




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


__UpperCamelCase      : int					      =   logging.get_logger(__name__)


if is_vision_available():
		import PIL

class 	__lowerCAmelCase			(			UpperCamelCase_				):
							UpperCamelCase__        =						["""pixel_values"""]
							def __init__(		self		:List[Any]	,   __magic_name__		:Optional[Any] = True	,   __magic_name__		:Optional[Any] = None	,   __magic_name__		:List[Any] = PILImageResampling.BICUBIC	,   __magic_name__		:int = True	,   __magic_name__		:Dict = None	,   __magic_name__		:List[str] = True	,   __magic_name__		:Optional[Any] = 1 / 255	,   __magic_name__		:int = True	,   __magic_name__		:List[str] = None	,   __magic_name__		:Optional[int] = None	,   __magic_name__		:List[str] = True	,   **__magic_name__		:int	,   ):







												'''simple docstring'''







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

												a		       =							do_resize
												a		       =							size
												a		       =							resample
												a		       =							do_center_crop
												a		       =							crop_size
												a		       =							do_rescale
												a		       =							rescale_factor
												a		       =							do_normalize
												a		       =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												a		       =							image_std if image_std is not None else OPENAI_CLIP_STD
												a		       =							do_convert_rgb
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Optional[int]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[str] = PILImageResampling.BICUBIC	,   __magic_name__		:str = None	,   **__magic_name__		:Optional[Any]	,   ):







												'''simple docstring'''







												a		       =							get_size_dict(__SCREAMING_SNAKE_CASE	,   default_to_square=__SCREAMING_SNAKE_CASE       )
												a		       =							get_resize_output_image_size(
												    __SCREAMING_SNAKE_CASE	,   size=(size["""height"""], size["""width"""])	,   default_to_square=__SCREAMING_SNAKE_CASE       )
												return resize(__SCREAMING_SNAKE_CASE	,   size=__SCREAMING_SNAKE_CASE	,   resample=__SCREAMING_SNAKE_CASE	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE       )
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:str	,   __magic_name__		:int	,   __magic_name__		:Optional[Any] = None	,   **__magic_name__		:Tuple	,   ):







												'''simple docstring'''







												a		       =							get_size_dict(__SCREAMING_SNAKE_CASE       )
												return center_crop(__SCREAMING_SNAKE_CASE	,   size=(size["""height"""], size["""width"""])	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE       )
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Any	,   __magic_name__		:Tuple	,   __magic_name__		:Dict = None	,   **__magic_name__		:Dict	,   ):







												'''simple docstring'''







												return rescale(__SCREAMING_SNAKE_CASE	,   scale=__SCREAMING_SNAKE_CASE	,   data_format=__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE       )
							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:str	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:int = None	,   **__magic_name__		:Dict	,   ):







												'''simple docstring'''







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





							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:Tuple	,   __magic_name__		:Any = None	,   __magic_name__		:Tuple = None	,   __magic_name__		:Any = None	,   __magic_name__		:Optional[int] = None	,   __magic_name__		:Optional[Any] = None	,   __magic_name__		:str = None	,   __magic_name__		:Tuple = None	,   __magic_name__		:Optional[Any] = None	,   __magic_name__		:List[str] = None	,   __magic_name__		:str = None	,   __magic_name__		:Tuple = None	,   __magic_name__		:Union[str, Any] = None	,   __magic_name__		:Any = ChannelDimension.FIRST	,   **__magic_name__		:Any	,   ):







												'''simple docstring'''







												a		       =							do_resize if do_resize is not None else self.do_resize
												a		       =							size if size is not None else self.size
												a		       =							get_size_dict(__SCREAMING_SNAKE_CASE	,   default_to_square=__SCREAMING_SNAKE_CASE       )
												a		       =							resample if resample is not None else self.resample
												a		       =							do_center_crop if do_center_crop is not None else self.do_center_crop
												a		       =							crop_size if crop_size is not None else self.crop_size
												a		       =							get_size_dict(__SCREAMING_SNAKE_CASE       )
												a		       =							do_rescale if do_rescale is not None else self.do_rescale
												a		       =							rescale_factor if rescale_factor is not None else self.rescale_factor
												a		       =							do_normalize if do_normalize is not None else self.do_normalize
												a		       =							image_mean if image_mean is not None else self.image_mean
												a		       =							image_std if image_std is not None else self.image_std
												a		       =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												a		       =							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_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:
																	a		       =							[convert_to_rgb(__SCREAMING_SNAKE_CASE       ) for image in images]

												# All transformations expect numpy arrays.
												a		       =							[to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

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

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

												a		       =							[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       ) for image in images]

												a		       =							{"""pixel_values""": images}
												return BatchFeature(data=__SCREAMING_SNAKE_CASE	,   tensor_type=__SCREAMING_SNAKE_CASE       )







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(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 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


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

