





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


__A     =    logging.get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["pixel_values"]

 def __init__(self    :       str	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       int = 32	,			UpperCAmelCase_    :       List[str]=PILImageResampling.BILINEAR	,			UpperCAmelCase_    :       bool = True	,			**UpperCAmelCase_    :       str	,			)	->None:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =do_resize
   lowerCamelCase__:				int        =do_rescale
   lowerCamelCase__:				int        =size_divisor
   lowerCamelCase__:				Optional[int]        =resample
   super().__init__(**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[ChannelDimension] = None	,			**UpperCAmelCase_    :       str)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Dict        =get_image_size(UpperCAmelCase_)
   # Rounds the height and width down to the closest multiple of size_divisor
   lowerCamelCase__:				Union[str, Any]        =height // size_divisor * size_divisor
   lowerCamelCase__:				Dict        =width // size_divisor * size_divisor
   lowerCamelCase__:				Optional[Any]        =resize(UpperCAmelCase_	,			(new_h, new_w)	,			resample=UpperCAmelCase_	,			data_format=UpperCAmelCase_	,			**UpperCAmelCase_)
   return image

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       Optional[ChannelDimension] = None	,			**UpperCAmelCase_    :       Any)	->np.ndarray:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       Optional[Union[TensorType, str]] = None	,			UpperCAmelCase_    :       ChannelDimension = ChannelDimension.FIRST	,			**UpperCAmelCase_    :       Any	,			)	->BatchFeature:



   '''simple docstring'''



   lowerCamelCase__:				str        =do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:				List[str]        =do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:				Optional[int]        =size_divisor if size_divisor is not None else self.size_divisor
   lowerCamelCase__:				Union[str, Any]        =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")

   lowerCamelCase__:				str        =make_list_of_images(UpperCAmelCase_)

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

   # All transformations expect numpy arrays.
   lowerCamelCase__:				int        =[to_numpy_array(UpperCAmelCase_) for img in images]

   if do_resize:
     lowerCamelCase__:				Optional[Any]        =[self.resize(UpperCAmelCase_	,			size_divisor=UpperCAmelCase_	,			resample=UpperCAmelCase_) for image in images]

   if do_rescale:
     lowerCamelCase__:				Dict        =[self.rescale(UpperCAmelCase_	,			scale=1 / 255) for image in images]

   lowerCamelCase__:				List[str]        =[to_channel_dimension_format(UpperCAmelCase_	,			UpperCAmelCase_) for image in images]

   lowerCamelCase__:				Union[str, Any]        ={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_	,			tensor_type=UpperCAmelCase_)








from math import pow





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   )					->		tuple[int, int]:







  """simple docstring"""





  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  lowerCamelCase__:				Optional[Any]        =int(pow(__a     ,   __a		)		)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    lowerCamelCase__  , lowerCamelCase__:				int        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    lowerCamelCase__  , lowerCamelCase__:				Dict        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
  return current_sum, solutions_count





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		int:







  """simple docstring"""





  if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
    raise ValueError(
        "Invalid input\n"
        "needed_sum must be between 1 and 1000, power between 2 and 10."		)

  return backtrack(__a     ,   __a     ,   1     ,   0     ,   0		)[1]  # Return the solutions_count


if __name__ == "__main__":
     import doctest

     doctest.testmod()

