
    sg(;                         d Z ddlmZmZmZmZ ddlZddlm	Z	m
Z
mZ ddlmZmZ ddlmZmZmZmZmZmZmZmZmZmZmZ ddlmZmZmZmZ  e       rddl Z  ejB                  e"      Z# G d	 d
e	      Z$y)zImage processor class for DeiT.    )DictListOptionalUnionN   )BaseImageProcessorBatchFeatureget_size_dict)resizeto_channel_dimension_format)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension
ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_arrayvalid_imagesvalidate_preprocess_arguments)
TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc                   x    e Zd ZdZdgZddej                  j                  dddddddf
dede	e
ef   ded	ed
e	e
ef   deeef   dededeeeee   f      deeeee   f      ddf fdZej                  ddfdej&                  de	e
ef   dedeee
ef      deee
ef      dej&                  fdZ e       dddddddddddej.                  dfdedede	e
ef   d	ed
e	e
ef   dedededeeeee   f      deeeee   f      deee
ef      dedeee
ef      dej                  j                  fd       Z xZS )DeiTImageProcessora	  
    Constructs a DeiT image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
            `do_resize` in `preprocess`.
        size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
            Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
        resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`):
            Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
            is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`.
        crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
            Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
            `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
            parameter in the `preprocess` method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method.
        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
    pixel_valuesTNgp?	do_resizesizeresampledo_center_crop	crop_sizerescale_factor
do_rescaledo_normalize
image_mean	image_stdreturnc                 0   t        |   di | ||nddd}t        |      }||nddd}t        |d      }|| _        || _        || _        || _        || _        || _        || _	        || _
        |	|	nt        | _        |
|
| _        y t        | _        y )N   )heightwidth   r#   
param_name )super__init__r
   r   r    r!   r"   r#   r%   r$   r&   r   r'   r   r(   )selfr   r    r!   r"   r#   r$   r%   r&   r'   r(   kwargs	__class__s               a/var/www/html/venv/lib/python3.12/site-packages/transformers/models/deit/image_processing_deit.pyr3   zDeiTImageProcessor.__init__R   s     	"6"'tc-JT"!*!6IsUX<Y	!)D	"	 ,"$,((2(>*DZ&/&;AV    imagedata_formatinput_data_formatc                     t        |      }d|vsd|vrt        d|j                                |d   |d   f}t        |f||||d|S )a  
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        r,   r-   zFThe `size` dictionary must contain the keys `height` and `width`. Got )r    r!   r:   r;   )r
   
ValueErrorkeysr   )r4   r9   r    r!   r:   r;   r5   output_sizes           r7   r   zDeiTImageProcessor.resizer   sy    F T"47$#6efjfofofqersttH~tG}5
#/
 
 	
r8   imagesreturn_tensorsc                    ||n| j                   }||n| j                  }||n| j                  }||n| j                  }||n| j                  }|	|	n| j
                  }	|
|
n| j                  }
||n| j                  }||n| j                  }t        |      }||n| j                  }t        |d      }t        |      }t        |      st        d      t        |||	|
||||||
       |D cg c]  }t        |       }}t!        |d         r|rt"        j%                  d       |t'        |d         }g }|D ]m  }|r| j)                  ||||      }|r| j+                  |||      }|r| j-                  |||	      }|	r| j/                  ||
||
      }|j1                  |       o |D cg c]  }t3        |||       }}d|i}t5        ||      S c c}w c c}w )a   
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Size of the image after `resize`.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                PILImageResampling filter to use if resizing the image Only has an effect if `do_resize` is set to
                `True`.
            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
                Whether to center crop the image.
            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
                Size of the image after center crop. If one edge the image is smaller than `crop_size`, it will be
                padded with zeros and then cropped
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - `None`: Return a list of `np.ndarray`.
                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        r#   r/   zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)
r%   r$   r&   r'   r(   r"   r#   r   r    r!   r   zIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r9   r    r!   r;   )r9   r    r;   )r9   scaler;   )r9   meanstdr;   )input_channel_dimr   )datatensor_type)r   r!   r"   r%   r$   r&   r'   r(   r    r
   r#   r   r   r=   r   r   r   loggerwarning_oncer   r   center_croprescale	normalizeappendr   r	   )r4   r@   r   r    r!   r"   r#   r%   r$   r&   r'   r(   rA   r:   r;   r9   
all_imagesrG   s                     r7   
preprocesszDeiTImageProcessor.preprocess   s>   B "+!6IDNN	'38+9+E4K^K^#-#9Zt
+9+E4K^K^'3'?|TEVEV#-#9Zt
!*!6IDNN	'tTYYT"!*!6IDNN	!)D	$V,F#:  	&!)%!)	
 6<<E.'<<6!9%*s
 $ >vay I
 	%E%dXars((u9Xi(j5ZkljiSd '  e$	%$ $
 ({N_`
 

 '>BBG =:
s   3G4G!)__name__
__module____qualname____doc__model_input_namesPILImageBICUBICboolr   strintr   r   floatr   r   r3   npndarrayr   r   r   FIRSTr   r   rP   __classcell__)r6   s   @r7   r   r   .   s   B (( #'*yy'8'8#$(,3!:>9=WW 38nW %	W
 W S>W c5j)W W W U5$u+#567W E%e"456W 
WH (:'A'A>BDH.
zz.
 38n.
 %	.

 eC)9$9:;.
 $E#/?*?$@A.
 
.
` %& ##$( $!:>9=;?(8(>(>DHECEC EC 38n	EC EC S>EC EC EC EC U5$u+#567EC E%e"456EC !sJ!78EC &EC $E#/?*?$@AEC  
!EC 'ECr8   r   )%rT   typingr   r   r   r   numpyr]   image_processing_utilsr   r	   r
   image_transformsr   r   image_utilsr   r   r   r   r   r   r   r   r   r   r   utilsr   r   r   r   rV   
get_loggerrQ   rI   r   r1   r8   r7   <module>rh      si    & . .  U U C    _ ^  
		H	%zC+ zCr8   