`iAK,dZddlmZmZddlZddlmZddlm Z ddl m Z m Z m Z ddlmZmZmZmZdd lmZmZmZmZmZmZmZmZmZmZmZdd lm Z m!Z!erddl"Z"e!j#e$Z%d e&e&efd Z'Gd de Z(dgZ)dS)z Image processor class for Vivit.)OptionalUnionN)is_vision_available) TensorType)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizerescaleresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imageis_valid_imageto_numpy_array valid_imagesvalidate_preprocess_arguments)filter_out_non_signature_kwargsloggingreturncjt|ttfr?t|dttfrt|ddr|St|ttfrt|dr|gSt|r|ggSt d|)Nrz"Could not make batched video from ) isinstancelisttupler ValueError)videoss /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/vivit/image_processing_vivit.py make_batchedr$5s&4-((Zq D%=-Q-QVdeklmenopeqVrVr FT5M * *~fQi/H/Hx   z B&BB C CCc#HeZdZdZdgZddejddddddddf dedee e e fded ed ee e e fd ed e e e fd ededee e ee fdee e ee fddffd Zejddfdejde e e fdedee e efdee e efdejf dZ ddejde e e fd edee e efdee e eff dZdddddddddddejdf dedeedee e e fdeed eed ee e e fd eed ee d eedeedee e ee fdee e ee fdeedee e efdejfdZeddddddddddddejdfdedeedee e e fdeed eed ee e e fd eed ee d eedeedee e ee fdee e ee fdee e efdedee e efdejjf dZxZS)VivitImageProcessoraC Constructs a Vivit 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 the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`): Size of the output image after resizing. The shortest edge of the image will be resized to `size["shortest_edge"]` while maintaining the aspect ratio of the original image. Can be overridden by `size` 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_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after applying the center crop. Can be overridden by the `crop_size` 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 `1/127.5`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. offset (`bool`, *optional*, defaults to `True`): Whether to scale the image in both negative and positive directions. Can be overridden by the `offset` 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? do_resizesizeresampledo_center_crop crop_size do_rescalerescale_factoroffset do_normalize image_mean image_stdrc  ^tjd i| ||nddi}t|d}||nddd}t|d}||_||_||_||_||_||_||_ ||_ | |_ | | nt|_ | | nt|_dS) N shortest_edgeFdefault_to_square)heightwidthr- param_name)super__init__r r)r*r,r-r+r.r/r0r1rr2rr3)selfr)r*r+r,r-r.r/r0r1r2r3kwargs __class__s r#r@zVivitImageProcessor.__init__ms ""6"""'ttos-CTU;;;!*!6IIsUX**DZ&/&;AVr%image data_formatinput_data_formatc t|d}d|vrt||dd|}n=d|vrd|vr|d|df}n$td|t |f||||d|S) a Resize an image. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the output image will have its shortest edge of length `s` while keeping the aspect ratio of the original image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use when resiizing the image. 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 (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Fr7r5)r8rFr:r;zDSize must have 'height' and 'width' or 'shortest_edge' as keys. Got )r*r+rErF)r r r!keysr )rArDr*r+rErFrB output_sizes r#r zVivitImageProcessor.resizes4TU;;; d " "6tO,YjKK  'T//>4=9KKqdhdmdmdodoqqrr r  #/       r%scalec 6t|f|||d|}|r|dz }|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. )rJrErF)r )rArDrJr0rErFrBrescaled_images r#r zVivitImageProcessor.rescalesJ>!  KK\  `f    0+a/Nr%c t||| | | ||||| | r|stdt|}|r)t|rtd|t |}|r|||||}|r||||}|r| ||| |}| r| || | |}t|| | }|S) zPreprocesses a single image.) r.r/r1r2r3r,r-r)r*r+z0For offset, do_rescale must also be set to True.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.N)rDr*r+rF)r*rF)rDrJr0rF)rDmeanstdrF)input_channel_dim) rr!rrlogger warning_oncerr center_cropr normalizer)rArDr)r*r+r,r-r.r/r0r1r2r3rErFs r#_preprocess_imagez%VivitImageProcessor._preprocess_images\& &!)%!)   Q* QOPP Pu%%  /%00    s     $ >u E E   pKKe$]nKooE  a$$UN_$``E  xLLuN6evLwwE  uNNZYbsNttE+E;Rcddd r%r"return_tensorsc njnjnjnjnj  nj  nj  nj  nj nj tdnj tdt|stdt|}   fd|D}d|i}t|| S) a Preprocess an image or batch of images. Args: videos (`ImageInput`): Video frames to preprocess. Expects a single or batch of video frames with pixel values ranging from 0 to 255. If passing in frames 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 applying resize. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`): Whether to centre crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after applying the centre crop. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between `[-1 - 1]` if `offset` is `True`, `[0, 1]` otherwise. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. offset (`bool`, *optional*, defaults to `self.offset`): Whether to scale the image in both negative and positive directions. 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: - 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: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the inferred 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. NFr7r-r<zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.cFg|]} fd|DS)cVg|]%}|    &S))rDr)r*r+r,r-r.r/r0r1r2r3rErF)rV).0imgr-rEr,r1r.r)r2r3rFr0r+r/rAr*s r# z=VivitImageProcessor.preprocess...}sj   "!&&'%#1')#1!!-)' +&7'   r%r>)r[videor-rEr,r1r.r)r2r3rFr0r+r/rAr*s r#r]z2VivitImageProcessor.preprocess..|s   ('                 "!#      r%r()data tensor_type)r)r+r,r.r/r0r1r2r3r*r r-rr!r$r )rAr"r)r*r+r,r-r.r/r0r1r2r3rWrErFr_s` ``````````` `` r# preprocesszVivitImageProcessor.preprocess!sH"+!6IIDN '388+9+E4K^#-#9ZZt +9+E4K^!-4;'3'?||TEV #-#9ZZt !*!6IIDN 'ttTYTU;;;!*!6IIDN !) DDD F## :  f%%                 ( )   .'>BBBBr%)TNN)__name__ __module__ __qualname____doc__model_input_namesrBILINEARboolrdictstrintrfloatrr@npndarrayrr r FIRSTrrVrrPILImagera __classcell__)rCs@r#r'r'Bs]&&P(()-'9'B#.2,5!:>9=WWWtCH~&W% W  W DcN+ WWc5j)WWWU5$u+#567WE%e"456W WWWWWWJ(:'B>BDH * * z* 38n* % * eC)9$9:; * $E#/?*?$@A *  * * * * b>BDH &&z&S%Z & & eC)9$9:; & $E#/?*?$@A &&&&V%))-15)-.2%)*.!%'+:>9=2B2HDH<<<D><tCH~& < -. < ! <DcN+<TN<!<<tn<U5$u+#567<E%e"456<./<$E#/?*?$@A< !<<<<|%$&&%))-15)-.2%)*.!%'+:>9=;?(8(>DH!rCrCrCD>rCtCH~& rC -. rC ! rCDcN+rCTNrC!rCrCtnrCU5$u+#567rCE%e"456rC!sJ!78rC&rC $E#/?*?$@A!rC" #rCrCrC'&rCrCrCrCrCr%r')*retypingrrnumpyrmtransformers.utilsrtransformers.utils.genericrimage_processing_utilsrr r image_transformsr r r r image_utilsrrrrrrrrrrrutilsrrrp get_loggerrbrRrr$r'__all__r>r%r#r}s'&""""""""222222111111UUUUUUUUUU                          >=======JJJ  H % % DDj!12 D D D DRCRCRCRCRC,RCRCRCj ! !r%