
'''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 (
    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, is_vision_available, logging


if is_vision_available():
				import PIL


__A        =     logging.get_logger(__name__)





class       A				(							_a					):
			lowerCamelCase				:				Union[str, Any]									=	["""pixel_values"""]



			def __init__(       self							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 0.9							,  lowerCamelCase__ = PILImageResampling.BICUBIC							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 1 / 255							,  lowerCamelCase__ = True							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  )      ->		Any:




									'''simple docstring'''



									super().__init__(**A_							)
									lowercase__							=							size if size is not None else {"""shortest_edge""": 224}
									lowercase__							=							get_size_dict(A_							,  default_to_square=A_							)
									lowercase__							=							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									lowercase__							=							get_size_dict(A_							,  param_name="""crop_size"""							)

									lowercase__							=							do_resize
									lowercase__							=							size
									lowercase__							=							crop_pct
									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 IMAGENET_DEFAULT_MEAN
									lowercase__							=							image_std if image_std is not None else IMAGENET_DEFAULT_STD



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = PILImageResampling.BICUBIC							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  )      ->		Any:




									'''simple docstring'''



									lowercase__							=							get_size_dict(A_							,  default_to_square=A_							)
									if "shortest_edge" not in size and ("height" not in size or "width" not in size):
															raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
									if crop_pct is not None:
															if "shortest_edge" in size:
																					lowercase__							=							int(size["""shortest_edge"""] / crop_pct							)
															elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																											lowercase__							=							int(size["""height"""] / crop_pct							)
																					else:
																											lowercase__							=							(int(size["""height"""] / crop_pct							), int(size["""width"""] / crop_pct							))
															else:
																					raise ValueError("""Invalid size for resize: {}""".format(A_							)							)

															lowercase__							=							get_resize_output_image_size(A_							,  size=A_							,  default_to_square=A_							)
									else:
															if "shortest_edge" in size:
																					lowercase__							=							get_resize_output_image_size(A_							,  size=size["""shortest_edge"""]							,  default_to_square=A_							)
															elif "height" in size and "width" in size:
																					lowercase__							=							(size["""height"""], size["""width"""])
															else:
																					raise ValueError("""Invalid size for resize: {}""".format(A_							)							)

									return resize(A_							,  size=A_							,  resample=A_							,  data_format=A_							,  **A_							)



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  )      ->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							=							get_size_dict(A_							)
									if "height" not in size or "width" not in size:
															raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''							)
									return center_crop(A_							,  size=(size["""height"""], size["""width"""])							,  data_format=A_							,  **A_							)



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  )      ->		Union[str, Any]:




									'''simple docstring'''



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



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  **lowerCamelCase__							,  )      ->		Tuple:




									'''simple docstring'''



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



			def A__			(       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__							,  )      ->		Any:




									'''simple docstring'''



									lowercase__							=							do_resize if do_resize is not None else self.do_resize
									lowercase__							=							crop_pct if crop_pct is not None else self.crop_pct
									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__							=							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__							=							size if size is not None else self.size
									lowercase__							=							get_size_dict(A_							,  default_to_square=A_							)
									lowercase__							=							crop_size if crop_size is not None else self.crop_size
									lowercase__							=							get_size_dict(A_							,  param_name="""crop_size"""							)

									lowercase__							=							make_list_of_images(A_							)

									if not valid_images(A_							):
															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 or resample is None:
															raise ValueError("""Size and resample must be specified if do_resize is True."""							)

									if do_center_crop and crop_pct is None:
															raise ValueError("""Crop_pct 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.
									lowercase__							=							[to_numpy_array(A_							) for image in images]

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

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

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

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

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

									lowercase__							=							{"""pixel_values""": images}
									return BatchFeature(data=A_							,  tensor_type=A_							)










def 		_A		(		_lowercase = 1_00 )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     __UpperCamelCase =			0
     for i in range(1					,  n + 1 ):
          sum_of_squares += i**2
          sum_of_ints += i
     return sum_of_ints**2 - sum_of_squares


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


