
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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase					:			Dict			      =		logging.get_logger(__name__)


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			int        =    ['''pixel_values''']




      def __init__(      self		:       List[str]				,       _snake_case		:       Optional[Any] = True				,       _snake_case		:       Optional[Any] = None				,       _snake_case		:       List[Any] = PILImageResampling.BICUBIC				,       _snake_case		:       Union[str, Any] = True				,       _snake_case		:       Optional[Any] = True				,       _snake_case		:       List[Any] = 1 / 255				,       _snake_case		:       Dict = None				,       _snake_case		:       Optional[int] = True				,       _snake_case		:       Union[str, Any] = None				,       _snake_case		:       Optional[Any] = None				,       **_snake_case		:       List[str]				,       ):
          super().__init__(**_snake_case				)
          __lowercase		:			Optional[int]	    =       size if size is not None else {'''height''': 224, '''width''': 224}
          __lowercase		:			Dict	    =       get_size_dict(_snake_case				)
          __lowercase		:			List[Any]	    =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          __lowercase		:			Union[str, Any]	    =       get_size_dict(_snake_case				,       default_to_square=_snake_case				,       param_name='''crop_size'''				)

          __lowercase		:			Union[str, Any]	    =       do_resize
          __lowercase		:			List[Any]	    =       do_rescale
          __lowercase		:			Tuple	    =       do_normalize
          __lowercase		:			List[Any]	    =       do_center_crop
          __lowercase		:			Optional[Any]	    =       crop_size
          __lowercase		:			Tuple	    =       size
          __lowercase		:			Optional[int]	    =       resample
          __lowercase		:			Optional[int]	    =       rescale_factor
          __lowercase		:			Optional[int]	    =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __lowercase		:			Union[str, Any]	    =       image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       List[str]				,       _snake_case		:       Tuple				,       _snake_case		:       str = PILImageResampling.BILINEAR				,       _snake_case		:       Optional[int] = None				,       **_snake_case		:       Optional[Any]				,       ):
          __lowercase		:			Tuple	    =       get_size_dict(_snake_case				)

          if "shortest_edge" in size:
              __lowercase		:			Any	    =       get_resize_output_image_size(_snake_case				,       size=size['''shortest_edge''']				,       default_to_square=_snake_case				)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
              __lowercase		:			List[Any]	    =       (size['''height'''], size['''width'''])
          else:
              raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'				)
          return resize(_snake_case				,       size=_snake_case				,       resample=_snake_case				,       data_format=_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[str]				,       _snake_case		:       int = None				,       **_snake_case		:       List[Any]				,       ):
          __lowercase		:			int	    =       get_size_dict(_snake_case				)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'				)
          return center_crop(_snake_case				,       size=(size['''height'''], size['''width'''])				,       data_format=_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Tuple				,       _snake_case		:       List[Any] = None				,       **_snake_case		:       Tuple				):
          return rescale(_snake_case				,       scale=_snake_case				,       data_format=_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       int				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       List[str]				,       _snake_case		:       int = None				,       **_snake_case		:       Dict				,       ):
          return normalize(_snake_case				,       mean=_snake_case				,       std=_snake_case				,       data_format=_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       int				,       _snake_case		:       Optional[int] = None				,       _snake_case		:       Optional[Any] = None				,       _snake_case		:       Union[str, Any] = None				,       _snake_case		:       str = None				,       _snake_case		:       int = None				,       _snake_case		:       Tuple = None				,       _snake_case		:       Tuple = None				,       _snake_case		:       Optional[Any] = None				,       _snake_case		:       int = None				,       _snake_case		:       Union[str, Any] = None				,       _snake_case		:       Tuple = None				,       _snake_case		:       Optional[Any] = ChannelDimension.FIRST				,       **_snake_case		:       Any				,       ):
          __lowercase		:			int	    =       do_resize if do_resize is not None else self.do_resize
          __lowercase		:			str	    =       do_rescale if do_rescale is not None else self.do_rescale
          __lowercase		:			Any	    =       do_normalize if do_normalize is not None else self.do_normalize
          __lowercase		:			Union[str, Any]	    =       do_center_crop if do_center_crop is not None else self.do_center_crop
          __lowercase		:			str	    =       crop_size if crop_size is not None else self.crop_size
          __lowercase		:			Dict	    =       get_size_dict(_snake_case				,       param_name='''crop_size'''				,       default_to_square=_snake_case				)
          __lowercase		:			List[Any]	    =       resample if resample is not None else self.resample
          __lowercase		:			int	    =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __lowercase		:			Tuple	    =       image_mean if image_mean is not None else self.image_mean
          __lowercase		:			str	    =       image_std if image_std is not None else self.image_std

          __lowercase		:			Optional[Any]	    =       size if size is not None else self.size
          __lowercase		:			str	    =       get_size_dict(_snake_case				)

          if not is_batched(_snake_case				):
              __lowercase		:			Any	    =       [images]

          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.'''				)

          # All transformations expect numpy arrays.
          __lowercase		:			Optional[int]	    =       [to_numpy_array(_snake_case				) for image in images]

          if do_resize:
              __lowercase		:			List[Any]	    =       [self.resize(image=_snake_case				,       size=_snake_case				,       resample=_snake_case				) for image in images]

          if do_center_crop:
              __lowercase		:			Optional[int]	    =       [self.center_crop(image=_snake_case				,       size=_snake_case				) for image in images]

          if do_rescale:
              __lowercase		:			str	    =       [self.rescale(image=_snake_case				,       scale=_snake_case				) for image in images]

          if do_normalize:
              __lowercase		:			Any	    =       [self.normalize(image=_snake_case				,       mean=_snake_case				,       std=_snake_case				) for image in images]

          __lowercase		:			Union[str, Any]	    =       [to_channel_dimension_format(_snake_case				,       _snake_case				) for image in images]

          __lowercase		:			int	    =       {'''pixel_values''': images}
          return BatchFeature(data=_snake_case				,       tensor_type=_snake_case				)




from math import factorial

__lowerCAmelCase					:			Dict			      =		{str(d): factorial(d) for d in range(10)}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						) ->						int:
    __lowercase		:			int	    =       7 * factorial(9	) + 1
    return sum(i for i in range(3	,					__lowerCAmelCase	) if sum_of_digit_factorial(__lowerCAmelCase	) == i	)


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


