
    sg#                         d Z ddlmZmZ ddlZddlmZmZ ddl	m
Z
mZmZ ddl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  ej4                  e      Z G d	 d
e      Zy)z"Image processor class for Swin2SR.    )OptionalUnionN   )BaseImageProcessorBatchFeature)get_image_sizepadto_channel_dimension_format)ChannelDimension
ImageInput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                   F    e Zd ZdZdgZ	 	 	 	 ddedeeef   dededdf
 fd	Z		 	 dd
e
j                  dedeeeef      deeeef      fdZ e       dddddej"                  dfdedee   dee   dee   dee   deeeef      deeef   deeeef      fd       Z xZS )Swin2SRImageProcessora  
    Constructs a Swin2SR image processor.

    Args:
        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.
    pixel_values
do_rescalerescale_factordo_padpad_sizereturnNc                 \    t        |   di | || _        || _        || _        || _        y )N )super__init__r   r   r   r   )selfr   r   r   r   kwargs	__class__s         g/var/www/html/venv/lib/python3.12/site-packages/transformers/models/swin2sr/image_processing_swin2sr.pyr!   zSwin2SRImageProcessor.__init__6   s2     	"6"$,     imagesizedata_formatinput_data_formatc                     t        ||      \  }}||z  dz   |z  |z
  }||z  dz   |z  |z
  }t        |d|fd|ffd||      S )a  
        Pad an image to make the height and width divisible by `size`.

        Args:
            image (`np.ndarray`):
                Image to pad.
            size (`int`):
                The size to make the height and width divisible by.
            data_format (`str` or `ChannelDimension`, *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.
            input_data_format (`str` or `ChannelDimension`, *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.

        Returns:
            `np.ndarray`: The padded image.
           r   	symmetric)moder)   r*   )r   r	   )	r"   r'   r(   r)   r*   
old_height	old_width
pad_height	pad_widths	            r%   r	   zSwin2SRImageProcessor.padE   sq    : !/u6G H
I D(1,4zA
$&*d2Y>	_q)n-#/
 	
r&   imagesreturn_tensorsc	                    ||n| j                   }||n| j                  }||n| j                  }||n| j                  }t	        |      }t        |      st        d      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                  |	||       }}	|D 	cg c]  }	t        |	||       }}	d|i}
t!        |
|	      S c c}	w c c}	w 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_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_pad (`bool`, *optional*, defaults to `True`):
                Whether to pad the image to make the height and width divisible by `window_size`.
            pad_size (`int`, *optional*, defaults to 32):
                The size of the sliding window for the local attention.
            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 typ, input_data_format=input_data_formate
                  `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.
        zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r   r   r   size_divisibilityr   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.)r'   scaler*   )r(   r*   )input_channel_dimr   )datatensor_type)r   r   r   r   r   r   
ValueErrorr   r   r   loggerwarning_oncer   rescaler	   r
   r   )r"   r3   r   r   r   r   r4   r)   r*   r'   r9   s              r%   
preprocessz Swin2SRImageProcessor.preprocessn   s   ^ $.#9Zt
+9+E4K^K^!-4;;'38$V,F#:  	&!)&		
 6<<E.'<<6!9%*s
 $ >vay I $ 5RcdF 
 gmn^cdhhu8GXhYnFn ou
ej'{N_`
 
 '>BB5 = o
s   5D0D5'D:D?)Tgp?T   )NN)__name__
__module____qualname____doc__model_input_namesboolr   intfloatr!   npndarrayr   strr   r	   r   FIRSTr   r   r?   __classcell__)r$   s   @r%   r   r   '   s   
 ((  ,3!! c5j)! 	!
 ! 
!& ?CDH'
zz'
 '
 eC)9$9:;	'

 $E#/?*?$@A'
R %& &**.!%"&;?4D4J4JDH\C\C TN\C !	\C
 \C 3-\C !sJ!78\C 3 001\C $E#/?*?$@A\C '\Cr&   r   )rD   typingr   r   numpyrI   image_processing_utilsr   r   image_transformsr   r	   r
   image_utilsr   r   r   r   r   r   r   r   utilsr   r   r   
get_loggerrA   r<   r   r   r&   r%   <module>rU      sV    ) "  F P P	 	 	 J I 
		H	%dC. dCr&   