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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__a  :     List[str]       =  logging.get_logger(__name__)


if is_vision_available():
		import PIL




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						['''pixel_values''']





			def __init__(   self      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = PILImageResampling.BICUBIC      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = 1 / 2_55      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = True      ,   **lowerCAmelCase__      ,   )     ->					int:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__						)
							__lowercase 			=							size if size is not None else {'''shortest_edge''': 2_24}
							__lowercase 			=							get_size_dict(lowerCAmelCase__      ,   default_to_square=lowerCAmelCase__						)
							__lowercase 			=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							__lowercase 			=							get_size_dict(lowerCAmelCase__      ,   default_to_square=lowerCAmelCase__      ,   param_name='''crop_size'''						)

							__lowercase 			=							do_resize
							__lowercase 			=							size
							__lowercase 			=							resample
							__lowercase 			=							do_center_crop
							__lowercase 			=							crop_size
							__lowercase 			=							do_rescale
							__lowercase 			=							rescale_factor
							__lowercase 			=							do_normalize
							__lowercase 			=							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							__lowercase 			=							image_std if image_std is not None else OPENAI_CLIP_STD
							__lowercase 			=							do_convert_rgb





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = PILImageResampling.BICUBIC      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__      ,   )     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							get_size_dict(lowerCAmelCase__      ,   default_to_square=lowerCAmelCase__						)
							if "shortest_edge" not in size:
											raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
							__lowercase 			=							get_resize_output_image_size(lowerCAmelCase__      ,   size=size['''shortest_edge''']      ,   default_to_square=lowerCAmelCase__						)
							return resize(lowerCAmelCase__      ,   size=lowerCAmelCase__      ,   resample=lowerCAmelCase__      ,   data_format=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							get_size_dict(lowerCAmelCase__						)
							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(lowerCAmelCase__      ,   size=(size['''height'''], size['''width'''])      ,   data_format=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__      ,   )     ->					List[str]:

							'''simple docstring'''


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





			def        _SCREAMING_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__ = None      ,   lowerCAmelCase__ = ChannelDimension.FIRST      ,   **lowerCAmelCase__      ,   )     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							do_resize if do_resize is not None else self.do_resize
							__lowercase 			=							size if size is not None else self.size
							__lowercase 			=							get_size_dict(lowerCAmelCase__      ,   param_name='''size'''      ,   default_to_square=lowerCAmelCase__						)
							__lowercase 			=							resample if resample is not None else self.resample
							__lowercase 			=							do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowercase 			=							crop_size if crop_size is not None else self.crop_size
							__lowercase 			=							get_size_dict(lowerCAmelCase__      ,   param_name='''crop_size'''      ,   default_to_square=lowerCAmelCase__						)
							__lowercase 			=							do_rescale if do_rescale is not None else self.do_rescale
							__lowercase 			=							rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowercase 			=							do_normalize if do_normalize is not None else self.do_normalize
							__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 			=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							__lowercase 			=							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:
											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.'''						)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											__lowercase 			=							[convert_to_rgb(lowerCAmelCase__						) for image in images]

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

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

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

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

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

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

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





def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							0
	while num > 0:
		digit_sum += num % 10
		num //= 10
	return digit_sum





def 		__A		(	__lowerCamelCase = 100    )      ->       int:
	a										=							1
	a										=							2

	for i in range(2							,			max_n + 1    ):
		a										=							pre_numerator
		a										=							2 * i // 3 if i % 3 == 0 else 1
		a										=							cur_numerator
		a										=							e_cont * pre_numerator + temp

	return sum_digits(__lowerCamelCase    )


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

