"""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, 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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  ["""pixel_values"""]

				def __init__(			self						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = PIL.Image.BICUBIC						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = 1 / 2_55						,      lowerCAmelCase = True						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     size if size is not None else {'height': 2_56, 'width': 2_56}
										snake_case        =     get_size_dict(lowerCAmelCase      )
										snake_case        =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

										snake_case        =     do_resize
										snake_case        =     size
										snake_case        =     resample
										snake_case        =     do_center_crop
										snake_case        =     crop_size
										snake_case        =     do_rescale
										snake_case        =     rescale_factor
										snake_case        =     do_normalize
										snake_case        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case        =     image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = PIL.Image.BICUBIC						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     get_size_dict(lowerCAmelCase      )
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
										return center_crop(lowerCAmelCase						,      size=(size['height'], size['width'])						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase=None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = ChannelDimension.FIRST						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

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

										snake_case        =     size if size is not None else self.size
										snake_case        =     get_size_dict(lowerCAmelCase      )
										snake_case        =     crop_size if crop_size is not None else self.crop_size
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

										snake_case        =     make_list_of_images(lowerCAmelCase      )

										if not valid_images(lowerCAmelCase      ):
																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_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'      )

										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.
										snake_case        =     [to_numpy_array(lowerCAmelCase      ) for image in images]

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

										if do_center_crop:
																snake_case        =     [self.center_crop(image=lowerCAmelCase						,      size=lowerCAmelCase      ) for image in images]

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

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

										snake_case        =     [to_channel_dimension_format(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										snake_case        =     {'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase						,      tensor_type=lowerCAmelCase      )



"""simple docstring"""
SCREAMING_SNAKE_CASE__   =					{str(digit): digit**5 for digit in range(10)}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     )   ->			int:





						"""simple docstring"""






						return sum(
						    number
						    for number in range(1_0_0_0	,     1_0_0_0_0_0_0   )
						    if number == digits_fifth_powers_sum(_UpperCamelCase   )   )


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


