code: 
  
  
  
  
  def lowercase						(     SCREAMING_SNAKE_CASE = 10_00   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	0
  			for i in range(SCREAMING_SNAKE_CASE__   ):
  						if i % 3 == 0 or i % 5 == 0:
  									result += i
  			return result
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
style_context: 
  
  
  
  
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					logging.get_logger(__name__)
  
  
  if is_vision_available():
  				import PIL
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''pixel_values''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 1 / 255			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				None:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'height': 2560, 'width': 1920}
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# The previous feature extractor size parameter was in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				if (output_width < output_height and input_width > input_height) or (
  				    output_width > output_height and input_width < input_height
  				):
  							SCREAMING_SNAKE_CASE_			=	np.rotaa(SCREAMING_SNAKE_CASE			,    3			)
  
  				if data_format is not None:
  							SCREAMING_SNAKE_CASE_			=	to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	output_width - input_width
  				SCREAMING_SNAKE_CASE_			=	output_height - input_height
  
  				if random_padding:
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_height + 1			)
  							SCREAMING_SNAKE_CASE_			=	np.random.randint(low=0			,    high=delta_width + 1			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	delta_height // 2
  							SCREAMING_SNAKE_CASE_			=	delta_width // 2
  
  				SCREAMING_SNAKE_CASE_			=	delta_height - pad_top
  				SCREAMING_SNAKE_CASE_			=	delta_width - pad_left
  
  				SCREAMING_SNAKE_CASE_			=	((pad_top, pad_bottom), (pad_left, pad_right))
  				return pad(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
  				return self.pad_image(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	size['height'], size['width']
  
  				# We always resize to the smallest of either the input or output size.
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				if height == input_height and width == input_width:
  							return image
  
  				if input_height > input_width:
  							SCREAMING_SNAKE_CASE_			=	int(input_width * height / input_height			)
  				elif input_width > input_height:
  							SCREAMING_SNAKE_CASE_			=	int(input_height * width / input_width			)
  
  				return resize(
  				    SCREAMING_SNAKE_CASE			,    size=(height, width)			,    resample=SCREAMING_SNAKE_CASE			,    reducing_gap=2.0			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(size['height']			,    size['width']			)
  				SCREAMING_SNAKE_CASE_			=	get_resize_output_image_size(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    default_to_square=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resize(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  				return resized_image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return rescale(SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return normalize(SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,    **SCREAMING_SNAKE_CASE			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	do_resize if do_resize is not None else self.do_resize
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else self.size
  				if isinstance(SCREAMING_SNAKE_CASE			,    (tuple, list)			):
  							# Previous feature extractor had size in (width, height) format
  							SCREAMING_SNAKE_CASE_			=	size[::-1]
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resample if resample is not None else self.resample
  				SCREAMING_SNAKE_CASE_			=	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  				SCREAMING_SNAKE_CASE_			=	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  				SCREAMING_SNAKE_CASE_			=	do_pad if do_pad is not None else self.do_pad
  				SCREAMING_SNAKE_CASE_			=	do_rescale if do_rescale is not None else self.do_rescale
  				SCREAMING_SNAKE_CASE_			=	rescale_factor if rescale_factor is not None else self.rescale_factor
  				SCREAMING_SNAKE_CASE_			=	do_normalize if do_normalize is not None else self.do_normalize
  				SCREAMING_SNAKE_CASE_			=	image_mean if image_mean is not None else self.image_mean
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else self.image_std
  
  				SCREAMING_SNAKE_CASE_			=	make_list_of_images(SCREAMING_SNAKE_CASE			)
  
  				if not valid_images(SCREAMING_SNAKE_CASE			):
  							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_rescale and rescale_factor is None:
  							raise ValueError('Rescale factor must be specified if do_rescale is True.'			)
  
  				if do_pad and size is None:
  							raise ValueError('Size must be specified if do_pad 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.
  				SCREAMING_SNAKE_CASE_			=	[to_numpy_array(SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_align_long_axis:
  							SCREAMING_SNAKE_CASE_			=	[self.align_long_axis(SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE_			=	[self.resize(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_thumbnail:
  							SCREAMING_SNAKE_CASE_			=	[self.thumbnail(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_pad:
  							SCREAMING_SNAKE_CASE_			=	[self.pad_image(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    random_padding=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE_			=	[self.rescale(image=SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	[self.normalize(image=SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	{'pixel_values': images}
  				return BatchFeature(data=SCREAMING_SNAKE_CASE			,    tensor_type=SCREAMING_SNAKE_CASE			)
  