code: 
  """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_   )
  
  
  
  
  
style_context: 
  """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() = }''')
  
  
  
  
  