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e	      Z#y)zImage processor class for ViT.    )DictListOptionalUnionN   )BaseImageProcessorBatchFeatureget_size_dict)convert_to_rgb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loggingc                   P    e Zd ZdZdgZddej                  ddddddf	dedee	e
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ef   ded	ee   d
ee   dee   deeeee   f      deeeee   f      deee
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ef      dee   fd       Z xZS )ViTImageProcessorax  
    Constructs a ViT image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
        size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
            method.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by the `resample` 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.
        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_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.
        do_convert_rgb (`bool`, *optional*):
            Whether to convert the image to RGB.
    pixel_valuesTNgp?	do_resizesizeresample
do_rescalerescale_factordo_normalize
image_mean	image_stddo_convert_rgbreturnc
                     t        |   di |
 ||nddd}t        |      }|| _        || _        || _        || _        || _        || _        ||nt        | _
        ||nt        | _        |	| _        y )N   )heightwidth )super__init__r
   r   r"   r$   r    r!   r#   r   r%   r   r&   r'   )selfr   r    r!   r"   r#   r$   r%   r&   r'   kwargs	__class__s              _/var/www/html/venv/lib/python3.12/site-packages/transformers/models/vit/image_processing_vit.pyr/   zViTImageProcessor.__init__M   s     	"6"'tc-JT""$(	 ,(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.BILINEAR`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            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!   r6   r7   )r
   
ValueErrorkeysr   )r0   r5   r    r!   r6   r7   r1   output_sizes           r3   r   zViTImageProcessor.resizeg   sy    F T"47$#6efjfofofqersttH~tG}5
#/
 
 	
r4   imagesreturn_tensorsc           
         ||n| j                   }||n| j                  }||n| j                  }||n| j                  }||n| j                  }||n| j
                  }|	|	n| j                  }	||n| j                  }||n| j                  }t        |      }t        |      }t        |      st        d      t        |||||	|||       |r|D cg c]  }t        |       }}|D cg c]  }t        |       }}t!        |d         r|rt"        j%                  d       |t'        |d         }|r"|D cg c]  }| j)                  ||||       }}|r!|D cg c]  }| j+                  |||       }}|r"|D cg c]  }| j-                  |||	|       }}|D cg c]  }t/        |||       }}d	|i}t1        ||

      S c c}w c c}w c c}w c c}w c c}w c c}w )aa  
        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`):
                Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
                resizing.
            resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
                `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
                an effect if `do_resize` is set to `True`.
            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 to use if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation to use if `do_normalize` is set to `True`.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                - Unset: 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:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - Unset: Use the channel dimension format of the input image.
            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.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
        zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r"   r#   r$   r%   r&   r   r    r!   r   zIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r5   r    r!   r7   )r5   scaler7   )r5   meanstdr7   )input_channel_dimr   )datatensor_type)r   r"   r$   r!   r#   r%   r&   r'   r    r
   r   r   r9   r   r   r   r   loggerwarning_oncer   r   rescale	normalizer   r	   )r0   r<   r   r    r!   r"   r#   r$   r%   r&   r=   r6   r7   r'   	size_dictr5   rC   s                    r3   
preprocesszViTImageProcessor.preprocess   s[   ~ "+!6IDNN	#-#9Zt
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!*!6IDNN	+9+E4K^K^'tTYY!$'	$V,F#:  	&!)%!		
 9?@nU+@F@ 6<<E.'<<6!9%*s
 $ >vay I $ %i(^opF 
  $ 5RcdF 
  $ U^opF  ou
ej'{N_`
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 '>BBM A =

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__module____qualname____doc__model_input_namesr   BILINEARboolr   r   strintr   floatr   r/   npndarrayr   r   r   FIRSTr   r   rJ   __classcell__)r2   s   @r3   r   r   *   s   @ (( )-'9'B'B,3!:>9=)--- tCH~&- %	-
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-< (:'B'B>BDH.
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 eC)9$9:;.
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   image_transformsr   r   r   image_utilsr   r   r   r   r   r   r   r   r   r   r   utilsr   r   r   
get_loggerrK   rE   r   r-   r4   r3   <module>r`      sa    % . .  U U S S    J I 
		H	%qC* qCr4   