ryy P3fE
")) E
P T/ T T8 D
# D
 D
N & \
!4 \
\
~ 8
!4 8
8
v"RYY "Jbii ""ryy "JR299 Rj8")) 8v N
"5 N
 N
b 
x
="5 x

x
vr#   