o
    ei3                  	   @   sP  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mZmZ ddlmZ eeZeeddG dd deZeeddG dd deZeeddG dd deZ eeddG dd deZ!G dd dej"Z#G dd dej"Z$dBd"ej%d#e&d$e'd%ej%fd&d'Z(G d(d) d)ej"Z)G d*d+ d+ej"Z*G d,d- d-ej"Z+G d.d/ d/ej"Z,G d0d1 d1eZ-G d2d3 d3ej"Z.eG d4d5 d5eZ/eG d6d7 d7e/Z0ed8dG d9d: d:e/Z1ed;dG d<d= d=e/Z2ed>dG d?d@ d@ee/Z3g dAZ4dS )CzPyTorch FocalNet model.    N)	dataclass)nn   )initialization)ACT2FN)BackboneMixin)GradientCheckpointingLayer)BackboneOutput)PreTrainedModel)ModelOutputauto_docstringlogging   )FocalNetConfigzC
    FocalNet encoder's outputs, with potential hidden states.
    )custom_introc                   @   sP   e Zd ZU dZdZejdB ed< dZe	ej dB ed< dZ
e	ej dB ed< dS )FocalNetEncoderOutputa  
    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reshaped_hidden_states)__name__
__module____qualname____doc__r   torchFloatTensor__annotations__r   tupler    r   r   l/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/focalnet/modeling_focalnet.pyr   $   s
   
 	r   zZ
    FocalNet model's outputs that also contains a pooling of the last hidden states.
    c                   @   b   e Zd ZU dZdZejdB ed< dZejdB ed< dZ	e
ej dB ed< dZe
ej dB ed< dS )FocalNetModelOutputa  
    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_outputr   r   )r   r   r   r   r   r   r   r   r!   r   r   r   r   r   r   r   r    9   s   
 r    z.
    FocalNet masked image model outputs.
    c                   @   r   )!FocalNetMaskedImageModelingOutputa  
    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reconstructionr   r   )r   r   r   r   r#   r   r   r   r$   r   r   r   r   r   r   r   r"   Q      
 r"   z4
    FocalNet outputs for image classification.
    c                   @   r   )FocalNetImageClassifierOutputa7  
    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#   logitsr   r   )r   r   r   r   r#   r   r   r   r'   r   r   r   r   r   r   r   r&   k   r%   r&   c                       sN   e Zd ZdZd fdd	Z	ddejdB dejdB deej	 fd	d
Z
  ZS )FocalNetEmbeddingszX
    Construct the patch embeddings and layernorm. Optionally, also the mask token.
    Fc              	      s|   t    t||j|j|j|j|jdd| _| jj	| _
|r(ttdd|jnd | _tj|j|jd| _t|j| _d S )NT)config
image_size
patch_sizenum_channels	embed_dimuse_conv_embedis_stemr   eps)super__init__FocalNetPatchEmbeddingsr*   r+   r,   r-   r.   patch_embeddings	grid_size
patch_gridr   	Parameterr   zeros
mask_token	LayerNormlayer_norm_epsnormDropouthidden_dropout_probdropout)selfr)   use_mask_token	__class__r   r   r3      s   

	 zFocalNetEmbeddings.__init__Npixel_valuesbool_masked_posreturnc           
      C   st   |  |\}}| |}| \}}}|d ur1| j||d}|d|}	|d|	  ||	  }| |}||fS )N      ?)r5   r=   sizer:   expand	unsqueezetype_asr@   )
rA   rE   rF   
embeddingsoutput_dimensions
batch_sizeseq_len_mask_tokensmaskr   r   r   forward   s   

zFocalNetEmbeddings.forward)FN)r   r   r   r   r3   r   r   
BoolTensorr   TensorrU   __classcell__r   r   rC   r   r(      s    r(   c                       sR   e Zd Z			d fdd	Zdd ZdejdB deejee	 f fd	d
Z
  ZS )r4   Fc	                    s
  t    t|tjjr|n||f}t|tjjr|n||f}|d |d  |d |d   }	|| _|| _|| _|	| _	|d |d  |d |d  f| _
|ri|rWd}
d}d}nd}
d}d}tj|||
||d| _n
tj||||d| _|rtj||jd	| _d S d | _d S )
Nr   r            r   )kernel_sizestridepadding)r]   r^   r0   )r2   r3   
isinstancecollectionsabcIterabler*   r+   r,   num_patchesr6   r   Conv2d
projectionr;   r<   r=   )rA   r)   r*   r+   r,   r-   add_normr.   r/   rd   r]   r_   r^   rC   r   r   r3      s0   
 "


z FocalNetPatchEmbeddings.__init__c                 C   s   || j d  dkrd| j d || j d   f}tj||}|| j d  dkr>ddd| j d || j d   f}tj||}|S )Nr   r   )r+   r   
functionalpad)rA   rE   heightwidth
pad_valuesr   r   r   	maybe_pad   s    z!FocalNetPatchEmbeddings.maybe_padrE   NrG   c                 C   s|   |j \}}}}|| jkrtd| |||}| |}|j \}}}}||f}|ddd}| jd ur:| |}||fS )NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.r[   r   )shaper,   
ValueErrorrm   rf   flatten	transposer=   )rA   rE   rR   r,   rj   rk   rN   rO   r   r   r   rU      s   



zFocalNetPatchEmbeddings.forward)FFF)r   r   r   r3   rm   r   r   r   rX   intrU   rY   r   r   rC   r   r4      s    *.	r4           Finput	drop_probtrainingrG   c                 C   sd   |dks|s| S d| }| j d fd| jd   }|tj|| j| jd }|  | || }|S )zc
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    rs   r   r   )r   )dtypedevice)rn   ndimr   randrw   rx   floor_div)rt   ru   rv   	keep_probrn   random_tensoroutputr   r   r   	drop_path   s   r   c                       sT   e Zd ZdZddedB ddf fddZdejdejfdd	Zde	fd
dZ
  ZS )FocalNetDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nru   rG   c                    s   t    || _d S rV   )r2   r3   ru   )rA   ru   rC   r   r   r3     s   

zFocalNetDropPath.__init__r   c                 C   s   t || j| jS rV   )r   ru   rv   )rA   r   r   r   r   rU     s   zFocalNetDropPath.forwardc                 C   s   d| j  S )Nzp=)ru   rA   r   r   r   
extra_repr  s   zFocalNetDropPath.extra_reprrV   )r   r   r   r   floatr3   r   rX   rU   strr   rY   r   r   rC   r   r     s
    r   c                       s&   e Zd Zd fdd	Zdd Z  ZS )	FocalNetModulationr[   Trs   c           	         s$  t    || _|j| | _|j| | _|| _|j| _|j	| _	t
j|d| | jd  |d| _t
j||dd|d| _t
 | _t
||| _t
|| _t
 | _g | _t| jD ](}| j| | j }| jt
t
j|||d||d ddt
  | j| qY| jrt
j||jd| _d S d S )Nr[   r   )bias)r]   r^   r   F)r]   r^   groupsr_   r   r0   )r2   r3   dimfocal_windowsfocal_windowfocal_levelsfocal_levelfocal_factor use_post_layernorm_in_modulationnormalize_modulatorr   Linearprojection_inre   projection_contextGELU
activationprojection_outr>   projection_dropout
ModuleListfocal_layerskernel_sizesrangeappend
Sequentialr;   r<   	layernorm)	rA   r)   indexr   r   r   r   kr]   rC   r   r   r3     s8   
 

zFocalNetModulation.__init__c                 C   s$  |j d }| |dddd }t|||| jd fd\}}}d}t| jD ]}| j| |}|||dd||d f   }q)| 	|j
dddj
ddd}	||	|dd| jdf   }| jrk|| jd  }| |}
||
 }|dddd }| jr| |}| |}| |}|S )	z
        Args:
            hidden_state:
                Input features with shape of (batch_size, height, width, num_channels)
        rH   r   r   r   r[   NT)keepdim)rn   r   permute
contiguousr   splitr   r   r   r   meanr   r   r   r   r   r   )rA   hidden_stater,   xqctxgatesctx_alllevel
ctx_global	modulatorx_outr   r   r   rU   5  s&   
 "



zFocalNetModulation.forward)r[   Trs   r   r   r   r3   rU   rY   r   r   rC   r   r     s    !r   c                       s&   e Zd Zd fdd	Zdd Z  ZS )FocalNetMlpNrs   c                    sR   t    |p|}|p|}t||| _t|j | _t||| _t	|| _
d S rV   )r2   r3   r   r   fc1r   
hidden_actr   fc2r>   drop)rA   r)   in_featureshidden_featuresout_featuresr   rC   r   r   r3   [  s   
zFocalNetMlp.__init__c                 C   s6   |  |}| |}| |}| |}| |}|S rV   )r   r   r   r   )rA   r   r   r   r   rU   d  s   




zFocalNetMlp.forward)NNrs   r   r   r   rC   r   r   Z  s    	r   c                       s*   e Zd ZdZd fdd	Zdd Z  ZS )FocalNetLayera  Focal Modulation Network layer (block).

    Args:
        config (`FocalNetConfig`):
            Model config.
        index (`int`):
            Layer index.
        dim (`int`):
            Number of input channels.
        input_resolution (`tuple[int]`):
            Input resolution.
        drop_path (`float`, *optional*, defaults to 0.0):
            Stochastic depth rate.
    rs   c                    s   t    || _|| _|| _|j| _|j| _tj	||j
d| _t|||| jd| _|dkr1t|nt | _tj	||j
d| _t||j }t|||| jd| _d| _d| _|jrwtj|jt| dd| _tj|jt| dd| _d S d S )Nr0   )r)   r   r   r   rs   )r)   r   r   r   rI   T)requires_grad)r2   r3   r)   r   input_resolutionr?   r   use_post_layernormr   r;   r<   norm1r   
modulationr   Identityr   norm2rr   	mlp_ratior   mlpgamma_1gamma_2use_layerscaler8   layerscale_valuer   ones)rA   r)   r   r   r   r   mlp_hidden_dimrC   r   r   r3   }  s.   
 zFocalNetLayer.__init__c           	   	   C   s   |\}}|j \}}}|}| jr|n| |}|||||}| |||| |}| js/|n| |}|| | j|  }|| | j| jrN| | 	|n| 	| |  }|S rV   )
rn   r   r   viewr   r   r   r   r   r   )	rA   r   input_dimensionsrj   rk   rP   rR   r,   shortcutr   r   r   rU     s   $zFocalNetLayer.forward)rs   )r   r   r   r   r3   rU   rY   r   r   rC   r   r   m  s     r   c                       sB   e Zd Z fddZdejdeeef deej fddZ  Z	S )FocalNetStagec              
      s"  t     | _t j| _ fddt| jD }| | jd k r+|d  nd }| jd k r6tnd }dd tj	d j
t jddD }|t jd  t jd d   t fddt j D | _|d ur| d	|d
 jdd| _nd | _d| _d S )Nc                    s   g | ]	} j d |  qS )r[   )r-   .0i)r)   r   r   
<listcomp>  s    z*FocalNetStage.__init__.<locals>.<listcomp>r   c                 S   s   g | ]}|  qS r   )item)r   r   r   r   r   r     s    r   cpu)rx   c              
      s0   g | ]}t  ttr| nd qS ))r)   r   r   r   r   )r   r`   listr   r)   r   r   r   r   r   r   r     s    r[   TF)r)   r*   r+   r,   r-   rg   r.   r/   )r2   r3   r)   lendepths
num_stagesr   r4   r   linspacedrop_path_ratesumr   r   layersr.   
downsamplepointing)rA   r)   r   r   r-   out_dimr   dprrC   r   r   r3     s6   
$,

zFocalNetStage.__init__r   r   rG   c           	      C   s|   |\}}| j D ]}|||}q|}| jd ur1|\}}|dd|jd d||}| |\}}n||||f}|||f}|S )Nr   r[   r   rH   )r   r   rq   reshapern   )	rA   r   r   rj   rk   layer_module!hidden_states_before_downsamplingrO   stage_outputsr   r   r   rU     s   


zFocalNetStage.forward)
r   r   r   r3   r   rX   r   rr   rU   rY   r   r   rC   r   r     s    .,r   c                       s`   e Zd Z fddZ			ddejdeeef dedB d	edB d
edB dee	B fddZ
  ZS )FocalNetEncoderc                    sH   t    t j| _ | _t fddt| jD | _	d| _
d S )Nc              	      s6   g | ]}t  |d  d|  d d|  fdqS )r   r[   r   )r)   r   r   )r   )r   i_layerr)   r6   r   r   r     s    z,FocalNetEncoder.__init__.<locals>.<listcomp>F)r2   r3   r   r   r   r)   r   r   r   stagesgradient_checkpointing)rA   r)   r6   rC   r   r   r3     s   

zFocalNetEncoder.__init__FTr   r   output_hidden_statesN(output_hidden_states_before_downsamplingreturn_dictrG   c                 C   sz  |rdnd }|r
dnd }|r1|j \}}	}
|j|g||
R  }|dddd}||f7 }||f7 }t| jD ]r\}}|||}|d }|d }|d }|d |d f}|r|r|j \}}	}
|j|g|d |d f|
R  }|dddd}||f7 }||f7 }q6|r|s|j \}}	}
|j|g||
R  }|dddd}||f7 }||f7 }q6|stdd	 ||fD S t|||d
S )Nr   r   r   r   r[   rH   c                 s   s    | ]	}|d ur|V  qd S rV   r   )r   vr   r   r   	<genexpr>6  s    z*FocalNetEncoder.forward.<locals>.<genexpr>)r   r   r   )rn   r   r   	enumerater   r   r   )rA   r   r   r   r   r   all_hidden_statesall_reshaped_hidden_statesrP   rR   hidden_sizereshaped_hidden_stater   stage_moduler   r   rO   r   r   r   rU     sP   





zFocalNetEncoder.forward)FFT)r   r   r   r3   r   rX   r   rr   boolr   rU   rY   r   r   rC   r   r     s$    
r   c                       s@   e Zd ZU eed< dZdZdZdgZe	
  fddZ  ZS )FocalNetPreTrainedModelr)   focalnetrE   Tr   c                    sv   t  | t|tr|jdurt|j dS dS t|tr7| jj	r9t
|j| jj t
|j| jj dS dS dS )zInitialize the weightsN)r2   _init_weightsr`   r(   r:   initzeros_r   r)   r   	constant_r   r   r   )rA   modulerC   r   r   r   G  s   


z%FocalNetPreTrainedModel._init_weights)r   r   r   r   r   base_model_prefixmain_input_namesupports_gradient_checkpointing_no_split_modulesr   no_gradr   rY   r   r   rC   r   r   ?  s   
 r   c                       sj   e Zd Zd fdd	Zdd Ze				ddejdB d	ejdB d
e	dB de	dB de
eB f
ddZ  ZS )FocalNetModelTFc                    s   t  | || _t|j| _t|jd| jd   | _t	||d| _
t|| j
j| _tj| j|jd| _|r<tdnd| _|   dS )z
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        use_mask_token (`bool`, *optional*, defaults to `False`):
            Whether to use a mask token for masked image modeling.
        r[   r   )rB   r0   N)r2   r3   r)   r   r   r   rr   r-   num_featuresr(   rN   r   r7   encoderr   r;   r<   r   AdaptiveAvgPool1dpooler	post_init)rA   r)   add_pooling_layerrB   rC   r   r   r3   V  s   zFocalNetModel.__init__c                 C   s   | j jS rV   )rN   r5   r   r   r   r   get_input_embeddingsk  s   z"FocalNetModel.get_input_embeddingsNrE   rF   r   r   rG   c                 K   s   |dur|n| j j}|dur|n| j j}|du rtd| j||d\}}| j||||d}|d }	| |	}	d}
| jdurM| |	dd}
t	
|
d}
|s[|	|
f|dd  }|S t|	|
|j|jdS )	z
        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).
        Nz You have to specify pixel_values)rF   r   r   r   r   r[   )r   r!   r   r   )r)   r   use_return_dictro   rN   r  r   r  rq   r   rp   r    r   r   )rA   rE   rF   r   r   kwargsembedding_outputr   encoder_outputssequence_outputpooled_outputr   r   r   r   rU   n  s6   

zFocalNetModel.forward)TFNNNN)r   r   r   r3   r	  r   r   r   rW   r   r   r    rU   rY   r   r   rC   r   r  T  s&    r  a  
    FocalNet Model with a decoder on top for masked image modeling.

    This follows the same implementation as 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dZe				ddejdB dejdB dedB dedB de	e
B f
d	d
Z  ZS )FocalNetForMaskedImageModelingc                    sz   t  | t|ddd| _t|j| _t|jd| jd   }t	
t	j||jd |j ddt	|j| _|   d S )NFT)r  rB   r[   r   )in_channelsout_channelsr]   )r2   r3   r  r   r   r   r   rr   r-   r   r   re   encoder_strider,   PixelShuffledecoderr  )rA   r)   r  rC   r   r   r3     s   
z'FocalNetForMaskedImageModeling.__init__NrE   rF   r   r   rG   c                 K   s4  |dur|n| j j}| j||||d}|d }|dd}|j\}}	}
t|
d  }}|||	||}| |}d}|durz| j j	| j j
 }|d||}|| j j
d| j j
dd }tjj||dd	}||  | d
  | j j }|s|f|dd  }|dur|f| S |S t|||j|jd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, FocalNetConfig, FocalNetForMaskedImageModeling
        >>> 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/focalnet-base-simmim-window6-192")
        >>> config = FocalNetConfig()
        >>> model = FocalNetForMaskedImageModeling(config)

        >>> 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.logits
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 192, 192]
        ```N)rF   r   r   r   r   r[   g      ?rH   none)	reductiongh㈵>)r#   r$   r   r   )r)   r  r   rq   rn   mathfloorr   r  r*   r+   repeat_interleaverL   r   r   rh   l1_lossr   r,   r"   r   r   )rA   rE   rF   r   r   r  outputsr  rP   r,   sequence_lengthrj   rk   reconstructed_pixel_valuesmasked_im_lossrJ   rT   reconstruction_lossr   r   r   r   rU     sB   '
 z&FocalNetForMaskedImageModeling.forwardr  )r   r   r   r3   r   r   r   rW   r   r   r"   rU   rY   r   r   rC   r   r    s$    r  z
    FocalNet Model with an image classification head on top (a linear layer on top of the pooled output) e.g. for
    ImageNet.
    c                       r  )FocalNetForImageClassificationc                    sP   t  | |j| _t|| _|jdkrt| jj|jnt | _	| 
  d S )Nr   )r2   r3   
num_labelsr  r   r   r   r  r   
classifierr  rA   r)   rC   r   r   r3     s   
"z'FocalNetForImageClassification.__init__NrE   labelsr   r   rG   c                 K   s   |dur|n| j j}| j|||d}|d }| |}d}	|dur)| ||| j }	|s?|f|dd  }
|	dur=|	f|
 S |
S t|	||j|jd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).
        Nr
  r   r[   )r#   r'   r   r   )r)   r  r   r&  loss_functionr&   r   r   )rA   rE   r(  r   r   r  r  r  r'   r#   r   r   r   r   rU   *  s(   
z&FocalNetForImageClassification.forwardr  )r   r   r   r3   r   r   r   
LongTensorr   r   r&   rU   rY   r   r   rC   r   r$    s$    r$  zG
    FocalNet backbone, to be used with frameworks like X-Decoder.
    c                
       sT   e Zd ZdZdef fddZe		ddejde	dB de	dB d	e
fd
dZ  ZS )FocalNetBackboneFr)   c                    s2   t  | |jg|j | _t|| _|   d S rV   )r2   r3   r-   hidden_sizesr  r  r   r  r'  rC   r   r   r3   ]  s   
zFocalNetBackbone.__init__NrE   r   r   rG   c                 K   s   |dur|n| j j}|dur|n| j j}| j|ddd}|j}d}t| jD ]\}}	|	| jv r6||| f7 }q&|sF|f}
|rD|
|jf7 }
|
S t	||rP|jddS ddd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/focalnet-tiny-lrf")
        >>> model = AutoBackbone.from_pretrained("microsoft/focalnet-tiny-lrf")

        >>> inputs = processor(image, return_tensors="pt")
        >>> outputs = model(**inputs)
        ```NTr
  r   )feature_mapsr   
attentions)
r)   r  r   r   r   r   stage_namesr   r   r	   )rA   rE   r   r   r  r  r   r-  idxstager   r   r   r   rU   f  s.   
zFocalNetBackbone.forward)NN)r   r   r   has_attentionsr   r3   r   r   rX   r   r	   rU   rY   r   r   rC   r   r+  U  s    	r+  )r$  r  r+  r  r   )rs   F)5r   collections.abcra   r  dataclassesr   r   r    r   r   activationsr   backbone_utilsr   modeling_layersr   modeling_outputsr	   modeling_utilsr
   utilsr   r   r   configuration_focalnetr   
get_loggerr   loggerr   r    r"   r&   Moduler(   r4   rX   r   r   r   r   r   r   r   r   r   r   r  r  r$  r+  __all__r   r   r   r   <module>   sz   
( HGEBKLe;C