
def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''







     if not isinstance(__snake_case       ,    __snake_case		):
          raise ValueError("Input must be an integer"		)
     if input_num <= 0:
          raise ValueError("Input must be positive"		)
     return sum(
         divisor for divisor in range(1       ,    input_num // 2 + 1		) if input_num % divisor == 0		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

A__      :	Dict          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    (list, tuple)		) and isinstance(videos[0]       ,    (list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

     elif isinstance(lowerCAmelCase       ,    (list, tuple)		) and is_valid_image(videos[0]		):
          return [videos]

     elif is_valid_image(lowerCAmelCase		):
          return [[videos]]

     raise ValueError(F"Could not make batched video from {videos}"		)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['pixel_values']



     def __init__(      self ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = 1 / 2_55 ,			__UpperCamelCase = True ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          super().__init__(**__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						size if size is not None else {"shortest_edge": 2_56}
          UpperCAmelCase__							: List[Any] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCAmelCase__							: int 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

          UpperCAmelCase__							: Dict 							=						do_resize
          UpperCAmelCase__							: Optional[int] 							=						size
          UpperCAmelCase__							: List[Any] 							=						do_center_crop
          UpperCAmelCase__							: str 							=						crop_size
          UpperCAmelCase__							: Optional[int] 							=						resample
          UpperCAmelCase__							: int 							=						do_rescale
          UpperCAmelCase__							: Union[str, Any] 							=						rescale_factor
          UpperCAmelCase__							: Union[str, Any] 							=						offset
          UpperCAmelCase__							: Dict 							=						do_normalize
          UpperCAmelCase__							: int 							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase__							: Union[str, Any] 							=						image_std if image_std is not None else IMAGENET_STANDARD_STD



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          if "shortest_edge" in size:
               UpperCAmelCase__							: Union[str, Any] 							=						get_resize_output_image_size(__UpperCamelCase ,			size["shortest_edge"] ,			default_to_square=__UpperCamelCase  )
          elif "height" in size and "width" in size:
               UpperCAmelCase__							: Any 							=						(size["height"], size["width"])
          else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
          return resize(__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[Any] 							=						get_size_dict(__UpperCamelCase  )
          if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
          return center_crop(__UpperCamelCase ,			size=(size["height"], size["width"]) ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					Tuple:
          UpperCAmelCase__							: str 							=						image.astype(np.floataa  )
          if offset:
               UpperCAmelCase__							: Tuple 							=						image - (scale / 2)
          return rescale(__UpperCamelCase ,			scale=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          return normalize(__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			)->					np.ndarray:
          if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."  )

          if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."  )

          if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."  )

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."  )

          if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."  )

          # All transformations expect numpy arrays.
          UpperCAmelCase__							: Optional[Any] 							=						to_numpy_array(__UpperCamelCase  )

          if do_resize:
               UpperCAmelCase__							: Union[str, Any] 							=						self.resize(image=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase  )

          if do_center_crop:
               UpperCAmelCase__							: int 							=						self.center_crop(__UpperCamelCase ,			size=__UpperCamelCase  )

          if do_rescale:
               UpperCAmelCase__							: List[str] 							=						self.rescale(image=__UpperCamelCase ,			scale=__UpperCamelCase ,			offset=__UpperCamelCase  )

          if do_normalize:
               UpperCAmelCase__							: List[Any] 							=						self.normalize(image=__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						to_channel_dimension_format(__UpperCamelCase ,			__UpperCamelCase  )
          return image



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			**__UpperCamelCase ,			)->					PIL.Image.Image:
          UpperCAmelCase__							: Optional[int] 							=						do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__							: int 							=						resample if resample is not None else self.resample
          UpperCAmelCase__							: Tuple 							=						do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__							: int 							=						do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__							: Any 							=						rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__							: Optional[int] 							=						offset if offset is not None else self.offset
          UpperCAmelCase__							: Dict 							=						do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__							: Dict 							=						image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__							: Optional[int] 							=						image_std if image_std is not None else self.image_std

          UpperCAmelCase__							: List[str] 							=						size if size is not None else self.size
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__							: Tuple 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

          if not valid_images(__UpperCamelCase  ):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."  )

          UpperCAmelCase__							: List[str] 							=						make_batched(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						[
              [
                  self._preprocess_image(
                      image=__UpperCamelCase ,			do_resize=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			do_center_crop=__UpperCamelCase ,			crop_size=__UpperCamelCase ,			do_rescale=__UpperCamelCase ,			rescale_factor=__UpperCamelCase ,			offset=__UpperCamelCase ,			do_normalize=__UpperCamelCase ,			image_mean=__UpperCamelCase ,			image_std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase__							: Dict 							=						{"pixel_values": videos}
          return BatchFeature(data=__UpperCamelCase ,			tensor_type=__UpperCamelCase  )



