
    i;              	          d Z ddlZddl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 ddlmZ dd	lmZ dd
lmZ ddlmZ ddlmZ ddlmZmZmZmZ ddlmZ  ej<                  e      Z e ed       G d de                    Z!e ed       G d de                    Z"e ed       G d de                    Z#e ed       G d de                    Z$d Z%d Z& G d d ejN                        Z( G d! d"ejN                        Z) G d# d$ejN                        Z*dJd%ejV                  d&e,d'e-d(ejV                  fd)Z. G d* d+ejN                        Z/ G d, d-ejN                        Z0 G d. d/ejN                        Z1 G d0 d1ejN                        Z2 G d2 d3ejN                        Z3 G d4 d5ejN                        Z4 G d6 d7ejN                        Z5 G d8 d9e      Z6 G d: d;ejN                        Z7e G d< d=e             Z8e G d> d?e8             Z9 ed@       G dA dBe8             Z: edC       G dD dEe8             Z; edF       G dG dHee8             Z<g dIZ=y)KzPyTorch Swin Transformer model.    N)	dataclass)nn   )initialization)ACT2FN)BackboneMixin)GradientCheckpointingLayer)BackboneOutput)PreTrainedModel)meshgrid)ModelOutputauto_docstringlogging	torch_int   )
SwinConfigzN
    Swin encoder's outputs, with potential hidden states and attentions.
    )custom_introc                       e Zd ZU dZdZej                  dz  ed<   dZe	ej                  df   dz  ed<   dZ
e	ej                  df   dz  ed<   dZe	ej                  df   dz  ed<   y)SwinEncoderOutputa  
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    Nlast_hidden_state.hidden_states
attentionsreshaped_hidden_states)__name__
__module____qualname____doc__r   torchFloatTensor__annotations__r   tupler   r        p/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/transformers/models/swin/modeling_swin.pyr   r   (   s}     37u((4/6:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr#   r   zV
    Swin model's outputs that also contains a pooling of the last hidden states.
    c                       e Zd ZU dZdZej                  dz  ed<   dZej                  dz  ed<   dZ	e
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   y)	SwinModelOutputa  
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
        Average pooling of the last layer hidden-state.
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    Nr   pooler_output.r   r   r   )r   r   r   r   r   r   r   r    r'   r   r!   r   r   r"   r#   r$   r&   r&   >   s    	 37u((4/6.2M5$$t+2:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr#   r&   z*
    Swin masked image model outputs.
    c                       e Zd ZU dZdZej                  dz  ed<   dZej                  dz  ed<   dZ	e
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   y)	SwinMaskedImageModelingOutputa  
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
        Masked image modeling (MLM) loss.
    reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
        Reconstructed pixel values.
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    Nlossreconstruction.r   r   r   )r   r   r   r   r*   r   r   r    r+   r   r!   r   r   r"   r#   r$   r)   r)   W   s     &*D%

d
")/3NE%%,3:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr#   r)   z0
    Swin outputs for image classification.
    c                       e Zd ZU dZdZej                  dz  ed<   dZej                  dz  ed<   dZ	e
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   dZe
ej                  df   dz  ed<   y)	SwinImageClassifierOutputa7  
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Classification (or regression if config.num_labels==1) loss.
    logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Classification (or regression if config.num_labels==1) scores (before SoftMax).
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    Nr*   logits.r   r   r   )r   r   r   r   r*   r   r   r    r.   r   r!   r   r   r"   r#   r$   r-   r-   r   s     &*D%

d
")'+FE$+:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr#   r-   c                     | j                   \  }}}}| j                  |||z  |||z  ||      } | j                  dddddd      j                         j                  d|||      }|S )z2
    Partitions the given input into windows.
    r   r   r            shapeviewpermute
contiguous)input_featurewindow_size
batch_sizeheightwidthnum_channelswindowss          r$   window_partitionr@      s}     /<.A.A+J|!&&Fk);8Lk[gM ##Aq!Q15@@BGGKYdfrsGNr#   c                     | j                   d   }| j                  d||z  ||z  |||      } | j                  dddddd      j                         j                  d|||      } | S )z?
    Merges windows to produce higher resolution features.
    r3   r   r   r   r0   r1   r2   r4   )r?   r:   r<   r=   r>   s        r$   window_reverserB      sn     ==$Lll2v4e{6JKYdfrsGooaAq!Q/::<AA"feUabGNr#   c            
            e Zd ZdZd fd	Zdej                  dededej                  fdZ	 	 dd	ej                  dz  d
ej                  dz  dedeej                     fdZ xZS )SwinEmbeddingszW
    Construct the patch and position embeddings. Optionally, also the mask token.
    c                 ~   t         |           t        |      | _        | j                  j                  }| j                  j
                  | _        |r4t        j                  t        j                  dd|j                              nd | _        |j                  r=t        j                  t        j                  d|dz   |j                              | _        nd | _        t        j                  |j                        | _        t        j"                  |j$                        | _        |j(                  | _        || _        y )Nr   )super__init__SwinPatchEmbeddingspatch_embeddingsnum_patches	grid_size
patch_gridr   	Parameterr   zeros	embed_dim
mask_tokenuse_absolute_embeddingsposition_embeddings	LayerNormnormDropouthidden_dropout_probdropout
patch_sizeconfig)selfrY   use_mask_tokenrJ   	__class__s       r$   rG   zSwinEmbeddings.__init__   s     3F ;++77//99O]",,u{{1a9I9I'JKcg))')||EKK;QR?TZTdTd4e'fD$'+D$LL!1!12	zz&"<"<= ++r#   
embeddingsr<   r=   returnc                    |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   Nr3         ?r   r   r0   bicubicF)sizemodealign_cornersdim)r5   rR   r   jit
is_tracingrX   r   reshaper7   r   
functionalinterpolater6   cat)rZ   r]   r<   r=   rJ   num_positionsclass_pos_embedpatch_pos_embedrf   
new_height	new_widthsqrt_num_positionss               r$   interpolate_pos_encodingz'SwinEmbeddings.interpolate_pos_encoding   s`    !&&q)A-0066q9A= yy##%+*F6UZ?+++221bqb59221ab59r"t.
T__,	&}c'9:)11!5GI[]`a)11!Q1=--33i(	 4 
 *11!Q1=BB1b#Nyy/?;CCr#   Npixel_valuesbool_masked_posrs   c                    |j                   \  }}}}| j                  |      \  }}	| j                  |      }|j                         \  }
}}|K| j                  j                  |
|d      }|j                  d      j                  |      }|d|z
  z  ||z  z   }| j                  (|r|| j                  |||      z   }n|| j                  z   }| j                  |      }||	fS )Nr3   g      ?)r5   rI   rT   rb   rP   expand	unsqueezetype_asrR   rs   rW   )rZ   rt   ru   rs   _r>   r<   r=   r]   output_dimensionsr;   seq_lenmask_tokensmasks                 r$   forwardzSwinEmbeddings.forward   s     *6););&<(,(=(=l(K%
%YYz*
!+!2
GQ&//00WbIK",,R088ED#sTz2[45GGJ##/''$*G*G
TZ\a*bb
'$*B*BB
\\*-
,,,r#   )FNF)r   r   r   r   rG   r   Tensorintrs   r   
BoolTensorboolr!   r   __classcell__r\   s   @r$   rD   rD      s    &&D5<< &D &DUX &D]b]i]i &DV 48).	-''$.- ))D0- #'	-
 
u||	-r#   rD   c                   v     e Zd ZdZ fdZd Zdej                  dz  deej                  ee
   f   fdZ xZS )rH   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)rF   rG   
image_sizerX   r>   rO   
isinstancecollectionsabcIterablerJ   rK   r   Conv2d
projection)rZ   rY   r   rX   r>   hidden_sizerJ   r\   s          r$   rG   zSwinPatchEmbeddings.__init__  s    !'!2!2F4E4EJ
$*$7$79I9Ik#-j+//:R:R#SZZdfpYq
#-j+//:R:R#SZZdfpYq
!!}
15*Q-:VW=:XY$$(&$Q-:a=8*Q-:VW=:XY))L+:^hir#   c                 n   || j                   d   z  dk7  rDd| j                   d   || j                   d   z  z
  f}t        j                  j                  ||      }|| j                   d   z  dk7  rFddd| j                   d   || j                   d   z  z
  f}t        j                  j                  ||      }|S )Nr   r   )rX   r   rj   pad)rZ   rt   r<   r=   
pad_valuess        r$   	maybe_padzSwinPatchEmbeddings.maybe_pad  s    4??1%%*T__Q/%$//!:L2LLMJ==,,\:FLDOOA&&!+Q4??1#5QRAS8S#STJ==,,\:FLr#   rt   Nr^   c                     |j                   \  }}}}| j                  |||      }| j                  |      }|j                   \  }}}}||f}|j                  d      j	                  dd      }||fS )Nr0   r   )r5   r   r   flatten	transpose)rZ   rt   rz   r>   r<   r=   r]   r{   s           r$   r   zSwinPatchEmbeddings.forward  s}    )5););&<~~lFEB__\2
(..1fe#UO''*44Q:
,,,r#   )r   r   r   r   rG   r   r   r   r!   r   r   r   r   r   s   @r$   rH   rH      sF    j	-E$5$5$< 	-u||UZ[^U_G_A` 	-r#   rH   c            	            e Zd ZdZej
                  fdee   dedej                  ddf fdZ	d Z
d	ej                  d
eeef   dej                  fdZ xZS )SwinPatchMerginga'  
    Patch Merging Layer.

    Args:
        input_resolution (`tuple[int]`):
            Resolution of input feature.
        dim (`int`):
            Number of input channels.
        norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
            Normalization layer class.
    input_resolutionrf   
norm_layerr^   Nc                     t         |           || _        || _        t	        j
                  d|z  d|z  d      | _         |d|z        | _        y )Nr1   r0   Fbias)rF   rG   r   rf   r   Linear	reductionrT   )rZ   r   rf   r   r\   s       r$   rG   zSwinPatchMerging.__init__7  sI     01s7AG%@q3w'	r#   c                     |dz  dk(  xs |dz  dk(  }|r.ddd|dz  d|dz  f}t         j                  j                  ||      }|S )Nr0   r   r   )r   rj   r   )rZ   r9   r<   r=   
should_padr   s         r$   r   zSwinPatchMerging.maybe_pad>  sU    qjAo:519>
Q519a!<JMM--mZHMr#   r9   input_dimensionsc                    |\  }}|j                   \  }}}|j                  ||||      }| j                  |||      }|d d dd ddd dd d f   }|d d dd ddd dd d f   }	|d d dd ddd dd d f   }
|d d dd ddd dd d f   }t        j                  ||	|
|gd      }|j                  |dd|z        }| j                  |      }| j                  |      }|S )Nr   r0   r   r3   r1   )r5   r6   r   r   rl   rT   r   )rZ   r9   r   r<   r=   r;   rf   r>   input_feature_0input_feature_1input_feature_2input_feature_3s               r$   r   zSwinPatchMerging.forwardF  s   ((5(;(;%
C%**:vulS}feD'14a4Aq(89'14a4Aq(89'14a4Aq(89'14a4Aq(89		?O_Ve"fhjk%**:r1|;KL		-0}5r#   )r   r   r   r   r   rS   r!   r   ModulerG   r   r   r   r   r   r   s   @r$   r   r   *  sr    
 XZWcWc (s (# (299 (hl (U\\ U3PS8_ Y^YeYe r#   r   input	drop_probtrainingr^   c                    |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)r5   ndimr   randr   r   floor_div)r   r   r   	keep_probr5   random_tensoroutputs          r$   	drop_pathr   a  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 )
SwinDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr   r^   c                 0    t         |           || _        y N)rF   rG   r   )rZ   r   r\   s     r$   rG   zSwinDropPath.__init__t  s    "r#   r   c                 D    t        || j                  | j                        S r   )r   r   r   rZ   r   s     r$   r   zSwinDropPath.forwardx  s    FFr#   c                      d| j                    S )Nzp=)r   rZ   s    r$   
extra_reprzSwinDropPath.extra_repr{  s    DNN#$$r#   r   )r   r   r   r   floatrG   r   r   r   strr   r   r   s   @r$   r   r   q  sG    b#%$, #$ #GU\\ Gell G%C %r#   r   c            
            e Zd Z fdZ	 	 d	dej
                  dej                  dz  dedz  deej
                     fdZ	d Z
 xZS )
SwinSelfAttentionc                    t         |           ||z  dk7  rt        d| d| d      || _        t	        ||z        | _        | j                  | j
                  z  | _        t        |t        j                  j                        r|n||f| _        t        j                  t        j                  d| j                  d   z  dz
  d| j                  d   z  dz
  z  |            | _        | j#                  d| j%                                t        j&                  | j                  | j                  |j(                        | _        t        j&                  | j                  | j                  |j(                        | _        t        j&                  | j                  | j                  |j(                        | _        t        j0                  |j2                        | _        y )	Nr   zThe hidden size (z6) is not a multiple of the number of attention heads ()r0   r   relative_position_indexr   )rF   rG   
ValueErrornum_attention_headsr   attention_head_sizeall_head_sizer   r   r   r   r:   r   rM   r   rN   relative_position_bias_tableregister_buffercreate_relative_position_indexr   qkv_biasquerykeyvaluerU   attention_probs_dropout_probrW   rZ   rY   rf   	num_headsr:   r\   s        r$   rG   zSwinSelfAttention.__init__  s   ?a#C5(^_h^iijk  $- #&sY#7 !558P8PP%k;??3K3KLKS^`kRl 	 -/LLKKT--a0014T=M=Ma=P9PST9TUW`a-
) 	68[8[8]^YYt1143E3EFOO\
99T//1C1C&//ZYYt1143E3EFOO\
zz&"E"EFr#   Nr   attention_maskoutput_attentionsr^   c                    |j                   \  }}}||d| j                  f}| 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                  | j                  j                  d         }|j                  | j                  d   | j                  d   z  | j                  d   | j                  d   z  d      }|j                  ddd      j                         }||j!                  d      z   }|r|j                   d   }|j                  ||z  || j"                  ||      }||j!                  d      j!                  d      z   }|j                  d| j"                  ||      }t$        j&                  j)                  |d      }| j+                  |      }t        j                  ||
      }|j                  dddd      j                         }|j-                         d d | j.                  fz   }|j                  |      }|r||f}|S |f}|S )Nr3   r   r0   r   re   r   )r5   r   r   r6   r   r   r   r   matmulmathsqrtr   r   r:   r7   r8   rx   r   r   rj   softmaxrW   rb   r   )rZ   r   r   r   r;   rf   r>   hidden_shapequery_layer	key_layervalue_layerattention_scoresrelative_position_bias
mask_shapeattention_probscontext_layernew_context_layer_shapeoutputss                     r$   r   zSwinSelfAttention.forward  s    )6(;(;%
C"CT-E-EF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!%!B!B4C_C_CdCdegCh!i!7!<!<Q$"2"21"55t7G7G7JTM]M]^_M`7`bd"
 "8!?!?1a!H!S!S!U+.D.N.Nq.QQ%'--a0J/44j(*d6N6NPSUX   0.2J2J12M2W2WXY2ZZ/44R9Q9QSVX[\ --//0@b/I ,,7_kB%--aAq9DDF"/"4"4"6s";t?Q?Q>S"S%**+BC6G=/2 O\M]r#   c                 r   t        j                  | j                  d         }t        j                  | j                  d         }t        j                  t	        ||gd            }t        j
                  |d      }|d d d d d f   |d d d d d f   z
  }|j                  ddd      j                         }|d d d d dfxx   | j                  d   dz
  z  cc<   |d d d d dfxx   | j                  d   dz
  z  cc<   |d d d d dfxx   d| j                  d   z  dz
  z  cc<   |j                  d      }|S )Nr   r   ij)indexingr0   r3   )	r   aranger:   stackr   r   r7   r8   sum)rZ   coords_hcoords_wcoordscoords_flattenrelative_coordsr   s          r$   r   z0SwinSelfAttention.create_relative_position_index  s)   << 0 0 34<< 0 0 34Xx&:TJKvq1(At4~aqj7QQ)11!Q:EEG1a D$4$4Q$7!$;; 1a D$4$4Q$7!$;; 1a A(8(8(;$;a$?? "1"5"5b"9&&r#   r   )r   r   r   rG   r   r   r   r   r!   r   r   r   r   s   @r$   r   r     s^    G: 48).	1||1 ))D01  $;	1
 
u||	1f'r#   r   c                   n     e Zd Z fdZdej
                  dej
                  dej
                  fdZ xZS )SwinSelfOutputc                     t         |           t        j                  ||      | _        t        j
                  |j                        | _        y r   )rF   rG   r   r   denserU   r   rW   rZ   rY   rf   r\   s      r$   rG   zSwinSelfOutput.__init__  s6    YYsC(
zz&"E"EFr#   r   input_tensorr^   c                 J    | j                  |      }| j                  |      }|S r   r   rW   )rZ   r   r   s      r$   r   zSwinSelfOutput.forward  s$    

=1]3r#   r   r   r   rG   r   r   r   r   r   s   @r$   r   r     s2    G
U\\  RWR^R^ r#   r   c            
            e Zd Z fdZ	 	 ddej
                  dej                  dz  dedz  deej
                     fdZ	 xZ
S )	SwinAttentionc                 j    t         |           t        ||||      | _        t	        ||      | _        y r   )rF   rG   r   rZ   r   r   r   s        r$   rG   zSwinAttention.__init__  s.    %fc9kJ	$VS1r#   Nr   r   r   r^   c                 h    | j                  |||      }| j                  |d   |      }|f|dd  z   }|S )Nr   r   )rZ   r   )rZ   r   r   r   self_outputsattention_outputr   s          r$   r   zSwinAttention.forward  sE     yy@QR;;|AF#%QR(88r#   r   )r   r   r   rG   r   r   r   r   r!   r   r   r   s   @r$   r  r    sW    2 48).		||	 ))D0	  $;		
 
u||		r#   r  c                   V     e Zd Z fdZdej
                  dej
                  fdZ xZS )SwinIntermediatec                    t         |           t        j                  |t	        |j
                  |z              | _        t        |j                  t              rt        |j                     | _        y |j                  | _        y r   )rF   rG   r   r   r   	mlp_ratior   r   
hidden_actr   r   intermediate_act_fnr   s      r$   rG   zSwinIntermediate.__init__  sa    YYsC(8(83(>$?@
f''-'-f.?.?'@D$'-'8'8D$r#   r   r^   c                 J    | j                  |      }| j                  |      }|S r   )r   r  r   s     r$   r   zSwinIntermediate.forward  s&    

=100?r#   r   r   s   @r$   r  r    s#    9U\\ ell r#   r  c                   V     e Zd Z fdZdej
                  dej
                  fdZ xZS )
SwinOutputc                     t         |           t        j                  t	        |j
                  |z        |      | _        t        j                  |j                        | _	        y r   )
rF   rG   r   r   r   r
  r   rU   rV   rW   r   s      r$   rG   zSwinOutput.__init__  sF    YYs6#3#3c#9:C@
zz&"<"<=r#   r   r^   c                 J    | j                  |      }| j                  |      }|S r   r   r   s     r$   r   zSwinOutput.forward  s$    

=1]3r#   r   r   s   @r$   r  r  
  s#    >
U\\ ell r#   r  c                        e Zd Zd fd	Zd Zd Zd Z	 	 ddej                  de	e
e
f   dedz  d	edz  d
e	ej                  ej                  f   f
dZ xZS )	SwinLayerc                    t         |           |j                  | _        || _        |j                  | _        || _        t        j                  ||j                        | _	        t        |||| j                        | _        |dkD  rt        |      nt        j                         | _        t        j                  ||j                        | _        t!        ||      | _        t%        ||      | _        y )Neps)r:   r   )rF   rG   chunk_size_feed_forward
shift_sizer:   r   r   rS   layer_norm_epslayernorm_beforer  	attentionr   Identityr   layernorm_afterr  intermediater  r   )rZ   rY   rf   r   r   drop_path_rater  r\   s          r$   rG   zSwinLayer.__init__  s    '-'E'E$$!-- 0 "Sf6K6K L&vsI4K[K[\9G#9Mn5SUS^S^S`!||CV5J5JK,VS9 -r#   c                    t        |      | j                  k  rgt        d      | _        t        j
                  j                         r(t	        j                   t	        j                  |            n
t        |      | _        y y Nr   )minr:   r   r  r   rg   rh   tensor)rZ   r   s     r$   set_shift_and_window_sizez#SwinLayer.set_shift_and_window_size$  s\     D$4$44'lDO=BYY=Q=Q=S		%,,'789Y\]mYn  5r#   c           	         | j                   dkD  rht        j                  d||df||      }t        d| j                         t        | j                   | j                          t        | j                    d       f}t        d| j                         t        | j                   | j                          t        | j                    d       f}d}|D ]  }	|D ]  }
||d d |	|
d d f<   |dz  }  t        || j                        }|j                  d| j                  | j                  z        }|j                  d      |j                  d      z
  }|j                  |dk7  d      j                  |dk(  d      }|S d }|S )Nr   r   r   r3   r0   g      Yr   )	r  r   rN   slicer:   r@   r6   rx   masked_fill)rZ   r<   r=   r   r   img_maskheight_sliceswidth_slicescountheight_slicewidth_slicemask_windows	attn_masks                r$   get_attn_maskzSwinLayer.get_attn_mask,  s   ??Q{{Avua#8fUHa$***+t'''$//)9:t&-M a$***+t'''$//)9:t&-L
 E - #/ K@EHQk1<=QJE
 ,Hd6F6FGL',,R1A1ADDTDT1TUL$..q1L4J4J14MMI!--i1nfEQQR[_`R`befI  Ir#   c                     | j                   || j                   z  z
  | j                   z  }| j                   || j                   z  z
  | j                   z  }ddd|d|f}t        j                  j                  ||      }||fS r!  )r:   r   rj   r   )rZ   r   r<   r=   	pad_right
pad_bottomr   s          r$   r   zSwinLayer.maybe_padH  s    %%0@0@(@@DDTDTT	&&$2B2B)BBdFVFVV
Ay!Z8
))-Dj((r#   r   r   r   Nalways_partitionr^   c                    |s| j                  |       n	 |\  }}|j                         \  }}}	|}
| j                  |      }|j                  ||||	      }| j	                  |||      \  }}|j
                  \  }}}}| j                  dkD  r1t        j                  || j                   | j                   fd      }n|}t        || j                        }|j                  d| j                  | j                  z  |	      }| j                  |||j                  |j                        }| j                  |||      }|d   }|j                  d| j                  | j                  |	      }t        || j                  ||      }| j                  dkD  r/t        j                  || j                  | j                  fd      }n|}|d   dkD  xs |d   dkD  }|r|d d d |d |d d f   j!                         }|j                  |||z  |	      }|
| j#                  |      z   }| j%                  |      }| j'                  |      }|| j)                  |      z   }|r	||d	   f}|S |f}|S )
Nr   )r   r0   )shiftsdimsr3   r   )r   r   r2   r   )r$  rb   r  r6   r   r5   r  r   rollr@   r:   r0  r   r   r  rB   r8   r   r  r  r   )rZ   r   r   r   r4  r<   r=   r;   rz   channelsshortcutr   
height_pad	width_padshifted_hidden_stateshidden_states_windowsr/  attention_outputsr  attention_windowsshifted_windows
was_paddedlayer_outputlayer_outputss                           r$   r   zSwinLayer.forwardO  s     **+;<("/"4"4"6
Ax --m<%**:vuhO %)NN=&%$P!z&3&9&9#:y!??Q$)JJ}tFVY]YhYhXhEipv$w!$1! !11FHXHX Y 5 : :2t?O?ORVRbRb?bdl m&&	)<)<EZEaEa ' 
	 !NN+@)_pNq,Q/,11"d6F6FHXHXZbc():D<L<LjZcd ??Q %

?DOOUYUdUdCelr s /]Q&;*Q-!*;
 1!WfWfufa2G H S S U-22:v~xX 4>>2C#DD++M:((6$t{{<'@@@Q'8';< YeWfr#   )r   r   FF)r   r   r   rG   r$  r0  r   r   r   r!   r   r   r   r   r   s   @r$   r  r    sz    .8) */(->||>  S/>  $;	>
 +> 
u||U\\)	*>r#   r  c                        e Zd Z fdZ	 	 d	dej
                  deeef   dedz  dedz  deej
                     f
dZ	 xZ
S )
	SwinStagec                 h   t         	|           || _        || _        t	        j
                  t        |      D cg c]-  }t        ||||||   |dz  dk(  rdn|j                  dz        / c}      | _	        |& |||t        j                        | _        d| _        y d | _        d| _        y c c}w )Nr0   r   )rY   rf   r   r   r  r  )rf   r   F)rF   rG   rY   rf   r   
ModuleListranger  r:   blocksrS   
downsamplepointing)
rZ   rY   rf   r   depthr   r   rL  ir\   s
            r$   rG   zSwinStage.__init__  s    mm u
  !%5'#,Q<%&UaZqf6H6HA6M

 !()9sr||\DO  #DO'
s   2B/r   r   r   Nr4  r^   c                    |\  }}t        | j                        D ]  \  }} |||||      }	|	d   } |}
| j                  )|dz   dz  |dz   dz  }}||||f}| j                  |
|      }n||||f}||
|f}|r|	dd  z  }|S )Nr   r   r0   )	enumeraterK  rL  )rZ   r   r   r   r4  r<   r=   rO  layer_modulerD  !hidden_states_before_downsamplingheight_downsampledwidth_downsampledr{   stage_outputss                  r$   r   zSwinStage.forward  s     )(5 	-OA|(8HJ[]mnM)!,M	-
 -:)??&5;aZA4EPQ	VWGW 1!'0BDU V OO,MO_`M!' >&(IK\]]12..Mr#   rE  )r   r   r   rG   r   r   r!   r   r   r   r   r   s   @r$   rG  rG    sb    < */(-||  S/  $;	
 + 
u||	r#   rG  c                        e Zd Z fdZ	 	 	 	 	 ddej
                  deeef   dedz  dedz  dedz  dedz  d	edz  d
ee	z  fdZ
 xZS )SwinEncoderc                    t         |           t        |j                        | _        || _        t        j                  d|j                  t        |j                        d      D cg c]  }|j                          }}t        j                  t        | j                        D cg c]  }t        |t        |j                   d|z  z        |d   d|z  z  |d   d|z  z  f|j                  |   |j"                  |   |t        |j                  d |       t        |j                  d |dz           || j                  dz
  k  rt$        nd        c}      | _        d| _        y c c}w c c}w )Nr   cpu)r   r0   r   )rY   rf   r   rN  r   r   rL  F)rF   rG   lendepths
num_layersrY   r   linspacer  r   itemr   rI  rJ  rG  r   rO   r   r   layersgradient_checkpointing)rZ   rY   rK   xdpri_layerr\   s         r$   rG   zSwinEncoder.__init__  sM   fmm,!&63H3H#fmmJ\ej!klAqvvxllmm  %T__5  !F,,q'z9:&/lq'z&BIaLUVX_U_D`%a --0$..w7!#fmmHW&=">V]]S`U\_`U`EaAbc4;dooPQ>Q4Q/X\
 ',#! ms   )E&(B*E+r   r   r   Noutput_hidden_states(output_hidden_states_before_downsamplingr4  return_dictr^   c                    |rdnd }|rdnd }	|rdnd }
|rE|j                   \  }}} |j                  |g|| }|j                  dddd      }||fz  }|	|fz  }	t        | j                        D ]  \  }} |||||      }|d   }|d   }|d   }|d   |d   f}|rP|rN|j                   \  }}} |j                  |g|d   |d   f| }|j                  dddd      }||fz  }|	|fz  }	nI|rG|sE|j                   \  }}} |j                  |g|| }|j                  dddd      }||fz  }|	|fz  }	|s|
|dd  z  }
 |st        d |||
fD              S t        |||
|		      S )
Nr"   r   r   r   r0   r   r3   c              3   &   K   | ]	  }||  y wr   r"   ).0vs     r$   	<genexpr>z&SwinEncoder.forward.<locals>.<genexpr>  s     mq_`_lms   )r   r   r   r   )r5   r6   r7   rQ  r`  r!   r   )rZ   r   r   r   re  rf  r4  rg  all_hidden_statesall_reshaped_hidden_statesall_self_attentionsr;   rz   r   reshaped_hidden_staterO  rR  rD  rS  r{   s                       r$   r   zSwinEncoder.forward  s0    #7BD+?RT"$5b4)6)<)<&J;$6M$6$6z$bDT$bVa$b!$9$A$A!Q1$M!-!11&+@*BB&(5 	9OA|(8HJ[]mnM)!,M0=a0@- -a 0 1" 57H7LM#(P-N-T-T*
A{ )O(I(N(N)"3A"68I!8L!M)OZ)% )>(E(EaAq(Q%!&G%II!*/D.FF*%.V-:-@-@*
A{(:(:(::(fHX(fZe(f%(=(E(EaAq(Q%!m%55!*/D.FF* #}QR'88#9	9< m]4EGZ$[mmm ++*#=	
 	
r#   )FFFFT)r   r   r   rG   r   r   r!   r   r   r   r   r   r   s   @r$   rX  rX    s    ,4 */,1@E(-#'<
||<
  S/<
  $;	<

 #Tk<
 37+<
 +<
 D[<
 
"	"<
r#   rX  c                   h     e Zd ZU eed<   dZdZdZdZdgZ	 e
j                          fd       Z xZS )SwinPreTrainedModelrY   swinrt   )imageTrG  c                    t         |   |       t        |t              rX|j                  t        j                  |j                         |j                   t        j                  |j                         yyt        |t              rNt        j                  |j                         t        j                  |j                  |j                                yy)zInitialize the weightsN)rF   _init_weightsr   rD   rP   initzeros_rR   r   r   copy_r   r   )rZ   moduler\   s     r$   rv  z!SwinPreTrainedModel._init_weights&  s     	f%fn-  ,F--.))5F667 6 12KK;;<JJv55v7\7\7^_ 3r#   )r   r   r   r   r    base_model_prefixmain_input_nameinput_modalitiessupports_gradient_checkpointing_no_split_modulesr   no_gradrv  r   r   s   @r$   rr  rr    sD    $O!&*#$U]]_
` 
`r#   rr  c                        e Zd Zd 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 )	SwinModelc                    t         |   |       || _        t        |j                        | _        t        |j                  d| j
                  dz
  z  z        | _        t        ||      | _
        t        || j                  j                        | _        t        j                  | j                  |j                         | _        |rt        j$                  d      nd| _        | j)                          y)a  
        add_pooling_layer (`bool`, *optional*, defaults to `True`):
            Whether or not to apply pooling layer.
        use_mask_token (`bool`, *optional*, defaults to `False`):
            Whether or not to create and apply mask tokens in the embedding layer.
        r0   r   )r[   r  N)rF   rG   rY   r[  r\  r]  r   rO   num_featuresrD   r]   rX  rL   encoderr   rS   r  	layernormAdaptiveAvgPool1dpooler	post_init)rZ   rY   add_pooling_layerr[   r\   s       r$   rG   zSwinModel.__init__6  s     	 fmm, 0 0119L3M MN(O"64??+E+EFd&7&7V=R=RS1Bb**1- 	r#   c                 .    | j                   j                  S r   r]   rI   r   s    r$   get_input_embeddingszSwinModel.get_input_embeddingsK      ///r#   Nrt   ru   r   re  rs   rg  r^   c                 @   ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }|t	        d      | j                  |||      \  }}	| j                  ||	|||      }
|
d   }| j                  |      }d}| j                  7| j                  |j                  dd            }t        j                  |d      }|s||f|
dd z   }|S t        |||
j                  |
j                  |
j                        S )	z
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        Nz You have to specify pixel_values)ru   rs   )r   re  rg  r   r   r0   )r   r'   r   r   r   )rY   r   re  use_return_dictr   r]   r  r  r  r   r   r   r&   r   r   r   )rZ   rt   ru   r   re  rs   rg  kwargsembedding_outputr   encoder_outputssequence_outputpooled_outputr   s                 r$   r   zSwinModel.forwardN  sJ    2C1N-TXT_T_TqTq$8$D $++JjJj 	 &1%<k$++B]B]?@@-1__/Tl .= .
** ,,/!5# ' 
 *!,..9;;" KK(A(A!Q(GHM!MM-;M%}58KKFM-')77&11#2#I#I
 	
r#   )TFNNNNFN)r   r   r   rG   r  r   r   r   r   r   r!   r&   r   r   r   s   @r$   r  r  4  s    *0  2637)-,0).#'6
''$.6
 ))D06
  $;	6

 #Tk6
 #'6
 D[6
 
	 6
 6
r#   r  ad  
    Swin Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://huggingface.co/papers/2111.09886).

    <Tip>

    Note that we provide a script to pre-train this model on custom data in our [examples
    directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

    </Tip>
    c                        e Zd Z 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 )SwinForMaskedImageModelingc                    t         |   |       t        |dd      | _        t	        |j
                  d|j                  dz
  z  z        }t        j                  t        j                  ||j                  dz  |j                  z  d      t        j                  |j                              | _        | j                          y )NFT)r  r[   r0   r   )in_channelsout_channelsr   )rF   rG   r  rs  r   rO   r]  r   
Sequentialr   encoder_strider>   PixelShuffledecoderr  )rZ   rY   r  r\   s      r$   rG   z#SwinForMaskedImageModeling.__init__  s     fdS	6++aF4E4E4I.JJK}}II(v7L7La7ORXReRe7est OOF112	
 	r#   Nrt   ru   r   re  rs   rg  r^   c                    ||n| j                   j                  }| j                  ||||||      }|d   }	|	j                  dd      }	|	j                  \  }
}}t        j                  |dz        x}}|	j                  |
|||      }	| j                  |	      }d}|| j                   j                  | j                   j                  z  }|j                  d||      }|j                  | j                   j                  d      j                  | j                   j                  d      j                  d      j                         }t        j                  j!                  ||d	      }||z  j#                         |j#                         d
z   z  | j                   j$                  z  }|s|f|dd z   }||f|z   S |S t'        |||j(                  |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).

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, SwinForMaskedImageModeling
        >>> 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/swin-base-simmim-window6-192")
        >>> model = SwinForMaskedImageModeling.from_pretrained("microsoft/swin-base-simmim-window6-192")

        >>> 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, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 192, 192]
        ```N)ru   r   re  rs   rg  r   r   r0   r`   r3   none)r   gh㈵>)r*   r+   r   r   r   )rY   r  rs  r   r5   r   floorri   r  r   rX   repeat_interleaverx   r8   r   rj   l1_lossr   r>   r)   r   r   r   )rZ   rt   ru   r   re  rs   rg  r  r   r  r;   r>   sequence_lengthr<   r=   reconstructed_pixel_valuesmasked_im_lossrb   r~   reconstruction_lossr   s                        r$   r   z"SwinForMaskedImageModeling.forward  s   P &1%<k$++B]B]))+/!5%=#  
 "!*)33Aq94C4I4I1
L/OS$899)11*lFTYZ &*\\/%B"&;;))T[[-C-CCD-55b$EO11$++2H2H!L""4;;#9#91=1	  #%--"7"7F`lr"7"s1D8==?488:PTCTUX\XcXcXpXppN02WQR[@F3A3M^%.YSYY,5!//))#*#A#A
 	
r#   r  )r   r   r   rG   r   r   r   r   r   r!   r)   r   r   r   s   @r$   r  r    s       2637)-,0).#'S
''$.S
 ))D0S
  $;	S

 #TkS
 #'S
 D[S
 
.	.S
 S
r#   r  a  
    Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.

    <Tip>

        Note that it's possible to fine-tune Swin on higher resolution images than the ones it has been trained on, by
        setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
        position embeddings to the higher resolution.

    </Tip>
    c                        e Zd Z 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 )SwinForImageClassificationc                 >   t         |   |       |j                  | _        t        |      | _        |j                  dkD  r4t        j                  | j                  j                  |j                        nt        j                         | _	        | j                          y r!  )rF   rG   
num_labelsr  rs  r   r   r  r  
classifierr  )rZ   rY   r\   s     r$   rG   z#SwinForImageClassification.__init__  sx      ++f%	 EKDUDUXYDYBIIdii,,f.?.?@_a_j_j_l 	
 	r#   Nrt   labelsr   re  rs   rg  r^   c                 V   ||n| j                   j                  }| j                  |||||      }|d   }	| j                  |	      }
d}|| j	                  ||
| j                         }|s|
f|dd z   }||f|z   S |S t        ||
|j                  |j                  |j                        S )a  
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        N)r   re  rs   rg  r   r0   )r*   r.   r   r   r   )	rY   r  rs  r  loss_functionr-   r   r   r   )rZ   rt   r  r   re  rs   rg  r  r   r  r.   r*   r   s                r$   r   z"SwinForImageClassification.forward  s    " &1%<k$++B]B]))/!5%=#  
  
/%%ffdkkBDY,F)-)9TGf$EvE(!//))#*#A#A
 	
r#   r  )r   r   r   rG   r   r   r   
LongTensorr   r!   r-   r   r   r   s   @r$   r  r    s      26*.)-,0).#',
''$.,
   4',
  $;	,

 #Tk,
 #',
 D[,
 
*	*,
 ,
r#   r  zM
    Swin backbone, to be used with frameworks like DETR and MaskFormer.
    c                   t     e Zd Zdef fdZd Z	 	 	 ddej                  dedz  dedz  dedz  d	e	f
d
Z
 xZS )SwinBackbonerY   c           	          t         |   |       |j                  gt        t	        |j
                              D cg c]  }t        |j                  d|z  z         c}z   | _        t        |      | _	        t        || j                  j                        | _        i }t        | j                  | j                        D ]  \  }}t!        j"                  |      ||<    t!        j$                  |      | _        | j)                          y c c}w )Nr0   )rF   rG   rO   rJ  r[  r\  r   r  rD   r]   rX  rL   r  zipout_featuresr9  r   rS   
ModuleDicthidden_states_normsr  )rZ   rY   rO  r  stager>   r\   s         r$   rG   zSwinBackbone.__init__O  s     #--.X]^abhbobo^pXq1rST#f6F6FA6M2N1rr(0"64??+E+EF !#&t'8'8$--#H 	DE<)+l)C&	D#%==1D#E  	 2ss   "Dc                 .    | j                   j                  S r   r  r   s    r$   r  z!SwinBackbone.get_input_embeddings_  r  r#   Nrt   re  r   rg  r^   c           	         ||n| j                   j                  }||n| j                   j                  }||n| j                   j                  }| j	                  |      \  }}| j                  |||dddd      }|j                  }	d}
t        | j                  |	      D ]  \  }}|| j                  v s|j                  \  }}}}|j                  dddd      j                         }|j                  |||z  |      } | j                  |   |      }|j                  ||||      }|j                  dddd      j                         }|
|fz  }
 |s|
f}|r||j                  fz  }|S t!        |
|r|j                  nd|j"                  	      S )
a  
        Returns:

        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("shi-labs/nat-mini-in1k-224")
        >>> model = AutoBackbone.from_pretrained(
        ...     "microsoft/swin-tiny-patch4-window7-224", 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, 7, 7]
        ```NT)r   re  rf  r4  rg  r"   r   r0   r   r   )feature_mapsr   r   )rY   r  re  r   r]   r  r   r  stage_namesr  r5   r7   r8   r6   r  r   r
   r   )rZ   rt   re  r   rg  r  r  r   r   r   r  r  hidden_stater;   r>   r<   r=   r   s                     r$   r   zSwinBackbone.forwardb  s   F &1%<k$++B]B]$8$D $++JjJj 	 2C1N-TXT_T_TqTq-1__\-J**,,/!%59!  
  66#&t'7'7#G 	0E<))):F:L:L7
L&%+33Aq!Q?JJL+00Ve^\Z>t77>|L+00VULY+33Aq!Q?JJL/	0 "_F#70022M%3G'//T))
 	
r#   )NNN)r   r   r   r   rG   r  r   r   r   r
   r   r   r   s   @r$   r  r  I  sj    z  0 -1)-#'L
llL
 #TkL
  $;	L

 D[L
 
L
r#   r  )r  r  r  rr  r  )r   F)>r   collections.abcr   r   dataclassesr   r   r    r   rw  activationsr   backbone_utilsr   modeling_layersr	   modeling_outputsr
   modeling_utilsr   pytorch_utilsr   utilsr   r   r   r   configuration_swinr   
get_loggerr   loggerr   r&   r)   r-   r@   rB   r   rD   rH   r   r   r   r   r   r   r   r   r  r  r  r  rG  rX  rr  r  r  r  r  __all__r"   r#   r$   <module>r     s   &   !   & ! + 9 . - % D D * 
		H	% 
H H H  
Hk H H& 
HK H H* 
H H H*	Y-RYY Y-x(-")) (-V3ryy 3nU\\ e T V[VbVb  %299 %Z'		 Z'z
RYY 
BII $ryy 	 	w		 wt4* 4nS
")) S
l `/ ` `, P
# P
 P
f 	e
!4 e
e
P <
!4 <
<
~ 
`
="5 `

`
Fr#   