



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 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, logging


_lowercase:					Union[str, Any]      =						logging.get_logger(__name__)



class       _lowercase				(  __SCREAMING_SNAKE_CASE      ):





	"""simple docstring"""
	__A		      =				["pixel_values"]




	def __init__(self						,			lowerCamelCase_ = True						,			lowerCamelCase_ = None						,			lowerCamelCase_ = PILImageResampling.BILINEAR						,			lowerCamelCase_ = True						,			lowerCamelCase_ = 1 / 255						,			lowerCamelCase_ = True						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(**__UpperCAmelCase						)
						a   	=  size if size is not None else {"height": 224, "width": 224}
						a   	=  get_size_dict(__UpperCAmelCase						)
						a   	=  do_resize
						a   	=  do_rescale
						a   	=  do_normalize
						a   	=  size
						a   	=  resample
						a   	=  rescale_factor
						a   	=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						a   	=  image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = PILImageResampling.BILINEAR						,			lowerCamelCase_ = None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  get_size_dict(__UpperCAmelCase						)
						if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''						)
						return resize(
						    __UpperCAmelCase						,			size=(size["height"], size["width"])						,			resample=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			**lowerCamelCase_						):
						"""simple docstring"""

						return rescale(__UpperCAmelCase						,			scale=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						return normalize(__UpperCAmelCase						,			mean=__UpperCAmelCase						,			std=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = ChannelDimension.FIRST						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  do_resize if do_resize is not None else self.do_resize
						a   	=  do_rescale if do_rescale is not None else self.do_rescale
						a   	=  do_normalize if do_normalize is not None else self.do_normalize
						a   	=  resample if resample is not None else self.resample
						a   	=  rescale_factor if rescale_factor is not None else self.rescale_factor
						a   	=  image_mean if image_mean is not None else self.image_mean
						a   	=  image_std if image_std is not None else self.image_std

						a   	=  size if size is not None else self.size
						a   	=  get_size_dict(__UpperCAmelCase						)

						a   	=  make_list_of_images(__UpperCAmelCase						)

						if not valid_images(__UpperCAmelCase						):
											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:
											raise ValueError("Size must be specified if do_resize is True."						)

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

						# All transformations expect numpy arrays.
						a   	=  [to_numpy_array(__UpperCAmelCase						) for image in images]

						if do_resize:
											a   	=  [self.resize(image=__UpperCAmelCase						,			size=__UpperCAmelCase						,			resample=__UpperCAmelCase						) for image in images]

						if do_rescale:
											a   	=  [self.rescale(image=__UpperCAmelCase						,			scale=__UpperCAmelCase						) for image in images]

						if do_normalize:
											a   	=  [self.normalize(image=__UpperCAmelCase						,			mean=__UpperCAmelCase						,			std=__UpperCAmelCase						) for image in images]

						a   	=  [to_channel_dimension_format(__UpperCAmelCase						,			__UpperCAmelCase						) for image in images]

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


"""simple docstring"""

UpperCamelCase      :							int          =					1_0**8






def 				A		(							snake_case :int						) ->      bool:
 if n % 1_0 == 0:
  return False
 __UpperCamelCase							    =			str(snake_case						)
 return s == s[::-1]






def 				A		(							) ->      int:
 __UpperCamelCase							    =			set()
 __UpperCamelCase							    =			1
 __UpperCamelCase							    =			5
 while sum_squares < LIMIT:
  __UpperCamelCase							    =			first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(snake_case						):
    answer.add(snake_case						)
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  __UpperCamelCase							    =			first_square**2 + (first_square + 1) ** 2

 return sum(snake_case						)


if __name__ == "__main__":
      print(solution())



