


'''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


_lowerCAmelCase	:		Optional[int]		  =						logging.get_logger(__name__)



class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       Tuple         =			["""pixel_values"""]



	def __init__(       self			,       snake_case__ = True			,       snake_case__ = None			,       snake_case__ = PIL.Image.NEAREST			,       snake_case__ = False			,       snake_case__ = None			,       snake_case__ = 1 / 255			,       snake_case__ = False			,       snake_case__ = True			,       snake_case__ = True			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = True			,       **snake_case__			,       ):
		super().__init__(**snake_case__     )
		lowerCAmelCase       :     int   				=			size if size is not None else {'height': 346, 'width': 346}
		lowerCAmelCase       :     Optional[int]   				=			get_size_dict(snake_case__     )
		lowerCAmelCase       :     str   				=			crop_size if crop_size is not None else {'height': 289, 'width': 289}
		lowerCAmelCase       :     int   				=			get_size_dict(snake_case__			,       param_name='crop_size'     )

		lowerCAmelCase       :     List[Any]   				=			do_resize
		lowerCAmelCase       :     Optional[int]   				=			size
		lowerCAmelCase       :     str   				=			resample
		lowerCAmelCase       :     Dict   				=			do_center_crop
		lowerCAmelCase       :     List[str]   				=			crop_size
		lowerCAmelCase       :     int   				=			do_rescale
		lowerCAmelCase       :     Dict   				=			rescale_factor
		lowerCAmelCase       :     Dict   				=			rescale_offset
		lowerCAmelCase       :     Optional[int]   				=			do_normalize
		lowerCAmelCase       :     int   				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		lowerCAmelCase       :     Optional[Any]   				=			image_std if image_std is not None else IMAGENET_STANDARD_STD
		lowerCAmelCase       :     Optional[Any]   				=			include_top



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = PIL.Image.NEAREST			,       snake_case__ = None			,       **snake_case__			,       ):
		lowerCAmelCase       :     Dict   				=			get_size_dict(snake_case__     )
		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(
		    snake_case__			,       size=(size['height'], size['width'])			,       resample=snake_case__			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = None			,       **snake_case__			,       ):
		lowerCAmelCase       :     Any   				=			get_size_dict(snake_case__     )
		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(snake_case__			,       size=(size['height'], size['width'])			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__ = True			,       snake_case__ = None			,       **snake_case__			,       ):
		if offset:
			lowerCAmelCase       :     Any   				=			(image - 1_2_7.5) * scale
			if data_format is not None:
				lowerCAmelCase       :     Tuple   				=			to_channel_dimension_format(snake_case__			,       snake_case__     )
			lowerCAmelCase       :     Union[str, Any]   				=			rescaled_image.astype(np.floataa     )
		else:
			lowerCAmelCase       :     int   				=			rescale(snake_case__			,       scale=snake_case__			,       data_format=snake_case__			,       **snake_case__     )
		return rescaled_image



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__			,       snake_case__ = None			,       **snake_case__			,       ):
		return normalize(snake_case__			,       mean=snake_case__			,       std=snake_case__			,       data_format=snake_case__			,       **snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__=None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = None			,       snake_case__ = ChannelDimension.FIRST			,       **snake_case__			,       ):
		lowerCAmelCase       :     Tuple   				=			do_resize if do_resize is not None else self.do_resize
		lowerCAmelCase       :     Union[str, Any]   				=			resample if resample is not None else self.resample
		lowerCAmelCase       :     Optional[Any]   				=			do_center_crop if do_center_crop is not None else self.do_center_crop
		lowerCAmelCase       :     Optional[int]   				=			do_rescale if do_rescale is not None else self.do_rescale
		lowerCAmelCase       :     List[str]   				=			rescale_factor if rescale_factor is not None else self.rescale_factor
		lowerCAmelCase       :     Any   				=			rescale_offset if rescale_offset is not None else self.rescale_offset
		lowerCAmelCase       :     Optional[Any]   				=			do_normalize if do_normalize is not None else self.do_normalize
		lowerCAmelCase       :     Union[str, Any]   				=			image_mean if image_mean is not None else self.image_mean
		lowerCAmelCase       :     Optional[int]   				=			image_std if image_std is not None else self.image_std
		lowerCAmelCase       :     Optional[int]   				=			include_top if include_top is not None else self.include_top

		lowerCAmelCase       :     List[Any]   				=			size if size is not None else self.size
		lowerCAmelCase       :     Tuple   				=			get_size_dict(snake_case__     )
		lowerCAmelCase       :     int   				=			crop_size if crop_size is not None else self.crop_size
		lowerCAmelCase       :     Tuple   				=			get_size_dict(snake_case__			,       param_name='crop_size'     )

		lowerCAmelCase       :     int   				=			make_list_of_images(snake_case__     )

		if not valid_images(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 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.
		lowerCAmelCase       :     Dict   				=			[to_numpy_array(snake_case__     ) for image in images]

		if do_resize:
			lowerCAmelCase       :     str   				=			[self.resize(image=snake_case__			,       size=snake_case__			,       resample=snake_case__     ) for image in images]

		if do_center_crop:
			lowerCAmelCase       :     Optional[Any]   				=			[self.center_crop(image=snake_case__			,       size=snake_case__     ) for image in images]

		if do_rescale:
			lowerCAmelCase       :     Union[str, Any]   				=			[self.rescale(image=snake_case__			,       scale=snake_case__			,       offset=snake_case__     ) for image in images]

		if do_normalize:
			lowerCAmelCase       :     Optional[Any]   				=			[self.normalize(image=snake_case__			,       mean=snake_case__			,       std=snake_case__     ) for image in images]

		if include_top:
			lowerCAmelCase       :     Union[str, Any]   				=			[self.normalize(image=snake_case__			,       mean=[0, 0, 0]			,       std=snake_case__     ) for image in images]

		lowerCAmelCase       :     Tuple   				=			[to_channel_dimension_format(snake_case__			,       snake_case__     ) for image in images]

		lowerCAmelCase       :     Dict   				=			{'pixel_values': images}
		return BatchFeature(data=snake_case__			,       tensor_type=snake_case__     )




'''simple docstring'''




from itertools import combinations, product

def       __UpperCamelCase     (     _A     :					int       , _A     :					int       , _A     :					int       , _A     :					int					)     ->   bool:




	"""simple docstring"""





	if xa == ya == 0 or xa == ya == 0:
		return False
	lowerCAmelCase       :     Optional[Any]   				=			xa * xa + ya * ya
	lowerCAmelCase       :     Tuple   				=			xa * xa + ya * ya
	lowerCAmelCase       :     List[str]   				=			(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       __UpperCamelCase     (     _A     :					int = 50					)     ->   int:




	"""simple docstring"""





	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() = }""")
