






"""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 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


A__     :      str             =			logging.get_logger(__name__)
class 				_UpperCAmelCase (	_A     ):

				"""simple docstring"""




				lowercase__    							=	["""pixel_values"""]




				def __init__(					self						:   Tuple,  lowerCamelCase						:   bool = True,  lowerCamelCase						:   Optional[Dict[str, int]] = None,  lowerCamelCase						:   PILImageResampling = PILImageResampling.BILINEAR,  lowerCamelCase						:   bool = True,  lowerCamelCase						:   Union[int, float] = 1 / 255,  lowerCamelCase						:   bool = True,  lowerCamelCase						:   Optional[Union[float, List[float]]] = None,  lowerCamelCase						:   Optional[Union[float, List[float]]] = None,  **lowerCamelCase						:   Optional[Any],  ):

								'''simple docstring'''
								super().__init__(**UpperCamelCase__  )
								lowercase__       			=      size if size is not None else {'''height''': 224, '''width''': 224}
								lowercase__       			=      get_size_dict(UpperCamelCase__  )
								lowercase__       			=      do_resize
								lowercase__       			=      do_rescale
								lowercase__       			=      do_normalize
								lowercase__       			=      size
								lowercase__       			=      resample
								lowercase__       			=      rescale_factor
								lowercase__       			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowercase__       			=      image_std if image_std is not None else IMAGENET_STANDARD_STD




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   np.ndarray,  lowerCamelCase						:   Dict[str, int],  lowerCamelCase						:   PILImageResampling = PILImageResampling.BILINEAR,  lowerCamelCase						:   Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase						:   Optional[int],  ):

								'''simple docstring'''
								lowercase__       			=      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     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   np.ndarray,  lowerCamelCase						:   float,  lowerCamelCase						:   Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								return rescale(UpperCamelCase__,  scale=UpperCamelCase__,  data_format=UpperCamelCase__,  **UpperCamelCase__  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   np.ndarray,  lowerCamelCase						:   Union[float, List[float]],  lowerCamelCase						:   Union[float, List[float]],  lowerCamelCase						:   Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase						:   Union[str, Any],  ):

								'''simple docstring'''
								return normalize(UpperCamelCase__,  mean=UpperCamelCase__,  std=UpperCamelCase__,  data_format=UpperCamelCase__,  **UpperCamelCase__  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   ImageInput,  lowerCamelCase						:   Optional[bool] = None,  lowerCamelCase						:   Dict[str, int] = None,  lowerCamelCase						:   PILImageResampling = None,  lowerCamelCase						:   Optional[bool] = None,  lowerCamelCase						:   Optional[float] = None,  lowerCamelCase						:   Optional[bool] = None,  lowerCamelCase						:   Optional[Union[float, List[float]]] = None,  lowerCamelCase						:   Optional[Union[float, List[float]]] = None,  lowerCamelCase						:   Optional[Union[str, TensorType]] = None,  lowerCamelCase						:   Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase						:   List[str],  ):

								'''simple docstring'''
								lowercase__       			=      do_resize if do_resize is not None else self.do_resize
								lowercase__       			=      do_rescale if do_rescale is not None else self.do_rescale
								lowercase__       			=      do_normalize if do_normalize is not None else self.do_normalize
								lowercase__       			=      resample if resample is not None else self.resample
								lowercase__       			=      rescale_factor if rescale_factor is not None else self.rescale_factor
								lowercase__       			=      image_mean if image_mean is not None else self.image_mean
								lowercase__       			=      image_std if image_std is not None else self.image_std

								lowercase__       			=      size if size is not None else self.size
								lowercase__       			=      get_size_dict(UpperCamelCase__  )

								lowercase__       			=      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.
								lowercase__       			=      [to_numpy_array(UpperCamelCase__  ) for image in images]

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

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

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

								lowercase__       			=      [to_channel_dimension_format(UpperCamelCase__,  UpperCamelCase__  ) for image in images]

								lowercase__       			=      {'''pixel_values''': images}
								return BatchFeature(data=UpperCamelCase__,  tensor_type=UpperCamelCase__  )












from collections import defaultdict






def   a      (						lowerCamelCase_ = 5    ):






				'''simple docstring'''
				lowercase__       			=      defaultdict(lowerCamelCase_    )
				lowercase__       			=      0

				while True:
								lowercase__       			=      get_digits(lowerCamelCase_    )
								freqs[digits].append(lowerCamelCase_    )

								if len(freqs[digits]    ) == max_base:
												lowercase__       			=      freqs[digits][0] ** 3
												return base

								num += 1






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return "".join(sorted(str(num**3    )    )    )


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




