



'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase							:int				    =					logging.get_logger(__name__)







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		int					=					['pixel_values']


	def __init__(self       ,      lowercase = True       ,      lowercase = None       ,      lowercase = None       ,      lowercase = PILImageResampling.BILINEAR       ,      lowercase = True       ,      lowercase = 1 / 255       ,      lowercase = True       ,      lowercase = None       ,      lowercase = None       ,      **lowercase       ,      ):
				super().__init__(**lowercase							)
				A_	:						Dict	=     size if size is not None else {"""shortest_edge""": 384}
				A_	:						Any	=     get_size_dict(lowercase       ,      default_to_square=lowercase							)

				A_	:						Optional[int]	=     do_resize
				A_	:						Tuple	=     size
				# Default value set here for backwards compatibility where the value in config is None
				A_	:						int	=     crop_pct if crop_pct is not None else 224 / 256
				A_	:						int	=     resample
				A_	:						Tuple	=     do_rescale
				A_	:						Tuple	=     rescale_factor
				A_	:						Dict	=     do_normalize
				A_	:						str	=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				A_	:						Optional[Any]	=     image_std if image_std is not None else IMAGENET_STANDARD_STD


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase = PILImageResampling.BICUBIC       ,      lowercase = None       ,      **lowercase       ,      ):
				A_	:						Any	=     get_size_dict(lowercase       ,      default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'							)
				A_	:						List[Any]	=     size["""shortest_edge"""]

				if shortest_edge < 384:
							# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
							A_	:						Dict	=     int(shortest_edge / crop_pct							)
							A_	:						Any	=     get_resize_output_image_size(lowercase       ,      size=lowercase       ,      default_to_square=lowercase							)
							A_	:						Union[str, Any]	=     resize(image=lowercase       ,      size=lowercase       ,      resample=lowercase       ,      data_format=lowercase       ,      **lowercase							)
							# then crop to (shortest_edge, shortest_edge)
							return center_crop(image=lowercase       ,      size=(shortest_edge, shortest_edge)       ,      data_format=lowercase       ,      **lowercase							)
				else:
							# warping (no cropping) when evaluated at 384 or larger
							return resize(
							    lowercase       ,      size=(shortest_edge, shortest_edge)       ,      resample=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      **lowercase       ,      ):
				return rescale(lowercase       ,      scale=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      **lowercase       ,      ):
				return normalize(lowercase       ,      mean=lowercase       ,      std=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = ChannelDimension.FIRST       ,      **lowercase       ,      ):
				A_	:						Dict	=     do_resize if do_resize is not None else self.do_resize
				A_	:						Dict	=     crop_pct if crop_pct is not None else self.crop_pct
				A_	:						List[str]	=     resample if resample is not None else self.resample
				A_	:						Optional[int]	=     do_rescale if do_rescale is not None else self.do_rescale
				A_	:						Tuple	=     rescale_factor if rescale_factor is not None else self.rescale_factor
				A_	:						List[Any]	=     do_normalize if do_normalize is not None else self.do_normalize
				A_	:						Union[str, Any]	=     image_mean if image_mean is not None else self.image_mean
				A_	:						int	=     image_std if image_std is not None else self.image_std

				A_	:						str	=     size if size is not None else self.size
				A_	:						List[Any]	=     get_size_dict(lowercase       ,      default_to_square=lowercase							)

				A_	:						Optional[int]	=     make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

				if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True."""							)

				if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
							raise ValueError("""crop_pct must be specified if size < 384."""							)

				if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

				# All transformations expect numpy arrays.
				A_	:						List[Any]	=     [to_numpy_array(lowercase							) for image in images]

				if do_resize:
							A_	:						Tuple	=     [self.resize(image=lowercase       ,      size=lowercase       ,      crop_pct=lowercase       ,      resample=lowercase							) for image in images]

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

				if do_normalize:
							A_	:						int	=     [self.normalize(image=lowercase       ,      mean=lowercase       ,      std=lowercase							) for image in images]

				A_	:						Union[str, Any]	=     [to_channel_dimension_format(lowercase       ,      lowercase							) for image in images]

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




'''simple docstring'''




from math import pow



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''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

			A_	:						Optional[int]	=     int(pow(lowerCamelCase__						,      lowerCamelCase__    )    )
			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
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
						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.
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
			return current_sum, solutions_count



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
						raise ValueError(
						    """Invalid input\n"""
						    """needed_sum must be between 1 and 1000, power between 2 and 10."""    )

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()