o پi'@sBddlZddlZddlmZddlmZmZddlZddlZ ddl Z ddl Z ddl Z ddlZddlmZeee jjedkr[e jjje jjje jjje jjje jjjdZne jje jje jje jje jjdZdee jje jjBde jfd d Zde jdejfd d Z de jejBde jejBfd dZ! d!de"e jjBdee jjge jjfdBde jjfddZ# d!de"deee jjgee jjfdBdee jjfddZ$  d"de jje jBejBde%de%dBde%dBde&e%e%ff ddZ'  d#de jje jBejBde%de%de"de"de jje jBejBf dd Z(dS)$N)Callable)unquoteurlparse)versionz9.1.0)linearbilinearbicubiclanczosnearestimagesreturncCs0t|ts|g}dd|D}tj|dd}|S)a4 Convert a PIL image or a list of PIL images to NumPy arrays. Args: images (`PIL.Image.Image` or `List[PIL.Image.Image]`): The PIL image or list of images to convert to NumPy format. Returns: `np.ndarray`: A NumPy array representation of the images. cSs"g|] }t|tjdqS)go@)nparrayastypefloat32).0imagere/home/ubuntu/.local/lib/python3.10/site-packages/sglang/multimodal_gen/runtime/models/vision_utils.py 2s"z pil_to_numpy..r)axis) isinstancelistr stack)r images_arrrrr pil_to_numpy$s rcCs,|jdkr |d}t|dddd}|S)z Convert a NumPy image to a PyTorch tensor. Args: images (`np.ndarray`): The NumPy image array to convert to PyTorch format. Returns: `torch.Tensor`: A PyTorch tensor representation of the images. ).Nr)ndimtorch from_numpy transposer rrr numpy_to_pt8s r$cCs d|dS)z Normalize an image array to [-1,1]. Args: images (`np.ndarray` or `torch.Tensor`): The image array to normalize. Returns: `np.ndarray` or `torch.Tensor`: The normalized image array. g@g?rr#rrr normalizeKs r%rconvert_methodcCst|tr1|ds|drtjtj|ddj}n#t j |r)tj|}nt d|dt|tjjr;|}nt dtj |}|durO||}|S|d }|S) au Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. convert_method (Callable[[PIL.Image.Image], PIL.Image.Image], *optional*): A conversion method to apply to the image after loading it. When set to `None` the image will be converted "RGB". http://https://TstreamIIncorrect path or URL. URLs must start with `http://` or `https://`, and  is not a valid path.zgIncorrect format used for the image. Should be a URL linking to an image, a local path, or a PIL image.NRGB)rstr startswithPILImageopenrequestsgetrawospathisfile ValueErrorImageOpsexif_transposeconvert)rr&rrr load_image[s&     r=videocCs|dp |d}tj|}d}|s|std|d|rwtj|dd}|jdkr4td |jt|}tj t |j}tj |d pJd }t j |dd } | j} |jd d} | D]} | | q^Wdn1spwY| }g} |drtj|}z | |||d qtyYn9wztjjWn tytddwt|}|D] }| tj |qWdn1swY|rt!| |dur|| } | S)a Loads `video` to a list of PIL Image. Args: video (`str`): A URL or Path to a video to convert to a list of PIL Image format. convert_method (Callable[[List[PIL.Image.Image]], List[PIL.Image.Image]], *optional*): A conversion method to apply to the video after loading it. When set to `None` the images will be converted to "RGB". Returns: `List[PIL.Image.Image]`: The video as a list of PIL images. r'r(Fr+r,Tr)z'Failed to download video. Status code: rz.mp4)suffixdeletei ) chunk_sizeNz.gifzf`Unable to find an ffmpeg installation on your machine. Please install via `pip install imageio-ffmpeg)"r/r6r7r8r9r3r4 status_coderbasenamersplitexttempfileNamedTemporaryFilename iter_contentwriteendswithr0r1r2appendcopyseektellEOFErrorimageiopluginsffmpegget_exeAttributeError get_reader fromarrayremove)r>r&is_urlis_filewas_tempfile_createdresponse parsed_url file_namer@ temp_file video_path video_datachunk pil_imagesgifreaderframerrr load_videosl            rgvae_scale_factorheightwidthcs|dur t|tjjr|j}nt|tjr|jd}n|jd}|dur@t|tjjr/|j}nt|tjr;|jd}n|jd}fdd||fD\}}||fS)a Returns the height and width of the image, downscaled to the next integer multiple of `vae_scale_factor`. Args: image (`Union[PIL.Image.Image, np.ndarray, torch.Tensor]`): The image input, which can be a PIL image, NumPy array, or PyTorch tensor. If it is a NumPy array, it should have shape `[batch, height, width]` or `[batch, height, width, channels]`. If it is a PyTorch tensor, it should have shape `[batch, channels, height, width]`. height (`Optional[int]`, *optional*, defaults to `None`): The height of the preprocessed image. If `None`, the height of the `image` input will be used. width (`Optional[int]`, *optional*, defaults to `None`): The width of the preprocessed image. If `None`, the width of the `image` input will be used. Returns: `Tuple[int, int]`: A tuple containing the height and width, both resized to the nearest integer multiple of `vae_scale_factor`. Nrrrc3s|] }||VqdSNr)rxrhrr s  z+get_default_height_width..)rr0r1rir Tensorshaperj)rrhrirjrrmrget_default_height_widths         rqdefaultr resize_moderesamplecCsh|dkrt|tjjstd|t|tjjsJ|dkr,|j||ft|d}|Std|d)a Resize image. Args: image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`): The image input, can be a PIL image, numpy array or pytorch tensor. height (`int`): The height to resize to. width (`int`): The width to resize to. resize_mode (`str`, *optional*, defaults to `default`): The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. If `crop`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only supported for PIL image input. Returns: `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`: The resized image. rrz2Only PIL image input is supported for resize_mode )rtz resize_mode z is not supported)rr0r1r9resizePIL_INTERPOLATION)rrirjrsrtrrrrusrurk)NN)rrr ))r6rFcollections.abcr urllib.parserrrQnumpyr PIL.Imager0 PIL.ImageOpsr3r packagingrparse __version__ base_versionr1 ResamplingBILINEARBICUBICLANCZOSNEARESTrvLINEARrndarrayrror$r%r.r=rginttuplerqrurrrrs   $ "  -   Q  4