"""simple docstring"""




from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase     :				Tuple    				=    logging.get_logger(__name__)

class    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						Tuple      =     ["pixel_values"]




					def __init__(						self    :  Dict	,    snake_case__    :  bool = True	,    snake_case__    :  Dict[str, int] = None	,    snake_case__    :  PILImageResampling = PILImageResampling.BICUBIC	,    snake_case__    :  bool = True	,    snake_case__    :  Dict[str, int] = None	,    snake_case__    :  bool = True	,    snake_case__    :  Union[int, float] = 1 / 255	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,    snake_case__    :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,    **snake_case__    :  str	,    ):
							super().__init__(**snake_case__  )
							lowerCAmelCase__   = size if size is not None else {"""shortest_edge""": 224}
							lowerCAmelCase__   = get_size_dict(snake_case__	,    default_to_square=snake_case__  )
							lowerCAmelCase__   = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCAmelCase__   = get_size_dict(snake_case__	,    param_name="""crop_size"""  )

							lowerCAmelCase__   = do_resize
							lowerCAmelCase__   = size
							lowerCAmelCase__   = resample
							lowerCAmelCase__   = do_center_crop
							lowerCAmelCase__   = crop_size
							lowerCAmelCase__   = do_rescale
							lowerCAmelCase__   = rescale_factor
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowerCAmelCase__   = image_std if image_std is not None else IMAGENET_DEFAULT_STD




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  np.ndarray	,    snake_case__    :  Dict[str, int]	,    snake_case__    :  PILImageResampling = PILImageResampling.BICUBIC	,    snake_case__    :  Optional[Union[str, ChannelDimension]] = None	,    **snake_case__    :  Dict	,    ):
							lowerCAmelCase__   = get_size_dict(snake_case__	,    default_to_square=snake_case__  )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
									lowerCAmelCase__   = int((256 / 224) * size["""shortest_edge"""]  )
									lowerCAmelCase__   = get_resize_output_image_size(snake_case__	,    size=snake_case__	,    default_to_square=snake_case__  )
									lowerCAmelCase__   = {"""height""": output_size[0], """width""": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""  )
							return resize(
							    snake_case__	,    size=(size_dict["""height"""], size_dict["""width"""])	,    resample=snake_case__	,    data_format=snake_case__	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  np.ndarray	,    snake_case__    :  Dict[str, int]	,    snake_case__    :  Optional[Union[str, ChannelDimension]] = None	,    **snake_case__    :  Optional[Any]	,    ):
							lowerCAmelCase__   = get_size_dict(snake_case__  )
							if "height" not in size or "width" not in size:
									raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""  )
							return center_crop(snake_case__	,    size=(size["""height"""], size["""width"""])	,    data_format=snake_case__	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  np.ndarray	,    snake_case__    :  Union[int, float]	,    snake_case__    :  Optional[Union[str, ChannelDimension]] = None	,    **snake_case__    :  int	,    ):
							return rescale(snake_case__	,    scale=snake_case__	,    data_format=snake_case__	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  np.ndarray	,    snake_case__    :  Union[float, List[float]]	,    snake_case__    :  Union[float, List[float]]	,    snake_case__    :  Optional[Union[str, ChannelDimension]] = None	,    **snake_case__    :  Optional[Any]	,    ):
							return normalize(snake_case__	,    mean=snake_case__	,    std=snake_case__	,    data_format=snake_case__	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  ImageInput	,    snake_case__    :  Optional[bool] = None	,    snake_case__    :  Optional[Dict[str, int]] = None	,    snake_case__    :  PILImageResampling = None	,    snake_case__    :  Optional[bool] = None	,    snake_case__    :  Optional[Dict[str, int]] = None	,    snake_case__    :  Optional[bool] = None	,    snake_case__    :  Optional[float] = None	,    snake_case__    :  Optional[bool] = None	,    snake_case__    :  Optional[Union[float, Iterable[float]]] = None	,    snake_case__    :  Optional[Union[float, Iterable[float]]] = None	,    snake_case__    :  Optional[TensorType] = None	,    snake_case__    :  ChannelDimension = ChannelDimension.FIRST	,    **snake_case__    :  Dict	,    ):
							lowerCAmelCase__   = do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase__   = resample if resample is not None else self.resample
							lowerCAmelCase__   = do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCAmelCase__   = do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase__   = rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase__   = do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase__   = image_mean if image_mean is not None else self.image_mean
							lowerCAmelCase__   = image_std if image_std is not None else self.image_std

							lowerCAmelCase__   = size if size is not None else self.size
							lowerCAmelCase__   = get_size_dict(snake_case__	,    default_to_square=snake_case__  )
							lowerCAmelCase__   = crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase__   = get_size_dict(snake_case__	,    param_name="""crop_size"""  )

							lowerCAmelCase__   = make_list_of_images(snake_case__  )

							if not valid_images(snake_case__  ):
									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_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.
							lowerCAmelCase__   = [to_numpy_array(snake_case__  ) for image in images]

							if do_resize:
									lowerCAmelCase__   = [self.resize(snake_case__	,    snake_case__	,    snake_case__  ) for image in images]

							if do_center_crop:
									lowerCAmelCase__   = [self.center_crop(snake_case__	,    snake_case__  ) for image in images]

							if do_rescale:
									lowerCAmelCase__   = [self.rescale(snake_case__	,    snake_case__  ) for image in images]

							if do_normalize:
									lowerCAmelCase__   = [self.normalize(snake_case__	,    snake_case__	,    snake_case__  ) for image in images]

							lowerCAmelCase__   = [to_channel_dimension_format(snake_case__	,    snake_case__  ) for image in images]

							lowerCAmelCase__   = {"""pixel_values""": images}
							return BatchFeature(data=snake_case__	,    tensor_type=snake_case__  )





"""simple docstring"""




from itertools import permutations







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		if num[3] % 2 != 0:
				return False

		if (num[2] + num[3] + num[4]) % 3 != 0:
				return False

		if num[5] % 5 != 0:
				return False

		lowerCAmelCase__   = [7, 11, 13, 17]
		for i, test in enumerate(lowerCamelCase__  ):
				if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
						return False
		return True







def 				_UpperCAmelCase		(							lowerCamelCase__ = 10  ):





		"""simple docstring"""




		return sum(
		    int("""""".join(map(lowerCamelCase__      ,	lowerCamelCase__  )  )  )
		    for num in permutations(range(lowerCamelCase__  )  )
		    if is_substring_divisible(lowerCamelCase__  )  )


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




