"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import Dict, List, Optional, Union
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (convert_to_rgb, resize, to_channel_dimension_format)
from ...image_utils import (OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, VideoInput, infer_channel_dimension_format,
is_scaled_image, is_valid_image, to_numpy_array, valid_images, validate_preprocess_arguments)
from ...utils import TensorType, is_vision_available, logging
logger = logging.get_logger(__name__)
if is_vision_available(): from PIL import Image
def make_batched_videos(videos) -> List[VideoInput]:
    if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]): return videos
    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
        if isinstance(videos[0], Image.Image) or len(videos[0].shape) == 3: return [videos]
        elif len(videos[0].shape) == 4: return [list(video) for video in videos]
    elif is_valid_image(videos) and len(videos.shape) == 4: return [list(videos)]
    raise ValueError(f"Could not make batched video from {videos}")
class LlavaOnevisionVideoProcessor(BaseImageProcessor):
    model_input_names = ["pixel_values_videos"]
    def __init__(self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_rescale: bool = True,
    rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None,
    do_convert_rgb: bool = True, **kwargs) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 384, "width": 384}
        size = get_size_dict(size, default_to_square=False)
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.do_convert_rgb = do_convert_rgb
    def _preprocess(self, images: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None,
    rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None,
    do_convert_rgb: bool = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None) -> Image.Image:
        if do_convert_rgb: images = [convert_to_rgb(image) for image in images]
        images = [to_numpy_array(image) for image in images]
        if is_scaled_image(images[0]) and do_rescale: logger.warning_once("It looks like you are trying to rescale already rescaled videos. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.")
        if input_data_format is None: input_data_format = infer_channel_dimension_format(images[0])
        if do_resize: images = [resize(image=image, size=size, resample=resample, input_data_format=input_data_format) for image in images]
        if do_rescale: images = [self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images]
        if do_normalize: images = [self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) for image in images]
        images = [to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images]
        return images
    def preprocess(self, videos: VideoInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None,
    rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None,
    do_convert_rgb: bool = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
    input_data_format: Optional[Union[str, ChannelDimension]] = None):
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        resample = resample if resample is not None else self.resample
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
        videos = make_batched_videos(videos)
        if not valid_images(videos[0]): raise ValueError("Invalid video type. Must be a list consisting of PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.")
        validate_preprocess_arguments(do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample)
        size_tuple = ((size["height"], size["width"]) if "height" in size and "width" in size else (size["shortest_edge"], size["shortest_edge"]))
        pixel_values = [self._preprocess(video, do_resize=do_resize, size=size_tuple, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor,
        do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_convert_rgb=do_convert_rgb, data_format=data_format, input_data_format=input_data_format) for video in videos]
        return BatchFeature(data={"pixel_values_videos": pixel_values}, tensor_type=return_tensors)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
