






'''simple docstring'''



from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = 8 ,   **lowerCamelCase ,   )							->List[str]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    do_pad
         __a				     =    pad_size




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase      )							->Dict:



         '''simple docstring'''
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None      )							->List[str]:



         '''simple docstring'''
         __a				     =    get_image_size(lowerCamelCase      )
         __a				     =    (old_height // size + 1) * size - old_height
         __a				     =    (old_width // size + 1) * size - old_width

         return pad(lowerCamelCase ,   ((0, pad_height), (0, pad_width)) ,   mode='symmetric' ,   data_format=lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    do_pad if do_pad is not None else self.do_pad
         __a				     =    pad_size if pad_size is not None else self.pad_size

         __a				     =    make_list_of_images(lowerCamelCase      )

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

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

         # All transformations expect numpy arrays.
         __a				     =    [to_numpy_array(lowerCamelCase      ) for image in images]

         if do_rescale:
            __a				     =    [self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase      ) for image in images]

         if do_pad:
            __a				     =    [self.pad(lowerCamelCase ,   size=lowerCamelCase      ) for image in images]

         __a				     =    [to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      ) for image in images]

         __a				     =    {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )







'''simple docstring'''



from maths.prime_factors import prime_factors



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""Input value of [number={number}] must be an integer"""
      raise TypeError(SCREAMING_SNAKE_CASE__    )
   if number < 1:
      raise ValueError('Input must be a positive integer'    )
   return -1 if len(prime_factors(SCREAMING_SNAKE_CASE__    )    ) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()