code: 
  
  
  
  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,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  lowerCAmelCase__	:  Optional[Any] 		=logging.get_logger(__name__)
  
  
  if is_vision_available():
  			import PIL
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				Dict       						=						['''pixel_values''']
  
  
  
  
  
  
  						def __init__(					self   ,							_A = True   ,							_A = None   ,							_A = PILImageResampling.BICUBIC   ,							_A = True   ,							_A = None   ,							_A = True   ,							_A = 1 / 255   ,							_A = True   ,							_A = None   ,							_A = None   ,							_A = True   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().__init__(**_A	)
  													__SCREAMING_SNAKE_CASE								=      size if size is not None else {'shortest_edge': 224}
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A   ,							default_to_square=_A	)
  													__SCREAMING_SNAKE_CASE								=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A	)
  
  													__SCREAMING_SNAKE_CASE								=      do_resize
  													__SCREAMING_SNAKE_CASE								=      size
  													__SCREAMING_SNAKE_CASE								=      resample
  													__SCREAMING_SNAKE_CASE								=      do_center_crop
  													__SCREAMING_SNAKE_CASE								=      crop_size
  													__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 OPENAI_CLIP_MEAN
  													__SCREAMING_SNAKE_CASE								=      image_std if image_std is not None else OPENAI_CLIP_STD
  													__SCREAMING_SNAKE_CASE								=      do_convert_rgb
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A = PILImageResampling.BICUBIC   ,							_A = None   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A   ,							default_to_square=_A	)
  													__SCREAMING_SNAKE_CASE								=      get_resize_output_image_size(
  													    _A   ,							size=(size['height'], size['width'])   ,							default_to_square=_A	)
  													return resize(_A   ,							size=_A   ,							resample=_A   ,							data_format=_A   ,							**_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A = None   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A	)
  													return center_crop(_A   ,							size=(size['height'], size['width'])   ,							data_format=_A   ,							**_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A = None   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return rescale(_A   ,							scale=_A   ,							data_format=_A   ,							**_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A = None   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													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 = ChannelDimension.FIRST   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''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
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A   ,							default_to_square=_A	)
  													__SCREAMING_SNAKE_CASE								=      resample if resample is not None else self.resample
  													__SCREAMING_SNAKE_CASE								=      do_center_crop if do_center_crop is not None else self.do_center_crop
  													__SCREAMING_SNAKE_CASE								=      crop_size if crop_size is not None else self.crop_size
  													__SCREAMING_SNAKE_CASE								=      get_size_dict(_A	)
  													__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								=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
  													__SCREAMING_SNAKE_CASE								=      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:
  																				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.'	)
  
  													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.'	)
  
  													# PIL RGBA images are converted to RGB
  													if do_convert_rgb:
  																				__SCREAMING_SNAKE_CASE								=      [convert_to_rgb(_A	) for image in images]
  
  													# All transformations expect numpy arrays.
  													__SCREAMING_SNAKE_CASE								=      [to_numpy_array(_A	) for image in images]
  
  													if do_resize:
  																				__SCREAMING_SNAKE_CASE								=      [self.resize(image=_A   ,							size=_A   ,							resample=_A	) for image in images]
  
  													if do_center_crop:
  																				__SCREAMING_SNAKE_CASE								=      [self.center_crop(image=_A   ,							size=_A	) for image in images]
  
  													if do_rescale:
  																				__SCREAMING_SNAKE_CASE								=      [self.rescale(image=_A   ,							scale=_A	) for image in images]
  
  													if do_normalize:
  																				__SCREAMING_SNAKE_CASE								=      [self.normalize(image=_A   ,							mean=_A   ,							std=_A	) for image in images]
  
  													__SCREAMING_SNAKE_CASE								=      [to_channel_dimension_format(_A   ,							_A	) for image in images]
  
  													__SCREAMING_SNAKE_CASE								=      {'pixel_values': images}
  													return BatchFeature(data=_A   ,							tensor_type=_A	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  from itertools import combinations, product
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__     )       ->				bool:
  							if xa == ya == 0 or xa == ya == 0:
  														return False
  							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
  							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
  							__SCREAMING_SNAKE_CASE								=      (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  							return (
  							    a_square + b_square == c_square
  							    or a_square + c_square == b_square
  							    or b_square + c_square == a_square
  							)
  
  
  
  
  
  
  def 			__lowercase     (				a__ = 50     )       ->				int:
  							return sum(
  							    1
  							    for pta, pta in combinations(product(range(limit + 1     )							,     repeat=2     )							,     2     )
  							    if is_right(*a__							,     *a__     )     )
  
  
  if __name__ == "__main__":
  			print(F'''{solution() = }''')
  
  
  
  
  
  
  