




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      ['''pixel_values''']






  def __init__(  self		:     int				,						_A		:     bool = True				,						_A		:     int = 32				,						_A		:     Optional[Any]=PILImageResampling.BILINEAR				,						_A		:     bool = True				,						**_A		:     Dict				,						)		->   None:






       """simple docstring"""







       lowercase :       int       =			do_resize
       lowercase :       Union[str, Any]       =			do_rescale
       lowercase :       int       =			size_divisor
       lowercase :       Tuple       =			resample
       super().__init__(**_A				)






  def  __a      (  self		:     int				,						_A		:     np.ndarray				,						_A		:     int				,						_A		:     Optional[int]				,						_A		:     Optional[ChannelDimension] = None				,						**_A		:     List[Any]				)		->   np.ndarray:






       """simple docstring"""







       lowercase      ,	lowercase :       int       =			get_image_size(_A				)
       # Rounds the height and width down to the closest multiple of size_divisor
       lowercase :       Tuple       =			height // size_divisor * size_divisor
       lowercase :       Tuple       =			width // size_divisor * size_divisor
       lowercase :       Optional[int]       =			resize(_A				,						(new_h, new_w)				,						resample=_A				,						data_format=_A				,						**_A				)
       return image






  def  __a      (  self		:     str				,						_A		:     np.ndarray				,						_A		:     float				,						_A		:     Optional[ChannelDimension] = None				,						**_A		:     Dict				)		->   np.ndarray:






       """simple docstring"""







       return rescale(image=_A				,						scale=_A				,						data_format=_A				,						**_A				)






  def  __a      (  self		:     Tuple				,						_A		:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]				,						_A		:     Optional[bool] = None				,						_A		:     Optional[int] = None				,						_A		:     int=None				,						_A		:     Optional[bool] = None				,						_A		:     Optional[Union[TensorType, str]] = None				,						_A		:     ChannelDimension = ChannelDimension.FIRST				,						**_A		:     Tuple				,						)		->   BatchFeature:






       """simple docstring"""







       lowercase :       Optional[Any]       =			do_resize if do_resize is not None else self.do_resize
       lowercase :       List[Any]       =			do_rescale if do_rescale is not None else self.do_rescale
       lowercase :       Union[str, Any]       =			size_divisor if size_divisor is not None else self.size_divisor
       lowercase :       List[str]       =			resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing'''				)

       lowercase :       str       =			make_list_of_images(_A				)

       if not valid_images(_A				):
            raise ValueError('''Invalid image(s)'''				)

       # All transformations expect numpy arrays.
       lowercase :       str       =			[to_numpy_array(_A				) for img in images]

       if do_resize:
            lowercase :       Dict       =			[self.resize(_A				,						size_divisor=_A				,						resample=_A				) for image in images]

       if do_rescale:
            lowercase :       str       =			[self.rescale(_A				,						scale=1 / 255				) for image in images]

       lowercase :       Tuple       =			[to_channel_dimension_format(_A				,						_A				) for image in images]

       lowercase :       str       =			{'''pixel_values''': images}
       return BatchFeature(data=_A				,						tensor_type=_A				)





def  snake_case(    __magic_name__			,		__magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       List[Any]       =			len(__magic_name__   )
     lowercase :       str       =			[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1
     for i in range(arr_len + 1   ):
          lowercase :       List[str]       =			True

     # sum is not zero and set is empty then false
     for i in range(1			,		required_sum + 1   ):
          lowercase :       str       =			False

     for i in range(1			,		arr_len + 1   ):
          for j in range(1			,		required_sum + 1   ):
               if arr[i - 1] > j:
                    lowercase :       Optional[Any]       =			subset[i - 1][j]
               if arr[i - 1] <= j:
                    lowercase :       Dict       =			subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

     return subset[arr_len][required_sum]


if __name__ == "__main__":
 import doctest

 doctest.testmod()