

'''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


__lowerCamelCase						    =      logging.get_logger(__name__)


class    A__ ( _snake_case     ):
    lowercase		   =						["pixel_values"]



    def __init__(   self						,			UpperCamelCase__ = True						,			UpperCamelCase__ = 1 / 255						,			UpperCamelCase__ = True						,			UpperCamelCase__ = 8						,			**UpperCamelCase__						,			)  -> None:





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

           A_		   = do_rescale
           A_		   = rescale_factor
           A_		   = do_pad
           A_		   = pad_size



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__    )  -> np.ndarray:





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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Union[str, Any]:





           '''simple docstring'''
           A_   ,     A_		   = get_image_size(UpperCamelCase__    )
           A_		   = (old_height // size + 1) * size - old_height
           A_		   = (old_width // size + 1) * size - old_width

           return pad(UpperCamelCase__						,			((0, pad_height), (0, pad_width))						,			mode="""symmetric"""						,			data_format=UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = ChannelDimension.FIRST						,			**UpperCamelCase__						,			)  -> Any:





           '''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(UpperCamelCase__    )

           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."""    )

           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(UpperCamelCase__    ) for image in images]

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

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

           A_		   = [to_channel_dimension_format(UpperCamelCase__						,			UpperCamelCase__    ) for image in images]

           A_		   = {"""pixel_values""": images}
           return BatchFeature(data=UpperCamelCase__						,			tensor_type=UpperCamelCase__    )



'''simple docstring'''







from functools import lru_cache
@lru_cache
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if num < 0:
              raise ValueError("""Number should not be negative."""			)

       return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
