
    sg{G                         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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 jD                  e#      Z$ G d	 d
e	      Z%y)z'Image processor class for EfficientNet.    )DictListOptionalUnionN   )BaseImageProcessorBatchFeatureget_size_dict)rescale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            #           e Zd ZdZdgZddej                  j                  dd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deeeee   f      deeeee   f      de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	 	 	 ddej&                  deeef   dedeee
ef      deee
ef      f
dZ e       dddddddddddddej0                  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deeeee   f      deeeee   f      dedeee
ef      dedeee
ef      dej                  j                  f d       Z xZS ) EfficientNetImageProcessoraN  
    Constructs a EfficientNet 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": 346, "width": 346}`):
            Size of the image after `resize`. Can be overridden by `size` in `preprocess`.
        resample (`PILImageResampling` filter, *optional*, defaults to 0):
            Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
        do_center_crop (`bool`, *optional*, defaults to `False`):
            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": 289, "width": 289}`):
            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.
        rescale_offset (`bool`, *optional*, defaults to `False`):
            Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. 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.
        include_top (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image again. Should be set to True if the inputs are used for image classification.
    pixel_valuesTNFgp?	do_resizesizeresampledo_center_crop	crop_sizerescale_factorrescale_offset
do_rescaledo_normalize
image_mean	image_stdinclude_topreturnc                 @   t        |   di | ||nddd}t        |      }||nddd}t        |d      }|| _        || _        || _        || _        || _        || _        || _	        || _
        |	| _        |
|
nt        | _        ||nt        | _        || _        y )NiZ  )heightwidthi!  r$   
param_name )super__init__r
   r    r!   r"   r#   r$   r'   r%   r&   r(   r   r)   r   r*   r+   )selfr    r!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r+   kwargs	__class__s                 q/var/www/html/venv/lib/python3.12/site-packages/transformers/models/efficientnet/image_processing_efficientnet.pyr4   z#EfficientNetImageProcessor.__init__W   s      	"6"'tc-JT"!*!6IsUX<Y	!)D	"	 ,"$,,((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.NEAREST`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.NEAREST`.
            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   )r5   r:   r!   r"   r;   r<   r6   output_sizes           r8   r   z!EfficientNetImageProcessor.resize{   sy    F T"47$#6efjfofofqersttH~tG}5
#/
 
 	
r9   scaleoffsetc                 4    t        |f|||d|}|r|dz
  }|S )a  
        Rescale an image by a scale factor.

        If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is
        1/127.5, the image is rescaled between [-1, 1].
            image = image * scale - 1

        If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1].
            image = image * scale

        Args:
            image (`np.ndarray`):
                Image to rescale.
            scale (`int` or `float`):
                Scale to apply to the image.
            offset (`bool`, *optional*):
                Whether to scale the image in both negative and positive directions.
            data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the image. If not provided, it will be the same as the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
        )rA   r;   r<      )r   )r5   r:   rA   rB   r;   r<   r6   rescaled_images           r8   r   z"EfficientNetImageProcessor.rescale   s;    > !
KK\
`f
 +a/Nr9   imagesreturn_tensorsc                    ||n| j                   }||n| j                  }||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         }|r"|D cg c]  }| j-                  ||||       }}|r!|D cg c]  }| j/                  |||       }}|r"|D cg c]  }| j1                  |||	|	       }}|
r"|D cg c]  }| j3                  ||||
       }}|r"|D cg c]  }| j3                  |d||
       }}|D cg c]  }t5        |||       }}d|i}t7        ||      S c c}w c c}w c c}w 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_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`.
            rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`):
                Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range].
            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.
            include_top (`bool`, *optional*, defaults to `self.include_top`):
                Rescales the image again for image classification if set to True.
            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$   r0   zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)
r'   r%   r(   r)   r*   r#   r$   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.)r:   r!   r"   r<   )r:   r!   r<   )r:   rA   rB   r<   )r:   meanstdr<   )input_channel_dimr   )datatensor_type)r    r"   r#   r'   r%   r&   r(   r)   r*   r+   r!   r
   r$   r   r   r>   r   r   r   loggerwarning_oncer   r   center_cropr   	normalizer   r	   )r5   rF   r    r!   r"   r#   r$   r'   r%   r&   r(   r)   r*   r+   rG   r;   r<   r:   rL   s                      r8   
preprocessz%EfficientNetImageProcessor.preprocess   s   N "+!6IDNN	'38+9+E4K^K^#-#9Zt
+9+E4K^K^+9+E4K^K^'3'?|TEVEV#-#9Zt
!*!6IDNN	%0%<k$BRBR'tTYYT"!*!6IDNN	!)D	$V,F#:  	&!)%!)	
 6<<E.'<<6!9%*s
 $ >vay I $ %dXYjkF 
 pvgl  u9Pa bF  
 $	  ~n`q  F   $ U^opF 
  $ U	UfgF  ou
ej'{N_`
 
 '>BBa =


s*   H;"I I)I
I1II)TNN)__name__
__module____qualname____doc__model_input_namesPILImageNEARESTboolr   strintr   r   floatr   r   r4   npndarrayr   r   r   r   FIRSTr   r   rR   __classcell__)r7   s   @r8   r   r   .   sb   $L (( #'*yy'8'8$$(,3$!:>9= !'!' 38n!' %	!'
 !' S>!' c5j)!' !' !' !' U5$u+#567!' E%e"456!' !' 
!'P (:'A'A>BDH.
zz.
 38n.
 %	.

 eC)9$9:;.
 $E#/?*?$@A.
 
.
h >BDH&zz& S%Z & 	&
 eC)9$9:;& $E#/?*?$@A&P %& ##$( $#!:>9= ;?(8(>(>DH#ZCZC ZC 38n	ZC ZC S>ZC ZC ZC ZC ZC U5$u+#567ZC E%e"456ZC ZC !sJ!78ZC  &!ZC" $E#/?*?$@A#ZC$ 
%ZC 'ZCr9   r   )&rV   typingr   r   r   r   numpyr_   image_processing_utilsr   r	   r
   image_transformsr   r   r   image_utilsr   r   r   r   r   r   r   r   r   r   r   utilsr   r   r   r   rX   
get_loggerrS   rN   r   r2   r9   r8   <module>rj      sl    . . .  U U L L    _ ^  
		H	%@C!3 @Cr9   