




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 :       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				)





lowerCAmelCase_						=			range(2, 20 + 1)
lowerCAmelCase_						=			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase_						=			{}






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     lowercase :       str       =			sum(a_i[j] for j in range(__magic_name__			,		len(__magic_name__   )   )   )
     lowercase :       Any       =			sum(a_i[j] * base[j] for j in range(min(len(__magic_name__   )			,		__magic_name__   )   )   )

     lowercase      ,	lowercase :       Optional[int]       =			0, 0
     lowercase :       str       =			n - i

     lowercase :       Optional[int]       =			memo.get(__magic_name__   )

     if sub_memo is not None:
          lowercase :       List[str]       =			sub_memo.get(__magic_name__   )

          if jumps is not None and len(__magic_name__   ) > 0:
               # find and make the largest jump without going over
               lowercase :       Dict       =			-1
               for _k in range(len(__magic_name__   ) - 1			,		-1			,		-1   ):
                    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                         lowercase :       Any       =			_k
                         break

               if max_jump >= 0:
                    lowercase      ,	lowercase      ,	lowercase :       List[str]       =			jumps[max_jump]
                    # since the difference between jumps is cached, add c
                    lowercase :       str       =			diff + c
                    for j in range(min(__magic_name__			,		len(__magic_name__   )   )   ):
                         lowercase      ,	lowercase :       Optional[Any]       =			divmod(__magic_name__			,		10   )
                    if new_c > 0:
                         add(__magic_name__			,		__magic_name__			,		__magic_name__   )

          else:
               lowercase :       Dict       =			[]
     else:
          lowercase :       Union[str, Any]       =			{c: []}
          lowercase :       Optional[Any]       =			sub_memo

     if dn >= max_dn or c + diff >= base[k]:
          return diff, dn

     if k > ks[0]:
          while True:
               # keep doing smaller jumps
               lowercase      ,	lowercase :       str       =			next_term(__magic_name__			,		k - 1			,		i + dn			,		__magic_name__   )
               diff += _diff
               dn += terms_jumped

               if dn >= max_dn or c + diff >= base[k]:
                    break
     else:
          # would be too small a jump, just compute sequential terms instead
          lowercase      ,	lowercase :       Optional[Any]       =			compute(__magic_name__			,		__magic_name__			,		i + dn			,		__magic_name__   )
          diff += _diff
          dn += terms_jumped

     lowercase :       Optional[Any]       =			sub_memo[c]

     # keep jumps sorted by # of terms skipped
     lowercase :       List[Any]       =			0
     while j < len(__magic_name__   ):
          if jumps[j][1] > dn:
               break
          j += 1

     # cache the jump for this value digitsum(b) and c
     sub_memo[c].insert(__magic_name__			,		(diff, dn, k)   )
     return (diff, dn)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[int]:





     '''simple docstring'''


     if i >= n:
          return 0, i
     if k > len(__magic_name__   ):
          a_i.extend([0 for _ in range(k - len(__magic_name__   )   )]   )

     # note: a_i -> b * 10^k + c
     # ds_b -> digitsum(b)
     # ds_c -> digitsum(c)
     lowercase :       Optional[Any]       =			i
     lowercase      ,	lowercase      ,	lowercase :       List[str]       =			0, 0, 0
     for j in range(len(__magic_name__   )   ):
          if j >= k:
               ds_b += a_i[j]
          else:
               ds_c += a_i[j]

     while i < n:
          i += 1
          lowercase :       List[str]       =			ds_c + ds_b
          diff += addend
          lowercase :       Tuple       =			0
          for j in range(__magic_name__   ):
               lowercase :       int       =			a_i[j] + addend
               lowercase      ,	lowercase :       Any       =			divmod(__magic_name__			,		10   )

               ds_c += a_i[j]

          if addend > 0:
               break

     if addend > 0:
          add(__magic_name__			,		__magic_name__			,		__magic_name__   )
     return diff, i - start_i






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Tuple:





     '''simple docstring'''


     for j in range(__magic_name__			,		len(__magic_name__   )   ):
          lowercase :       Any       =			digits[j] + addend
          if s >= 10:
               lowercase      ,	lowercase :       List[str]       =			divmod(__magic_name__			,		10   )
               lowercase :       List[str]       =			addend // 10 + quotient
          else:
               lowercase :       Optional[Any]       =			s
               lowercase :       Tuple       =			addend // 10

          if addend == 0:
               break

     while addend > 0:
          lowercase      ,	lowercase :       str       =			divmod(__magic_name__			,		10   )
          digits.append(__magic_name__   )






def  snake_case(    __magic_name__ = 10**15   )			->							int:





     '''simple docstring'''


     lowercase :       List[Any]       =			[1]
     lowercase :       List[Any]       =			1
     lowercase :       str       =			0
     while True:
          lowercase      ,	lowercase :       str       =			next_term(__magic_name__			,		20			,		i + dn			,		__magic_name__   )
          dn += terms_jumped
          if dn == n - i:
               break

     lowercase :       str       =			0
     for j in range(len(__magic_name__   )   ):
          a_n += digits[j] * 10**j
     return a_n


if __name__ == "__main__":
 print(f'''{solution() = }''')