






from string import ascii_uppercase

__lowerCamelCase	:		List[Any]										=   {str(ord(c) - 55): c for c in ascii_uppercase}







def 			A__							(      _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if isinstance(_a		,  _a	):
  raise TypeError("""int() can't convert non-string with explicit base"""	)
 if num < 0:
  raise ValueError("""parameter must be positive int"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'str' object cannot be interpreted as an integer"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'float' object cannot be interpreted as an integer"""	)
 if base in (0, 1):
  raise ValueError("""base must be >= 2"""	)
 if base > 36:
  raise ValueError("""base must be <= 36"""	)
 snake_case__  :    Union[str, Any]							       =""""""
 snake_case__  :    List[str]							       =0
 snake_case__  :    List[Any]							       =0
 while div != 1:
  snake_case__       ,   snake_case__  :    Union[str, Any]							       =divmod(_a		,  _a	)
  if base >= 11 and 9 < mod < 36:
   snake_case__  :    Dict							       =ALPHABET_VALUES[str(_a	)]
  else:
   snake_case__  :    Optional[Any]							       =str(_a	)
  new_value += actual_value
  snake_case__  :    Dict							       =num // base
  snake_case__  :    Tuple							       =div
  if div == 0:
   return str(new_value[::-1]	)
  elif div == 1:
   new_value += str(_a	)
   return str(new_value[::-1]	)

 return new_value[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for base in range(2, 37):
              for num in range(10_00):
                     assert int(decimal_to_any(num, base), base) == num, (
                         num,
                         base,
                         decimal_to_any(num, base),
                         int(decimal_to_any(num, base), base),
                     )













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	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =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"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

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

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





