
"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__magic_name__		       =				logging.get_logger(__name__)


class    _lowerCAmelCase						(						lowerCamelCase_						):
							lowercase_     :     Dict       =							["""pixel_values"""]



							def __init__(      self ,						a_ = True ,						a_ = None ,						a_ = PIL.Image.NEAREST ,						a_ = False ,						a_ = None ,						a_ = 1 / 255 ,						a_ = False ,						a_ = True ,						a_ = True ,						a_ = None ,						a_ = None ,						a_ = True ,						**a_ ,						)							->							str:
								super().__init__(**a_   )
								_UpperCAmelCase =						size if size is not None else {"height": 346, "width": 346}
								_UpperCAmelCase =						get_size_dict(a_   )
								_UpperCAmelCase =						crop_size if crop_size is not None else {"height": 289, "width": 289}
								_UpperCAmelCase =						get_size_dict(a_ ,						param_name="crop_size"   )

								_UpperCAmelCase =						do_resize
								_UpperCAmelCase =						size
								_UpperCAmelCase =						resample
								_UpperCAmelCase =						do_center_crop
								_UpperCAmelCase =						crop_size
								_UpperCAmelCase =						do_rescale
								_UpperCAmelCase =						rescale_factor
								_UpperCAmelCase =						rescale_offset
								_UpperCAmelCase =						do_normalize
								_UpperCAmelCase =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCAmelCase =						image_std if image_std is not None else IMAGENET_STANDARD_STD
								_UpperCAmelCase =						include_top



							def       _a   (      self ,						a_ ,						a_ ,						a_ = PIL.Image.NEAREST ,						a_ = None ,						**a_ ,						)							->							Union[str, Any]:
								_UpperCAmelCase =						get_size_dict(a_   )
								if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"   )
								return resize(
								    a_ ,						size=(size["height"], size["width"]) ,						resample=a_ ,						data_format=a_ ,						**a_   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							Tuple:
								_UpperCAmelCase =						get_size_dict(a_   )
								if "height" not in size or "width" not in size:
									raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"   )
								return center_crop(a_ ,						size=(size["height"], size["width"]) ,						data_format=a_ ,						**a_   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ = True ,						a_ = None ,						**a_ ,						)							->							str:







								if offset:
									_UpperCAmelCase =						(image - 127.5) * scale
									if data_format is not None:
										_UpperCAmelCase =						to_channel_dimension_format(a_ ,						a_   )
									_UpperCAmelCase =						rescaled_image.astype(np.floataa   )
								else:
									_UpperCAmelCase =						rescale(a_ ,						scale=a_ ,						data_format=a_ ,						**a_   )
								return rescaled_image



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							Dict:
								return normalize(a_ ,						mean=a_ ,						std=a_ ,						data_format=a_ ,						**a_   )



							def       _a   (      self ,						a_ ,						a_ = None ,						a_ = None ,						a_=None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = None ,						a_ = ChannelDimension.FIRST ,						**a_ ,						)							->							Optional[Any]:
								_UpperCAmelCase =						do_resize if do_resize is not None else self.do_resize
								_UpperCAmelCase =						resample if resample is not None else self.resample
								_UpperCAmelCase =						do_center_crop if do_center_crop is not None else self.do_center_crop
								_UpperCAmelCase =						do_rescale if do_rescale is not None else self.do_rescale
								_UpperCAmelCase =						rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCAmelCase =						rescale_offset if rescale_offset is not None else self.rescale_offset
								_UpperCAmelCase =						do_normalize if do_normalize is not None else self.do_normalize
								_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 =						include_top if include_top is not None else self.include_top

								_UpperCAmelCase =						size if size is not None else self.size
								_UpperCAmelCase =						get_size_dict(a_   )
								_UpperCAmelCase =						crop_size if crop_size is not None else self.crop_size
								_UpperCAmelCase =						get_size_dict(a_ ,						param_name="crop_size"   )

								_UpperCAmelCase =						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_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.
								_UpperCAmelCase =						[to_numpy_array(a_   ) for image in images]

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

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

								if do_rescale:
									_UpperCAmelCase =						[self.rescale(image=a_ ,						scale=a_ ,						offset=a_   ) for image in images]

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

								if include_top:
									_UpperCAmelCase =						[self.normalize(image=a_ ,						mean=[0, 0, 0] ,						std=a_   ) for image in images]

								_UpperCAmelCase =						[to_channel_dimension_format(a_ ,						a_   ) for image in images]

								_UpperCAmelCase =						{"pixel_values": images}
								return BatchFeature(data=a_ ,						tensor_type=a_   )






"""simple docstring"""
def     __lowerCamelCase  (		UpperCamelCase__ = 400_0000      ):


	"""simple docstring"""






	_UpperCAmelCase =						1
	_UpperCAmelCase =						2
	_UpperCAmelCase =						0
	while j <= n:
		if j % 2 == 0:
			total += j
		_UpperCAmelCase ,      _UpperCAmelCase =						j, i + j

	return total


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




