


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


__snake_case            =    logging.get_logger(__name__)




class    UpperCAmelCase					(       lowercase_    ):
			lowercase						= ["""pixel_values"""]




			def __init__(						self							:		int       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Dict[str, int]] = None       ,       __magic_name__							:		PILImageResampling = PILImageResampling.BICUBIC       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[int, float] = 1 / 2_5_5       ,       __magic_name__							:		Dict[str, int] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[float, List[float]]] = None       ,       __magic_name__							:		Optional[Union[float, List[float]]] = None       ,       **__magic_name__							:		Dict       ,       ):







						"""simple docstring"""







						super().__init__(**lowerCamelCase_						)
						UpperCamelCase         =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
						UpperCamelCase         =					get_size_dict(lowerCamelCase_						)
						UpperCamelCase         =					crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
						UpperCamelCase         =					get_size_dict(lowerCamelCase_       ,       default_to_square=lowerCamelCase_       ,       param_name="""crop_size"""						)

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




			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		Dict[str, int]       ,       __magic_name__							:		PILImageResampling = PILImageResampling.BILINEAR       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					get_size_dict(lowerCamelCase_						)

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




			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		Dict[str, int]       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		Optional[int]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		np.ndarray       ,       __magic_name__							:		float       ,       __magic_name__							:		Optional[Union[str, ChannelDimension]] = None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







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




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







						"""simple docstring"""







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





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







						"""simple docstring"""







						UpperCamelCase         =					do_resize if do_resize is not None else self.do_resize
						UpperCamelCase         =					do_rescale if do_rescale is not None else self.do_rescale
						UpperCamelCase         =					do_normalize if do_normalize is not None else self.do_normalize
						UpperCamelCase         =					do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCamelCase         =					crop_size if crop_size is not None else self.crop_size
						UpperCamelCase         =					get_size_dict(lowerCamelCase_       ,       param_name="""crop_size"""       ,       default_to_square=lowerCamelCase_						)
						UpperCamelCase         =					resample if resample is not None else self.resample
						UpperCamelCase         =					rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCamelCase         =					image_mean if image_mean is not None else self.image_mean
						UpperCamelCase         =					image_std if image_std is not None else self.image_std

						UpperCamelCase         =					size if size is not None else self.size
						UpperCamelCase         =					get_size_dict(lowerCamelCase_						)

						if not is_batched(lowerCamelCase_						):
									UpperCamelCase         =					[images]

						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."""						)

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

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

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

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

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

						UpperCamelCase         =					[to_channel_dimension_format(lowerCamelCase_       ,       lowerCamelCase_						) for image in images]

						UpperCamelCase         =					{"""pixel_values""": images}
						return BatchFeature(data=lowerCamelCase_       ,       tensor_type=lowerCamelCase_						)








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 100	):





			"""simple docstring"""




			UpperCamelCase         =					sum(i * i for i in range(1       ,  n + 1	)	)
			UpperCamelCase         =					int(math.pow(sum(range(1       ,  n + 1	)	)       ,  2	)	)
			return square_of_sum - sum_of_squares


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




